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Category : Protein Folding

Mapping out the mystery of blood stem cells – Science Codex

Princess Margaret scientists have revealed how stem cells are able to generate new blood cells throughout our life by looking at vast, uncharted regions of our genetic material that hold important clues to subtle biological changes in these cells.

The finding, obtained from studying normal blood, can be used to enhance methods for stem cell transplantation, and may also shed light into processes that occur in cancer cells that allow them to survive chemotherapy and relapse into cancer growth many years after treatment.

Using state-of-the art sequencing technology to perform genome-wide profiling of the epigenetic landscape of human stem cells, the research revealed important information about how genes are regulated through the three-dimensional folding of chromatin.

Chromatin is composed of DNA and proteins, the latter which package DNA into compact structures, and is found in the nucleus of cells. Changes in chromatin structure are linked to DNA replication, repair and gene expression (turning genes on or off).

The research by Princess Margaret Cancer Centre Senior Scientists Drs. Mathieu Lupien and John Dick is published in Cell Stem Cell, Wednesday, November 25, 2020.

"We don't have a comprehensive view of what makes a stem cell function in a specific way or what makes it tick," says Dr. Dick, who is also a Professor in the Department of Molecular Genetics, University of Toronto.

"Stem cells are normally dormant but they need to occasionally become activated to keep the blood system going. Understanding this transition into activation is key to be able to harness the power of stem cells for therapy, but also to understand how malignant cells change this balance.

"Stem cells are powerful, potent and rare. But it's a knife's edge as to whether they get activated to replenish new blood cells on demand, or go rogue to divide rapidly and develop mutations, or lie dormant quietly, in a pristine state."

Understanding what turns that knife's edge into these various stem cell states has perplexed scientists for decades. Now, with this research, we have a better understanding of what defines a stem cell and makes it function in a particular way.

"We are exploring uncharted territory," says Dr. Mathieu Lupien, who is also an Associate Professor in the Department of Medical Biophysics, University of Toronto. "We had to look into the origami of the genome of cells to understand why some can self-renew throughout our life while others lose that ability. We had to look beyond what genetics alone can tell us."

In this research, scientists focused on the often overlooked noncoding regions of the genome: vast stretches of DNA that are free of genes (i.e. that do not code for proteins), but nonetheless harbour important regulatory elements that determine if genes are turned on or off.

Hidden amongst this noncoding DNA - which comprise about 98% of the genome - are crucial elements that not only control the activity of thousands of genes, but also play a role in many diseases.

The researchers examined two distinct human hematopoietic stem cells or immature cells that go through several steps in order to develop into different types of blood cells, such as white or red blood cells, or platelets.

They looked at long-term hematopoietic stem cells (HSCs) and short-term HSCs found in the bone marrow of humans. The researchers wanted to map out the cellular machinery involved in the "dormancy" state of long-term cells, with their continuous self-renewing ability, as compared to the more primed, activated and "ready-to-go" short-term cells which can transition quickly into various blood cells.

The researchers found differences in the three-dimensional chromatin structures between the two stem cell types, which is significant since the ways in which chromatin is arranged or folded and looped impacts how genes and other parts of our genome are expressed and regulated.

Using state-of-the-art 3D mapping techniques, the scientists were able to analyze and link the long-term stem cell types with the activity of the chromatin folding protein CTCF and its ability to regulate the expression of 300 genes to control long-term, self-renewal.

"Until now, we have not had a comprehensive view of what makes a stem cell function in a particular way," says Dr. Dick, adding that the 300 genes represent what scientists now think is the "essence" of a long-term stem cell.

He adds that long-term dormant cells are a "protection" against malignancy, because they can survive for long periods and evade treatment, potentially causing relapse many years later.

However, a short-term stem cell that is poised to become active, dividing and reproducing more quickly than a long-term one, can gather up many more mutations, and sometimes these can progress to blood cancers, he adds.

"This research gives us insight into aspects of how cancer starts and how some cancer cells can retain stem-cell like properties that allow them to survive long-term," says Dr. Dick.

He adds that a deeper understanding of stem cells can also help with stem cells transplants for the treatment of blood cancers in the future, by potentially stimulating and growing these cells ex vivo (out of the body) for improved transplantation.

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Mapping out the mystery of blood stem cells - Science Codex

Recommendation and review posted by Alexandra Lee Anderson

Are Proteins Attracted to Function? – Discovery Institute

Photo: Douglas Axe.

Doug Axe showed that functional space is a tiny fraction of sequence space in proteins. Evolutionists think they found a shortcut as simple as dropping down a funnel. Proteins dont have to search all of sequence space at random; a ring attractor pulls them down the thermodynamic funnel into functional glory land.

Richard Dawkins has been criticized for years now for his Weasel analogy (see Jonathan Witts critique). And yet the myth lives on. Miracles happen with the words, It evolves! while waving the magic wand, Natural Selection. Heres a new instance involving protein folds.

Dawkinss main error was with setting a target sequence for random letters (the Hamlet sequence Methinks it is like a weasel), and then preserving the randomly changing letters that matched the target. Natural selection as Darwin envisioned it has no target sequence. Each step must be functional, or it is not selected. All the intermediate phrases in Dawkinss computer simulation were gibberish. They had no function in language. They would never converge on the target phrase by unguided natural processes.

The same is true with random sequences of amino acids, called polypeptides. They have no function and are not called proteins or enzymes unless and until they fold into a functional shape. Before considering the following hypothesis by two chemists, remember that without guidance from genes, polypeptides fall into the vast neverland called sequence space where nothing happens (the amino acids, furthermore, must be left-handed, or homochiral). Functional space is but a tiny fraction of sequence space. Doug Axe discussed this in his book Undeniable, based on his own research at Cambridge. He experimentally determined how much change was necessary to break a functional protein with mutations. It led to his estimate that a random polypeptide 150 amino acids in length, which is modest for a protein, has only a 1 in 1074 chance of arriving at a functional fold. That probability drops to an impossible 1 in 10148 chance if the sequence must be homochiral, and even lower if the sequence also has to consist of only peptide bonds. In short, it would be a miracle.

In their paper in PNAS, Funneled energy landscape unifies principles of protein binding and evolution, Zhiqiang Yan and Jin Wang think they have found a shortcut to the miraculous. Natural selection will push the polypeptide down a thermodynamic funnel, like a golfer putting a ball into the cup. Why? Because, clearly, proteins have evolved. Anything that has evolved would have had the magic wand of natural selection to do the magic.

Most proteins have evolved to spontaneously fold into native structure and specifically bind with their partners for the purpose of fulfilling biological functions. According to Darwin, protein sequences evolve through random mutations, and only the fittest survives. The understanding of how the evolutionary selection sculpts the interaction patterns for both biomolecular folding and binding is still challenging. In this study, we incorporated the constraint of functional binding into the selection fitness based on the principle of minimal frustration for the underlying biomolecular interactions. Thermodynamic stability and kinetic accessibility were derived and quantified from a global funneled energy landscape that satisfies the requirements of both the folding into the stable structure and binding with the specific partner. The evolution proceeds via a bowl-like evolution energy landscape in the sequence space with a closed-ring attractor at the bottom. The sequence space is increasingly reduced until this ring attractor is reached. The molecular-interaction patterns responsible for folding and binding are identified from the evolved sequences, respectively. The residual positions participating in the interactions responsible for folding are highly conserved and maintain the hydrophobic core under additional evolutionary constraints of functional binding. The positions responsible for binding constitute a distributed network via coupling conservations that determine the specificity of binding with the partner. This work unifies the principles of protein binding and evolution under minimal frustration and sheds light on the evolutionary design of proteins for functions. [Emphasis added.]

Methinks these are weasel words. This is like the following syllogism. Major premise: Everything evolves by natural selection. Minor premise: Proteins occupy a tiny fraction of sequence space that permits folding and binding to specific partners. Conclusion: Natural selection pushed proteins to fulfill these constraints. Anything circular here? What if one does not accept the major premise?

To make their point, Yan and Wang know that they have to satisfy the laws of thermodynamics, which militate against functional folds by accident. Sure enough, the paper has lovely equations. But if the premise is wrong, equations only provide window dressing on a fake storefront. Here is the weasel-like target sequence:

To realize the principle of minimal frustration in protein evolution, one of the typical naturally occurring protein domains (WW domain) and its binding complex were chosen as the evolution model. WW domains preferably bind Pro-rich peptide. The native structure of the binding complex was considered as the evolved and functional structures (SI Appendix, Fig. S2). The evolution simulation is to mimic how nature selects and optimizes the sequences of the WW domain, which can spontaneously fold and preferably bind to the specific Pro-rich peptide.

Their principle of minimal frustration refers to optimization of protein sequences. The principle is useful for analyzing proteins, but not for accounting how they became optimized.

The principle of minimal frustration has been fruitful in illustrating how the global pattern of interactions determines thermodynamic stability and kinetic accessibility of protein folding and binding. The principle requires that energetic conflicts are minimized in folded native states, so that a sequence can spontaneously fold. Because of the functional necessity, naturally occurring sequences are actually in the tradeoff for coding the capacity to simultaneously satisfy stable folding and functional binding. From the view of localized frustration, naturally occurring proteins maintain a conserved network of minimally frustrated interactions at the hydrophobic core. In contrast, highly frustrated interactions tend to be clustered on the surface, often near binding sites that become less frustrated upon binding. A natural question is how the evolution sculpts the interaction patterns that conflict with the overall folding of minimal frustration but are specific for protein binding.

This principle is an ID principle: proteins are sculpted to have stable cores, but flexible surfaces. They are optimized for this. To make evolution the sculptor begs the question. Its like saying, proteins must fulfill requirements for thermodynamic stability and kinetic accessibility; therefore, evolution fulfilled these requirements. Its like saying, We take minimal frustration to be a measure of fitness, and since natural selection always moves toward higher fitness, proteins evolved the observed optimization. How do they not recognize the circular reasoning here? They are following a principle of maximal frustration for critical thinkers! Its incredible that this kind of circular argument was published in the premiere journal of the National Academy of Sciences and survived the editing scrutiny of David A. Weitz of Harvard.

Protein function is the ultimate goal of protein evolution via mutagenesis for survival. This work has proposed and quantified the selection fitness of protein evolution with the principle of minimal frustration. The selection fitness of thermodynamic stability and kinetic accessibility incorporates both folding and binding requirements. Driven by the selection fitness, the evolution dynamics in sequence space can be depicted and visualized as a bowl-like energy landscape where the sequence space is increasingly reduced until the closed-ring attractor is reached at the bottom. The evolved sequences located in the basin of the attractor faithfully reproduce the interaction patterns as those extracted from naturally occurring sequences. The consistency validates the principle of minimal frustration as the selection fitness of protein evolution. To fulfill the folding and function, evolution sculpts the interaction patterns with the minimal-frustration principle to develop the hydrophobic core for folding and the coupling network for functional binding.

Comparing this to Dawkins Weaselology, this is like saying, The goal of sentences is to express meaning. Driven by this selection fitness, evolution dynamics guarantee that random letters will fall through a bowl-like semantics landscape where the randomness is reduced until a closed ring of meaningful sentences naturally occurs. The fact that natural sentences convey meaning validates this principle. Evolution sculpts meaning from random letters because it must, and lo and behold, it does. Aaagggh! How does this notion pass peer review?

To make their circularity seem practical, they show what else could be done by reasoning in a circle in the wide-angle view:

In addition, the evolution of a protein binding/assembling system generally involves the evolution of each binding/assembling partner. Therefore, the evolution of one partner is constrained or coupled to the evolution of its partners, i.e., coevolution of the partners, such as a toxinantitoxin system. In this case, the selection fitness of protein evolution involves the constraints not only from its own folding and binding but also from those of its partners. The study of this more complex issue would bridge the evolution of a single protein to the evolution of a protein network.

The whole world is circular. Isnt that a useful idea!

This kind of reasoning is not limited to this paper. Five authors, including Joseph W. Thornton (whom Michael Behe says threw a monkey wrench into Darwinian evolution), wrote a preprint on bioRxiv with similar fallacies. In Chance, contingency, and necessity in the experimental evolution of ancestral proteins, they assert that varieties of BCL-2 (an anti-apoptosis protein) arrived at their optimum fitness by convergent evolution, even though they recognize that there was no way to expect that because of the inevitability of chance and contingency in unguided natural processes:

Finally, our observations suggest that the sequence-structure-function associations apparent in sequence alignments are, to a significant degree, the result of shared but contingent constraints that were produced by chance events during history. Present-day proteins are physical anecdotes of a particular history: they reflect the interaction of accumulated chance events during descent from common ancestors with necessity imposed by physics, chemistry and natural selection. Apparent design principles in extant or evolved proteins express not how things must be or even how they would be best but rather the contingent legacy of the constraints and opportunities that those molecules just happen to have inherited.

Once again, they treat natural selection as a sculptor with a guiding hand. The constraints to get sequences that work must have chosen working products out of the vast sea of possibilities. They evolved because they evolved. They look designed, but the design principles are only apparent.

Dawkins would be pleased that his fallacy continues to be fruitful. His critics worry about the overpopulation of weasels in science. Hawks, flying overhead the infested area, casting a wide view over the creatures running in circles, make good weasel predators.

Link:
Are Proteins Attracted to Function? - Discovery Institute

Recommendation and review posted by Alexandra Lee Anderson

The 3.2- resolution structure of human mTORC2 – Science Advances

RESULTSStructure determination of mTORC2 by cryo-EM

To investigate the structure of mTORC2 and the mechanism of its regulation, we coexpressed recombinant components of human mTORC2 (mTOR, mLST8, Rictor, and SIN1) in Spodoptera frugiperda cells. The assembled complex, purified using tag-directed antibody affinity followed by size exclusion chromatography, was analyzed by cryo-EM (Fig. 1B and figs. S1, A to C, and S2) in the presence of ATPS and either the full-length substrate Akt1 (fig. S1, D and E) or an Akt1 variant missing the PH domain (PH-Akt1), or in the absence of Akt1 with and without ATPS (fig. S2). The sample prepared in the presence of ATPS and PH-Akt1 yielded the highest overall resolution of 3.2 (density A in fig. S2).

mTORC2 forms a rhomboid-shaped dimer (Fig. 1C) as observed in lower-resolution mTORC2 reconstructions (2022). The mTOR kinase consists of the N-terminal Horn and Bridge domains followed by the FAT, FRB, and kinase domains (Fig. 1A). mTOR forms the core of mTORC2 with mLST8 on the periphery, close to the active site cleft, similar to mTOR-mLST8 in mTORC1 (16, 23). In the overall reconstruction, as a consequence of EM refinement of a flexible molecule, one-half of the dimer showed better local resolution (Fig. 1B, fig. S3, A to C, and movie S1). Therefore, focused refinement on a unique half of the assembly improved the resolution to 3.0 (density C in fig. S2), and these maps were used for structural modeling (fig. S3, D to F). Previous mTORC2 and yeast TORC2 reconstructions (2022) revealed that the two mTOR FAT domains are in closer proximity to each other than observed in mTORC1 (16, 23, 27), and in the current structure, the distance between the mTOR FAT domains is further reduced (fig. S3G). Irrespective of these structural differences between the two mTORCs, the catalytic site in mTORC2 closely resembles the catalytic site in mTORC1 without Rheb-mediated activation (23), suggesting that mTORC2 may be activated by a yet to be defined mechanism.

Previous studies of mTORC2 subunits Rictor and SIN1 or their yeast orthologs were not of sufficient resolution to allow de novo model building, resulting in ambiguous or inconsistent interpretations (20, 22, 28). Here, we unambiguously model all structured regions of Rictor and the N-terminal region of SIN1 (Fig. 2, A to C), whereas the middle and C-terminal part of SIN1 retain high flexibility and are not resolved. The fold of Rictor differs substantially from previous interpretations (fig. S3, H and I) (20). Rictor is composed of three interacting stacks of -helical repeats, here referred to as the ARM domain (AD), the HEAT-like domain (HD), and the C-terminal domain (CD) (Fig. 2, A to C). The N-terminal AD (residues 26 to 487) forms a large superhelical arrangement of nine ARM repeats (Fig. 2, A and B) that structurally separates the HD and CD. The HD (residues 526 to 1007), interpreted as two separate domains in previous lower-resolution studies (20, 22), is composed of 10 HEAT-like repeats. In sequence space, the HD and CD of Rictor are separated by an extended stretch of residues (1008 to 1559) that are predicted to be disordered and are not resolved in our reconstruction. We refer to this region as the phosphorylation site region (PR) because it contains most of Rictors phosphorylation sites (29). The two ends of the PR are anchored by a two-stranded -sheet at the top of the HD, which is thus termed the PR anchor (Fig. 2, B and C, and fig. S4A). From here, a partially flexible linker wraps around the AD and the mTOR FRB domain extending toward the CD (Fig. 2B and fig. S4C).

(A) Sequence-level domain organization of Rictor. Flexible and unresolved regions are indicated as dotted lines. Interactions with other proteins in the complex are highlighted below the sequences. Asterisks indicate residues interacting with the N-terminal region of SIN1. (B) Two views of Rictor, colored by domains. The structured part of Rictor forms three domains: an N-terminal Armadillo repeat domain (AD, magenta), a HEAT-like repeat domain (HD, dark magenta), and a C-terminal domain (CD, light red); the phosphorylation site region (PR) remains disordered. The sequences flanking the nonresolved PR are highlighted in red, and the PR anchor is colored in gold. Bound ligands are shown as cyan spheres. (C) Schematic representation of Rictor and SIN1 domain topology. (D) The Rictor CD occupies the FRB domain and sterically blocks FKBP-rapamycin binding (26).

The structured parts of the CD form a four-helix bundle and a zinc finger, with bound Zn2+, in the vicinity of the Rictor N terminus (Fig. 2A and fig. S4, B and C). Residues coordinating the zinc ion are highly conserved in metazoan Rictor (fig. S4F). In earlier work, this domain had been interpreted as representing the SIN1 domain (20). The complete CD is absent in sequences of fungal Rictor orthologs, but other large extensions in yeast Rictor and SIN1 sequences may occupy the equivalent location in yeast TORC2, as observed in an intermediate-resolution reconstruction of budding yeast TORC2 (fig. S4, D and E) (21). Increased levels of Zn2+ have been reported to stimulate Akt1 S473 phosphorylation in cells (30, 31), but no direct involvement of mTORC2 activation has been demonstrated.

Contacts between Rictor and mTOR are made by the Rictor AD, which sits between the Horn domain of the proximal mTOR subunit and the Bridge domain of the distal mTOR subunit (Fig. 2B). With its positioning on top of the mTOR FRB domain, the CD of Rictor blocks the binding space of FKBP12-rapamycin in mTORC1, thereby explaining the absence of an mTORC1-like mode of sensitivity to rapamycin for mTORC2 (Fig. 2D) (5, 8, 28).

The SIN1 subunit of mTORC2 exhibits an unexpected structural organization. The N-terminal region (residues 2 to 137), contrary to earlier interpretations, does not form an independently folding domain but interacts tightly with Rictor and mLST8 in an extended conformation (Figs. 2, A to C, and 3, A to E). The CRIM, Ras-binding domain (RBD), and PH domains of SIN1, however, remain flexibly disposed. The N terminus of SIN1 is inserted into a deep cleft at the interface of the AD and HD of Rictor. The N-terminal Ala2 with a structurally resolved acetylated N terminus and Phe3 of SIN1 are buried in a hydrophobic pocket of Rictor (Fig. 3, C and D, and fig. S5A). The anchored N-terminal region of SIN1 forms two short helices (residues 6 to 33) inserted into grooves on the surface of the Rictor AD (Fig. 3D) and then continues with a flexible sequence segment toward the Rictor CD (Figs. 2, B and C, and 3C and fig. S5B). Protruding from the Rictor CD, SIN1 forms a helical segment, referred to as the traverse, that spans the distance to mLST8 across the mTORC2 kinase cleft (Fig. 3C and fig. S5, B and C). The next region of SIN1 interacts with the fourth strand of the second blade of the mLST8 propeller by -strand complementation, leading to displacement of an mLST8 loop relative to the structure of mLST8 in mTORC1 (Fig. 3, C and E, and fig. S5D). SIN1 then follows the surface of the mLST8 propeller, finally forming an -helix anchored between the first and seventh blades of mLST8.

(A) Sequence-level domain organization of SIN1. Flexible and unresolved regions are shown above each domain representation as dotted lines in two colors as indicated. Interactions with other proteins in the complex are indicated below the domain representation. (B) Extension of the processed SIN1 N terminus disrupts assembly of Rictor and SIN1 with mTOR/mLST8 into mTORC2. SDS-polyacrylamide gel of a FLAG bead pulldown from lysates of insect cells expressing mTORC2 comprising SIN1 variants. Levels of Rictor are drastically reduced in the mTOR-based pulldown for mTORC2 carrying variants of SIN1 N-terminally extended by a tryptophan (mTORC2 SIN1_W), two consecutive arginines (mTORC2 SIN1_2R), and three consecutive arginines (mTORC2 SIN1_3R). (C) Surface representation of mTORC2. SIN1 (shown as green cartoon) interacts via two N-terminal helices with Rictor, winds around Rictor, traverses the catalytic site cleft, and winds around mLST8. The field of view of subpanel D is indicated. (D) Close-up view of the SIN1 N-terminal residues, which are deeply inserted between Rictor AD and HD. Acetylated Ala2 and Phe3 are bound in a hydrophobic pocket, while Asp5 interacts via salt bridges (yellow dashes). (E) Top view of mLST8 -propeller (orange) and the interaction regions with SIN1 (green). The nomenclature for WD40 -propeller repeats is indicated. (F) Top view of the catalytic site with the structure shown as surface together with the density of a subclass (light gray). The lower-resolution extra density is consistent with a placement of the SIN1 CRIM domain, here shown in dark green (PDB: 2RVK). Unassigned extra density protrudes from the CRIM domain to the mTOR active site and Rictor.

SIN1 integrates into the Rictor fold and connects Rictor with mLST8, suggesting a direct role in stabilizing mTORC2. To test the relevance of the anchoring of the N terminus of SIN1 on Rictor, we extended the N terminus of SIN1 using tryptophan or arginine residues to exploit steric hindrance or charge-charge repulsion to prevent the insertion into the Rictor pocket. Insertion of residues impairs critical interactions observed for the acetylated N terminus of SIN1 and prevents Rictor integration into mTORC2, as observed in baculovirus-mediated expression of mTOR components followed by pull-down assays (Fig. 3B and fig. S5E). Therefore, SIN1 acts as an integral part of the Rictor structure that critically stabilizes interdomain interactions, explaining the difficulties observed in purifying isolated Rictor (20).

These observations are also consistent with the locations of posttranslational modifications or mutations that affect mTORC2 activity. SIN1 phosphorylation at Thr86 and Thr398 has been reported to reduce mTORC2 integrity and kinase activity toward Akt1 Ser473 (32). Thr86 in SIN1, which is a target for phosphorylation by S6 kinase (32), is bound to a negatively charged pocket of the Rictor CD (Fig. 3C and fig. S5C). Phosphorylation of Thr86 would lead to repulsion from this pocket, destabilizing the interaction between Rictor and mTOR-mLST8 and presumably the entire mTORC2 assembly, in agreement with earlier in vivo and in vitro observations (32). The importance of SIN1 in connecting Rictor to mLST8, and therefore also indirectly to mTOR, is also consistent with the requirement of mLST8 for mTORC2 integrity (33, 34).

A poorly resolved density linked to the SIN1 helix anchored to mLST8 is observed in all reconstructions. In previous structural studies of yeast TORC2, a similar region of density was associated with the CRIM domain of Avo1, the yeast SIN1 ortholog (21, 28). Most likely, it represents the mobile substrate-binding CRIM domain that directly follows the helix in the SIN1 sequence and has a matching shape based on the solution structure of the Schizosaccharomyces pombe SIN1 CRIM domain (Fig. 3F and fig. S6, A to C) (25, 26). The positions of the SIN1 RBD and PH domains remain unresolved. In the dataset collected for samples with added full-length Akt1 (dataset 2 in fig. S2), we observed additional low-resolution density (Fig. 3F and fig. S6, B and C) between the hypothetic CRIM domain and Rictor AD and CD in the vicinity of the mTOR active site. This density, not of sufficient resolution to assign specific interactions, may represent parts of bound Akt1 or SIN1 domains (fig. S6C).

A proposed regulatory mechanism for mTORC2 involves ubiquitylation of mLST8 on Lys305 and Lys313 (35). Loss of ubiquitylation by K305R and/or K313R mutation, or truncation of mLST8 at Tyr297, leads to mTORC2 hyperactivation and increased AKT phosphorylation (35). mLST8 Lys305 is proximal to the SIN1 helix anchoring the CRIM domain. Ubiquitylation of Lys305 would prevent association of the SIN1 helix, leading to dislocation of the SIN1 CRIM domain required for substrate recruitment (Figs. 3C and 4A). Ubiquitylation of Lys313, which is found on the lower face of mLST8 (Figs. 3C and 4A), presumably also interferes with positioning of the CRIM domain (fig. S6A).

(A) Overview of mTORC2 architecture and ligand interaction sites. Each half of the dimeric mTORC2 has three small-molecule binding sites. The kinase active site and the A-site, which is located in the peripheral region of Rictor, bind to ATP (or ATP analogs). The I-site in the middle of the FAT domain of mTOR binds InsP6. The indicated modifications on SIN1 and mLST8 affect mTORC2 assembly. Extra-density region following the CRIM domain is indicated as a gray outline. (B). Close-up view of the A-site on the periphery of the Rictor HD with bound ATPS. A hydrogen bond between ATPS and Asn543 is shown as dashed yellow lines. (C) Close-up view of the I-site in the FAT domain of mTOR. InsP6 is surrounded by a cluster of positively charged amino acids. It only directly interacts with residues of the FAT domain.

We observed two previously uncharacterized, small-molecule binding sites outside the mTOR catalytic site, which is itself occupied by ATPS. The first (A-site) (Fig. 4B and fig. S7, A and B) is located in the HD of Rictor and is thus specific to mTORC2. The second (I-site) (Fig. 4C and fig. S7C) is located in the FAT domain of mTOR and is thus common to mTORC1 and mTORC2.

The density of the small molecule in the A-site matched that of an ATP molecule and was confirmed to be ATP (or ATPS) through a comparison of cryo-EM reconstructions of mTORC2 with and without ATPS added at a near physiological concentration of 2 mM (datasets 1 and 4, figs. S2 and S7A). The A-site does not resemble any known ATP-binding site. Positively charged amino acids (Lys541, Arg575, Arg576, and Arg572) of the A-site are conserved in Rictor orthologs from yeast to human (figs. S4E and S8). Other residues are not conserved, hinting at the possibility for interactions with alternative negatively charged ligands. The A-site is located approximately 100 from the mTOR catalytic site. Ligand binding to the A-site caused neither long-range allosteric change affecting the kinase site nor local structural perturbations (fig. S9, I to L).

To investigate the effect of ligand binding to the A-site, we generated a series of Rictor variants with a mutated A-site (table S1). Variants with three or four mutated residues (A3 and A4) assembled into mTORC2 (fig. S10B), while variant A5 was defective in assembly (fig. S10, B to D). Cryo-EM reconstructions of variants A3 and A4 in the presence of ATPS (fig. S9, I to L) confirmed that the chosen mutations abolish ligand binding under near physiological conditions (figs. S7A and S9, J and L). Purified mTORC2 containing Rictor variant A3 or A4 exhibited thermal stability and kinase activity, in an Akt1 in vitro phosphorylation assay, comparable to wild-type (WT) mTORC2 (fig. S10, F to H). Complementation of a Rictor knockout (KO) in human embryonic kidney (HEK) 293T cells by transfected Rictor-WT, or Rictor variant A3 yielded comparable levels of Akt1-S473 phosphorylation (table S1 and fig. S11). Together, the above analyses indicate that ligand binding to the A-site does not directly influence mTORC2 kinase activity, suggesting rather a role in the interaction with other, yet unidentified, partner proteins of mTORC2.

The I-site is formed entirely by the FAT domain of mTOR, where a large, positively charged, pocket is lined by six lysine and two arginine residues to bind an extended ligand (Fig. 4C and fig. S7C). The I-site was still partially occupied in our reconstruction of mTORC2 prepared without addition of exogenous ATPS or other relevant ligands (fig. S7A). The copurified molecule was identified by map appearance and by ion mobility spectrometrymass spectrometry (IMS-MS) as inositol hexakisphosphate (InsP6) (figs. S7, D to F, and S12). InsP6 binds in a region, which is incomplete in related PI3Ks (36), but is generally conserved in members of the PIKK family of kinases (37). InsP6 was previously reported to associate with DNA-PKcs (38). Recently, structure determination of the PIKK family kinase SMG1 revealed InsP6 binding in a region corresponding to the I-site and led the authors to postulate a corresponding binding site in mTOR but involving both the kinase domain and FAT domain (37). InsP6 has previously been observed as a structural component of multi-subunit assemblies, including the spliceosome (39) and proteasome activator complex (40), and helical repeat regions have been identified as InsP6 interaction sites (41).

To investigate the function of InsP6 interaction, we purified recombinant mTORC2 containing mTOR I-site mutations (table S1). mTOR variants with two and three mutations, I2 and I3, yielded intact mTORC2 complexes (fig. S10A), while a variant with five mutations, I5, failed to assemble into mTORC2 (fig. S10, A and D). mTORC2 containing mTOR variants I2 and I3 displayed normal kinase activity toward Akt1 in vitro (fig. S10E). Notably, the mutations in I2 are equivalent to those reported previously to abolish completely the kinase activity of an N-terminally truncated naked mTOR fragment toward a C-terminal peptide of Akt1 (37). A possible explanation for this apparent discrepancy is provided by a reduced stability of mTORC2 assembled using the I2 variant (but not the I3 variant) (fig. S10G). This destabilizing effect might be more pronounced in an mTOR fragment than in the context of an assembled mTORC2 (fig. S10G).

To investigate a possible role of InsP6 metabolism on mTORC2 activity in HEK293T cells, we knocked down (KD) and knocked out (KO) inositol-pentakisphosphate 2-kinase (IPPK) and multiple inositol polyphosphate phosphatase 1 (MINPP1), respectively. The former enzyme generates InsP6, whereas the latter degrades it (fig. S13). These manipulations of InsP6-metabolizing enzymes did not alter mTORC2 kinase activity in nonstimulated cells or in cells stimulated with fetal calf serum (FCS) and insulin (fig. S13). These biochemical results are consistent with the observed stable binding of InsP6 to mTORC2 and suggest a role of InsP6 in mTOR folding or mTOR complex assembly, rather than as an acute transient metabolic input signal to mTORC1 or mTORC2.

Insect cell vectors from the MultiBac Baculovirus expression system (42) (Geneva Biotech, Geneva, Switzerland) have been used to clone internally FLAG-tagged pAceBAC-mTOR (FLAG after Asp258), pIDK-Rictor, pIDC-mLST8, and pAceBAC1-SIN1 using Gateway Cloning (Thermo Fisher Scientific, USA). Rictor was originally amplified from myc-Rictor, which was a gift from D. Sabatini (8) (Addgene plasmid no. 11367). Site-directed mutagenesis was used to generate mTORC2 A- and I-site variants. The following set of A-site mutants with pIDK-Rictor as template was created: Rictor_R572E_R575E_R576E (A3), Rictor_R572E_R575E_R576E_Y579A (A4), and Rictor_R572E_R575E_R576E_Y579A_L587W (A5). The following I-site mutants with FLAG-tagged pAceBAC-mTOR were generated: mTOR_K1753E_K1788E (I2), mTOR_R1628E_K1655E_K1662E (I3), and mTOR_R1628E_K1655E_K1662E_K1706E_K1735E (I5). WT Rictor and mutants A3 and A5 were subcloned into plasmid MX01 (Addgene plasmid no. 158624). SIN1 N-terminal variants were generated by inserting a tryptophan (SIN1_W), two consecutive arginines (SIN1_2R), or three consecutive arginines (SIN1_3R) using site-directed mutagenesis and pAceBAC1-SIN1 as template. Plasmids encoding FLAG-tagged mTOR, Rictor, and mLST8 were fused to a MultiBac expression plasmid using Cre-recombinase (New England Biolabs, Ipswich, USA) and transposed into a bacmid for baculovirus production. Baculovirus encoding untagged SIN1 was produced separately.

Sf21 insect cells (Expression Systems) were grown in HyClone insect cell media (GE Life Sciences), and baculovirus was generated according to Fitzgerald et al. (42). For the expression of recombinant human WT mTORC2, A- and I-site mTORC2 mutants, and mTORC2 carrying SIN1 N-terminal variants, Sf21 cells were infected at a cell density of 1 Mio/ml. Cells were coinfected with 1:100 (v/v) ratio of two undiluted supernatants from cells previously infected with baculovirus encoding FLAG-mTOR, Rictor, and mLST8 or infected with baculovirus encoding untagged SIN1, respectively. WT mTORC2, A-site mutants A3, A4, and A5, and I-site mutants I2, I3, and I5 were purified as follows: Insect cells were harvested 72 hours after infection by centrifugation at 800g for 25 min and stored at 80C until further use. Cell pellets were lysed in 50 mM bicine (pH 8.5), 200 mM NaCl, and 2 mM MgCl2 by sonication, and the lysate was cleared by ultracentrifugation. Soluble protein was incubated with 10 ml of anti-DYKDDDDK agarose beads (Genscript, Piscataway, USA) for 1 hour at 4C. The beads were transferred to a 50-ml gravity flow column (Bio-Rad) and washed four times with 200 ml of wash buffer containing 50 mM bicine (pH 8.5), 200 mM NaCl, and 2 mM EDTA. Protein was eluted by incubating beads for 30 min with 10 ml of wash buffer supplemented with synthetic DYKDDDDK peptide (0.6 mg/ml) (Genscript, Piscataway, USA). The eluate was combined with three additional elution steps using synthetic DYKDDDDK peptide (0.1 mg/ml) and 5-min incubation time. The eluted protein was concentrated using a 100,000-Da molecular mass cutoff centrifugal concentrator (Amicon) of regenerated cellulose membrane and purified by size exclusion chromatography on a custom-made Superose 6 Increase 10/600 GL gel filtration column equilibrated with 10 mM bicine (pH 8.5), 150 mM NaCl, 0.5 mM EDTA, and 2 mM tris(2-carboxyethyl)phosphine (TCEP). Purified WT mTORC2 was concentrated in gel filtration buffer to a final concentration of 3 to 3.5 mg/ml determined by A280 absorption using NanoDrop 2000 (Thermo Fisher Scientific). Sample was supplemented with 5% (v/v) glycerol and stored at 80C for later cryo-EM use. Purified mTORC2 variants with A- and I-site mutants were concentrated in gel filtration buffer to a final concentration of 0.4 to 2 mg/ml as determined by absorption at 280-nm wavelength using NanoDrop 2000 (Thermo Fisher Scientific). The resulting samples were supplemented with 5% (v/v) glycerol and stored at 80C for later use.

The coding sequence for Akt1 (43) was cloned into a pAceBAC1 expression vector (Geneva Biotech, Geneva, Switzerland) with an N-terminal His10-Myc-FLAG tag by Gateway cloning. Baculovirus was produced as described for mTORC2. Akt1 was purified with anti-DYKDDDDK agarose beads as described for mTORC2. The eluted protein was concentrated using a 10,000-Da molecular mass cutoff centrifugal concentrator (Amicon) of regenerated cellulose membrane and further purified by size exclusion chromatography with a Superdex 75 Increase column equilibrated with 10 mM bicine (pH 8.5), 150 mM NaCl, 0.5 mM EDTA, and 2 mM TCEP. Purified Akt1 was concentrated in gel filtration buffer, supplemented with 5% (v/v) glycerol, and stored at 80C for further experiments. Dephosphorylated Akt1 was obtained after overnight incubation of 4.5 mg of protein with 6 g of -protein phosphatase (New England Biolabs) in the presence of PMP buffer (New England Biolabs) and 1 mM MnCl2 before size exclusion chromatography. Successful Akt1 dephosphorylation was confirmed by Western blot with antibodies against phosphoAKT-Ser473 (no. 4060; Cell Signaling Technology, Beverly, USA) and phosphoAKT-Thr450 (no. 9267; Cell Signaling Technology, Beverly, USA) at a dilution of 1:1000 in 5 ml of Tris-buffered saline with 0.1% Tween20 (TBST). Human (Delta-PH) Akt1 protein (residues 144 to 480, mono-phosphorylated on T450), as described by Lui et al. (44) (therein referred to as Akt1KD), was provided by T. Leonard (Max-Perutz Labs, Vienna).

A-site mutants A3, A4, and A5 and I-site mutants I2, I3, and I5, and mTORC2 carrying SIN1 N-terminal variants extended by a tryptophan (SIN1_W), two consecutive arginines (SIN1_2R), and three consecutive arginines (SIN1_3R) inserted between the processed Met1 and Ala2, were immunoprecipitated in small scale using FLAG beads. Five-gram wet weight of pellets from insect cells expressing A- and I-site mutants and SIN1 N-terminal variants was lysed in 50 mM bicine (pH 8.5), 200 mM NaCl, and 2 mM MgCl2 using a Dounce homogenizer. The lysate was cleared by ultracentrifugation for 45 min at 35,000g. Soluble protein was incubated with 125 l of anti-DYKDDDDK agarose beads (Genscript, Piscataway, USA) for 1 hour at 4C. The beads were transferred to a 5-ml gravity flow column (Pierce Centrifuge Columns, Thermo Fisher Scientific) and washed with 50 ml of buffer containing 50 mM bicine (pH 8.5), 200 mM NaCl, and 2 mM EDTA. Protein was eluted by 30-min incubation of the beads with 400-l wash buffer supplemented with synthetic DYKDDDDK peptide (0.6 mg/ml) (Genscript, Piscataway, USA). Total lysate, soluble supernatant after ultracentrifugation, flow through from FLAG column, buffer wash, and elution fraction were loaded onto a 4 to 15% SDS polyacrylamide gel (Bio-Rad Laboratories). In addition, total lysate, supernatant after ultracentrifugation, and elution fraction of mTORC2 WT, SIN1 N-terminal variants, and mutants A5 and I5 were analyzed by immunoblotting using antibodies against mTOR (no. 2972; Cell Signaling Technology, Beverly, USA), SIN1 (A300-910A; Bethyl), Rictor (A300-458A; Bethyl), and actin (MAB1501; Merck Millipore) at a dilution of 1:1000 in 5 ml of TBST. A goat anti-rabbit horseradish peroxidase (HRP)labeled antibody (ab6721; Abcam, Cambridge, UK) was used as the secondary antibody at a dilution of 1:3000 in 5 ml of TBST.

mTORC2 kinase activity assays were conducted in 100 mM Hepes (pH 7.4), 1 mM EGTA, 1 mM TCEP, 0.0025% Tween 20, and 10 mM MnCl2 using dephosphorylated Akt1 as a substrate. In a 60-l reaction volume, 0.05 M of either WT or A- and I-site mutant mTORC2 was mixed with 1 M Akt1 and, where indicated, either dimethyl sulfoxide or 25 M Torin1. The mixture was preincubated for 5 min at room temperature, and the reaction was initiated by the addition of 10 M ATP. After 20 min at 37C, the reaction was terminated by the addition of 60 l of 2 Laemmli sample buffer. The reactions were analyzed by Western blotting using primary antibodies against phosphoAKT-Ser473 (no. 4060; Cell Signaling Technology, Beverly, USA), phosphoAKT-Thr450 (no. 9267; Cell Signaling Technology, Beverly, USA), AKT (no. 4685), and mTOR (no. 2972; Cell Signaling Technology, Beverly, USA), anti-FLAG antibodies (Sigma-Aldrich, F1804), SIN1 (Bethyl, A300-910A), and Rictor (Bethyl, A300-458A) at a dilution of 1:1000 in 5 ml of TBST. A goat anti-rabbit HRP-labeled antibody (ab6721; Abcam, Cambridge, UK) was used as the secondary antibody at a dilution of 1:3000 in 5 ml of TBST. Signals were detected using the Enhanced Chemiluminescence (ECL) Kit SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific). Images were acquired using a Fusion FX (Vilber) imaging system.

Thermal unfolding was monitored by differential scanning fluorimetry (DSF) based on internal tryptophane fluorescence on a Prometheus NT.48 instrument (NanoTemper Technologies). Purified WT mTORC2 or mTORC2 containing mutations in A- or I-site was diluted to 0.1 mg/ml in 10 mM bicine (pH 8.5), 150 mM NaCl, 0.5 mM EDTA, and 2 mM TCEP. High-precision capillaries (NanoTemper Technologies) were filled with 10-l sample and placed on the sample holder. A temperature gradient of 0.1C/min from 22 to 65C was applied, and fluorescence intensity at 330 and 350 nm was recorded. A plot of the ratio of fluorescence intensities at those wavelengths (F350/F330) was generated using a Python script. The experiment was repeated two times with five replicates per sample run each time. Melting points were calculated using PR.ThermControl software version 2.1.2. Data were analyzed using GraphPad Prism version 8.0.0 (GraphPad Software, San Diego, CA, USA) to generate the mean and SD of the melting points. One outlier, likely resulting from capillary handling, for sample A4 was excluded from data analysis.

HEK293T cells were cultured and maintained in Dulbeccos modified Eagles medium (DMEM) high glucose with 10% FCS, 4 mM glutamine, 1 mM sodium pyruvate, and 1 penicillin/streptomycin. RICTOR KO cells were generated as described by Bossler et al. (45). Four micrograms of plasmids harboring RICTOR-WT, RICTOR-A_3, and RICTOR-A_5 was transfected with jetPRIME (Polyplus). Twenty-four hours after transfection, cells were starved for serum for overnight and stimulated with 10% FCS and 100 nM insulin for 15 min. Total cell lysates were prepared in lysis buffer containing 100 mM tris-HCl (pH 7.5), 2 mM EDTA, 2 mM EGTA, 150 mM NaCl, 1% Triton X-100, complete inhibitor cocktail (Roche), and PhosSTOP (Roche). Protein concentration was determined by a Bradford assay, and equal amounts of protein were separated by SDSpolyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto nitrocellulose membranes (GE Healthcare). Antibodies used were as follows: AKT (1:1000 dilution, catalog no. 2920, Cell Signaling Technology), AKT-pS473 (1:1000, catalog no. 4060, Cell Signaling Technology), RICTOR (1:1000, catalog no. 2040, Cell Signaling Technology), ACTIN (1:2000, catalog no. MAB1501, Millipore), IRDye 800CW goat anti-rabbit immunoglobulin G (IgG) (1:20,000, catalog no. 926-32211, LI-COR), and IRDye 680RD goat anti-mouse IgG (1:20,000, catalog no. 926-68070). All antibodies were diluted in 10 ml of TBST and Licor intercept (TBS) blocking buffer (1:1). Signals were detected by LI-COR Fc (LI-COR Biosciences).

HEK293T cells were cultured and maintained in DMEM high glucose with 10% FCS, 4 mM glutamine, 1 mM sodium pyruvate, and 1 penicillin/streptomycin. For KD of IPPK and MINPP1, 0.1 106 cells per well were seeded in a six-well plate and transfected with 100 nM small interfering RNA (siRNA) using the jetPRIME (Polyplus) system. After 32 hours, cells were washed twice with phosphate-buffered saline (PBS) (/) and starved for serum for 16 hours. Forty-eight hours after transfection, cells were incubated at 37C with PBS (+/+) for 10 min followed by stimulation with 10% FCS and 100 nM insulin for 15 min at 37C. Cells were washed with ice-cold PBS (/) and harvested for SDS-PAGE or RNA isolation for quantitative polymerase chain reaction (qPCR) analysis. KO experiments were conducted as described above, using generated KO cells instead of transfection with siRNA. Total cell lysates were prepared in M-PER lysis buffer (Thermo Fisher Scientific) containing complete inhibitor cocktail (Roche) and PhosSTOP (Roche), and protein concentrations were determined by Bradford assay. Equal amounts of protein were separated by SDS-PAGE and transferred onto nitrocellulose membranes (GE Healthcare), and signals were detected by LI-COR Fc (LI-COR Biosciences). Antibodies used were as follows: AKT (1:1000, catalog no. 2920, Cell Signaling Technology), AKT-pS473 (1:1000, catalog no. 4060, Cell Signaling Technology), ACTIN (1:5000, catalog no. MAB1501, Millipore), IRDye 800CW goat anti-rabbit IgG (1:20,000, catalog no. 926-32211, LI-COR), and IRDye 680RD goat anti-mouse IgG (1:20,000, catalog no. 926-68070). All antibodies were diluted in 10 ml of TBST and Licor intercept (TBS) blocking buffer (1:1).

For qPCR, total RNA was isolated using the RNeasy Kit (Qiagen). RNA was reverse-transcribed to complementary DNA (cDNA) using the iScript cDNA Synthesis Kit (Bio-Rad). Semiquantitative real-time PCR analysis was performed using Fast SYBR Green (Applied Biosystems). Relative expression levels were determined by normalizing each CT values to POLR2A using the CT method. The sequence for the primers used in this study was as follows: IPPK-fw, 5-AATGAATGGGGGTACCACGG-3; IPPK-rv, 5-AACTTCAGAAACCGCAGCAC-3; MINPP1-fw, 5-AGCTACTTTGCAAGTGCCAG-3; MINPP1-rv, 5-TGCATGACCAAACTGGAGGA-3.

KO cells were generated using the LentiCRISPR system as described by Sanjana et al. (46). Guide RNAs (gRNAs) against IPPK and MINPP1 were expressed from LentiCRISPRv2 (gifts from F. Zhang; Addgene plasmid nos. 49535 and 52961) by transfection of HEK293T cells with 1 g of DNA using jetPRIME. The following gRNA target sequences were used: IPPK gRNA, 5-TCGGCCGGTGCTCTGCAAAG-3; MINPP1 gRNA, 5-ATCCAGTCCGCGTACCACAA-3. Following transfection, cells were selected with puromycin, propagated, and screened for loss of target protein by qPCR. DNA sequencing of PCR products confirmed insertions or deletions leading to interrupted sequencing reactions. Pools of KO cells were used to avoid clonal variation. HEK293T cells transfected with empty vector were used as control.

Ten micrograms of mTORC2 I-site mutants I2 and I3 and A-site mutants A3, A4, and A5 was dissolved in 50 l of digestion buffer [1% sodium deoxycholate (SDC), 0.1 M tris, 10 mM TCEP, 15 mM chloroacetamide (CAA) (pH 8.5)] using vortexing for trypsin digestion. For endoproteinase GluC and chymotrypsin digestion, the same protein aliquots were dissolved in 20 l of a digestion buffer consisting of 1 M urea, 0.1 M ammonium bicarbonate, 10 mM TCEP, and 15 mM CAA. Samples were either incubated for 10 min at 95C (trypsin) or 1 hour at 37C (GluC and chymotrypsin) to reduce and alkylate disulfide bonds. Protein aliquots were digested overnight at 37C by incubation with sequencing-grade modified trypsin, GluC, and chymotrypsin (all 1:50, w/w; Promega). Then, the peptides were cleaned up using iST cartridges (PreOmics, Munich) according to the manufacturers instructions. Samples were dried under vacuum and dissolved in LC-buffer A (0.1% formic acid) at a concentration of 0.05 g/l.

To enhance the sensitivity of the liquid chromatographyMS (LC-MS) analysis, a label-free targeted LC-MS approach was carried out. Therefore, three lists of peptides considering the cleavage specificity of the three proteases used and containing all mutation sites were generated. The peptide sequences were imported into Skyline (version 20.1; https://brendanx-uw1.gs.washington.edu/labkey/project/home/software/Skyline/begin.view) to generate a mass isolation list of all doubly and triply charged precursor ions for each protease. These were then loaded into a Q Exactive plus LC-MS platform and analyzed using the following settings: The setup of the RPLC-MS system was as described previously (47). Chromatographic separation of peptides was carried out using an EASY nano-LC 1000 system (Thermo Fisher Scientific), equipped with a heated RP-HPLC column (75 m by 30 cm) packed in-house with 1.9-m C18 resin (Reprosil-AQ Pur, Maisch). Peptides were analyzed per LC-MS/MS run using a linear gradient ranging from 95% solvent A (0.15% formic acid and 2% acetonitrile) and 5% solvent B (98% acetonitrile, 2% water, and 0.15% formic acid) to 45% solvent B over 60 min at a flow rate of 200 nl/min. MS analysis was performed on a Q Exactive plus mass spectrometer equipped with a nano-electrospray ion source (both Thermo Fisher Scientific). Each MS cycle consisted of one MS1 scan followed by high-collision dissociation of the selected precursor ions in the isolation mass lists. Total cycle time was approximately 2 s. For MS1, 3 106 ions were accumulated in the Orbitrap cell over a maximum time of 50 ms and scanned at a resolution of 35,000 FWHM [at 200 mass/charge ratio (m/z)]. MS2 scans were acquired at a target setting of 3 106 ions, accumulation time of 110 ms, and a resolution of 35,000 FWHM (at 200 m/z). The normalized collision energy was set to 27%, the mass isolation window was set to 0.4 m/z, and one microscan was acquired for each spectrum.

The acquired raw files were converted to the mascot generic file (mgf) format using the msconvert tool [part of ProteoWizard, version 3.0.4624 (2013-6-3)]. Using the MASCOT algorithm (Matrix Science, version 2.4.1), the mgf files were searched against a decoy database containing normal and reverse sequences of the predicted SwissProt entries of Homo sapiens (www.ebi.ac.uk, release date 9 December 2019), the mTOR and Rictor mutations, and commonly observed contaminants (in total 41,556 sequences for H. sapiens) generated using the SequenceReverser tool from the MaxQuant software (version 1.0.13.13). The precursor ion tolerance was set to 10 ppm, and fragment ion tolerance was set to 0.02 Da. The search criteria were set as follows: Full tryptic specificity was required (cleavage after lysine or arginine residues unless followed by proline), three missed cleavages were allowed, carbamidomethylation (C) was set as a fixed modification, and oxidation (M) was set as a variable modification. Next, the database search results were imported to the Scaffold Q+ software (version 4.3.2, Proteome Software Inc., Portland, OR), and the protein false identification rate was set to 1% based on the number of decoy hits. Specifically, peptide identifications were accepted if they could be established at greater than 97.0% probability to achieve a false discovery rate less than 1.0% by the scaffold local FDR algorithm. Protein identifications were accepted if they could be established at greater than 65.0% probability to achieve an FDR less than 1.0% and contained at least one identified peptide. Protein probabilities were assigned by the Protein Prophet program (48). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters. Last, a spectral library (*.blib) was generated from the assigned MS/MS spectra and imported to Skyline together with the acquired raw data files. Only precursor ions confidently identified by database searching and present in the spectral library were used for quantitative analysis. Quantitative result reports were further analyzed by Microsoft Excel and PRISM (GraphPad Software, San Diego, USA).

Different conditions were screened for mTORC2 in the presence and absence of substrates (fig. S2). For all conditions, freshly thawed mTORC2 aliquots were used to prepare samples with an mTORC2 concentration of 0.37 mg/ml. Shortly before grid preparation, the samples were diluted to reach a final mTORC2 concentration of 0.12 mg/ml.

For each grid, a small piece of continuous carbon was floated on top of the sample for 1 min. The carbon was then picked with a Quantifoil R2/2 holey carbon copper grid (Quantifoil Micro Tools), which was swiftly mounted in a Vitrobot (Thermo Fischer Scientific) whose chamber was set to 4C and 100% humidity. Five microliters of buffer was then added on top of the grid on the side showing the carbon covered with particles, which was immediately blotted with a setting of 0- to 6-s blotting time and rapidly plunge-frozen in a mixture 2:1 of propane:ethane (Carbagas).

Data were collected using a Titan Krios (Thermo Fisher Scientific) transmission electron microscope equipped with either a K2 Summit direct electron detector (Gatan), a K3 direct electron detector (Gatan), or a Falcon 3EC direct electron detector (Thermo Fisher Scientific) using either EPU (Thermo Fisher Scientific) or SerialEM (fig. S2) (49). Cameras were used in counting and/or super-resolution mode. During data collection, the defocus was varied between 1 and 3 m and four exposures were collected per holes. Stacks of frames were collected with a pixel size of 0.84 /pixel and a total dose of about 70 e/2.

For all datasets, the initial processing was done in similar fashion. First, the stacks of frames were aligned and dose-weighted using Motioncor2 (50). GCTF (51) was used to estimate the contrast transfer function (CTF) of the nondose-weighted micrographs. After a selection of good micrographs using both the quality of the power spectra and the quality of the micrographs themselves as criteria, particles were picked using batchboxer from the EMAN1.9 package (52) using particle averages from manually picked particles as references. Particles were extracted using Relion3.0 (53), followed by two rounds of two-dimensional (2D) classification using cryoSPARCv2 (Structura Biotechnology Inc.) (table S2) (54). The first reference was generated by ab initio reconstruction using cryoSPARCv2. Good particles from 2D classification were then used for a homogeneous 3D refinement followed by nonuniform refinement using cryoSPARCv2. Two masks were then generated manually around each half of the pseudo-dimeric mTORC2 using UCSF Chimera (55), and two focused refinements around each half of the complex using cryoSPARCv2 were performed using those masks. For dataset 1, which contained PH-Akt1, the resolution was further improved by performing Bayesian particle polishing (53) followed by CTF refinement using Relion3.1. Those particles were again subjected to a round of nonuniform refinement and local refinement using cryoSPARC v2. For each reconstruction, the maps were sharpened using phenix.auto_sharpen (56) or were transformed to structure factors using phenix.map_to_structure_factors (56) and sharpened in COOT (57).

Further 3D classifications without alignment for local structural variability close to the catalytic center were performed using the particles from the datasets containing the purified Akt1 and, independently, the ones from the dataset with PH-Akt1 using Relion3.0 (53) and using a mask manually created in UCSF Chimera (55). After classification, the particles were used for refinement using cryoSPARCv2 (Structura Biotechnology Inc.). To compare the density of the sample with and without ATPS, the final density (volume A) was filtered to 4.2 and compared to the density without ATPS (volume F). Difference density was calculated using UCSF ChimeraX (58).

First, mTOR and mLST8 models were taken from the EM structure of mTORC2 [Protein Data Bank (PDB): 5ZCS (20)] and each fold was rigid bodyfitted into the better half of the density. Minor changes in mTOR conformation were done manually to fit the density, and then Rictor and SIN1 were manually built de novo using COOT (57). Map quality enabled direct model building for structured regions, and lower-resolution density provided connectivity information for assigning and linking regions of Sin1 and Rictor as shown in figs. S4C and S5B. The second half of mTORC2 was made by copying and rigid body fitting each chain of the first half in the second one. Last, the structure of either one- or two-sided mTORC2 was refined using phenix.real_space_refine (table S2) (56), using Ramachandran and secondary structure restraints. As the horns of mTOR were flexible and their local resolutions were considerably lower, additional reference restraints were applied, using PDB: 6BCX (23) as reference. The model was then validated by comparing the Fourier Shell Correlations (FSC) calculated for the experimental density and the models (fig. S3). In addition, both the half and full structure were also refined in their respective half map (half map 1) and the FSCs of this structure against the same half map (half map 1), the other half (half map 2), and the full map were compared. The similarity of the curves shows that the structure was not overfitted.

InsP6 (Sigma-Aldrich) was directly dissolved in 10 mM ammonium acetate (pH 8.5) and diluted to 50 M. mTORC2 in cryo-EM buffer was buffer-exchanged and concentrated in 10 mM ammonium acetate (pH 8.5) using an Amicon Ultra-0.5 mLMWCO 100kDa. The concentrated complex was mixed with an equal volume of Phenol at pH 8, thoroughly vortexed for 30 s, and incubated at room temperature for 30 min. The tube was then centrifuged for 5 min at 15,000g. The aqueous phase was then used for MS. A sample containing only buffer and no protein was subjected to the same treatment for reference. The samples were then mixed with four volumes of injection buffer [90% acetonitrile, 9% methanol, 50 mM ammonium acetate (pH 7)] and directly injected using a Hamilton syringe in Synapt G2-SI HDMS (Waters) in negative mode and using the T-Wave IMS.

All density and structure representations were generated using UCSF ChimeraX (58). Difference densities were calculated in ChimeraX using the volume subtract command. Local resolutions were estimated using cryoSPARC v2 (Structura Biotechnology Inc.). The electrostatic surface representation of Rictor was generated using APBS [Adaptive Poisson-Boltzmann Solver (59)]. Multiple sequence alignment was performed using Clustal Omega (60) and visualized with Espript (61). Conservation analysis was done with AL2CO (62) and visualized in UCSF ChimeraX (58).

Acknowledgments: We thank T. Sharpe at the Biophysics facility and A. Schmidt at the Proteomics Core Facility of Biozentrum and the sciCORE scientific computing facility, all from University of Basel. We thank M. Leibundgut for advice with model building, A. Jomaa and S. Mattei for advice on cryo-EM data processing, the ETH scientific center for optical and electron microscopy (ScopeM), and, in particular, M. Peterek and P. Tittmann for technical support. We are indebted to E. Laczko and J. Hu of the Functional Genomics Center Zrich for the help with mass spectrometry. We thank I. Lui and T. Leonard (Max F. Perutz Laboratories, Vienna) for providing (Delta-PH) Akt1 protein. Funding: F.M. and K.B. are recipients of a fellowship from the Biozentrum International PhD program. This work was supported by the Swiss National Science Foundation (SNSF) via the National Center of Excellence in RNA and Disease (project funding 138262) to N.B. and M.N.H. and SNSF project funding 179323 and 177084 to T.M. Author contributions: A.S. designed the experiments, prepared the sample for cryo-EM, and carried out data processing and structure modeling. A.S. and D.B. performed data collection. F.M. designed the experiments; cloned Akt1, mTORC2 mutants, and Rictor mutants; expressed and purified proteins; and performed the activity assays and the nano-DSF measurements. E.S. established the mTORC2 purification procedure. S.I. cloned mTORC2 and contributed to data analysis and manuscript preparation. M.S. performed the in-cell analysis of mTORC2 activity. K.B. and M.S. performed the KO/KD of MINPP1 and IPPK. A.S., F.M., D.B., S.I., N.B., M.N.H., and T.M. participated in the writing of the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The high-resolution cryo-EM map of the half-mTORC2 (density C) and full-mTORC2 (density A) has been deposited in the Electron Microscopy Data Bank as EMD-11492 and EMD-11488, respectively, while the corresponding models are in the Protein Data Bank as PDB ID 6ZWO and 6ZWM. In addition, the density of mTORC2 in the absence of ATPS (density F), as well as the densities showing extra density (densities G and H) were deposited in the Electron Microscopy Data Bank as EMD-11489, EMD-11491, and EMD-11490, respectively. Plasmid MX01 is available from Addgene. Requests for materials should be addressed to T.M.

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The 3.2- resolution structure of human mTORC2 - Science Advances

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Cross-reactive neutralization of SARS-CoV-2 by serum antibodies from recovered SARS patients and immunized animals – Science Advances

Abstract

The current coronavirus disease 2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus genetically close to SARS-CoV. To investigate the effects of previous SARS-CoV infection on the ability to recognize and neutralize SARS-CoV-2, we analyzed 20 convalescent serum samples collected from individuals infected with SARS-CoV during the 2003 SARS outbreak. All patient sera reacted strongly with the S1 subunit and receptor binding domain (RBD) of SARS-CoV; cross-reacted with the S ectodomain, S1, RBD, and S2 proteins of SARS-CoV-2; and neutralized both SARS-CoV and SARS-CoV-2 S proteindriven infections. Analysis of antisera from mice and rabbits immunized with a full-length S and RBD immunogens of SARS-CoV verified cross-reactive neutralization against SARS-CoV-2. A SARS-CoVderived RBD from palm civets elicited more potent cross-neutralizing responses in immunized animals than the RBD from a human SARS-CoV strain, informing strategies for development of universal vaccines against emerging coronaviruses.

The global outbreak of the coronavirus disease 2019 (COVID-19) was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is a new coronavirus (CoV) genetically close to SARS-CoV that emerged in 2002 (13). As of 25 May 2020, a total of 5,307,298 confirmed COVID-19 cases, including 342,070 deaths, have been reported from 216 countries or regions, and the numbers are still growing rapidly (https://who.int). Unfortunately, even though 17 years passed, we have not developed effective prophylactics and therapeutics in preparedness for the reemergence of SARS or SARS-like CoVs. A vaccine is urgently needed to prevent the human-to-human transmission of SARS-CoV-2.

Like SARS-CoV and many other CoVs, SARS-CoV-2 uses its surface spike (S) glycoprotein to gain entry into host cells (46). Typically, the S protein forms a homotrimer with each protomer consisting of S1 and S2 subunits. The N-terminal S1 subunit is responsible for virus binding to the cellular receptor angiotensin-converting enzyme 2 (ACE2) through an internal receptor binding domain (RBD) that is capable of functional folding independently, whereas the membrane-proximal S2 subunit mediates membrane fusion events. While SARS-CoV-2 and SARS-CoV share about 80% homology in full-length genome sequences, their S proteins have about 76% amino acid identity (2, 3). The RBD sequences of the two viruses are only about 74% identical, with most mutations occurring in the receptor-binding motifs (RBMs) (~50% amino acid identity). It was found that the ACE2-binding affinity of the SARS-CoV-2 RBD is 10- to 20-fold higher than that of the SARS-CoV RBD, which may contribute to the higher transmissibility of SARS-CoV-2 (7). Very recently, the prefusion structure of the SARS-CoV-2 S protein was determined by cryoelectron microscopy, which revealed an overall similarity to that of SARS-CoV (5, 7); the crystal structure of the SARS-CoV-2 RBD in complex with ACE2 was also determined by several independent groups, and the residues or motifs critical for the higher-affinity RBD-ACE2 interaction were identified (810). As seen, the SARS-CoV-2 RBD binds ACE2 in the same orientation with the SARS-CoV RBD and relies on conserved, mostly aromatic, residues. The structures have also provided evidence to support a mechanism of infection triggering that is thought to be conserved among the Coronaviridae, wherein the S protein undergoes distinct conformational states with the RBD closed (receptor-inaccessible) or opened (receptor-accessible).

The S protein of CoVs is also a main target of neutralizing antibodies (nAbs), thus being considered an immunogen for vaccine development (5, 11). During the SARS-CoV outbreak in 2002, we took immediate actions to characterize the immune responses in infected SARS patients and in inactivated virus vaccine- or S proteinimmunized animals (1220). We demonstrated that the S protein RBD dominates the nAb response against SARS-CoV infection and thus proposed an RBD-based vaccine strategy (11, 1522). Our follow-up studies verified a potent and persistent anti-RBD response in recovered SARS patients (2325). Although SARS-CoV-2 and SARS-CoV share substantial genetic and functional similarities, their S proteins, especially in the RBD sequences, display relatively larger divergences. Toward developing vaccines and immunotherapeutics against emerging CoVs, it is fundamentally important to characterize the antigenic cross-reactivity between SARS-CoV-2 and SARS-CoV.

A panel of serum samples collected from 20 patients who recovered from SARS-CoV infection was analyzed for the antigenic cross-reactivity with SARS-CoV-2. First, we examined the convalescent sera by a commercial diagnostic enzyme-linked immunosorbent assay (ELISA) kit, which uses a recombinant nucleocapsid (N) protein of SARS-CoV-2 as detection antigen. As shown in Fig. 1A, all the serum samples at a 1:100 dilution displayed high reactivity, verifying that the N antigen is highly conserved between SARS-CoV and SARS-CoV-2. As tested by ELISA, each of the patient sera also reacted with the SARS-CoV S1 subunit and its RBD strongly (Fig. 1B). Then, we determined the cross-reactivity of the patient sera with four recombinant protein antigens derived from the S protein of SARS-CoV-2, including S ectodomain (designated S), S1 subunit, RBD, and S2 subunit. As shown in Fig. 1C, all the serum samples also reacted strongly with the S and S2 proteins, but they were less reactive with the S1 and RBD proteins.

(A) Reactivity of sera from 20 recovered patients with SARS-CoV (P01 to P20) with the nucleoprotein (N) of SARS-CoV-2 was measured by a commercial ELISA kit. The positive (pos) or negative (neg) control serum sample provided in the kit was collected from a convalescent SARS-CoV-2infected individual or healthy donor. (B) Reactivity of convalescent SARS sera with the recombinant S1 and RBD proteins of SARS-CoV. (C) Reactivity of convalescent SARS sera with the S ectodomain (designated S), S1, RBD, and S2 proteins of SARS-CoV-2. Serum samples from two healthy donors were used as negative control (Ctrl-1 and Ctrl-2). The experiments were performed with duplicate samples and repeated three times, and data are shown as means with SDs. OD450, optical density at 450 nm.

Limited by facility that can handle authentic viruses, we developed a pseudovirus-based single-cycle infection assay to determine the cross-neutralizing activity of the convalescent SARS sera on SARS-CoV and SARS-CoV-2. A control lentivirus was pseudotyped with vesicular stomatitis virus G protein (VSV-G). Initially, the serum samples were analyzed at a 1:20 dilution. As shown in Fig. 2A, all the sera efficiently neutralized both the SARS-CoV and SARS-CoV-2 pseudoviruses to infect 293T/ACE2 cells, and in comparison, each serum had lower efficiency in inhibiting SARS-CoV-2 as compared to SARS-CoV. None of the immune sera showed appreciable neutralizing activity on VSV-G pseudovirus. The neutralizing titer for each patient serum was then determined. As shown in Fig. 2B, the patient sera could neutralize SARS-CoV with titers ranging from 1:120 to 1:3240 and could cross-neutralized SARS-CoV-2 with titers ranging from 1:20 to 1:360. In a highlight, the patient P08 serum had the highest titer to neutralize SARS-CoV (1:3240) when it neutralized SARS-CoV-2 with a titer of 1:120; the patient P13 serum showed the highest titer on SARS-CoV-2 (1:360) when it had a 1:1080 titer to efficiently neutralize SARS-CoV.

(A) Neutralizing activities of convalescent patient sera (1:20 dilution) against SARS-CoV, SARS-CoV-2, and VSV-G control were tested by a single-cycle infection assay. (B) Neutralizing titers of each of the convalescent patient sera on the three pseudotypes were measured. The experiments were performed with triplicate samples and repeated three times, and data are shown as means with SDs.

To comprehensively characterize the cross-reactivity between the S proteins of SARS-CoV and SARS-CoV-2, we generated mouse antisera against the S protein of SARS-CoV by immunization. Here, three mice (M-1, M-2, and M-3) were immunized with a recombinant full-length S protein in the presence of MPL-TDM adjuvant (monophosphoryl lipid A plus trehalose dicorynomycolate), while two mice (M-4 and M-5) were immunized with the S protein plus alum adjuvant (fig. S1). Binding of antisera to diverse S antigens were initially examined by ELISA. As shown in Fig. 3A, the mice immunized by the S protein with the MPL-TDM adjuvant developed relatively higher titers of antibody responses as compared to the two mice with the alum adjuvant. It was expected that the adjuvanticity of alum formulation was weaker than that of MPL-TDM. Apparently, each of the mouse antisera had high cross-reactivity with the SARS-CoV-2 S and S2 proteins, but the cross-reactive antibodies specific for the SARS-CoV-2 S1 and RBD were relatively lower except that in mouse M-3. Subsequently, the neutralizing capacity of mouse anti-S sera was measured with pseudoviruses. As shown in Fig. 3 (B to F), all the antisera, diluted at 1:40, 1:160, or 1:640, potently neutralized SARS-CoV, and consistently, they were able to cross-neutralize SARS-CoV-2 although with reduced capacity relative to SARS-CoV.

(A) Binding activity of mouse anti-S sera at a 1:100 dilution to SARS-CoV (S1 and RBD) and SARS-CoV-2 (S, S1, RBD, and S2) antigens was determined by ELISA. A healthy mouse serum was tested as control. (B to F) Neutralizing activity of mouse anti-S sera at indicated dilutions against SARS-CoV, SARS-CoV-2, and VSV-G pseudoviruses was determined by a single-cycle infection assay. The experiments were performed in triplicate and repeated three times, and data are shown as means with SDs. Statistical significance was tested by two-way ANOVA with Dunnett posttest. **P 0.01 and ***P 0.001.

As the S protein RBD dominates the nAb response to SARS-CoV, we sought to characterize the RBD-mediated cross-reactivity and neutralization on SARS-CoV-2. To this end, we first generated mouse anti-RBD sera by immunization with two RBD-Fc fusion proteins: one encoding the RBD sequence of a palm civet SARS-CoV strain SZ16 (SZ16-RBD) and the second one with the RBD sequence of a human SARS-CoV strain GD03 (GD03-RBD). Both the fusion proteins were expressed in 293T cells and purified to apparent homogenicity (fig. S1). As shown in Fig. 4A, all eight mice developed robust antibody responses against the SARS-CoV S1 and RBD, and in comparison, four mice (M-1 to M-4) immunized with SZ16-RBD exhibited higher titers of antibody responses than the mice (M-5 to M-8) immunized with GD03-RBD. Each of the anti-RBD sera cross-reacted well with the S protein of SARS-CoV-2, suggesting that SARS-CoV and SARS-CoV-2 do share antigenically conserved epitopes in the RBD sites. Noticeably, while the SZ16-RBD immune sera also reacted with the SARS-CoV-2 S1 and RBD antigens, the cross-reactivity of the GD03-RBD immune sera was low. However, while the mouse anti-RBD sera at 1:50 dilutions were measured with increased coating antigens in ELISA, they reacted with the SARS-CoV-2 S1 and RBD efficiently, which verified the cross-reactivity (Fig. 4B). Similarly, the neutralizing activity of mouse antisera was determined by pseudovirus-based single-cycle infection assay. As shown in Fig. 4 (C and D), both the SZ16-RBD and GD03-RBDspecific antisera displayed very potent activities to neutralize SARS-CoV; they also cross-neutralized SARS-CoV-2 with relatively lower efficiencies. As judged by the neutralizing activity at the highest serum dilution, the SZ16-RBD antisera were more potent than the GD03-RBD antisera in neutralizing SARS-CoV; however, the two antisera had no significant difference in neutralizing SARS-CoV-2 (Fig. 4, E and F).

(A) Binding activity of mouse antisera at a 1:100 dilution to SARS-CoV (S1 and RBD) and SARS-CoV-2 (S, S1, and RBD) antigens was determined by ELISA. A healthy mouse serum was tested as control. (B) The cross-reactivity of mouse antisera with the SARS-CoV-2 S1 and RBD proteins. The antisera were diluted at 1:50, and the S1 and RBD antigens were coated at 100 ng per ELISA plate well. (C and D) Neutralizing activities of mouse antisera at indicated dilutions against SARS-CoV, SARS-CoV-2, and VSV-G pseudoviruses were determined by a single-cycle infection assay. The experiments were performed in triplicate and repeated three times, and data are shown as means with SDs. (E and F) Comparison of neutralizing activities of the mouse antiSZ16-RBD and antiGD03-RBD sera. Statistical significance was tested by two-way ANOVA with Dunnett posttest. ns, not significant. *P 0.05, **P 0.01, and ***P 0.001.

We further developed rabbit antisera by immunizations, in which two rabbits were immunized with SZ16-RBD (R-1 and R-2) or with GD03-RBD (R-3 and R-4). Each RBD protein elicited antibodies highly reactive with both the SARS-CoV and SARS-CoV-2 antigens (Fig. 5A), which were different from their immunizations in mice. As expected, all of the rabbit antisera potently neutralized SARS-CoV and SARS-CoV-2 in a similar profile with that of the mouse anti-S and anti-RBD sera (Fig. 5, B and C). Obviously, the neutralizing activity of rabbit antiSZ16-RBD sera against both the viruses was higher than that of the rabbit antiGD03-RBD sera (Fig. 5, D and E). Together, the results verified that the SARS-CoV S protein and its RBD immunogens can induce cross-neutralizing antibodies toward SARS-CoV-2 by vaccination.

(A) Binding activity of rabbit antisera at a 1:100 dilution to SARS-CoV (S1 and RBD) and SARS-CoV-2 (S protein and RBD) antigens was determined by ELISA. A healthy rabbit serum was tested as control. (B and C) Neutralizing activities of rabbit antisera or control serum at indicated dilutions on SARS-CoV, SARS-CoV-2, and VSV-G pseudoviruses were determined by a single-cycle infection assay. The experiments were done in triplicate and repeated three times, and data are shown as means with SDs. (D and E) Comparison of neutralizing activities of the rabbit antiSZ16-RBD and antiGD03-RBD sera. Statistical significance was tested by two-way ANOVA with Dunnett posttest. *P 0.05, **P 0.01, and ***P 0.001.

To validate the observed cross-reactive neutralization and explore the underlying mechanism, we purified anti-RBD antibodies from the rabbit antisera above. As shown in Fig. 6 (A and B), both purified rabbit antiSZ16-RBD and antiGD03-RBD antibodies reacted strongly with the SARS-CoV RBD protein and cross-reacted with the SARS-CoV-2 S and RBD but not S2 proteins in a dose-dependent manner. Moreover, the purified antibodies dose-dependently neutralized SARS-CoV and SARS-CoV-2 but not VSV-G (Fig. 6, C and D). Consistent with their antisera, the rabbit antiSZ16-RBD antibodies were more active than the rabbit antiGD03-RBD antibodies against both SARS-CoV and SARS-CoV-2 (Fig. 6, E and F). Next, we investigated whether the rabbit anti-RBD antibodies block RBD binding to 293T/ACE2 cells by flow cytometry. As expected, both the SARS-CoV and SARS-CoV-2 RBD proteins could bind to 293T/ACE2 cells in a dose-dependent manner and, in line with previous findings, that the RBD of SARS-CoV-2 bound to ACE2 more efficiently (fig. S2). Unexpectedly, the antibodies purified from SZ16-RBDimmunized rabbits (R-1 and R-2) potently blocked the binding of both the RBD proteins, whereas the antibodies from GD03-RBDimmunized rabbits (R-3 and R-4) had no such blocking functionality except a high concentration of the rabbit R-3 antibody on the SARS-CoV RBD binding (Fig. 7).

Binding titers of purified rabbit antiSZ16-RBD (A) and antiGD03-RBD (B) antibodies (Abs) to the SARS-CoV (RBD) and SARS-CoV-2 (S, RBD, and S2) antigens were determined by ELISA. A healthy rabbit serum was tested as control. (C and D) Neutralizing titers of purified rabbit antiSZ16-RBD and antiGD03-RBD antibodies on SARS-CoV, SARS-CoV-2, and VSV-G pseudoviruses were determined by a single-cycle infection assay. The experiments were done in triplicate and repeated three times, and data are shown as means with SDs. (E and F) Comparison of neutralizing activities of the rabbit antiSZ16-RBD and antiGD03-RBD antibodies.

(A) Blocking activity of rabbit anti-RBD antibodies on the binding of SARS-CoV RBD (first two panels) or SARS-CoV-2 RBD (last two panels) to 293T/ACE2 cells was determined by flow cytometry. FITC-A, fluorescein isothiocyanate-labeled concanavalin A. (B) Purified rabbit anti-RBD antibodies inhibited the RBD-ACE2 binding dose-dependently. The experiments were repeated three times, and data are shown as means with SDs. Statistical significance was tested by two-way ANOVA with Dunnett posttest. *P 0.05 and **P 0.01.

To develop effective vaccines and immunotherapeutics against emerging CoVs, the antigenic cross-reactivity between SARS-CoV-2 and SARS-CoV is a key scientific question that needs to be addressed as soon as possible. However, after the SARS-CoV outbreak more than 17 years ago, there are very limited blood samples from SARS-CoVinfected patients available for such studies. At the moment, Hoffmann et al. (26) analyzed three convalescent patient with SARS sera and found that both SARS-CoV-2 and SARS-CoV S protein-driven infections were inhibited by diluted sera, but the inhibition of SARS-CoV-2 was less efficient; Ou et al. (27) detected one patient with SARS serum that was collected at 2 years after recovery, which showed a serum neutralizing titer of >1:80 dilution for SARS-CoV pseudovirus and of 1:40 dilution for SARS-CoV-2 pseudovirus. While these studies supported the cross-neutralizing activity of the convalescent SARS sera on SARS-CoV-2, a just published study with the plasma from seven SARS-CoVinfected patients suggested that cross-reactive antibody binding responses to the SARS-CoV-2 S protein did exist, but cross-neutralizing responses could not be detected (28). In this study, we first investigated the cross-reactivity and neutralization with a panel of precious immune sera collected from 20 recovered SARS patients. As shown, all the patient sera displayed high titers of antibodies against the S1 and RBD proteins of SARS-CoV and cross-reacted strongly with the S protein of SARS-CoV-2. In comparison, the patient sera had higher reactivity with the S2 subunit of SARS-CoV-2 relative to its S1 subunit and RBD protein, consistent with a higher sequence conservation between the S2 subunits of SARS-CoV-2 and SARS-CoV than that of their S1 subunits and RBDs (3, 5). Each of the patient sera could cross-neutralize SARS-CoV-2 with serum titers ranging from 1:20 to 1:360 dilutions, verifying the cross-reactive neutralizing activity of the patient with SARS sera on the S protein of SARS-CoV-2.

Now, two strategies are being explored for developing vaccines against emerging CoVs. The first one is based on a full-length S protein or its ectodomain, while the second uses a minimal but functional RBD protein as vaccine immunogen. Our previous studies revealed that the RBD site contains multiple groups of conformation-dependent neutralizing epitopes: Some epitopes are critically involved in RBD binding to the cell receptor ACE2, whereas other epitopes have a neutralizing function but do not interfere with the RBD-ACE2 interaction (15, 18). Most neutralizing monoclonal antibodies (mAbs) previously developed against SARS-CoV target the RBD epitopes, while a few are directed against the S2 subunit or the S1/S2 cleavage site (29, 30). The cross-reactivity of such mAbs with SARS-CoV-2 has been characterized, and it was found that many SARS-CoVneutralizing mAbs exhibit no cross-neutralizing capacity (8, 31). For example, CR3022, a nAb isolated from a convalescent patient with SARS, cross-reacted with the RBD of SARS-CoV-2 but did not neutralize the virus (31, 32). Nonetheless, a new human anti-RBD mAb, 47D11, has just been isolated from transgenic mice immunized with a SARS-CoV S protein, which neutralizes both SARS-CoV-2 and SARS-CoV (33). The results of polyclonal antisera from immunized animals are quite inconsistent. For example, Walls et al. (5) reported that plasma from four mice immunized with a SARS-CoV S protein could completely inhibit SARS-CoV pseudovirus and reduced SARS-CoV-2 pseudovirus to ~10% of control, thus proposing that immunity against one virus of the sarbecovirus subgenus can potentially provide protection against related viruses; two rabbit antisera raised against the S1 subunit of SARS-CoV also reduced SARS-CoV-2 Sdriven cell entry although with lower efficiency compared to SARS-CoV S (26). Moreover, four mouse antisera against the SARS-CoV RBD cross-reacted efficiently with the SARS-CoV-2 RBD and neutralized SARS-CoV-2, suggesting the potential to develop a SARS-CoV RBDbased vaccine preventing SARS-CoV-2 (34). Differently, it was reported that plasma from mice infected or immunized by SARS-CoV failed to neutralize SARS-CoV-2 infection in Vero E6 cells (28), and mouse antisera raised against the SARS-CoV RBD were even unable to bind to the S protein of SARS-CoV-2 (8). In the present studies, several panels of antisera against the SARS-CoV S and RBD proteins were comprehensively characterized. Although the use of pseudovirus-based neutralization assay might not fully reflect the complexity of authentic SARS-CoV-2 infection, our results, altogether, did provide reliable data to validate the cross-reactivity and cross-neutralization between SARS-CoV and SARS-CoV-2. Meaningfully, this work found that the RBD proteins derived from different SARS-CoV strains can elicit antibodies with unique functionalities: While the RBD from a palm civet SARS-CoV (SZ16) induced potent antibodies capable of blocking the RBD-receptor binding, the antibodies elicited by the RBD derived from a human strain (GD03) had no such effect despite their neutralizing activities. SZ16-RBD shares an overall 74% amino acid sequence identity with the RBD of SARS-CoV-2, when their internal RBMs display more marked substitutions (~50% sequence identity); however, SZ16-RBD and GD03-RBD only differ from three amino acids, all located within the RBM (fig. S3). Further research is needed to determine how these mutations change the antigenicity and immunogenicity of the S protein and RBD immunogens.

Three more questions invite further investigation. First, it would be intriguing to know whether individuals who recovered from previous SARS-CoV infection can direct their acquired SARS-CoV immunity against SARS-CoV-2 infection. To address this question, an epidemiological investigation of populations exposed to SARS-CoV-2 would provide valuable insights. Second, it would be important to determine whether a universal vaccine can be rationally designed by engineering the S protein RBD sequences. Third, although antibody-dependent infection enhancement was not observed during our studies with the human and animal serum antibodies, the possibility of such effects should be carefully addressed in vaccine development.

Two RBD-Fc fusion proteins, which contain the RBD sequence of Himalayan palm civet SARS-CoV strain SZ16 (accession number: AY304488.1) or the RBD sequence of human SARS-CoV strain GD03T0013 (AY525636.1, denoted GD03) linked to the Fc domain of human immunoglobulin G1 (IgG1), were expressed in transfected 293T cells and purified with protein ASepharose 4 Fast Flow in our laboratory as previously described (15). A full-length S protein of SARS-CoV Urbani (AY278741) was expressed in expressSF+ insect cells with recombinant baculovirus D3252 by the Protein Sciences Corporation (Bridgeport, CT, USA) (16). A panel of recombinant proteins with a C-terminal polyhistidine (His) tag, including S1 and RBD of SARS-CoV (AAX16192.1) and S ectodomain (S-ecto), S1, RBD, and S2 of SARS-CoV-2 (YP_009724390.1), were purchased from the Sino Biological Company (Beijing, China) and characterized for quality control by SDSpolyacrylamide gel electrophoresis (fig. S4).

Twenty patients with SARS were enrolled in March 2003 for a follow-up study at the Peking Union Medical College Hospital, Beijing. Serum samples were collected from recovered patients at 3 to 6 months after discharge, with the patients written consent and the approval of the ethics review committee (23, 24). The samples were stored in aliquots at 80C and were heat-inactivated at 56C before performing experiments.

Multiple immunization protocols were conducted in compliance with the Institutional Animal Care and Use Committee guidelines and are summarized in fig. S1B. First, five Balb/c mice (6 weeks old) were subcutaneously immunized with 20 g of full-length S protein resuspended in phosphate-buffered saline (PBS; pH 7.2) in the presence of MPL-TDM adjuvant or alum adjuvant (Sigma-Aldrich). Second, eight Balb/c mice (6 weeks old) were subcutaneously immunized with 20 g of SZ16-RBD or GD03-RBD fusion proteins and MPL-TDM adjuvant. The mice were boosted two times with 10 g of the same antigens and the MPL-TDM adjuvants at 3-week intervals. Third, four New Zealand White rabbits (12 weeks old) were immunized intradermally with 150 g of SZ16-RBD or GD03-RBD resuspended in PBS (pH 7.2) in the presence of Freunds complete adjuvant and boosted two times with 150 g of the same antigens and incomplete Freunds adjuvant at 3-week intervals. Mouse and rabbit antisera were collected and stored at 40C.

Binding activity of serum antibodies with diverse S protein antigens was detected by ELISA. In brief, 50 or 100 ng of a purified recombinant protein (SARS-CoV S1 or RBD and SARS-CoV-2 S-ecto, S1, RBD, or S2) was coated into a 96-well ELISA plate overnight at 4C. Wells were blocked with 5% bovine serum albumin in PBS for 1 hour at 37C, followed by incubation with diluted antisera or purified rabbit antibodies for 1 hour at 37C. A diluted horseradish peroxidaseconjugated goat anti-human, mouse, or rabbit IgG antibody was added for 1 hour at room temperature. Wells were washed five times between each step with 0.1% Tween 20 in PBS. Wells were developed using 3,3,5,5-tetramethylbenzidine and read at 450 nm after termination with 2 M H2SO4.

Neutralizing activity of serum antibodies was measured by pseudovirus-based single-cycle infection assay as previously described (35). The pseudovirus particles were prepared by cotransfecting 293T cells with a backbone plasmid (pNL4-3.luc.RE) that encodes an Env-defective, luciferase reporter-expressing HIV-1 genome and a plasmid expressing the S protein of SARS-CoV-2 (IPBCAMS-WH-01; accession number: QHU36824.1) or SARS-CoV (GD03T0013) or the VSV-G. Cell culture supernatants containing virions were harvested 48 hours after transfection, filtrated, and stored at 80C. To measure the neutralizing activity of serum antibodies, a pseudovirus was mixed with an equal volume of serially diluted sera or purified antibodies and incubated at 37C for 30 min. The mixture was then added to 293T/ACE2 cells at a density of 104 cells/100 l per plate well. After culture at 37C for 48 hours, the cells were harvested and lysed in reporter lysis buffer, and luciferase activity (relative luminescence unit) was measured using luciferase assay reagents and a luminescence counter (Promega, Madison, WI). Percent inhibition of serum antibodies compared to the level of the virus control subtracted from that of the cell control was calculated. The highest dilution of the serum sample that reduced infection by 50% or more was considered to be positive.

Blocking activity of purified rabbit anti-RBD antibodies on the binding of RBD proteins with a His tag to 293T/ACE2 cells was detected by flow cytometry assay. Briefly, SARS-CoV-2 RBD protein (2 g/ml) or SARS-CoV RBD protein (10 g/ml) was added to 4 105 cells and incubated for 30 min at room temperature. After washing two times with PBS, cells were incubated with a 1:500 dilution of Alexa Fluor 488labeled rabbit antiHis tag antibody (Cell Signaling Technology, Danvers, MA) for 30 min at room temperature. After two washes, cells were resuspended in PBS and analyzed by FACSCantoII instrument (Becton Dickinson, Mountain View, CA).

Statistical analyses were carried out using GraphPad Prism 7 Software. One-way or two-way analysis of variance (ANOVA) was used to test for statistical significance. Only P values of 0.05 or lower were considered statistically significant [P > 0.05 (ns, not significant), *P 0.05, **P 0.01, and ***P 0.001].

Acknowledgments: Funding: This work was supported by grants from the National Natural Science Foundation of China (81630061 and 82041006) and the CAMS Innovation Fund for Medical Sciences (2017-I2M-1-014). Author contributions: Conceptualization: Y. He and T.L. Formal analysis: Y.Z., D.Y., and Y. He. Investigation: Y.Z., D.Y., Y. Han, H.Y., H.C., and L.R. Resources: H.C., L.R., J.W., T.L., and Y. He. Writingoriginal draft: Y. He. Writingreview and editing: all authors. Funding acquisition: Y. He and T.L. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Cross-reactive neutralization of SARS-CoV-2 by serum antibodies from recovered SARS patients and immunized animals - Science Advances

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Bootcamp allows Oswego researchers to explore fighting COVID-19 – NNY360

OSWEGO - SUNY Oswego students, faculty and recent alumni were part of a research team that spans many institutions and disciplines to research the COVID-19 pandemic resulting from the SARS-CoV-2 virus.

SUNY Oswego students Emily Fingar, Michael Kirsch and Charlotte Labrie-Cleary and recent graduates Ali Khan and Santiago Soto joined Julia Koeppe of the chemistry faculty for the weeklong bootcamp hosted by the Institute for Quantitative Biomedicine at Rutgers University this summer.

Other institutions in Oswegos group included Rochester Institute of Technology, Ursinus College, Hope College, Grand View University and Xavier University. The goal was to bring together teams of interdisciplinary researchers with complementary skills and interests to investigate the virus. Carried out completely remotely, participants interacted with experts and learned how to use various bioinformatics tools to answer pertinent research questions.

Research focused on the SARS-CoV-2 main protease (an enzyme that breaks down proteins into smaller units), which is essential for viral activity and a promising drug target. By understanding the differences in this protease resulting from the rapid evolution, researchers can move closer to developing an antiviral medicine to help COVID-19 patients, Koeppe said.

Students learned to work remotely (in Zoom and Zoom breakout rooms) with a group of their peers and a faculty mentor to study the structure and function of the main protease from the virus, Koeppe said. Students learned about computer programs used to view macromolecules such as proteins and enzymes; key principles of bioinformatics, such as sequence alignments that can show the evolution of proteins; and computer programs that model protein folding to determine three-dimensional structures.

At the end of the boot camp, all of the students gave a short presentation with their group members on some specific questions that they explored when looking at changes in the amino acid sequence of the SARS-CoV-2 protease and how they expected these changes would or would not affect the function of the protease, Koeppe said.

Preparing young researchers

Khan, a May graduate who is starting Ph.D. work in cancer biology at the University of Iowas Carver Medical School, worked in a team with two other students and Koeppe.

We were given daily tasks in which we used various structural visualizing tools to understand different mutations of coronavirus with respect to bond length, change in heat energy, etc., Khan said. There were a couple of mutations assigned per group and we had to analyze those and came up with a conclusion. We then gave a mini-presentation at the end of the week for our group about our findings.

Khan said the knowledge and interactions all were fruitful for his future plans.

This Bootcamp taught me how to interface with a scientist in a different field, Khan said. I also got an opportunity to attend various lectures which taught me the importance of research and how impactful research can be. I was also able to learn how to use visualization softwares and python programming language which will definitely come in handy in my Ph.D.

For Santiago Soto, who earned his biology degree in May and is already working in the field professionally as a clinical laboratory technologist with Acutis Diagnostics, the bootcamp helped with his important everyday work with live SARS-CoV-2 samples.

I really enjoyed the opportunity to observe the mutation and evolution process of SARS-CoV-2s over the past six months and its main protease Nsp5 while comparing it to the original viral isolate to 161 unique sequence/structure variants, Soto said. This was done by analyzing amino acid sequences using 3D atomic level structures using several bioinformatic tools. The research found Nsp5 could be a promising drug target for vaccine development, he added.

This bootcamp allowed me to better understand the use of bioinformatics/biostatistics, Soto noted. Its the base principle on being able to make identifications on the genetic basis of diseases, their desirable properties and unique adaptations. I would like to pursue sometime in the future a graduate degree and career in epidemiology, biomedical engineering or genetics, where the use of bioinformatics is constantly being used to assist in progression.

A member of Koeppes research team, senior biology and health science major Emily Fingar was immediately interested when Koeppe reached out with the opportunity. She learned Foldit, PyRosetta, and Mol visualization software programs so that we could take our assigned mutants, where we had the DNA sequence but not necessarily a structure, and force those mutations into the known protease structure, she said.

My team specifically was assigned 11 mutations in the SARS-CoV-2 main protease to characterize, Fingar said. Our goal was to model, using these programs, how each of the assigned mutations of the SARS-CoV-2 main protease might be changing at the protein level as well as the stability of that protein. We also used this data to examine if there are regions in the protein structure that are mutating more often than other regions.

Fingar said the bootcamp helped her continue to broaden computational skills for research. Ill be the first to admit Im not the best with computers, Fingar said. This opportunity has shown me that I am capable of learning and effectively utilizing them in a meaningful way that is relevant to my research. My next challenge will be to tackle the statistical programming language R.

Senior biochemistry major Charlotte Labrie-Cleary found the opportunity to work in remote teams and gain experience relevant to research were key takeaways.

I learned how to use incredibly powerful bioinformatic tools that I hope to learn more about in the future, Labrie-Cleary said. I learned about the evolution of viruses with a focus on coronaviruses. We learned in depth about the SARS-CoV-2 main protease as well as its spike protein and why theyre important. We learned about testing techniques for COVID-19 and how they work.

For Labrie-Cleary. learning so much at a fast pace was exhilarating and I feel lucky to have been able to participate, she said. It has shed light into the world of bioinformatics, which is something I have always been super interested in. This experience will give me a head start when considering graduate programs, and it excites me to learn more about it. As an undergraduate, I am fortunate to have been offered such a valuable experience, as many students at our level are not offered such during undergraduate studies.

Senior biochemistry major Michael Kirsch appreciated learning about topics such as the evolution of RNA viruses, development of testing for COVID-19, what parts of COVID-19 might be the best to target with medicines that are being developed to treat it, and how phylogenetic trees can be used to help piece together when different mutations in a virus branch off from one another, he said.

His team used Mol and Foldit to examine the protein 6YB7, the COVID-19 main protease, which could lead to research on what affects its ability to do its job as a protease, and future research can then be done on how to disrupt this protein from doing its job, Kirsch said.

Virology, the study of viruses, is among the future fields Kirsch is considering, and the bootcamp has further encouraged him. I now know how to use several new programs to visualize or otherwise analyze proteins, which will be useful in my last semester at Oswego, as the research I do with Dr. Koeppe is focused on determining the function of protein 3DL1, he said. Being able to better visualize it can only help my research efforts, which Im excited about.

The bootcamp will allow Koeppe to provide better lab experiences and topical opportunities for her students.

I am currently modifying some of the bootcamp materials to use them as online lab experiments in our biochemistry lab courses for the fall semester, and I will also create a unit on the novel coronavirus and the main protease for my masters-level enzymes course for the fall semester, Koeppe noted. Students who are interested in further study will be welcome to join my research group where they can begin with computational experiments to study the viral proteins with a goal of identifying a possible drug target.

Koeppe and chemistry faculty member Kestutis Bendinskas have been using tools developed at the boot camp to design experiments for studying SARS-CoV-2 in their biochemistry lab courses.

The experience can help Koeppe develop a unit on computational software for protein folding into our biochemistry lab curriculum that focuses on enzymes of unknown function, she said. The software we used for protein folding in the bootcamp was new to all of us, and we think it will be a good addition to what weve already been using in the lab.

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Real Progress In Crowdsourcing Scientific Tasks To Gamers – Bio-IT World

By Deborah Borfitz

November 4, 2020 | Gaming and sciencetwo seemingly incompatible areas of activityhave come together nicely in the case of citizen science games such as Foldit, Phylo, and Borderlands Science, as reported by academics close to the action who presented at the recent Bio-IT World Conference & Expo Virtual. The games are all played online, involve analyzing large sets of data, and endeavoring to solve real scientific problems. And players get credit individually (when willing) or as a crowd when findings appear in scholarly, peer-reviewed publications.

Whats not to love about the concept? Its certainly a great way to redirect the attention of people already spending untold hours on video games, says Seth Cooper, assistant professor in the Khoury College of Computer Sciences at Northeastern University. A pioneer of the field of scientific discovery games, he has demonstrated that video game enthusiasts are able to outperform purely computational methods for certain types of structural biochemistry problems, effectively codify their strategies, and integrate with the lab to help design real synthetic proteins.

Cooper is co-creator of Foldit, where the competition is about protein folding and design. Its hard for a computer to search all the possibilities without the aid of human creativity and reason, he says. The game is built on chemistry software called Rosetta and has been out for over a decade with more than half a million players, Cooper continues. It has evolved into a multi-institutional collaboration.

The goal, as with most games, is to get a high score, Cooper says. Players compete, and often collaborate, to build the best protein structures.

The process begins with a biochemist identifying a problem that gets turned into a game or puzzle that gets posted online, he explains. Each puzzle is only available for about a week, and generally a couple are up for play at any one time. Data generated by the Foldit players continually improve the game for better scientific results, Cooper notes. The levels of play get progressively harder.

Anyone can participate and most have no formal background in biochemistry, yet theyre contributing to science, he says. Back in 2011, players famously came up with an elegant, low-energy model for a monkey-virus enzyme, solving a longstanding scientific problem potentially useful for the design of retroviral drugs for AIDSand accomplished the feat inside of three weeks.

Players have also successfully redesigned existing enzymes, Cooper adds, as well as designed several protein structures from scratch that have been confirmed by X-ray crystallography. Theyre now working on designing an enzyme that will bind to the spike protein of SARS-CoV-2.

Vanderbilt University is also using Foldit to design small molecules and the University of California, Davis is studying the impact of adding a narrative to the competition. In the future, Cooper says, Foldit users might start working in a virtual reality environment. An educational version of Foldit with more contextual science information is available for classroom use, says Cooper, as is a standalone version that is completely separate from the game.

Burning Task Use

At McGill University, associate professor and computational scientist Jerome Waldispuehl is championing the gamification of genomics research with citizen science video game Phylo and its newest iteration called Borderlands Science. His focus is on multiple sequence alignment, one of the most challenging problems in bioinformatics that involves discovering similarities between a set of protein or DNA sequences.

Phylo presents players with DNA puzzles where they manipulate patterns consisting of colored tiles so that they almost forget the scientific context, Waldispuehl says. The abstraction task is to minimize the mismatch of colors to avoid a penalty.

Every alignment submitted by players is eventually reinserted into an existing algorithm as an optimization, says Waldispuehl. Alignments up for play contain sections of human DNA thought to be linked to various genetic disorders. Since 2010, Phylo has had 350,000 participants and generated one million solutions by improving alignments by 40%-95% over a computer algorithm, he reports.

Borderlands Science, launched in April for purposes of education and science outreach, quickly hit the one million mark with players and has come up with 50 million solutions, he adds. Collaborators include video game science company Massively Multiplayer Online Science, Gearbox Software and The Microsetta Initiative of the University of California, San Diego.

The Borderlands version of the game is played vertically rather than horizontally and rewards success with in-game currency that is important to some players, Waldispuehl says. It is currently aimed at improving 16S ribosomal RNA gene sequences from human microbiome alignments.

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Image of the Month: The right place of human Man1b1 – Baylor College of Medicine News

Location, location, location! It is especially important in the world of cells. The Man1b1 protein, known to be involved in regulating a balanced, functional network of cellular proteins, was assumed to localize in the endoplasmic reticulum.

Dr. Richard Siferss group challenged this widespread view by showing that Man1b1 is actually located in the Golgi, a cellular structure functionally associated with but physically separate from the endoplasmic reticulum. The findings sharpened the appreciation of the dynamic process that regulates protein folding and the handling of misfolded, defective proteins, known to be involved in a number of conditions such as conformational diseases.

Conformational diseases include common conditions associated with accumulation of defective proteins, including neurological disorders, such as Alzheimers disease. Human Man1b1 has been linked to the causes of multiple congenital disorders of intellectual disability and HIV infection, and to poor prognosis in patients with bladder cancer. A better understanding of how Man1b1 works can potentially open new doors into developing improved treatments.

Learn more about the research conducted at the Sifers lab here, including the recent discovery of an unexpected new function of Man1b1.

Dr. Richard Sifers is professor of pathology & immunologyand member of theDan L Duncan Comprehensive Cancer CenteratBaylor College of Medicine.

By Ana Mara Rodrguez, Ph.D.

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Angelika Amon, cell biologist who pioneered research on chromosome imbalance, dies at 53 – MIT News

Angelika Amon, professor of biology and a member of the Koch Institute for Integrative Cancer Research, died on Oct. 29 at age 53, following a two-and-a-half-year battle with ovarian cancer.

"Known for her piercing scientific insight and infectious enthusiasm for the deepest questions of science, Professor Amon built an extraordinary career and in the process, a devoted community of colleagues, students and friends," MIT President L. Rafael Reif wrote in a letter to the MIT community.

Angelika was a force of nature and a highly valued member of our community, reflects Tyler Jacks, the David H. Koch Professor of Biology at MIT and director of the Koch Institute. Her intellect and wit were equally sharp, and she brought unmatched passion to everything she did. Through her groundbreaking research, her mentorship of so many, her teaching, and a host of other contributions, Angelika has made an incredible impact on the world one that will last long into the future.

A pioneer in cell biology

From the earliest stages of her career, Amon made profound contributions to our understanding of the fundamental biology of the cell, deciphering the regulatory networks that govern cell division and proliferation in yeast, mice, and mammalian organoids, and shedding light on the causes of chromosome mis-segregation and its consequences for human diseases.

Human cells have 23 pairs of chromosomes, but as they divide they can make errors that lead to too many or too few chromosomes, resulting in aneuploidy. Amons meticulous and rigorous experiments, first in yeast and then in mammalian cells, helped to uncover the biological consequences of having too many chromosomes. Her studies determined that extra chromosomes significantly impact the composition of the cell, causing stress in important processes such as protein folding and metabolism, and leading to additional mistakes that could drive cancer. Although stress resulting from aneuploidy affects cells ability to survive and proliferate, cancer cells which are nearly universally aneuploid can grow uncontrollably. Amon showed that aneuploidy disrupts cells usual error-repair systems, allowing genetic mutations to quickly accumulate.

Aneuploidy is usually fatal, but in some instances extra copies of specific chromosomes can lead to conditions such as Down syndrome and developmental disorders including those known as Patau and Edwards syndromes. This led Amon to work to understand how these negative effects result in some of the health problems associated specifically with Down syndrome, such as acute lymphoblastic leukemia. Her expertise in this area led her to be named co-director of the recently established Alana Down Syndrome Center at MIT.

Angelikas intellect and research were as astonishing as her bravery and her spirit. Her labs fundamental work on aneuploidy was integral to our establishment of the center, say Li-Huei Tsai, the Picower Professor of Neuroscience and co-director of the Alana Down Syndrome Center. Her exploration of the myriad consequences of aneuploidy for human health was vitally important and will continue to guide scientific and medical research.

Another major focus of research in the Amon lab has been on the relationship between how cells grow, divide, and age. Among other insights, this work has revealed that once cells reach a certain large size, they lose the ability to proliferate and are unable to reenter the cell cycle. Further, this growth contributes to senescence, an irreversible cell cycle arrest, and tissue aging. In related work, Amon has investigated the relationships between stem cell size, stem cell function, and tissue age. Her labs studies have found that in hematopoetic stem cells, small size is important to cells ability to function and proliferate in fact, she posted recent findings on bioRxiv earlier this week and have been examining the same questions in epithelial cells as well.

Amon lab experiments delved deep into the mechanics of the biology, trying to understand the mechanisms behind their observations. To support this work, she established research collaborations to leverage approaches and technologies developed by her colleagues at the Koch Institute, including sophisticated intestinal organoid and mouse models developed by the Yilmaz Laboratory, and a microfluidic device developed by the Manalis Laboratory for measuring physical characteristics of single cells.

The thrill of discovery

Born in 1967, Amon grew up in Vienna, Austria, in a family of six. Playing outside all day with her three younger siblings, she developed an early love of biology and animals. She could not remember a time when she was not interested in biology, initially wanting to become a zoologist. But in high school, she saw an old black-and-white film from the 1950s about chromosome segregation, and found the moment that the sister chromatids split apart breathtaking. She knew then that she wanted to study the inner workings of the cell and decided to focus on genetics at the University of Vienna in Austria.

After receiving her BS, Amon continued her doctoral work there under Professor Kim Nasmyth at the Research Institute of Molecular Pathology, earning her PhD in 1993. From the outset, she made important contributions to the field of cell cycle dynamics. Her work on yeast genetics in the Nasmyth laboratory led to major discoveries about how one stage of the cell cycle sets up for the next, revealing that cyclins, proteins that accumulate within cells as they enter mitosis, must be broken down before cells pass from mitosis to G1, a period of cell growth.

Towards the end of her doctorate, Amon became interested in fruitfly genetics and read the work of Ruth Lehmann, then a faculty member at MIT and a member of the Whitehead Institute. Impressed by the elegance of Lehmanns genetic approach, she applied and was accepted to her lab. In 1994, Amon arrived in the United States, not knowing that it would become her permanent home or that she would eventually become a professor.

While Amons love affair with fruitfly genetics would prove short, her promise was immediately apparent to Lehmann, now director of the Whitehead Institute. I will never forget picking Angelika up from the airport when she was flying in from Vienna to join my lab. Despite the long trip, she was just so full of energy, ready to talk science, says Lehmann. She had read all the papers in the new field and cut through the results to hit equally on the main points.

But as Amon frequently was fond of saying, yeast will spoil you. Lehmann explains that because they grow so fast and there are so many tools, your brain is the only limitation. I tried to convince her of the beauty and advantages of my slower-growing favorite organism. But in the end, yeast won and Angelika went on to establish a remarkable body of work, starting with her many contributions to how cells divide and more recently to discover a cellular aneuploidy program.

In 1996, after Lehmann had left for New York Universitys Skirball Institute, Amon was invited to become a Whitehead Fellow, a prestigious program that offers recent PhDs resources and mentorship to undertake their own investigations. Her work on the question of how yeast cells progress through the cell cycle and partition their chromosomes would be instrumental in establishing her as one of the worlds leading geneticists. While at Whitehead, her lab made key findings centered around the role of an enzyme called Cdc14 in prompting cells to exit mitosis, including that the enzyme is sequestered in a cellular compartment called the nucleolus and must be released before the cell can exit.

I was one of those blessed to share with her a eureka moment, as she would call it, says Rosella Visintin, a postdoc in Amons lab at the time of the discovery and now an assistant professor at the European School of Molecular Medicine in Milan. She had so many. Most of us are lucky to get just one, and I was one of the lucky ones. Ill never forget her smile and scream neither will the entire Whitehead Institute when she saw for the first time Cdc14 localization: You did it, you did it, you figured it out! Passion, excitement, joy everything was in that scream.

In 1999, Amons work as a Whitehead Fellow earned her a faculty position in the MIT Department of Biology and the MIT Center for Cancer Research, the predecessor to the Koch Institute. A full professor since 2007, she also became the Kathleen and Curtis Marble Professor in Cancer Research, associate director of the Paul F. Glenn Center for Biology of Aging Research at MIT, a member of the Ludwig Center for Molecular Oncology at MIT, and an investigator of the Howard Hughes Medical Institute.

Her pathbreaking research was recognized by several awards and honors, including the 2003 National Science Foundation Alan T. Waterman Award, the 2007 Paul Marks Prize for Cancer Research, the 2008 National Academy of Sciences (NAS) Award in Molecular Biology, and the 2013 Ernst Jung Prize for Medicine. In 2019, she won the Breakthrough Prize in Life Sciences and the Vilcek Prize in Biomedical Science, and was named to the Carnegie Corporation of New Yorks annual list of Great Immigrants, Great Americans. This year, she was given the Human Frontier Science Program Nakasone Award. She was also a member of the NAS and the American Academy of Arts and Sciences.

Lighting the way forward

Amons perseverance, deep curiosity, and enthusiasm for discovery served her well in her roles as teacher, mentor, and colleague. She has worked with many labs across the world and developed a deep network of scientific collaboration and friendships. She was a sought-after speaker for seminars and the many conferences she attended. In over 20 years as a professor at MIT, she has mentored more than 80 postdocs, graduate students, and undergraduates, and received the School of Sciences undergraduate teaching prize.

Angelika was an amazing, energetic, passionate, and creative scientist, an outstanding mentor to many, and an excellent teacher, says Alan Grossman, the Praecis Professor of Biology and head of MITs Department of Biology. Her impact and legacy will live on and be perpetuated by all those she touched.

Angelika existed in a league of her own, explains Kristin Knouse, one of Amons former graduate students and a current Whitehead Fellow. She had the energy and excitement of someone who picked up a pipette for the first time, but the brilliance and wisdom of someone who had been doing it for decades. Her infectious energy and brilliant mind were matched by a boundless heart and tenacious grit. She could glance at any data and immediately deliver a sharp insight that would never have crossed any other mind. Her positive attributes were infectious, and any interaction with her, no matter how transient, assuredly left you feeling better about yourself and your science.

Taking great delight in helping young scientists find their own eureka moments, Amon was a fearless advocate for science and the rights of women and minorities and inspired others to fight as well. She was not afraid to speak out in support of the research and causes she believed strongly in. She was a role model for young female scientists and spent countless hours mentoring and guiding them in a male-dominated field. While she graciously accepted awards for women in science, including the Vanderbilt Prize and the Women in Cell Biology Senior Award, she questioned the value of prizes focused on women as women, rather than on their scientific contributions.

Angelika Amon was an inspiring leader, notes Lehmann, not only by her trailblazing science but also by her fearlessness to call out sexism and other -isms in our community. Her captivating laugh and unwavering mentorship and guidance will be missed by students and faculty alike. MIT and the science community have lost an exemplary leader, mentor, friend, and mensch.

Amons wide-ranging curiosity led her to consider new ideas beyond her own field. In recent years, she has developed a love for dinosaurs and fossils, and often mentioned that she would like to study terraforming, which she considered essential for a human success to life on other planets.

It was always amazing to talk with Angelika about science, because her interests were so deep and so broad, her intellect so sharp, and her enthusiasm so infectious, remembers Vivian Siegel, a lecturer in the Department of Biology and friend since Amons postdoctoral days. Beyond her own work in the lab, she was fascinated by so many things, including dinosaurs dreaming of taking her daughters on a dig lichen, and even life on Mars.

Angelika was brilliant; she illuminated science and scientists, says Frank Solomon, professor of biology and member of the Koch Institute. And she was intense; she warmed the people around her, and expanded what it means to be a friend.

Amon is survived by her husband Johannes Weis, and her daughters Theresa and Clara Weis, and her three siblings and their families.

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ProMIS Neurosciences adds Dr. David Wishart to its Scientific Advisory Board – GlobeNewswire

TORONTO and CAMBRIDGE, Mass., Oct. 29, 2020 (GLOBE NEWSWIRE) -- ProMIS Neurosciences, Inc. (TSX: PMN) (OTCQB: ARFXF), a biotechnology company focused on the discovery and development of antibody therapeutics targeting toxic oligomers implicated in the development of neurodegenerative diseases, welcomes Dr. David Wishart, Distinguished University Professor in the Departments of Biological Sciences and Computing Science at the University of Alberta, to its Scientific Advisory Board (SAB). Identified as one of the worlds most highly cited scientists for each of the past 7 years, Dr. Wishart brings more than three decades in protein folding and misfolding research to ProMIS, creating industry-leading depth in this area of therapeutic development for neurodegenerative and other diseases.

The commitment and talent of our advisory board has been instrumental to the ongoing development of our broad portfolio of highly specific therapeutic, vaccine and diagnostic candidates, said Eugene Williams, Executive Chairman of ProMIS Neurosciences. Dr. Wisharts world-recognized expertise in protein folding and misfolding combined with Dr. Neil Cashmans complementary leadership will place ProMIS among the most accomplished within this arena. Their combined expertise will advance our platforms application to an even broader scope of diseases caused by protein misfolding.

Dr. Wishart will play a pivotal role in advising ProMIS on the application and further development of its drug discovery and development platform, which is uniquely capable of identifying the sequence and shape (conformation) of novel binding targetscalled peptide antigenson misfolded proteins implicated in the development of neurodegenerative diseases such as Alzheimers, Parkinsons and ALS. ProMIS has leveraged its novel platform to create a portfolio of antibody, intrabody and vaccine candidates that are highly selective for the misfolded protein aggregates driving pathogenesis. With Dr. Wisharts support, ProMIS will continue to expand the application of its platform to the biology of additional misfolded protein diseases.

Never before has there been a more urgent need for therapy, diagnostic and vaccine candidates that are highly specific for their intended target, said Dr. Wishart. I look forward to working with Dr. Neil Cashman and his team and such an accomplished SAB as we continue to seek new opportunities to apply ProMIS unique platform technology to misfolded protein diseases with high unmet need.

ProMIS SAB includes distinguished, highly published and cited contributors to the current scientific understanding of Alzheimers, Parkinsons, ALS, protein misfolding diseases in general, vaccines and diagnostics. Dr. Wishart joins the following current members:

About Dr. David WishartDr. Wishart has been studying protein folding and misfolding for more than 30 years using a combination of computational and experimental approaches. These experimental approaches include NMR spectroscopy, circular dichroism, fluorescence spectroscopy, electron microscopy, protein engineering and molecular biology. The computational methods include molecular dynamics, agent-based modeling, bioinformatics and machine learning. Over the course of his career, Dr. Wishart has published more than 430 scientific papers, cited more than 78,000 times, covering many areas of protein science including structural biology, protein metabolism and computational biochemistry. He has been with the University of Alberta since 1995 and is currently a Distinguished University Professor in the Departments of Biological Sciences and Computing Science. He also holds adjunct appointments with the Faculty of Pharmaceutical Sciences and the Department of Pathology and Laboratory Medicine.

Dr. Wishart has been awarded research grants totaling more than $130 million from a number of funding agencies. He has also led or directed a number of core facilities and centers and currently co-directs The Metabolomics Innovation Centre (TMIC), Canadas national metabolomics laboratory. Dr. Wishart held the Bristol-Myers Squibb Research Chair in Pharmaceutical Sciences from 1995-2005, received the Astra-Zeneca-CFPS Young Investigator Prize in 2001, was awarded a Lifetime Honorary Fellowship by the Metabolomics Society in 2014 and elected as a Fellow of the Royal Society of Canada in 2017.

About ProMIS NeurosciencesProMIS Neurosciences, Inc. is a development stage biotechnology company whose unique core technology is the ability to rationally predict the site and shape (conformation) of novel targets known as Disease Specific Epitopes (DSEs) on the molecular surface of proteins. In neurodegenerative diseases, such as Alzheimers, ALS and Parkinsons disease, the DSEs are misfolded regions on toxic forms of otherwise normal proteins. In the infectious disease setting, these DSEs represent peptide antigens that can be used as an essential component to create accurate and sensitive serological assays to detect the presence of antibodies that arise in response to a specific infection, such as COVID-19. ProMIS proprietary peptide antigens can also be used to create potential therapeutic antibodies, as well as serve as the basis for development of vaccines. ProMIS is headquartered in Toronto, Ontario, with offices in Cambridge, Massachusetts. ProMIS is listed on the Toronto Stock Exchange under the symbol PMN, and on the OTCQB Venture Market under the symbol ARFXF.Visit us atwww.promisneurosciences.com, follow us onTwitterandLinkedIn. To learn more about protein misfolding diseases, listen to Episodes 11, 24, of Saving Minds, a podcast available atiTunesorSpotify.

For media inquiries, please contact:Shanti Skiffingtonshanti.skiffington@gmail.comTel. 617 921-0808

The TSX has not reviewed and does not accept responsibility for the adequacy or accuracy of this release. This information release contains certain forward-looking information. Such information involves known and unknown risks, uncertainties and other factors that may cause actual results, performance or achievements to be materially different from those implied by statements herein, and therefore these statements should not be read as guarantees of future performance or results. All forward-looking statements are based on the Companys current beliefs as well as assumptions made by and information currently available to it as well as other factors. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of the date of this press release. Due to risks and uncertainties, including the risks and uncertainties identified by the Company in its public securities filings, actual events may differ materially from current expectations. The Company disclaims any intention or obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise.

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If AlphaFold Is a Product of Design, Maybe Our Bodies Are Too – Walter Bradley Center for Natural and Artificial Intelligence

Recently, weve been looking at tech philosopher George Gilders new Gaming AI about what AI canand cantdo for us. It cant do our thinking for us but it can do many jobs we dont even try because no human being has enough time or patience to motor through all the calculations.

Which brings us to the massive complexity of the proteins that carry out our genetic instructionsbetter knowledge of which would help us battle many diseases.

Gilder notes that when DeepMinds AlphaGo beat humans at the board game Go in 2016, it wasnt just for the fun of winning a game. DeepMind cofounder Demis Hassabis (pictured in 2018) is more interested in real-life uses such as medical research (p. 11). The human body is very complex and a researcher can be confronted with thousands of possibilities. Which ones matter?

The area the DeepMind team decided to focus on is protein folding: Human DNA has 64 codons that program little machines in our cells (ribosomes) to create specific proteins out of the standard twenty amino acids. But, to do their jobs, the proteins fold themselves into many, many different shapes. Figuring it all out is a real problem for researchers and the DeepMind crew hope that AI will help:

Over the past five decades, researchers have been able to determine shapes of proteins in labs using experimental techniques like cryo-electron microscopy, nuclear magnetic resonance and X-ray crystallography, but each method depends on a lot of trial and error, which can take years of work, and cost tens or hundreds of thousands of dollars per protein structure. This is why biologists are turning to AI methods as an alternative to this long and laborious process for difficult proteins. The ability to predict a proteins shape computationally from its genetic code alonerather than determining it through costly experimentationcould help accelerate research.

As Gilder recounts, the biotech industry conducts annual global protein-folding competitions among molecular biologists and in 2019 DeepMind defeated all teams of relatively unaided human rivals:

Advancing from the unaided human level of two or three correct protein configurations out of forty, DeepMind calculated some thirty-three correct solutions out of forty. This spectacular advance opens the way to major biotech gains in custom-built protein molecules adapted to particular people with particular needs or diseases. It is the most significant biotech invention since the complementary CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) method for using enzymes directly to edit strands of DNA.

But now that we have found a way to tackle one aspect of the immense complexity of human bodily existence, heres an interesting problem to think about: We are told by many philosophers that life came to exist on Earth purely by chance. How likely is that, given the intricacy of the machinery that governs our bodies?

Kirk Durston, a biophysicist who studies protein folds, comments:

As we all know from probabilities, you can get lucky once, but not thousands of times

As real data shows, the probability of finding a functional sequence for one average protein family is so low, there is virtually zero chance of obtaining it anywhere in this universe over its entire history never mind finding thousands of protein families.

Yet thats what we have. All those protein families. As we learn more about the world we live in, we may find ourselves confronting more challenges like this: We had to invent a really complex machine to even begin to figure out protein folding in our bodies and we know that the machine did not happen by chance. So why should we believe that our bodies happened that way? Probably not.

Note: While medicine may be the most important way AI can help us, it also helps us in other areas where huge numbers of calculations are essential for success. For example, it can help recover lost languages and interpret charred scrolls. It can continuously scan the skies, sparing astronomers for more human-friendly work like interpreting the results. It can restore blurred images and help with cold case files. As with anything, the trick is to take advantage of what it can really do. We dont need the courtroom sentencing robot or the AI Jesusbut then we never did. As our information resources become larger and more complex, we do need some help with the sheer volume and thats where AI is bound to succeed.

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Why AI geniuses havent created true thinking machines. The problems have been hinting at themselves all along. Quantum computers play by the same rules as digital ones: Meaningful information still requires an interpreter (observer) to relate the map to the territory.

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