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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.

Are Proteins Attracted to Function? - Discovery Institute

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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; 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 (, 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 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


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 ( 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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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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Global AI Medicine Software Market Analysis of Key Players, Type, Application, Demand and Consumption By 2026 – Stock Market Vista

There are millions of them around the globe waiting for clutching on to some of the latest vital information circulating across the globe. The up-to-the-minute AI Medicine Software market report based on the growth and the development of theAI Medicine Software marketis systematically listed down. The AI Medicine Software market report comprises statistically verified facts such the unique essence including topological investigations, worldwide market share, government stringent norms, applications, current trends, futuristic plans, market bifurcations, and so on mentioned in a crystal clear pattern.

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4 Reasons To Use Cannabis For Treating Women’s Health Issues – Longevity LIVE

Specific dietary changes, like including natural supplements, can help in enhancing sexual energy in women. Aphrodisiacs are the foods, drinks, or drugs that help increase female libido. Cannabis is another such natural aphrodisiac that can help increase womens libido. Cannabis can also help in reducing the pain during penetration experienced by some women.

According to a study, women who ate cannabis experienced two times higher sex drives, with satisfactory orgasms than those with irregular cannabis usage. Cannabis interacts with endocannabinoids in the body, which help regulate sexual function. Women can use cannabis as an aphrodisiac in many ways. Earlier, people used to take it in the form of a beverage, but today, there are many different consumption methods. Women can also use cannabis as lubricants, helping in reducing pain during sex.

Mental health disorders affect both men and women, but theyre more severe in women. Various factors like career, family responsibility, and abuse are significant causes of mental issues in women. The most common mental illness in women is depression, bipolar disorder, and schizophrenia, often challenging to diagnose.

According to an article, cannabis has antidepressant traits that help decrease anxiety and depression in women. The depletion of serotonin in the brain leads to depression. However, taking cannabis in low doses helps release serotonin, which causes anti-depressant effects. This can help in alleviating stress and anxiety, as well as assisting in doing day-to-day activities.

These are some of the benefits of cannabis for women. Research states that one should always use cannabis from low doses to reap the right health benefits. Buy cannabis products from reputed shops that will provide authentic cannabis products. Make sure to check the THC and CBD content in the product to understand the dosage. You may need to think twice if you have any medical condition or are taking any medication. If you fall into either of these groups, make sure to consult a health professional before starting cannabis.

Denson, T. F., & Earleywine, M. (2006). Decreased depression in marijuana users.Addictive behaviors,31(4), 738742.

Lynn, B. K., Lpez, J. D., Miller, C., Thompson, J., & Campian, E. C. (2019). The Relationship between Marijuana Use Prior to Sex and Sexual Function in Women.Sexual medicine,7(2), 192197.

Olh, A., Tth, B. I., Borbr, I., Sugawara, K., et al. (2014). Cannabidiol exerts sebostatic and antiinflammatory effects on human sebocytes. The Journal of clinical investigation,124(9), 37133724.

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US listing set to support and accelerate ongoing development of 4D Pharma’s live biotherapeutics –

The move follows the merger it announced late last month with the NASDAQ listed, Longevity, a special purpose acquisition company (SPAC). 4D Pharma will become dual-listed and ordinary shares will continue to be traded on AIM.

Live biotherapeutics, a novel class of drug derived from the microbiome,are defined by the US Food and Drug Administration (FDA) as being biological products that contain a live organism, such as a bacterium, that is applicable to the prevention, treatment or cure of a disease.

The merger is set to be completed and effective in early 2021, said the UK firm. But it is subject to approval by 4D Pharma and Longevity shareholders and it is also bound by the SEC review process.

The US listing is set to support and accelerate ongoing development of its live biotherapeutics, said the CEO, Duncan Peyton.

It is a big step for 4D Pharma, it is a big step for any European biotech business to step across the Atlantic and become a US listed business. It is something we have worked on for a while, he told BioPharma-Reporter.

The benefits are evident, he said. We get access to strong healthcare funds that understand innovative technologies like live biotherapeutics, we get access to more capital."

4D Pharma, which was officially founded in 2014, is at a pivotal time in its growth and development, said the CEO.

4D Pharma's live biotherapeutic products (LBPs) are orally delivered single strains of bacteria that are naturally found in the healthy human gut, and it has developed a proprietary platform, MicroRx (R), that identifies live biotherapeutics based on a deep understanding of function and mechanism.

Its clinical programs target cancer, including a clinical collaboration with Merck, respiratory diseases such as COVID-19 and asthma, gastrointestinal diseases, and pre-clinical programs targeting neurological diseases like Parkinsons disease and auto-immune diseases. It is also engaging in a vaccine research collaborative alliance with Merck.

Peyton said that despite the operational and economic challenges in 2020 created by the global pandemic, the biotech has made significant strides on several fronts.

This includes 4D Pharma presenting clinical data for live biotherapeutics for the treatment of cancer in combination with Mercks Keytruda, as well as positive Phase II results for the first drug candidate able to treat both IBS-C and IBS-D. In addition, earlier in 2020, 4D Pharma was able to quickly instigate a Phase II clinical trial in COVID-19 based on our detailed understanding of our asthma program.

What are the big next steps, and what is the regulatory approval timeline looking like for 4D Pharmas live biotherapeutics?

That really depends on the pathway we take, in the coming weeks and months, in relation to our oncology program, in particular.

We want to expand on the data we have seen recently in relation to oncology and IBS and take that through to approval trials.

The IBS trial will probably take longer, as there is need to address a large patient population. The oncology space is slightly different, and there is [an option] to get an accelerated approval.

Product registration is within reach then. I would say we are just a couple of years, not a decade, away from approval.

4D Pharma is a leader in the field, he said. From a research point of view, we are streets ahead.

The company understands the mechanisms of action of its strains and can demonstrate that preclinically and in patients, he said.

Those capabilities are supported by nearly 1,000 granted patents. I think we have a similar number of patents in development, and all of our IP is generated in-house, our team is pushing forward mechanistic understanding.

And Mercks engagement is a validation of 4Ds clinical pipeline and research capabilities, he added.

The companys platform, he said, goes beyond just addressing diseases of the gut, to explore oncology and immune system related areas. We are also starting to understand and unpick how our bacteria actually act on the gut-brain axis, continued Peyton.

Such insights could potentially lead to innovation in the neurodegenerative disease field, he said.

And the CEO sees Big Pharma's attitudes to the live biotherapeutics field changing.

We have been working with Merck, who I believe to be one of the most innovative of Big Pharma companies, for the past two and half years. We have been working with them on Keytruda, which is probably the biggest selling drug in the world right now, we are looking at how we can expand the patient groups into which Keytruda is active or regenerate a response for the drug, as it does not work in all patients. That is a signal of Mercks belief in the microbiome.

On top of that, Merck asked us to explore what we could do for it in relation to its vaccine business. We have that vaccine program [set up] now, we are doing all the research, and Merck will take it on from the preclinical stage.

As we publish more data on oncology and IBS, we are getting more incoming calls.

The landscape continues to change, some Big Pharma companies want to wait and see, others want to get involved and Merck is one of those.

4D Pharma has six clinical programs, namely a Phase I/II study of MRx0518 in combination with Keytruda (pembrolizumab) in solid tumors, a Phase I study of MRx0518 in a neoadjuvant setting for patients with solid tumors, a Phase I study of MRx0518 in patients with pancreatic cancer, a Phase I/II study of MRx-4DP0004 in asthma, a Phase II study of MRx-4DP0004 in patients hospitalized with COVID-19, and Blautix (R) in Irritable Bowel Syndrome (IBS) which has completed a successful Phase II trial.

Preclinical stage program include candidates for CNS disease such as Parkinson's disease and other neurodegenerative conditions.

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Global Adrenomyeloneuropathy Treatment Market Size, Share, Trends, CAGR by Technology, Key Players, Regions, Cost, Revenue and Forecast 2020 to 2025 -…

New Market Research Report: on Adrenomyeloneuropathy Treatment Market size | Segment by Applications (Hospitals, Clinics, Ambulatory Surgical Centers and Others), by Type (Steroid Replacement Therapy and Stem Cell Transplant), Regional Outlook Opportunity, Market Demand, Latest Trends, Adrenomyeloneuropathy Treatment Industry Share & Revenue by Manufacturers, Leading Companies Profiles, Analysis, Growth Forecast 2025. Analyzes current market size and upcoming Few years' growths of this industry.

Adrenomyeloneuropathy Treatment Market Size report is the best source that gives CAGR values with variations during the forecast period of 2020-2025 for the market. The study encompasses market drivers and restraints by using SWOT analysis, along with their impact on the demand over the forecast period. This global market report endows with a profound overview of product specification, product type, production analysis, and technology by taking into consideration the major factors such as revenue, cost, and gross margin. The Adrenomyeloneuropathy Treatment advertising report has been prepared based on the market type, size of the organization, availability on-premises, and the end-users organization type.

Some of the prominent players operating in the global Adrenomyeloneuropathy Treatment market include Ascend Biopharmaceuticals, Immatics Biotechnologies, Human Longevity, Novadip Biosciences, BioRestorative Therapies, Eureka Therapeutics, Cytori Therapeutics, Allogene Therapeutics, Regeneus, NewLink Genetics and Talaris Therapeutics among others.

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Competitive Analysis: Adrenomyeloneuropathy Treatment Market

Global Adrenomyeloneuropathy Treatment market Size is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of Adrenomyeloneuropathy Treatment market Share for global, Europe, North America, Asia-Pacific, South America, and Middle East & Africa.

Global Adrenomyeloneuropathy Treatment Market Split by Product Type and Applications:

Based on Types:

Based on Application:

The latest research on the Adrenomyeloneuropathy Treatment Market Size fundamentally delivers insights that can empower stakeholders, business owners, and field marketing executives to make effective investment decisions driven by facts and extremely thorough research. The study aims to provide an evaluation and deliver essential information on the competitive landscape to meet the unique requirements of the companies and individuals operating in the Adrenomyeloneuropathy Treatment Market for the forecast period, 20202025. To help firms comprehend the Adrenomyeloneuropathy Treatment industry in multiple ways, the report exhaustively assesses the share, size, and growth rate of the business worldwide.

Report Highlights:

The report covers the shifting industry dynamics

Detailed information about the market segmentation

Covers past, present and projected industry size Recent industry trends

Key Competition landscape

Business strategies of key players and product offerings

Potential and niche segments/regions exhibiting promising growth

A neutral perspective towards market performance

Queries Resolved In This Report:

Which will be the specialties at which Adrenomyeloneuropathy Treatment Market players profiling with intensive designs, financials, and ongoing headways should set nearness?

Which will be the foreseen development rates for your own Adrenomyeloneuropathy Treatment economy out and out and each portion inside?

Which will be the Adrenomyeloneuropathy Treatment application and sorts and estimate joined intently by makers?

Which will be the dangers which will attack growth?

The length of the global Adrenomyeloneuropathy Treatment market opportunity?

How Adrenomyeloneuropathy Treatment Market share advance vacillations of their value from various assembling brands?

Chapters Define in TOC (Table of Content) of the Report:

Chapter 1: Market Survey, Drivers, Restraints and Good fortune, Segmentation overview

Chapter 2: Market competitiveness by Manufacturers

Chapter 3: Production by Regions

Chapter 4: Consumption by Regions

Chapter 5: Production, By Types, Revenue and Market share by Types

Chapter 6: Consumption, By Applications, Market share (%) and Growth Rate by Applications

Chapter 7: Overall profiling and analysis of Manufacturers

Chapter 8: Manufacturing cost analysis, Raw materials analysis, Region-wise manufacturing expenses

Chapter 9: Supply Chain, Sourcing Strategy and Downstream Buyers

Chapter 10: Marketing Strategy Analysis, Distributors/Traders

Chapter 11: Market Influence Factors Analysis

Chapter 12: Market Predict

Chapter 13: Adrenomyeloneuropathy Treatment Research Findings and Conclusion, Appendix, methodology and data source

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What’s Wrong With Health Care in the United States? – Psychiatric Times

Health care in the United States is in poor health, its quality steadily declining since the 1960s. According to the World Economic Forum , in the 1950s the United States ranked first among the developed nations in the quality of health care. But now, despite enlisting renowned physicians, public health experts, economists, business leaders, and specialists in the treatment of ailing systems, we rank near the bottom.

What is the explanation? Is something being overlooked in the current effort to improve medical care? Do we not understand the cause of this fatal condition? The authors believe it is the importance of relationships that is being ignored; the physician-patient relationship, family relationships, and relationship systems in the community. Four key elements in our current health care system are responsible for this failure to acknowledge these critical relationships.

Interchangeability of parts

The foundation of the Industrial Revolution was the ability to create interchangeable parts. For example, workers on an assembly line build excellent automobiles cost-effectively. Health care administrators are using that model in medicine. The model of industrialized medicine works poorly when dealing with human beings, and is cost-effective only in the short run. Patients and practitioners are not interchangeable parts. Patients and their illnesses are too complex for a one-size-fits-all design. A health care model that does not recognize each patient as a unique person existing in a unique network of relationshipswith medical practitioners, family, and communityresults in increasingly poor outcomes, as the data demonstrate.

Systematic ignoring of the physician-patient and physician-family relationships

Treatment outcomes decline when patients see a different physician at each visit. The industrialized model excludes the importance of relatedness between physician and patient, losing the stabilizing relational linkages. For example, patients in hospitals cared for by their primary care physician are more likely to be discharged home and less likely to die within 30 days than those cared for by hospitalists who do not have an ongoing relationship with the patient and family.

The lack of sustained relationships between physicians and patients, and physicians and families, in the context of their community, impairs the effectiveness of treatment. It is costly to the physicians sense that their work is meaningful, and thus costly to physician morale. Systems that fail to attend to the physician-patient relationship lead to decreased treatment effectiveness, physician burnout, early retirement, and increased rates of physician suicide.

Healing is more likely in the context of families than healing in isolation

Without attention to context, our words, actions, and experience have no meaning. Family and community relationships are the context of our patients lives. Strong social connections are associated with increased longevity. For example, marriage is a protective factor against cardiac disease and the death of a spouse is associated with increased rates of physical illness in the surviving spouse. For individuals with a chronic illness, treatment without consideration of family and community appears simpler and more efficient, but leads to sterile, out-of-context, uninformed treatment, blinding both the patient and the physician from seeing both the illness and the solution.

The undermining and erosion of family and community

Regulations and reimbursement systems that reduce the medical interaction to a specific procedure diminish the involvement of families and communities in a patients recovery. The Health Information Portability and Accountability Act (HIPAA), for example, which was established to protect patient confidentiality, immediately became an obstacle to communication with the people in the patients life who can help with follow-up appointments, medication adherence, continuity of care, and emotional support.

Treating the correct condition

The 4 elements described point to both the fatal condition and its solution. Attention to the whole person, focusing on relationships with physicians and nurses, patients and families, and the web of community relationships available in every community to every patient, helps people get and stay healthy. Any potential solution to the health care system that ignores relationships will always be ineffective. If industrial medicine worked, and at this point weve done that experiment, costs would not be rising, patients and physicians would not be dissatisfied, and health would be improving.

Dr Copansis Adjunct Associate Professor, Geisel School of Medicine at Dartmouth, Hanover, NH.Dr Rahmaniis training director of Child and Adolescent Psychiatry at the University of Florida.

*Members of the Research Committee, Group for the Advancement of Psychiatry also include Dr John Beahrs, Emeritus Professor of Psychiatry, Oregon Health and Science University, Portland; Dr Allan Josephson, (Former) Professor and Chief, Division of Child and Adolescent Psychiatry, University of Louisville School of Medicine; Dr David Keith, Professor Emeritus, Department of Psychiatry and Behavioral Sciences, Upstate Medical Center, Syracuse, NY; Dr Patrick Malone (19442016); Dr Alan Swann, Professor of Psychiatry, Baylor College of Medicine, and staff psychiatrist, Houston VA Medical Center; Dr William Swift, Emeritus Clinical Professor of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison; Dr Johan Verhulst (19382019); and Dr Douglas Kramer, Emeritus Clinical Professor of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, and Chair, GAP Research Committee.

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4D Pharma to host virtual KOL event reviewing new data presented at the Society for Immunotherapy of Cancer annual meeting 2020 from Live Biotherapeut…

4D Pharma to host virtual KOL event reviewing new data presented at the Society for Immunotherapy of Cancer annual meeting 2020 from Live Biotherapeutic MRx0518 clinical programs

Leeds, UK, November 5, 2020, 4D pharma plc(AIM: DDDD), a pharmaceutical company leading the development of Live Biotherapeutic products (LBPs) - a novel class of drug derived from the microbiome, today announces that it will host a virtual Key Opinion Leader (KOL) event to review data presented from two ongoing clinical trials of MRx0518, the Companys lead immuno-oncology single strain Live Biotherapeutic, as both a neoadjuvant monotherapy and combination therapy in patients refractory to checkpoint inhibitors at the Society for Immunotherapy of Cancer (SITC) Annual Meeting 2020. The event will take place on Wednesday, November 11, 2020 at 1:00pm GMT (8:00am ET).

The event will feature presentations from 4D pharma management and KOLs, Dr Mark P Lythgoe, Academic Clinical Fellow in Medical Oncology and Pharmacist at Imperial College London, and Dr Shubham Pant, Associate Professor Department of Investigational Cancer Therapeutics and Department of Gastrointestinal Medical Oncology at the University of Texas MD Anderson Cancer Center.

A live webcast of the event will be available on the Reports and Presentations section of the 4D Pharma website To access the call, please dial 1-877-270-2148 (United States) or 1-412-902-6510 (international) and reference the 4D pharma conference call to join. A replay of the webcast and accompanying slides will be available on the 4D pharma website following the event.

About 4D pharma

Founded in February 2014, 4D pharma is a world leader in the development of Live Biotherapeutics, a novel and emerging class of drugs, defined by the FDA as biological products that contain a live organism, such as a bacterium, that is applicable to the prevention, treatment or cure of a disease. 4D has developed a proprietary platform, MicroRx(R), that rationally identifies Live Biotherapeutics based on a deep understanding of function and mechanism.

4D pharma's Live Biotherapeutic products (LBPs) are orally delivered single strains of bacteria that are naturally found in the healthy human gut.The Company has six clinical programmes, namely a Phase I/II study of MRx0518 in combination with KEYTRUDA (pembrolizumab) in solid tumours, a Phase I study of MRx0518 in a neoadjuvant setting for patients with solid tumours, a Phase I study of MRx0518 in patients with pancreatic cancer, a Phase I/II study of MRx-4DP0004 in asthma, a Phase II study of MRx-4DP0004 in patients hospitalised with COVID-19, and Blautix(R) in Irritable Bowel Syndrome (IBS) which has completed a successful Phase II trial. Preclinical-stage programmes include candidates for CNS disease such as Parkinson's disease and other neurodegenerative conditions. The Company has a research collaboration with MSD, a tradename of Merck & Co., Inc., Kenilworth, NJ, USA, to discover and develop Live Biotherapeutics for vaccines.

In October 2020 4D pharma announced its intention to merge with Longevity Acquisition Corporation (NASDAQ: LOAC), a special purpose acquisition company (SPAC), and seek a NASDAQ listing.The merger is expected to be completed and the NASDAQ listing of 4D pharma American Depositary Shares (ADSs) under the ticker symbol LBPS is currently expected to become effective in early 2021, subject to approval of 4D Shareholders and Longevity Shareholders, and the SEC review process.

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