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Category : Protein Folding

The function of folding | Feature – Chemistry World

Molecules that fold are fundamental to life. If you look at biology as a chemist, you cant escape the conclusion that almost every complicated thing that biology does at the molecular level is carried out by a sequence-specific folded heteropolymer, says Sam Gellman from the University of WisconsinMadison in the US. Chemists have been trying to learn a few of these folding tricks from biology, but according to Jonathan Clayden from the University of Bristol in the UK, rather than just replicating these polymers, the aim now is [to] do better than nature with a bit of chemical ingenuity. Using a wider spectrum of starting blocks he and others are creating molecules called foldamers that might one day beat biology at its own folding game.

The idea of synthesising molecules that could fold into secondary structures stems from work on protein folding carried out in the 1980s. A key contribution was simulations from protein [modelling] specialist Ken Dill, says Gellman, an early adopter of the approach, who came up with the name foldamer.

Dill, now at Stony Brook University in New York state, US, had been working on protein folding and concluded that the process was driven by the juxtaposition of hydrophobic and hydrophilic amino acids in proteins. Before that, the view had been that hydrogen bonding was the magic that dictated how proteins get their structure, says Dill. Work carried out by his collaborator Ron Zuckermann, then at pharma company Chiron, showed this was not the case. He used peptoids made from poly-N-substituted glycines, which have side chains appended to the backbone nitrogen atom rather than the carbon. These molecules could adopt stable helices without the presence of hydrogen bonding, which convinced Dill and Zuckermann that folding was primarily due to the nature of amino acid side chains, with the backbone hydrogen bonding acting only as additional glue.

We walk in proteins footsteps, but we lag far behind

These ideas led Gellman to wonder what other molecules might be able to fold like peptides and he remembers questioning Dill after a conference talk, asking If I could make a polystyrene with a hydrophobic styrene sub-unit and a hydrophilic styrene sub-unit, would they fold? The response was Yes, I think so.

For Dill and collaborator Zuckermann, the folding process is where life started and is responsible for the chemistry to biology transition. While the prevailing theory marks RNA as the first self-replicating molecule, Dill thinks that there must have been a stage before the RNA world where molecules started folding, publishing his foldamer hypothesis in 2017.1 Dividing monomers into those with hydrophilic (polar) and those with hydrophobic side chains, he used a simple computer model to create chains where similar subunits were attracted to each other and found that even short chains can collapse into relatively compact structures.

Theres a natural elongation mechanism that is also selective and auto catalytic, Dill explains. This is because the collapsed structures expose what he calls landing pads for catalysing other nascent polymers, ultimately creating primitive enzymes. What it means is youre going to have a whole ensemble of potential protein functions that are coming out of this soup, just naturally, because of the variability of hydrophobicpolar sequences themselves. For biology you ultimately needed information storage via DNA, but first you needed folding, Dill says.

So if biology is about folding, could chemists also harness this power? Gellman started trying in the 1990s, coming up with the name foldamer for these types of synthetic molecules, typically 1020 monomer units. It turns out, you cant do this with polystyrene because nobody knows how to make a polystyrene where you [can] control which monomer goes where, so a lot of this work has ended up focusing on polyamides, explains Gellman. He has focused on -amino acids which have their amino group bonded to the -carbon rather than the as found in biology, but still fold into helices of various shapes, comparable to those found in proteins.

Others, such as supramolecular chemist Ivan Huc, from the University of Munich in Germany, have designed more exotic structures using aromatic oligoamides, and monomers bearing proteinogenic side chains that provide the folding impetus. Hucs apple peel helical capsule can be tuned in diameter according to monomer size, and specific attractive and repulsive interactions between the amide and the other functional groups can be substituted onto the aromatic rings. These foldamers can house a guest molecule in the resulting cavity.2 These shapes are very trivial to obtain with aromatic amides and they are completely out of the reach of peptides or nucleotides, says Huc.

Designing foldamers is still a mostly trial and error process based on an understanding of local conformational preferences. Computational tools are gaining ground but arent as advanced as tools to model proteins and peptides. We walk in their footsteps, but we lag far behind, says Huc.

One of the obvious dreams is to create catalytic versions [of foldamers], says Gellman, who recently took up this difficult challenge. In some cases, enzymes speed reactions up a million times by organising molecules within enclosed pockets. While Gellman cannot make this sort of tertiary structure yet, he did create a foldamer that allows two functional groups to be arranged in proximity to each other tethered to a helix.3 Gellmans foldamer contained a and amino acids, including residues with five-membered rings, which stabilised the foldamers helical structure by constraining the backbones flexibility.

This was used to catalyse the formation of large macrocycles, which are useful as potential drugs but difficult to make as the two ends of long chain molecules need to be close together to react. Using a primary and secondary amine group each attached to a residue, the foldamer is able to correctly position the ends and form a carboncarbon bond via an aldol condensation, creating 1222 carbon rings. Previous work had shown that such foldamer systems allowed similar reactions to proceed at least 100 times faster than using small molecule catalysts. The foldamers performance is still a long way from that of an enzyme though.

Gellman and others are also working on how foldamers could out-smart biology as drug molecules. There is a whole host of peptides which sometimes are used as drugs but they break down [in the body] very fast, says Dimitri Dimitriou, chief executive of Swiss drug company Immupharma. If you can effectively create a peptide analogue, which is stable, then [foldamers] have the potential to be as big as the monoclonal antibody industry thats the excitement from the commercial side. He is confident that within five years foldamer drugs will be on the market.

Gellman co-founded Longevity Biotech in 2010 to develop peptide drugs incorporating -amino acids.4 These peptides only have a quarter to a third of the residue, but because theyre distributed along the backbone, proteolytic enzymes will cut [them] very slowly, he explains.The company call these helical foldamers hybridtides and are trying to design hybridtide drugs that bind to G protein-coupled receptors (GPCRs), transmembrane proteins that transmit signals inside a cell when stimulated by molecules outside. They are currently conducting a pre-clinical biomarker study for a Parkinsons disease drug candidate.

During the coronavirus shutdown Gellman has continued to work on foldamers that may block the Sars-CoV-2 virus that causes Covid-19. The approach is based on work carried out in 2009 inspired by a drug for HIVAids.5 A 36-residue peptide, enfuvirtide, is effective in blocking the virus attaching itself to cells, but the drug has such a short half-life that patients needed to be injected twice a day. We made variants that were 300-fold less susceptible to proteolysis [digestion] because of the a [backbone] and thats what were trying to do with the coronavirus, says Gellman.

Its a very complicated and difficult challenge but this is what we are trying

Immupharma are also developing foldamer drugs alongside subsidiary company Ureka, based on the work of Giles Guichard at the University of Bordeaux in France. But their foldamers swap some amino acids for ureas, which have two amino groups joined by a carbonyl. Oligourea is particularly good to form helices and those helices are similar to peptide helices you have a good mixture of rigidity coming from the urea [backbone] and some flexibility coming from the sidechain groups, which can be substituted a little bit like an amino acid, explains Sebastien Goudreau, head of research at Ureka.

As proof-of-concept Ureka has started with glucagon-like peptide-1 (GLP-1), the 31-amino-acid hormone found in the pancreas that enhances the secretion of insulin and is used for the treatment of type 2 diabetes and the liver disease non-alcoholic-steatohepatitis. Their foldamer replaces four consecutive GLP-1 amino acids with three urea residues.6 We have shown that it works and proved that it can extend the half-life dramatically [in mice], say Dimitriou. This could mean a dose would only be needed once a month and if resistant enough to digestive enzymes it might be able to be taken orally, although Dimitriou says they have not proven this yet.

Also on the radar are complex proteinprotein interactions, traditionally considered undruggable. Its a very complicated and difficult challenge, says Huc. But this is what we are trying. He has been designing foldamer molecules that can match a binding site in terms of their size, shape and proteinogenic side chains as far they can predict, but the final trick is to tether it to the protein. Using disulfide linkers, foldamers bearing different proteinogenic side chains were attached via a cysteines thiol side chain. Hucs achiral foldamer will resonate between a left-handed and right-handed helix, but if it interacts with the protein surface, one version will become more favourable and predominate; this can be detected using circular dichroism spectroscopy.7 The sign of an interaction doesnt mean tight binding, says Huc, but from these interactions, I can design.

Not only has nature created folded molecules, but also molecules that can change their shapes. For example, GPCRs will undergo conformational switching as they respond to hormones and the molecules that stimulate our senses of taste and smell. Clayden has been using foldamers to try and recreate the action of these receptors. Weve been designing molecules that have exactly the same sort of features when they pick up a ligand for example, they change shape and as a result they transmit information through the structure of the molecule thats what we call dynamic foldamers.

Unlike nature, Clayden starts with an achiral amino acid, -aminoisobutyric acid (AIB). You end up with a helix that can either be left- or right-handed and can actually inter-convert very rapidly between those, he says. The switching mechanism is provided by a large cyclic amino-borate group on the amine end of the foldamer. When a bulky chiral diol ligand is added it will form a boronate ester which then forms a methanol-bridge to the amine group. The steric bulk of the ligand forces the foldamer to switch to one helical sense.8 Clayden has shown these artificial receptors work when embedded in phospho-lipid vesicles.9 [In] the long term we would like to get these things into real cells. Weve done some very preliminary work, he says. These dynamic foldamers could lead to smart drugs that could independently switch enzyme pathways on or off within cells depending on a specific stimulus.

Clayden has used the same approach to imitate our colour vision, which in nature relies on the GPCR receptor rhodopsin in the retinal rods. Our molecule is an azobenzene chromophore and thats attached to an AIB foldamer that changes shape when the azobenzene responds to light, he explains. In UV light the molecule switches to its cis conformation which induces a screw sense in the foldamer making what Clayden calls a conformational photo diode.10 He envisions future smart chemical systems made from dynamic foldamers for example, simply using different coloured lights to turn reactions on and off or switch from one enantiomeric product to another. Were currently working on a system that binds a catalyst, but releases it when its prompted to switch. That sort of idea could be used to release, for example, an enzyme inhibitor.

Dills foldamer hypothesis for the early stages of life supposes a move from secondary folded structures to the proteins we have today, with their complex tertiary structures, combining helices and sheets made from defined peptide sequences. The real power of biology in my view, and where I would love to see foldamers go, is hooking domains together, he says. But chemists are some way from this. Most proteins are over 100 residues thats pretty hard for chemical synthesis, says Gellman.

Most labs are using solid-phase synthetic methods and starting to introduce automation but synthesising the relevant monomers isnt trivial. Small molecule synthesis is not nearly as advanced a field as it should be, says Gellman. For peptide chemistry, many of the starting blocks are commercially available but for foldamers that isnt the case. We can buy some of the amino acids we need, but many of them, particularly when they have rings to constrain their local conformation, we cant, and we dont know how to make [them].

Most proteins are over 100 residues thats pretty hard for chemical synthesis

Nevertheless chemists are attempting some simple tertiary structures. Several groups have produced foldamers that mimic the zinc finger domain (a protein motif that is able to coordination one or more zinc ions and binds a wide variety of biological molecules). Foldamers have also re-created the four-helix bundle motif, with hydrophobic residues buried in the core. Huc has even formed helical bundles in non-polar organic solvents showing these structures can form in very different environments to nature.11

To create larger structures, Huc has suggested borrowing natures solution: ribosomes, the cells protein factory. [My] long term dream is to hijack this machinery, and teach or modify the ribosome to produce [non-natural] chemical entities. This hasnt been done yet and might not be so easy. Ribosomes are complexes of RNA and protein that are able to link amino acids together. They start with a messenger RNA (mRNA) template which base pairs with transfer RNA (tRNA) molecules that carry individual amino acids.

We need to think of other things that nature doesnt do at all

Hucs initial work with ribosomes in 2018 used novel RNA enzymes known as flexizymes, designed by Hiroaki Suga at the University of Tokyo in Japan, that are capable of attaching non-natural amino acids to tRNA. Huc was able to attach a dipeptide-appended aromatic helical foldamer. He then used an E. coli ribosome to synthesise a foldamerpeptide hybrid the foldamer needed to unfold to get through the ribosome exit tunnel.12 While the ribosome is not forming bonds within the foldamer itself, its certainly a small step in that direction.

Going back 30 years the question was whether biological polymers and their ability to fold were unique. Chemists have answered that: we can tell many different types of chemical backbones have a propensity to fold, says Huc. The question is now whether we can make increasingly complex large folded molecules and what can we do with them. Nature has taught us some tricks, but chemists have a wider palette to work from. [We need to] think of other things that nature doesnt do at all, suggests Huc. Perhaps the key developments will be in high temperature materials or micro-processors, who knows?

Rachel Brazil is a science writer based in London, UK

1 E Guseva, R N Zuckermann and K A Dill, Proc. Natl Acad. Sci. USA, 2017, 114, E7460 (DOI: 10.1073/pnas.1620179114)

2 J Garric, J-M Lger and I Huc, Angew. Chem. Int. Ed., 2005, 44, 1954 (DOI: 10.1002/anie.200462898)

3 Z C Girvin, M K Andrews, X Liu, S H Gellman, Science, 2019, 366, 1528 (DOI: 10.1126/science.aax7344)

4 R Cheloha et al, Nat. Biotechnol., 2014, 32, 653 (DOI: 10.1038/nbt.2920)

5 S W Horne et al, Proc. Natl Acad. Sci. USA, 2009, 106, 14751 (DOI: 10.1073/pnas.0902663106)

6 J Fremaux et al, Nat Commun., 2019, 10, 924 (DOI: 10.1038/s41467-019-08793-y)

7 M Vallade et al, Bioconj. Chem., 2019, 30, 54 (DOI: 10.1021/acs.bioconjchem.8b00710)

8 R Brown et al, Nat. Chem., 2013, 5, 853 (DOI: 10.1038/nchem.1747)

9 F Lister et al, Nat. Chem., 2017, 9, 420 (DOI: 10.1038/nchem.2736)

10 D Mazzier et al, J. Am. Chem. Soc., 2016, 138, 8007 (DOI: 10.1021/jacs.6b04435)

11 S De et al, Nat. Chem., 2018, 10, 51 (DOI: 10.1038/nchem.2854)

12 J M Rogers et al, Nat. Chem., 2018, 10, 405 (DOI: 10.1038/s41557-018-0007-x)

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The function of folding | Feature - Chemistry World

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DeepMinds Protein Folding AI Is Going After Coronavirus

In late December last year, Dr. Li Wenliang began warning officials about a novel coronavirus in Wuhan, China, but was silenced by the police before tragically succumbing to the disease two months later. Meanwhile, almost simultaneously, a computer server halfway across the world started issuing worrying alerts of a potential new outbreak. The server runs software by BlueDot, a company based in San Francisco that uses AI to monitor infectious disease outbreaks for signs of early trouble.

Not enough people listened to either human expertise or AI. Then cases skyrocketed in Wuhan and spread across the world, and people had to take note.

Hindsight is 20/20, but it is remarkable that BlueDot and other machine learning-based services are beginning to catch early signs of infectious disease outbreaksalmost within the same time frame as health experts, if just for COVID-19. We often hear about AI as the next second coming of healthcare, where it can catch cases early, accelerate drug development, and personalize treatment. Yet COVID-19 is the first global pandemic to ever hold healthcare AIs feet to the flame in a global, serious, and urgent real-world test case. In a head-to-head race, can AI actually accelerate new anti-virals or vaccines for COVID-19, something the world has never previously seen? Or will traditional biotech measures excel, in turn unveiling that AIs hype massively outstrips reality?

MIT Technology Review recently reported an excellent piece that comprehensively looks at how AIat its current ability levelcan help us predict, diagnose, and treat novel viral threats. Im on board with the general idea: AIs potential is enormous.

Yet for now, dont look to AI to help tackle COVID-19; its simply not ready.

That said, it is enormously helpful to see how major machine learning companies are utilizing or repositioning their technologies for tackling the crisis. People often critique AI tested in toy cases, or standardized, limited datasets that may have limited significance in the real world. With companies working on COVID-19, thats no longer the case.

Ready, player, go? Heres how one major AI player in healthtech, DeepMind, is trying to knee-cap COVID-19.

The promise of AI for accelerating medical drug discovery is almost a universally supported idea. One caveat: so far, though new drugs have been discovered using AI, no AI-based drug candidate has made it through the approval process (yet), or even demonstrated that the tech makes the whole process faster to market (yet).

In very broad strokes, AI could be enormously helpful for initial drug discovery in two main ways: one, screening through millions of chemical compounds for potential drugs in simulation tests, far faster than any human expert; two, identifying targets that new drugs can latch onto, either to reduce their impact (making people less sick), or to slow their spread among people.

For COVID-19, DeepMind is focusing on the second route. Known mostly for its algorithms that beat human players at Go, DOTA, and other games, DeepMind has nevertheless been working directly on solutions for drug discovery. Their secret sauce? AlphaFold, a deep learning system that tries to predict protein structures accurately when no similar proteins exist.

Stay with me. How a protein looks in 3D is essential for developing new drugs, especially for new viruses. COVID-19, for example, has really spikey proteins that jut out from its surface. Normally, human cells dont carethey wont let the virus inside. But COVID-19s spikey proteins also harbor a Trojan Horse that activates it in certain cells with a complementary component. Lung cells have an abundance of these factors, which is why theyre susceptible to invasion.

Bottom line: if a drug is going to fit into a protein like a key into a lock to trigger a whole cascade of nasty reactions, then the first step is to figure out the structure of the lock. Thats what DeepMinds AlphaFold is doing.

Thanks to a surge of global collaboration, China released the genomic blueprint of the COVID-19 virus in open-access databases, whereas others have posted online the structure of some of its proteinseither determined by experiments or through computational modeling. DeepMind is taking these data to the next level by focusing on a few understudied but potentially important proteins that could become drug or vaccine targets using machine learning.

Protein folding has been a decades-long, fundamental problem in biochemistry and drug discovery. Almost all of our existing drugs grab onto certain proteins to work, so identifying protein structure is akin to surveying the enemy landscape and figuring out best attack point simultaneously. The problem is the genetic code doesnt translate to how proteins look. When it comes to a new virus, without predicting protein structures were basically fighting viruses and diseases as if they were the Invisible Man.

Traditional methods use high-tech microscopes, freezing proteins into crystal-looking entities, and other strange and expensive ways to understand their structure. Under the scope, a protein is basically a chain of chemical letters that wrap around itself into intricate structureskinda like how your headphones always tangle into inconceivable structures while youre sleeping. For DeepMind and other protein-folding efforts, the key is to predictand then find methods to decipher drug targets fromthose structures.

AlphaFold stands out as a union of decades of deep learning progress, but guided by expertise from protein structure databases in the public domain. In a nutshell, AlphaFold uses genome sequences (available for COVID-19 and relatively easy to get) to predict the properties of resulting proteins that actually do the work, by looking at the distance of each letter or component that makes up a certain protein. It doesnt predict specific sequences with special powerssuch as those that bind to a cellbut offers a quick police sketch of the virus perp in sight.

Theres no doubt that AlphaFold is new to the protein-folding game. Even DeepMind itself stresses that these structure predictions have not been experimentally verified, but could galvanize efforts at making anti-virals and/or vaccines. For now, its difficult to judge how much AlphaFold will contribute to the pandemic, if at all. But by automating a critical aspect of drug discovery, its also en route to becoming a much larger player in the next epidemic.

Of note: all of this would not be possible without public, open-source databases of protein structures (like UniProt and the Protein Data Bank) thats been building for decades. DeepMinds release, posted with open access, has been lauded by fellow scientists as a way of giving back to the community.

Chinas long-time Google surrogate and AI behemoth, Baidu, is using an algorithm to predict the structure of another important biomolecule, mRNA. mRNA shuttles information from the genome to protein factories, so shoot the mRNA messenger, then the viral proteins are never born. Similarly, AI could one day potentially predict epidemics and how a virus changes over timebut it will only help if theres enough trust to listen to the models.

Various AI companies are also making a play towards efficient diagnosticsidentifying COVID-19 signs in medical scansor other measures to support at-risk and overworked medical frontline heroes. The problem is that with any new outbreak, we dont have enough data to train an AI, which means that they will struggle to find subtle differences in imperfect medical scans, at least for now.

So, is AI our savior? Not in this pandemic. Similar to the 2003 SARS outbreak, the best response is something that has existed for centuries: social distancing. As I mentioned previously, before COVID-19 exploded into a pandemic, science was ready to provide answers for COVID-19 as long as governments were also ready to respond. And because AI is based on scientific data and helping otherwise difficult efforts, machine learning is rapidly learning to do the same.

But perhaps ironically, COVID-19 is exposing both the best and weakest parts of AI in our current society for healthcare: great models that in theory should work, solid predictions that can be tested, but not without any recommendations without a heavy dose of skepticism. COVID-19 presents a brutal test case for AI in healthcare.

But for now, the toughest case is that of government management and what we do in response.

Note: To learn more about the Covid-19 pandemic, tune into Singularity Universitys free virtual summit: Covid-19: The State & Future of Pandemics.

Image Credit: Vektor Kunst from Pixabay

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DeepMinds Protein Folding AI Is Going After Coronavirus

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Bioinformatics Platforms Market: Year 2017-2027 and its detail analysis by focusing on top key players like Illumina, Qiagen, ID Business Solutions,…

Bioinformatics Platforms Market 2020 Global Industry research report explores analysis of historical data along with size, share, growth, demand, revenue and forecast of the global Bioinformatics Platforms and estimates the future trend of market on the basis of this detailed study. The study shares market performance both in terms of volume and revenue and this factor which is useful & helpful to the business.

Bioinformatics is one of the branch of information technology which deals with the development of software solutions in order to process biological data. Some of the applications included in the bioinformatics research includes, genome annotation, modeling, molecular folding, expression profiling, and gene/protein prediction. The emergence and advancements in bioinformatics are associated with the computerized programming which are specially designed to handle large volumes of DNAs, RNAs, proteins, and metabolites.

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Table Of Content

1.INTRODUCTION

2. KEY TAKEAWAYS3. RESEARCH METHODOLOGY4. BIOINFORMATICS PLATFORMS LANDSCAPE

5. BIOINFORMATICS PLATFORMS KEY MARKET DYNAMICS

6. BIOINFORMATICS PLATFORMS GLOBAL MARKET ANALYSIS

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Explained: How corona of the virus changes into a hairpin shape and why – The Indian Express

Written by Kabir Firaque | New Delhi | Updated: July 24, 2020 10:45:13 am Structure of SARS-CoV-2, including the spike protein. (Source: Wikipedia)

The spike protein of SARS-CoV-2 the corona in the coronavirus that causes Covid-19 disease has just revealed new secrets. Researchers have found that the spike protein changes its form after it attaches itself to a human cell, folding in on itself and assuming a rigid hairpin shape. The researchers have published their findings in the journal Science, and believe the knowledge can help in vaccine development.

It is a protein that protrudes from the surface of a coronavirus, like the spikes of a crown or corona hence the name coronavirus. In the SARS-CoV-2 coronavirus, it is the spike protein that initiates the process of infection in a human cell. It attaches itself to a human enzyme, called the ACE2 receptor, before going on to enter the cell and make multiple copies of itself.

Using the technique of cryogenic electron microscopy (cryo-EM), Dr Bing Chen and colleagues at Boston Childrens Hospital have freeze-framed the spike protein in both its shapes before and after fusion with the cell.

The images show a dramatic change to the hairpin shape after the spike protein binds with the ACE2 receptor. In fact, the researchers found that the after shape can also show itself before fusion without the virus binding to a cell at all. The spike can go into its alternative form prematurely.

Dr Chen suggests that assuming the alternative shape may help keep SARS-CoV-2 from breaking down. Studies have shown that the virus remains viable on various surfaces for various periods of time. Chen suggests that the rigid shape may explain this.

More significantly, the researchers speculate that the postfusion form may also protect SARS-CoV-2 from our immune system.

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The postfusion shape could induce antibodies that do not neutralise the virus. In effect, the spikes in this form may act as decoys that distract the immune system.

Antibodies specifically targeting the postfusion state would not be able to block membrane fusion (viral entry) since it would be too late in the process. This is well established in the field of other viruses, such as HIV, Chen told The Indian Express, by email.

In principle, if both conformations shared neutralising epitopes (the part of the virus targeted by antibodies), then the postfusion form too could induce neutralising antibodies, Chen said. But because the two structures are often very different, in particular, in case of SARS-CoV-2 and HIV, I think it is not very likely that the postfusion form would be useful as an immunogen, he explained.

Yes, both the before and after forms have sugar molecules, called glycans, at evenly spaced locations on their surface. Glycans are another feature that helps the virus avoid immune detection.

The researchers believe the findings have implications for vaccine development. Many vaccines that are currently in development use the spike protein to stimulate the immune system. But these may have varying mixes of the prefusion and postfusion forms, Chen said. And that may limit their protective efficacy.

Chen stressed the need for stabilising the spike protein in its prefusion structure in order to block the conformational changes that lead to the postfusion state. If the protein is not stable, antibodies may be induced but they will be less effective in terms of blocking the virus, he said.

Using our prefusion structure as a guide, we should be able to do better (introducing stabilizing mutations) to mimic the prefusion state, which could be more effective in eliciting neutralizing antibody responses, Chen told The Indian Express. We are in the process of doing this in case the first round of vaccines are not as effective as we all hope.

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Explained: How corona of the virus changes into a hairpin shape and why - The Indian Express

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Microsoft Executive Vice President Jason Zander: Digital Transformation Accelerating Across the Energy Spectrum; Being Carbon Negative by 2030; The…

WASHINGTON--(BUSINESS WIRE)--Microsoft Executive Vice President Jason Zander says the company has never been more busy partnering with the energy industry on cloud technologies and energy transition; the combination of COVID-19 and the oil market shock has condensed years of digital transformation into a two-month period; the companys return to its innovative roots and its goal to have removed all of the companys historic carbon emissions by 2050 in the latest edition of CERAWeek Conversations.

In a conversation with IHS Markit (NYSE: INFO) Vice Chairman Daniel Yergin, Zanderwho leads the companys cloud services business, Microsoft Azurediscusses Microsofts rapid and massive deployment of cloud-based apps that have powered work and commerce in the COVID-19 economy; how cloud technologies are optimizing business and vaccine research; the next frontiers of quantum computing and its potential to take problems that would take, literally, a thousand years, you might be able to solve in 10 seconds, and more.

The complete video is available at: http://www.ceraweek.com/conversations

Selected excerpts:Interview Recorded Thursday, July 16, 2020

(Edited slightly for brevity only)

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Weve already prepositioned in over 60 regions around the world hundreds of data center, millions and millions of server nodestheyre already there. If you can imagine COVID, if you had to go back and do a procurement exercise and figure out a place to put the equipment, and the supply chains were actually shut down for a while because of COVID. Thats why I say, even three to five years ago we as industries would have been pretty challenged to respond as quickly as we had.

Thats on the more tactical end of the spectrum. On the other end weve also done a lot of things around data sets and advanced data work. How do we find a cure? Weve done things like [protein] folding at home and making sure that those things could be hosted on the cloud. These are thingsthat will be used in the search of a vaccine for the virus. Those are wildly different spectrums from the tactical 'we need to manage and do logistics' to 'we need a search for things that are going to get us all back to basically normal.'

Theres also a whole bunch of stimulus packages and payment systems that are getting created and deployed. Weve had financial services companies that run on top of the cloud. They may have been doing a couple of hundred big transactions a day; weve had them do tens to hundreds of thousands a day when some of this kicked in.

The point is with the cloud I can just go to the cloud, provision it, use it, and eventually when things cool back down, I can just shut it off. I dont have to worry about having bought servers, find a place for them to live, hiring people to take care of them.

There was disruption in supply chain also. Many of us saw this at least in the Statesif you think even the food supply chain, every once in a while, youd see some hiccups. Theres a whole bunch of additional work that weve done around how do we do even better planning around that, making sure we can hit the right levels of scale in the future? God forbid we should have another one of these, but I think we can and should be responsible to make sure that weve got it figured out.

The policy and investment sideit has never been more important for us to collaborate with healthcare, universities, and with others. Weve kicked off a whole bunch of new partnerships and work that will benefit us in the future. This was a good wake up call for all of us in figuring out how to marshal and be able to respond even better in the future.

Weve had a lot of cases where people have been moving out of their own data centers and into ours. Let us basically take care of that part of the system. We can run it cheaply and efficiently. Im seeing a huge amount of data center accelerationfolks that really want to move even faster on getting their workloads removed. Thats true for oil and gas but its also true for the financial sector and retail.

Specifically, for oil and gas, one of the things that were trying to do in particular is bring this kind of cloud efficiency, this kind of AI, and especially help out with places where you are doing exploration. What these have in common is the ability to take software especially from the [independent software vendors] that work in the spacereservoir simulation, explorationand marry that to these cloud resources where I can spin things up and spin things down. I can take advantage of that technology that Ive got, and I am more efficient. I am not spending capex; I can perhaps do even more jobs than I was doing before. That allows me to go do that scale. If youre going to have less resources to do something, you of course want to increase your hit rate; increase your efficiency. Those are some of the core things that were seeing.

A lot of folks, especially in oil and gas, have some of the most sophisticated high-performance computing solutions that are out there today. What we want to be able to do with the cloud is to be able to enable you to do even more of those solutions in a much more efficient way. Weve got cases where people have been able to go from running one reservoir simulation job a day on premises [to] where they can actually go off to the cloud and since we have all of this scale and all of this equipment, you can spin up and do 100 in one day. If that is going to be part of how you drive your efficiency, then being able to subscribe to that and go up and down its helping you do that job much more efficiently than you used to and giving you a lot more flexibility.

Were investing in a $1 billion fund over the next four years for carbon removal technology. We also are announcing a Microsoft sustainability calculator for cloud customers. Basically, you can help get transparency into your Scope 1,2, and 3 carbon emissions to get control. You can think of us as we want to hit this goal, we want to do it ourselves, we want to figure out how we build technology to help us do that and then we want to share that technology with others. And then all along the way we want to partner with energy companies so that we can all be partnering together on this energy transition.

From a corporate perspective weve made pledges around being carbon negative, but then also working with our energy partners. The way that we look at this is youre going to have continued your requirements and improvements in standards of living around the entire planet. One of the core, critical aspects to that is energy. The world needs more energy, not less. There are absolutely the existing systems that we have out there that we need to continue to improve, but they are also a core part of how things operate.

What we want to do is have a very responsible program where were doing things like figuring out how to go carbon negative and figuring out ways that we as a company can go carbon negative. At the same time, taking those same techniques and allowing others to do the same and then partnering with energy companies around energy transformation. We still want the investments in renewables. We want to figure out how to be more efficient at the last mile when we think about the grid. I generally find that when you get that comprehensive answer back to our employees, they understand what we are doing and are generally supportive.

Coming up is a digital feedback loop where you get enough data thats coming through the system that you can actually start to be making smart decisions. Our expectation is well have an entire connected environment. Now we start thinking about smart cities, smart factories, hospitals, campuses, etc. Imagine having all of that level of data thats coming through and the ability to do smart work shedding or shaping of electrical usage, things where I can actually control brownout conditions and other things based on energy usage. Theres also the opportunity to be doing smart sharing of systems where we can do very efficient usage systemsintelligent edge and edge deployments are a core part of that.

How do we keep all the actual equipment that people are using safe? If you think about 5G and additional connectivity, were getting all this cool new technology thats there. You have to figure out a way in which youre leveraging silicon, youre leveraging software and the best in securityand were investing in all three.

The idea of being able to harness particle physics to do computing and be able to figure out things in minutes that would literally take centuries to go pull off otherwise in classical computing is kind of mind-blowing. Were actually working with a lot of the energy companies on figuring out how could quantum inspired algorithms make them more efficient today. As we get to full scale quantum computing then they would run natively in hardware and would be able to do even more amazing things. That one has just the potential to really, really change the world.

The meta point is problems that would take, literally, a thousand years, you might be able to solve in 10 seconds. Weve proven how that kind of technology can work. The quantum-inspired algorithms therefore allow us to take those same kind of techniques, but we can run them on the cloud today using some of the classic cloud computers that are there. Instead of taking 1,000 years, maybe its something that we can get done in 10 days, but in the future 10 seconds.

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Proteasomal degradation of the intrinsically disordered protein tau at single-residue resolution – Science Advances

INTRODUCTION

Intrinsically disordered proteins (IDPs) are abundant in the human proteome and are implicated as therapeutic targets in major human diseases (1). IDPs have amino acid sequences of low complexity and lack an ordered three-dimensional (3D) structure (1). This allows IDPs to dynamically bind to diverse interaction partners and thus influence many biological processes (1). The activity of IDPs is regulated by posttranslational modifications including phosphorylation and truncation (1, 2). Because of their structural instability, IDPs are particularly sensitive to proteolytic degradation (35).

Aggregation of IDPs into insoluble deposits is the hallmark of neurodegenerative diseases (3). Aggregates of the IDP tau are linked to the progression of Alzheimers disease (AD) and are found in other age-related disorders termed tauopathies (6). The longest tau isoform in the human central nervous system comprises 441 residues (7). The N-terminal ~150 residues of tau project away from the microtubule surface and are thus termed projection domain (8). The central part of the tau sequence is formed by pseudo-repeats, which bind to microtubules (8, 9) and are essential for pathogenic aggregation and folding into cross- structure in tau amyloid fibrils (10, 11). Phosphorylated tau accumulates during the development of AD (6, 12).

The 20S proteasome forms the proteolytic core particle of the 26S proteasome holoenzyme (13). In contrast to the proteasomal degradation of most cellular proteins, IDPs can be degraded by the 20S proteasome in an ubiquitin- and adenosine triphosphate (ATP)independent process without the necessity of the 19S regulatory particle (35). Soluble tau is degraded by the 20S proteasome (14, 15), while phosphorylation and aggregation of tau inhibit its turnover by the proteasome (2, 1517). Decline of proteasomal activity and accumulation of tau have been linked to neurodegeneration (2, 18, 19): Decreased proteasomal activity results in tau accumulation, neurotoxicity, and cognitive dysfunction in cell and animal models of neurodegenerative disorders. Pharmacological activation of the 20S proteasome, direct administration of proteasome, or targeted proteasomal degradation of tau is therefore the focus of current therapeutic strategies targeting tauopathies (20, 21).

Here, we study the degradation of the IDP tau by the 20S proteasome through a residue-specific and quantitative approach that combines nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). We provide detailed insights into the identity and properties of the proteasomal degradation products of tau, the single-residue degradation kinetics, and their specific regulation by phosphorylation in different tau domains/by different kinases.

The 20S proteasome (20S) is a barrel-shaped complex comprised by two stacked heptameric -rings that are sandwiched by two heptameric -rings (Fig. 1A) (13). The proteolytic sites, which hydrolyze the peptide bonds of substrates, are located in the subunits. IDPs thus traverse through the -rings to reach the active sites in the interior of the 20S proteasome (Fig. 1B). To study degradation of the IDP tau, we recombinantly prepared 20S from Thermoplasma acidophilum, which contains only one type of subunit and one type of subunit. This 20S particle thus has 14 identical chymotrypsin-like active sites, which are positioned at equal distances around the -rings (Fig. 1B). Electron microscopy (EM) showed intact barrel-shaped 20S complexes (Fig. 1C). The 441-residue isoform of tau (hTau40; also termed 2N4R tau; Fig. 1D) was also expressed in Escherichia coli.

(A) Schematic representation depicting the architecture of the 20S proteasome (20S) comprising 28 subunits arranged in four heptameric rings (7777). (B) The proteolytic active sites of the 20S proteasome are located in its interior, thus enabling degradation of hTau40 into short peptides once it has entered the 20S core. (C) Negatively stained EM micrograph of the 20S proteasome. (D) Domain organization of full-length hTau40 composed of 441 amino acids (aa) (UniProt ID 10636-8). N1 and N2 are the two inserts in the N-terminal projection domain, P1 and P2 correspond to the two proline-rich regions, and R1 to R are five pseudo-repeats. (E) (Left) SDS-PAGE gel showing hTau40 (1) and the degradation of (2 to 5) hTau40 by the 20S proteasome over time. The samples were incubated at 37C for 30 min (2), 90 min (3), and 150 min (4) and were subsequently put at 4C for additional 48 hours (5). After 48 hours, two well-resolved bands at ~28 and ~30 kDa (red lined box) appeared. (Right) The amino acid sequences of the upper (~30 kDa) and lower bands were identified with in-gel analysis and marked in red. Both intermediates correspond to the N-terminal domain of hTau40.

Recombinant hTau40 was incubated with the 20S proteasome, and degradation was followed by SDSpolyacrylamide gel electrophoresis (PAGE) (Fig. 1E, left). After ~150 min, a clear decrease in the intensity of the hTau40 band at ~60 kDa was apparent (lane 4 in Fig. 1E). In addition, two bands running at ~30 and 28 kDa appeared. Analysis after 48 hours of incubation confirmed the presence of the two new bands, while the full-length protein was degraded to near completion (lane 5 in Fig. 1E).

The two intermediate bands were precisely and independently excised from the gel, subjected to in-gel digestion using trypsin, which specifically cleaves at the peptide bond C terminus of lysine or arginine residues, and analyzed using liquid chromatography (LC)MS/MS. For both bands, the MS analysis confidently identified several peptides from the N-terminal domain (Fig. 1E, right). No peptides were identified in the region from 127 to 210, which contains multiple lysine and arginine residues such that trypsin digestion will produce too short sequences to be analyzed by LC-MS/MS. In the case of the upper band, the additional peptide RTPSLPTPPTR (residues 211 to 221 of hTau40) was identified (Fig. 1E, right).

We also separated the two long fragments using LC and detected their molecular weight by intact MS, giving masses of 25.782 and 22.257 kDa (fig. S1). Manual matching of the determined masses to N-terminal sequences of hTau40 showed that the long fragment contains residues 1 to 251, and the short one has residues 1 to 218. Previous studies showed that the upper band is recognized by the antibody Tau-5 (14), which binds to residues in the region from 218 to 225 (22).

To gain insight into the structural properties of the long tau fragments generated during 20S degradation, we recombinantly prepared a tau protein comprising residues 1 to 239 of hTau40. Tau(1239) contains the full epitope for the Tau-5 antibody (residues 218 to 225) and has a length in between the two long N-terminal fragments. Particle size analysis by dynamic light scattering (fig. S2A) showed that both hTau40 and Tau(1239) are more compact than the average size values for IDPs (fig. S2B) (23). hTau40, with an experimental size of 5.2 nm and an expected size for its number of residues of 5.5 nm, is 5% more compact than expected, while Tau(1239) is 18% more compact than expected with 3.3 and 4 nm as experimental and expected sizes, respectively. Despite the stronger compaction of Tau(1239), both proteins present the typical pattern of random coil conformation in circular dichroism spectra (fig. S2C).

Figure S2D shows the 1H-15N heteronuclear single-quantum coherence (HSQC) spectrum of 15N-labeled Tau(1239). The backbone cross peaks are located in the region between 7.6 and 8.6 parts per million (ppm), which is characteristic for IDPs. When compared to hTau40, chemical shift perturbation was restricted to the most C-terminal residues of Tau(1239) (fig. S2E), i.e., residues where Tau(1239), but not hTau40, ends. Analysis of the secondary structure propensities using the chemical shifts of carbonyl and C (fig. S2F) furthermore showed that both hTau40 and Tau(1239) are mainly random coil, in agreement with circular dichroism spectra (fig. S2C).

In addition, the single-residue analysis showed that Tau(1239) contains elements of transient secondary structure: residues 116 to 119 with a tendency for helical structure and two short stretches (residues 150 to 152 and 225 to 230) with extended conformation. The same transiently structured regions were detected in hTau40 (fig. S2F). TALOS+ also identified four regions with preference for extended conformation (residues 275 to 279, 306 to 310, 337 to 339, and 392 to 399) and one with helical content (residues 431 to 437) in hTau40, in agreement with previous analysis (24). The presence of extended conformations in the repeat region has previously been suggested to be responsible for the observation that the repeat region of tau, which is not present in Tau(1239), is less compact when compared to a pure random coil conformation. The combined data thus point to a compaction of the N-terminal cleavage intermediates of hTau40 (fig. S2, A and B).

To identify short tau peptides generated by 20S, we analyzed the released peptides in the supernatant after incubation of hTau40 and 20S using MS. The largest fraction of identified peptides was from hTau40s pseudo-repeat region (Fig. 2, A and B). In addition, peptides from the C-terminal domain and the residue regions 2 to 13, 84 to 103, and 167 to 192 were detected but with very low responses in MS in the supernatant (Fig. 2C). The tau peptides and their cleavage sites identified by MS are generally in good agreement with the proteasomal cleavage sites predicted by NetChop 3.1 (Fig. 2B) (25).

(A) Domain organization of hTau40. (B) Amino acid sequence of hTau40 depicting in color [color code as in (A)] the 20S-generated peptides, which were identified by LC-MS/MS. The peptides underlined with black dots were also present in the in-solution sample but with low intensities. The slashes depict all identified cleavage sites. Cleavage sites predicted by the NetChop server are marked by arrows. The bar on top of the VQIVYK sequence indicates the ability of this sequence to form amyloid-like filaments (26). (C) (Left) Histogram representation of the peak area of 20S-generated tau peptides [color code as in (A)] identified by in-solution analysis. Insert depicting the sequences of the identified peptides and the cleavage sites (marked with slashes). (Right) Histogram representing the most intense peptides in the R3 region. A.U., arbitrary units. (D) ThT fluorescence during incubation of the peptide 309VYKPVDL315. The peptide (50, 100, and 150 M) was incubated with heparin (peptide:heparin molar ratio of 4:1) in triplicates.

The peptide with the highest ion peak area was 309VYKPVDL315 (Fig. 2C, right). It partially overlaps with the hexapeptide sequence 306VQIVYK311 at the beginning of pseudo-repeat R3 (Fig. 2B).

The 306VQIVYK311 sequence is the most hydrophobic residue stretch of tau, is a major driving force for pathogenic tau aggregation, and can form amyloid-like filaments in isolation (26). We therefore tested whether the 20S-generated tau peptide 309VYKPVDL315 can aggregate into amyloid fibrils. To this end, the 309VYKPVDL315 peptide was incubated with heparin at a molar ratio of 4:1.

Figure 2D shows the results from thioflavin-T (ThT) fluorescence measurements of 309VYKPVDL315/heparin samples at three different peptide concentrations during incubation at 37C for 6 days. For all of the samples, the background-corrected ThT intensity was very low and did not increase during incubation (Fig. 2D). No increase in ThT intensity was detected even when the peptide was incubated for 6 days in the absence of heparin (fig. S3). Because ThT fluorescence intensity increases upon binding to amyloid fibrils, the data show that the 20S-generated peptide 309VYKPVDL315 is not able/has a very low propensity to form amyloid fibrils.

To gain insight into the kinetics of degradation of tau by the 20S proteasome and define its residue specificity, we used NMR spectroscopy. Figure 1A displays the 2D 1H-15N HSQC spectrum of 15N-labeled hTau40. The NMR spectrum was recorded at 5C to attenuate the exchange of amide protons with solvent and thus exchange-induced NMR signal broadening. Comparison of the HSQC spectrum of hTau40 alone with the spectra recorded after 30 min and 66 hours (red) in the presence of 20S (hTau40:20S molar ratio of 4:1) showed that after 30 min, the spectrum of hTau40 was essentially unchanged (fig. S4), but after 66 hours, additional sharp cross peaks were present. Four of the newly appearing cross peaks overlapped with signals observed in a natural abundance 1H-15N HSQC spectrum of the 309VYKPVDL315 peptide, i.e., the peptide with the highest ion peak area in MS (fig. S5). The degradation-associated cross peaks were not observed for a separate sample, which additionally contained the proteasome inhibitor oprozomib (Fig. 3A, right spectrum).

(A) Superposition of 2D 1H-15N HSQC spectra of hTau40 at 5C in the presence of the 20S proteasome after 3 hours (black) and 66 hours (red) in the absence (left) and presence (right) of the proteasome inhibitor oprozomib. (B) (Top) Evolution of relative peak intensities, I(t)/I0, in 2D 1H-15N HSQC spectra of hTau40 in the presence of 20S with increasing incubation time at 5C. I0 is the cross-peak intensity observed in the first HSQC. (Middle) Residue-specific rate constants of a first-order model of the 20S degradation kinetics of hTau40. Correlation coefficients for the fit to the first-order model are color-coded (color code bar to the right). Error bars represent SD. (Bottom) Evolution of relative peak intensities in 2D 1H-15N HSQC spectra of hTau40 in the presence of the 20S proteasome and the proteasome inhibitor oprozomib.

When tau is degraded by the proteasome into small peptides, the chemical environment of residues changes. To gain insights into the kinetics of 20S degradation, the intensity of IDP cross peaks at their location in the absence of 20S can be analyzed (27). Because a 1H-15N backbone correlation can be observed for every non-proline residue in the 2D 1H-15N HSQC, up to 397 (441 residues minus the C terminus and 43 prolines, and depending on signal overlap) sequence-specific probes for tau degradation are thus available.

The top panel in Fig. 3B displays the decrease of NMR signal intensities along the hTau40 sequence with increasing 20S incubation time. The fastest decrease occurred in the repeat domain. To derive residue-specific degradation rates, we fitted first-order decay kinetics via linear regression to the residue-specific intensity data. The highest rates occurred in repeat R3 and reached up to 0.015 hours1 at 5C (Fig. 3B, middle, and table S1). Fast degradation kinetics were also observed in the other pseudo-repeats, in agreement with similar sequence compositions. In addition, taus C terminus as well as residues ~220 to 250 at the end of the proline-rich region were rapidly affected by degradation.

Oprozomib predominantly inhibits the chymotrypsin-like activity of the 20S proteasome (28). Detailed analysis of the hTau40 spectra in the presence of both 20S and the small-molecule oprozomib showed that the cross peaks of residues in R2 and R3 decreased in intensity by up to 20% after 66 hours (Fig. 3B, bottom, and table S1). Thus, the 20S complex has residual proteolytic activity, which is not inhibited by oprozomib.

A large number of kinases can phosphorylate tau (29). These include proline-directed kinases [e.g., glycogen synthase kinase 3 (GSK3) and cyclin-dependent kinase 5 (cdk5)] that phosphorylate proline-serine/threonine motifs, notably in the proline-rich region of tau, as well as non-prolinedirected kinases [e.g., microtubule affinity-regulating kinase (MARK), protein kinase A (PKA), and Ca2+/calmodulin-dependent protein kinase II (CaMKII)], which phosphorylate the KXGS motifs in the pseudo-repeats. CaMKII phosphorylates tau at several sites (30) and colocalizes with neurofibrillary tangles (NFTs) in AD brains (31).

To gain insight into the influence of substrate phosphorylation on 20S degradation, we phosphorylated recombinant hTau40 with CaMKII in vitro. SDS-PAGE demonstrated an upfield shift in the hTau40 band, confirming successful phosphorylation (Fig. 4A). According to MS/MS analysis, CaMKII phosphorylates S131 and T135 in the projection domain, T212 and S214 in P2, S262 in R1, and S356 in R4 (30). 1H-15N NMR spectroscopy further showed that S214, S356, and S413 are fully phosphorylated in hTau40 (Fig. 4B). In addition, S262, S324, and S352 were found to be partially phosphorylated (Fig. 4B).

(A) SDS-PAGE gel demonstrating phosphorylation of hTau40 by CaMKII in the presence of calmodulin. (B) (Left) Enlarged region with phosphorylated residues taken from the first 2D 1H-15N HSQC recorded at 5C for a total duration of 3 hours on CaMKII-phosphorylated hTau40 in the presence of 20S. On top, the location of the phosphorylated residues is marked by short black lines in the context of the domain diagram of hTau40. (Right) Superposition of the first 2D 1H-15N HSQC spectrum (black; total measurement time: 3 hours) of CaMKII-phosphorylated hTau40 in the presence of 20S with the spectrum completed after 66 hours (red). (C) Relative peak intensities in 2D 1H-15N HSQC spectra of CaMKII-phosphorylated hTau40 in the presence of the 20S proteasome with increasing time of incubation at 5C (from red to blue).

We then incubated CaMKII-phosphorylated hTau40 with 20S proteasome at 5C. Even after 66 hours, no degradation peaks were observed in the 1H-15N HSQC spectrum (Fig. 4B and fig. S6). In addition, hTau40 cross-peak intensities remained largely unaffected (Fig. 4C and fig. S6). Similarly, CaMKII phosphorylation of the tau construct K18, which only contains the repeat domain, attenuated its degradation by the 20S proteasome (fig. S7). Thus, phosphorylation of tau by CaMKII interferes with the degradation of tau by the 20S proteasome.

GSK3 is ubiquitously expressed in mammalian tissue and has been implicated as a major tau kinase in AD (32). In vitro modification of hTau40 by GSK3 results in phosphorylation of S46, T175, T181, S202, T205, T212, T217, T231, S235, S396, S400, and S404 (33). NMR confirmed complete phosphorylation of S396, S400, and S404 (Fig. 5A). In contrast to CaMKII phosphorylation (Fig. 4), phosphorylation by GSK3 did not block proteasomal processing of hTau40 [Figs. 5A (red spectrum) and 6]. Analysis of cross-peak intensities at increasing 20S incubation times further showed that rapid degradation occurred in repeats R2 and R3 of hTau40 (Fig. 5B).

(A) (Left) Enlarged region with phosphorylated residues taken from the first 2D 1H-15N HSQC recorded at 5C for a total duration of 3 hours on GSK3-phosphorylated hTau40 in the presence of 20S. On top, the cartoon depicts the sites of phosphorylation of hTau40 by GSK3. (Right) Superposition of the first 2D 1H-15N HSQC spectrum (black; total measurement time: 3 hours) of GSK3-phosphorylated hTau40 in the presence of 20S with the spectrum completed after 66 hours (red). (B) Relative peak intensities in 2D 1H-15N HSQC spectra of GSK3-phosphorylated hTau40 in the presence of the 20S proteasome with increasing time of incubation at 5C (from red to blue).

(A and B) Per-residue rate constants for degradation of tau by the 20S proteasome. Residue-specific rate constants of a first-order model of the 20S degradation kinetics of hTau40 at 5C (A, top; same as in Fig. 3B), in the presence of the inhibitor oprozomib (A, bottom), of hTau40 phosphorylated by CaMKII (B, top), and of hTau40 phosphorylated by GSK3 (B, bottom). Correlation coefficients for the fit to the first-order model are color-coded (color code bars to the right). Error bars represent SD. (C) Schematic representation illustrating the phosphorylation-dependent degradation of the AD-related protein tau by the 20S proteasome: Wild-type tau (hTau40) is degraded by the 20S proteasome starting from the pseudo-repeat region and the C-terminal domain, producing short peptides (blue, pink, and orange) from those regions, followed by degradation of the N-terminal domain, which generates two long N-terminal fragments. Depending on the sites of phosphorylation, 20S degradation of tau is inhibited (CaMKII; top) or attenuated (GSK3; bottom). The color code of different hTau40 domains is described in Fig. 1.

Figure 6 (A and B) compares the residue-specific degradation rates of unmodified hTau40 in the presence of the 20S proteasome (Fig. 6A, top), unmodified hTau40 in the presence of 20S and the inhibitor oprozomib (Fig. 6A, bottom), CaMKII-phosphorylated hTau40 and 20S (Fig. 6B, top), and GSK3-phosphorylated hTau40 and 20S (Fig. 6B, bottom, and table S1). As calculated from the time-dependent decrease in cross-peak intensities, GSK3-phosphorylated hTau40 is most efficiently processed by the 20S proteasome in repeats R2 and R3. The phosphorylation of selected residues in taus C-terminal domain, however, blocks cleavage of peptide bonds in this region. In addition, the decay of NMR signals in the proline-rich region was strongly attenuated (Fig. 5B and fig. S4), in agreement with phosphorylation of T212, T217, T231, and S235 by GSK3 (33).

Within the cell, IDPs are constantly synthesized and degraded by the proteasome. Because they lack a globular structure, IDPs can directly be processed by the 20S proteasome without the need for previous ubiquitination and unfolding by the 26S proteasome (35, 34). In parallel, IDPs can be degraded in a ubiquitin-dependent manner by the 26S proteasome. Aggregates of IDPs cannot properly be degraded by the proteasome and are instead processed through autophagy (18, 19). In addition, tau aggregates might inhibit the activity of proteasomes and thereby contribute to neurodegeneration (2, 17, 18). Detailed insights into the processing of tau and other IDPs by the 20S proteasome may therefore be important for treating neurodegeneration and other human diseases (34).

Inhibition of the proteasome by small molecules results in increased amounts of tau in SH-SY5Y cells and rat brain (14, 35). In addition, the four-repeat isoform hTau43 (also termed 0NR4 tau) was shown to be degraded by the human 20S proteasome in vitro without previous ubiquitination (14). In agreement with the latter study, which used human 20S (14), we observed two relatively stable populations of long tau fragments from the N terminus when incubating hTau40 with the 20S proteasome from T. acidophilum (Fig. 1). To determine the identity of the two hTau40 fragments, we performed MS analysis and found that the long and short fragments contain residues 1 to 251 and 1 to 218, respectively (Fig. 1).

Proteasomes cleave their substrates to short peptides with mean lengths between 6 and 10 amino acids (4, 36). Longer (>50 amino acids) degradation intermediates are rarely detected, because the substrate is thought not to dissociate from the proteasome during the degradation process. The presence of two long truncated tau fragments during 20S degradation is therefore unexpected. The more than 200-residue-long tau fragments contain multiple, potential proteasomal cleavage sites (Fig. 2B). To investigate whether the generation of these fragments is the result of specific structural properties of the N-terminal domain of hTau40, we characterized this domain at a single-residue level by NMR spectroscopy. The analysis showed that Tau(1239) is more compact than hTau40 (fig. S2). We speculate that the more compact structure might interfere with 20S cleavage of the N-terminal fragments.

The short ~6- to 10-residue tau peptides generated by the 20S proteasome can further be cleaved by other proteases (2). In parallel, they might itself contain activity, which is relevant for pathological processes. Consistent with this hypothesis, the six-residue tau peptide 306VQIVYK311 can form insoluble amyloid-like filaments in vitro (26). We therefore used MS to identify the tau peptides generated by 20S degradation (Fig. 2). From the large number of different 20S-generated peptides, the tau peptide with the highest ion peak area was 309VYKPVDL315. Consistent with the high abundance of the 309VYKPVDL315 peptide generated by 20S degradation, signals corresponding to this peptide were identified in the NMR spectra of degraded tau (fig. S4). The 309VYKPVDL315 peptide lacks the first three amino acids of the filament-forming 306VQIVYK311 sequence but has four additional N-terminal residues including the two hydrophobic residues V313 and L315. Despite an overall high hydrophobicity, however, the tau peptide 309VYKPVDL315 did not aggregate into amyloid-like filaments in the presence of the aggregation enhancer heparin (Fig. 2D). Notably, all of the other 20S-generated peptides in the region from 308 to 320 also contain residue P312, i.e., a proline with known -strandbreaking property (Fig. 2C, right). Cleavage of tau by the 20S proteasome thus generates peptides that are unable to aggregate into amyloid-like filaments.

A wide range of assays have been developed to follow protein degradation. These assays often sample the degradation reaction at discrete time points using SDS-PAGE and antibody binding, autoradiography, protein staining, or Western blotting (37). In addition, proteasome activity can be analyzed through the measurement of fluorescence anisotropy of small-molecule dyes attached to substrate proteins. The identity of degradation products can furthermore be determined using MS. Here, we combined MS with NMR to (i) gain insight into the structural properties of the long degradation intermediates of tau identified by MS and (ii) quantify degradation kinetics in the IDP tau with single-residue and high temporal resolution. MS and NMR spectroscopy are thereby complementary, because MS enables large-scale identification of substrate fragments and peptides generated by proteasomal degradation, but cannot identify all released peptides, lacks single-residue resolution, and is limited in temporal resolution. NMR spectroscopy makes it possible to follow substrate degradation, while the reaction occurs in the test tube, and quantify degradation kinetics at high spatial/per-residue and temporal resolution. On the other hand, a high number of generated peptides and fragments complicate their identification by NMR especially for large IDPs, such as tau, which have many cross peaks. In addition, it has to be taken into account that the cleavage of a peptide bond can be sensed by residues that are several positions removed from the site of proteolysis (27). Because of the abovementioned aspects, we believe that the combination of MS and NMR will also be useful to investigate differences in the degradation pattern and substrate selectivity of 20S proteasomes from different organisms.

Using NMR spectroscopy, we found that the 20S degradation of many tau residues follows first-order decay kinetics (Fig. 3). The maximum degradation rate reached ~0.015 hours1 at 5C, which corresponds to a degradation half-time of ~46 hours. The reported half-life of tau in HT22 cells is 60 hours (15). The analysis further showed that the 20S proteasome from T. acidophilum preferentially cleaves tau in the pseudo-repeat region, with the fastest rates observed in repeat R3 (Fig. 3). Repeat R3 is part of the cross- structure of heparin-induced tau fibrils (38). In addition, R3 is located in the core of paired helical filaments purified from the brains of patients with AD (10). The data suggest that the 20S proteasome preferentially degrades the regions of tau, which are important for pathogenic aggregation.

SDS-PAGE analysis, in combination with antibody binding, was used to suggest that the degradation of tau by the 20S proteasome is bidirectional (14), supporting degradation models in which 20S degradation has a preference for the free NH2 or COOH terminus of a substrate (39). In contrast, we find that the proteasome degradation of tau is most efficient in the repeat domain (followed by the C-terminal domain; Fig. 3). Our results are thus in agreement with reports showing that the 20S proteasome can initiate endoproteolytic cleavage at internal sites of IDPs (5). The efficient cleavage of the pseudo-repeat region also enables the generation of the two long fragments from the N terminus of tau (Fig. 1).

The strength of the quantitative, combined MS/NMR approach was further supported by the experiments, in which we studied the influence of phosphorylation of tau on its degradation by the 20S proteasome (Figs. 4 and 5). Tau molecules found in NFTs in the brains of patients with AD are hyperphosphorylated, and dysregulation of tau phosphorylation has been linked to neuronal toxicity (6). Consistent with the hypothesis that impaired proteasomal degradation results in tau accumulation, phosphomimetic tau variants were less efficiently degraded by the proteasome in autophagy-deficient mouse embryonic fibroblasts (16).

The quantitative NMR-based degradation analysis showed that phosphorylation of tau by the non-prolinedirected serine/threonine kinase CaMKII inhibits degradation of tau by the 20S proteasome (Figs. 4 and 6). When the proteasome cannot degrade tau, autophagy becomes important, in agreement with the observation that autophagy is the primary route for clearing phosphorylated tau in neurons (16). However, using the same quantitative approach, we found that tau phosphorylated by GSK3, which phosphorylates Pro-Ser/Thr epitopes seen in NFTs in AD (32), only blocks cleavage in certain regions but does not interfere with tau cleavage in the pseudo-repeats R2 and R3 (Figs. 5 and 6). The regions of tau, which are no longer cleaved such as the C-terminal domain and the proline-rich domain, contain residues phosphorylated by GSK3 (Fig. 5). While GSK3 does not phosphorylate residues in the repeat region, CaMKII phosphorylates S262, S324, S352, and S356 and blocks degradation by the 20S proteasome (Figs. 4 and 6). Phosphorylation of S262, S324, S352, and S356 therefore appears to play an important role in the inhibition of tau degradation by the 20S proteasome. S262, S324, S352, and S356 are also phosphorylated by microtubule-associated protein/MARKs, and their phosphorylation affects tau aggregation as well as microtubule binding of tau (40). Currently, the mechanism of impaired degradation of CaMKII-phosphorylated tau is unknown but could involve (i) an impaired/restricted entry through the 20S gate formed by the first 12 amino acids of the subunit and (ii) a blocked interaction with the catalytic sites in the subunit. Our study provides the basis to quantify with single-residue resolution the degradation of tau and other IDPs, their different isoforms, and posttranslationally modified variants and thus gain mechanistic insight into disease-associated accumulation of IDPs.

Unlabeled and 15N-labeled Tau protein (hTau40, UniProt ID 10636-8, 441 residues) were expressed in E. coli strain BL21(DE3) from a pNG2 vector (a derivative of pET-3a, Merck-Novagen, Darmstadt) in the presence of an antibiotic. In case of unlabeled protein, cells were grown in 1 to 10 liters of LB and induced with 0.5 mM IPTG (isopropyl--d-thiogalactopyranoside) at OD600 (optical density at 600 nm) of 0.6 to 0.8. To obtain 15N-labeled protein, cells were grown in LB until an OD600 of 0.6 to 0.8 was reached, then centrifuged at low speed, washed with M9 salts (Na2HPO4, KH2PO4, and NaCl), and resuspended in minimal medium M9 supplemented with 15NH4Cl as the only nitrogen source and induced with 0.5 mM IPTG. After induction, the bacterial cells were harvested by centrifugation, and the cell pellets were resuspended in lysis buffer [20 mM MES (pH 6.8), 1 mM EGTA, and 2 mM dithiothreitol (DTT)] complemented with protease inhibitor mixture, 0.2 mM MgCl2, lysozyme, and deoxyribonuclease (DNase) I. Subsequently, cells were disrupted with a French pressure cell press (in ice-cold conditions to avoid protein degradation). In the next step, NaCl was added to a final concentration of 500 mM and boiled for 20 min. Denaturated proteins were removed by ultracentrifugation at 4C. The supernatant was dialyzed overnight at 4C against dialysis buffer [20 mM MES (pH 6.8), 1 mM EDTA, 2 mM DTT, 0.1 mM phenylmethylsulfonyl fluoride (PMSF), and 50 mM NaCl] to remove salt. The following day, the sample was filtered and applied onto a previously equilibrated ion-exchange chromatography column, and the weakly bound proteins were washed out with buffer A (same as the dialysis buffer). Tau protein was eluted with a linear gradient of 60% final concentration of buffer B [20 mM MES (pH 6.8), 1 M NaCl, 1 mM EDTA, 2 mM DTT, and 0.1 mM PMSF]. Protein samples were concentrated by ultrafiltration (5 kDa Vivaspin from Sartorius) and purified by gel filtration chromatography. Last, the protein was dialyzed against 50 mM sodium phosphate (NaP) (pH 6.8).

20S proteasomes from T. acidophilum were expressed from pRSETA containing the bicistronic gene including psmA and psmB. Transformed BL21 cells were induced with 0.1 mM IPTG and incubated for 18 hours at 37C. Harvested cells were resuspended in 3 ml of lysis buffer (50 mM Na2HPO4 pH 8.0, 300 mM NaCl) per 1 g of cells and lysed with the French press. The lysate was incubated at 65C for 15 min. Heat-denatured proteins were removed by centrifugation at 30,000g at 4C. Polyethylenimine (0.1%, w/v) was added to the supernatant to precipitate contaminating nucleic acids. Precipitated nucleic acids were removed by centrifugation at 100,000g for 1 hour. The supernatant was subjected to differential precipitation with polyethylene glycol 400 (PEG; number signifies the mean molecular weight of the PEG polymer). PEG400 was added to a concentration of 20% (v/v) to the supernatant under stirring at 18C and incubated for 30 min. Precipitated proteins were removed by centrifugation at 30,000g for 30 min at 4C. The supernatant was then precipitated by raising the concentration of PEG400 to 40% (v/v). The precipitate of this step contained the 20S proteasomes and was recovered by centrifugation at 30,000g for 30 min at 4C and resuspended in purification buffer (0.05 M BisTris pH 6.5, 0.05 M K(OAc), 0.01 M Mg(OAc)2, 0.01 M -Glycerophosphate) containing 5% (w/v) sucrose, 10 mM DTT, and 0.01% (w/v) lauryl maltose neopentyl glycol (LMNG) on an orbital shaker at 18C. The resuspended material was loaded on 10 to 30% (w/v) sucrose gradients in purification buffer containing 5 mM DTT, which are centrifuged at 284,000g for 16 hours at 4C. Gradients were harvested in 400 l of fractions. SDS-PAGE was used to identify fractions containing 20S proteasomes. Selected fractions were pooled and precipitated by the addition of 40% (v/v) PEG400. After centrifugation (30,000g, 20 min), the supernatant was removed and the precipitate was resuspended in purification buffer containing 5% (w/v) sucrose, 10 mM DTT, and 0.01% (w/v) LMNG. The resuspended material was loaded on linear 10 to 40% (w/v) sucrose gradients in purification buffer containing 5 mM DTT, which are centrifuged at 284,000g for 18 hours at 4C. Fractions containing 20S proteasomes are yet again identified by SDS-PAGE, precipitated and concentrated by the addition of 40% PEG400, and resuspended in purification buffer containing 5% (w/v) sucrose and 5 mM DTT, yielding the final purified protein preparation at 26 mg/ml. Protein concentrations were determined by the Bradford assay (Bio-Rad, Munich, Germany) using bovine serum albumin (BSA) as a standard.

For grid preparation, a protein stock solution (6 mg/ml) was diluted to 0.25 mg/ml with standard buffer without sucrose. Glutaraldehyde was added to the diluted protein solution to a concentration of 0.1% (v/v). After incubation for 2.5 min at room temperature, the reaction was quenched by the addition of 50 mM l-aspartate (pH 6.5). A continuous carbon foil was floated on the protein solution for 1 min at 4C. A holey carbon copper grid was used to remove the continuous carbon foil from the protein solution. Excess liquid was removed with a tissue paper. Proteins were stained by floating the grid on a saturated uranyl formate solution for 1 min at 4C. Remaining staining solution was removed with a tissue, and the grid was dried under ambient conditions. Negative-stain EM images were taken with a Philips CM200 microscope (160 kV). Images were acquired at a magnification of 66,000. The pixel size corresponds to 3.34 per pixel. The TVIPS charge-coupled device camera was used to record the micrographs.

hTau40 was phosphorylated by CaMKII (recombinant human CaMKII alpha protein from Abcam) and GSK3 [recombinant human GSK3 beta protein (active) from Abcam]. The reaction was performed by mixing 0.2 mM hTau40 with 0.02 mg/ml kinase, 2 mM DTT, 2 mM ATP, 1 mM PMSF, and 5 mM MgCl2 in 40 mM Hepes (pH 7.4). In case of CaMKII, we additionally used 2 M calmodulin (bovine calmodulin, recombinant from Sigma), 1 mM CaCl2, and, in case of GSK3, 2 mM EGTA. The samples were incubated at 30C overnight and buffer-exchanged to 50 mM NaP (pH 6.8). Protein concentrations were determined by the Bradford assay using BSA as a standard.

For detection of hTau40 degradation products/fragments generated by the 20S proteasome (hTau40:20S molar ratio of 3:1), we used a 18% separating gel [ddH2O, 30% acrylamide, 1.5 M tris (pH 8.8), 10% SDS, 10% ammonium persulfate (APS), and tetramethylethylenediamine (TEMED)] and a 4% stacking gel [ddH2O, 30% acrylamide, 1 M tris (pH 6.8), 10% SDS, 10% APS, and TEMED]. For validation of hTau40 phosphorylation, we used a 12% separating gel and a 4% stacking gel.

hTau40 was incubated with 20S proteasome for 150 min at 37C and 1 day at 4C. The resulting reaction sample was in 50 mM NaP (pH 6.8). The buffer was exchanged to MS compatible sample buffer using Amicon Ultra centrifugal filters with a molecular weight cutoff of 3000. The filter was first washed using water. The reaction sample and 300 l of sample buffer [0.1% formic acid (FA)] were then added to the filter and centrifuged at 7500g for 30 min. After removing the buffer, 300 l of sample buffer was added and centrifuged for 30 min. The buffer exchange was then repeated one more time. Last, the samples were diluted to 100 ng/l for the following MS analysis.

The intact MS experiment was performed on Q Exactive HF-X2 (Thermo Fisher Scientific) coupled to a Dionex UltiMate 3000 UHPLC system (Thermo Fisher Scientific) equipped with a PepSwift Monolithic Trap Column [200 m inside diameter (ID) 5 mm] and a ProSwift RP-4H Monolithic Nano Column (100 m ID 25 cm). The flow rate was set to 1 l/min. Mobile phase A and mobile phase B were 0.1% (v/v) FA and 80% (v/v) acetonitrile (ACN), 0.08% FA, respectively. The gradient started at 20% B and increased to 50% B in 33 min and then kept B constant at 90% for 4 min, followed by re-equilibration of the column with 5% B. MS spectra were acquired with the following settings: microscans, 1; resolution, 120,000; mass analyzer, Orbitrap; automatic gain control (AGC) target, 3 106; injection time, 100 ms; mass range, 450 to 2000 mass/charge ratio (m/z).

hTau40 samples were incubated with 20S proteasome (molar ratio of 3:1) for different times (30, 90, and 150 min at 37C and, additionally, 48 hours at 4C). The samples were then analyzed by SDS-PAGE electrophoresis as described above. The two fragments (around 25 to 30 kDa) were carefully cut from the gel and used for in-gel analysis. The in-gel digestion of the two bands was performed using trypsin (Promega) to the gels. In the next step, the extracted peptides were desalted by using stage tips. In the last step, the samples were dried (SpeedVac) and readied for further analysis.

hTau40 was incubated with 20S proteasome (molar ratio of 3:1) at 37C for 3 hours before the analysis. The samples were precipitated by acetone and put at 30C overnight. Then, the samples were centrifuged at 14,000g for 10 min, and the supernatant was collected and dried. In the next step, contaminates were removed by the sp3 method, followed by direct injection into the mass spectrometer.

In-geldigested peptides were analyzed using an Orbitrap Fusion Tribrid (Thermo Fisher Scientific) instrument. In-solution samples were analyzed using Orbitrap Fusion Lumos (Thermo Fisher Scientific). Both instruments are coupled to a Dionex UltiMate 3000 UHPLC system (Thermo Fisher Scientific) equipped with an in-housepacked C18 column (ReproSil-Pur 120 C18-AQ, 1.9 m pore size, 75 m inner diameter, 30 cm length, Dr. Maisch GmbH). Both Orbitrap Fusions (Tribrid and Lumos) were operating in data-dependent mode for MS2. Dried samples were resuspended in 5% ACN, 0.1% FA. Samples were centrifuged for 10 min at 14,000g, and the supernatants were transferred to new sample tubes. In both cases, the flow rate was set to 300 nl/min. Mobile phase A and mobile phase B were 0.1% FA (v/v) and 80% ACN, 0.08% FA (v/v), respectively. The gradient in Orbitrap Fusion Tribrid (in-gel samples) started at 10% B and increased to 42% B in 43 min and then kept B constant at 90% for 6 min, followed by re-equilibration of the column with 5% B. MS1 spectra were acquired with the following settings: resolution, 120,000; mass analyzer, Orbitrap; mass range, 380 to 1500 m/z; injection time, 50 ms; AGC target, 4 105; S-Lens radio frequency (RF) levels, 60; charge state, +2 to +7; dynamic exclusion after n time, n = 1, dynamic exclusion duration = 60 s. MS2 parameters were as follows: first mass, 120; activation type, higher-energy collisional dissociation (HCD); collision energy, 35; Orbitrap resolution, 30,000; maximum injection time, 250 ms; AGC target, 100,000. The gradient in Orbitrap Fusion Lumos (in-solution samples) increased to 30% B in 42 min and further to 40% B in 4 min and then kept B constant at 90% for 6 min, followed by re-equilibration of the column with 5% B. MS1 spectra were acquired with the following settings: resolution, 120,000; mass analyzer, Orbitrap; mass range, 350 to 1600 m/z; injection time, 50 ms; AGC target, 5 105; S-Lens RF levels, 30; charge state, +2 to +7; dynamic exclusion after n time, n = 1, dynamic exclusion duration = 30 s. MS2 parameters were as follows: first mass, 120; activation type, HCD; collision energy, 30; Orbitrap resolution, 15,000; maximum injection time, 120 ms; AGC target, 100,000.

Thermo Proteome Discoverer (2.1.0.81) was used for database searching. In Proteome Discoverer, the Sequest HT, fixed value peptide spectrum match validator, and Precursor Ions Area Detector nodes were used. Parameters for database searching were as follows: the hTau40 protein sequence (P10636-8) was downloaded from Swiss-Prot. Mass tolerance for precursors and fragment ions was set as 10 and 20 ppm, respectively. Maximal missed cleavage was 4. Dynamic modifications were set as oxidation (M) and acetylation (protein N terminus). For in-gel samples, fixed modification was carbamidomethylation (C). Trypsin was used as the enzyme, and its specificity was set as semi-specific. For in-solution sample, no enzyme was set. For precursor ions area detector, mass precision was 2 ppm. Only the peptides that were identified with high confidence were used in this study. For in-solution samples, the peak area of precursors was used for quantification of the identified peptides.

The peptide VYKPVDL was synthesized as trifluoroacetic acid salts by GenScript, and the stock solution (1 mM) was made in 25 mM Hepes (pH 7.4). To test whether the peptide can aggregate into amyloid fibrils, we used 50, 100, and 150 M of the peptide in 25 mM Hepes (pH 7.4). The stock solution of ThT (purchased from Sigma) was prepared in ddH2O, and for the binding assay, 50 M was used. When heparin (~20 kDa, Roth) was added to the sample, the molar ratio of the peptide to heparin was 4:1. ThT fluorescence was then measured with excitation at 440 nm and emission at 482 nm at 37C using a multimode microplate reader (Spark 20M, TECAN).

2D 1H-15N HSQC and 3D spectra (HNCO and HNCA) of hTau40 and Tau(1239) were acquired at 5C on a Bruker 800 MHz spectrometer equipped with triple-resonance 5-mm cryogenic probe. The protein concentration was 125 M in 50 mM NaP buffer (pH 6.8), 5% D2O, 0.1% NaN3, and 50 M dextran sulfate sodium. Spectra were processed with TopSpin 3.5 (Bruker) and analyzed using Sparky.

NMR degradation experiments with 20S proteasome involving hTau40, phosphorylated hTau40, and hTau40 in the presence of the proteasome inhibitor were acquired and processed as explained above. 2D 1H-15N HSQC spectra were recorded for 15N-labeled hTau40 and 20S proteasome in a molar ratio of 4:1 in 50 mM NaP buffer (pH 6.8) and 10% D2O. The dead time between mixing hTau40 and 20S proteasome and starting the first HSQC experiments was ~30 min.

To study the kinetics of the degradation of hTau40 by the 20S proteasome, 60-min HSQCs were measured every hour during the first 24 hours and then for 180-min HSQCs every 3 hours (for a total of 38 measurements) up to 66 hours. In case of the sample with the inhibitor as well as the phosphorylated samples, 180-min HSQCs were recorded every 3 hours for a total of 22 measurements (66 hours). For our control sample, we used the proteasome inhibitor oprozomib (ApexBio), which was incubated for 2 hours at 37C in 250 molar excess before the experiment.

Peak intensities were extracted from a series of 1H-15N HSQC datasets at predetermined time intervals. After peak assignment with the software Sparky, the peak intensities were normalized with respect to the initial peak intensity for each residue, taking into account the duration of each HSQC. A residue was excluded from plotting and further analysis if a consecutively recorded peak intensity increased to more than 115% of the relative intensity of the preceding measurement. Such an increase in peak intensity when compared to the preceding measurement can arise from more favorable relaxation properties in the generated peptides when compared to full-length tau. In addition, peak overlap can potentially cause fluctuating intensities.

The peak intensities at all recorded times of the remaining (i.e., not excluded) residues were analyzed by fitting to first-order decay kinetics via linear regression of the data with respect to the analytic solution of the normalized first-order decay model. The fitted first-order decay reaction constants were plotted for all nonexcluded residues of hTau40. The statistical uncertainty in the determined degradation rates expressed in terms of SDs of fits was estimated as follows. For each sample, we randomly excluded five (in case of samples hTau40 + 20S in molar ratios of 4:1 and 4.5:1) or three (in case of samples hTau40 + 20S + inhibitor, hTau40 + CaMKII + 20S, and hTau40 + 20S + GSK3) intensity profiles collected at the various time intervals from the fitting procedure and repeated this procedure 20 times. The selection was performed by randomly drawing five (three, respectively) numbers from a uniform distribution over all profiles measured at different time intervals, and the fitting procedure was carried out on each of these subsamples and each amino acid residue. From the 21 fits per residue obtained this way (20 undersampled plus 1 fit based on all measured profiles), we calculated the sample SD and depicted it as error bars. The plots depicting degradation rates were plotted as the full-data fit (declared here as the mean estimated value) plus/minus the SD. In addition, we determined the Pearson correlation coefficients for all respective fits, which are encoded in the color. Fits with an incorrect sign of (i.e., implying an incorrect/unphysical trend) were excluded from the plot.

Acknowledgments: We thank N. Rezaei-Ghaleh for help with NMR experiments and the Max Planck society for support. Funding: The financial support from the German Research Foundation (DFG) through the Emmy Noether Program GO 2762/1-1 (to A.G.) is acknowledged. P.F. is supported by a Manfred-Eigen-Fellowship from the Max Planck Institute for Biophysical Chemistry. M.Z. was supported by the advanced grant 787679-LLPS-NMR of the European Research Council. Author contributions: T.U.-G. performed tau phosphorylation, NMR experiments, and data analysis. P.F. and K.-T.P. performed MS and data analysis. A.I.d.O. analyzed Tau(1239) and performed NMR experiments and K18 degradation. F.H. prepared 20S proteasome. A.G. performed NMR data analysis. M.-S.C.-O. prepared Tau(1239). A.C. supervised 20S preparation. H.U. supervised MS. E.M. and M.Z. designed the study. The manuscript was written through contributions of all authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015349. The chemical shifts of Tau(1239) were deposited in the BMRB (identifier: 28065). 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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Proteasomal degradation of the intrinsically disordered protein tau at single-residue resolution - Science Advances

Recommendation and review posted by Alexandra Lee Anderson

Solve Puzzles for Science | Foldit

(This post was originally sent out on July 3 to our mailing list. You can sign up for the mailing list here to receive weekly updates about Foldit, including tips and tricks and see the top-scoring solutions to the week's puzzles. Don't forget to join our Discord as well to stay in the chat even when you're not folding!)

Hey folders!

Dev Josh here with your weekly Foldit update.

This week we saw the introduction of the Reaction Design tool. The devs are working hard on polishing it up and making it more usable! As always, thanks for your feedback and bug reports. You can submit more feedback here.

In this puzzle, I accidentally evo'ed on a broken developer build and got the top score. Whoops, sorry about that!Here are some of the solutions at the top of the leaderboards. [A note from our scientists: the top of the leaderboards doesn't always mean the most scientifically useful. These highlights are not scientific feedback and are not officially endorsed as scientifically valid designs by the Foldit team.]

Join the mailing list to see what others are folding!

This week's recipe is an oldie but a goodie from drjr. The recipe is called Reset, and it does what it says on the tin: reset to the best score, unfreeze the protein, remove all your bands, and set the CI to 1. A simple recipe, but a handy quality of life tool for when you just need to backtrack a little.

Quick shoutout to argyrw for always being a friendly voice in chat! Say hi to her in global or veteran chat.

Beginner: Are you still using Pull to draft your protein in the early game? Try making cutpoints and moving pieces around with the Move tool, it's so much easier! Don't forget to disable cutpoint bands in the Behavior tab, or they'll all come together again when you wiggle.

Intermediate: It can be really tempting mid-game to just switch to running recipes. But give some time to carefully inspect every acceptor and donor (the red and blue dots) to see what hydrogen bonds you can form, and manually mutate as needed. Not only will this lower your BUNS, but it'll help form a strong hbond network. The scientists love this, and your rank will too!

Expert: If you haven't already, read bkoep's blog on binder design metrics. DDG, SASA, and SC are going to become really important soon since we're looking to add objectives for them. So understanding and practicing these principles now can help you get a headstart on the competition! Use the protein design sandbox to try out some ideas.

Have a tip to share or a recipe to recommend? Reply with your suggestions or make a wiki page for your ideas! Reaction Design doesn't have a page yet, so if you understand this tool, help out your community by writing about it! (Since writing this post, LociOiling has graciously created the page for Reaction Design puzzles.)

Until next time, happy folding!

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Solve Puzzles for Science | Foldit

Recommendation and review posted by Alexandra Lee Anderson

Setting the bar in education – The Star Online

Cheahs belief in working with the best and learning from the best also birthed the appointments of the Jeffrey Cheah distinguished professors.

Under the collaboration between Jeffrey Cheah Foundation and globally acclaimed academic institutions, eminent experts and scholars - who have contributed to solving critical global issues in health, disease and economy amongst others - are appointed to share their knowledge and expertise with Malaysian academics, students and the general public.

Among the prominent names on the list are:

Prof Jeffrey David Sachs

As a world renowned economist and director of the UN Sustainable Development Solutions Network, Prof Sachs is one of the worlds most influential experts on sustainable economic development.

A passionate leader in the fight against poverty and the special advisor to the UN secretary-general on sustainable development, he has advised heads of states and governments on economic strategy for more than a quarter century.

Appointed as an honorary Jeffrey Cheah distinguished professor of sustainable development at Sunway University this year, he is also the chairman of the Jeffrey Sachs Centre on Sustainable Development.

Prof Sir Leszek Borysiewicz

The chairman of Cancer Research United Kingdom (UK) since 2016, Prof Borysiewicz is an Honorary Jeffrey Cheah distinguished professor who is now the emeritus vice-chancellor of the University of Cambridge, after serving as its vice-chancellor from 2011 to 2017.

A founding fellow of the Academy of Medical Sciences, he has been chief executive of the UKs Medical Research Council since 2007 and was knighted in 2001 for his breakthroughs in vaccines, including developing Europes first trial of a vaccine to treat cervical cancer.

Prof Sir Alan Fersht

World leading protein scientist Prof Fersht, also an honorary Jeffrey Cheah distinguished professor and life fellow of Gonville and Caius College Cambridge, is widely regarded as one of the main pioneers of protein engineering, which is a process to analyse the structure, activity and folding of proteins.

His current research involves a fusion of protein engineering, structural biology, biophysics and chemistry to study the structure, activity, stability and folding of proteins, as well as the role of protein misfolding and instability in cancer and disease.

Prof Kay-Tee Khaw

Prof Khaw, a leading expert in the field of health and disease, is a Jeffrey Cheah professorial fellow in Gonville and Caius College, Cambridge. She is currently one of the principal UK scientists working on the European Prospective Investigation into Cancer and Nutrition, a Europe-wide project investigating the links between diet, lifestyle and cancer.

Appointed as a Commander of the order of the British Empire in 2003, Prof Khaw has been recognised for developing improved methods for collecting information on peoples diets and levels of exercise and relating this to the number of diagnosed cancer cases.

Prof Rema Hanna

A highly distinguished economist, Prof Hanna is the Jeffrey Cheah professor of South East Asia Studies and chair of the Harvard Kennedy School International Development Area, as well as the faculty director of evidence for policy design at Harvards Centre for International Development and the co-scientific director of the Abdul Latif Jameel Poverty Action Lab South East Asia office in Indonesia.

Her focus is on improving overall service delivery, understanding the impacts of corruption, bureaucratic absenteeism and discrimination against disadvantaged minority groups on delivery outcomes.

Prof Ketan J Patel

Prof Patel is a Jeffrey Cheah professorial fellow in Gonville and Caius College, Cambridge and the principal research scientist at the famous MRC Laboratory of Molecular Biology in the University of Cambridge.

His research, which focuses on the molecular basis of inherited genomic instability and the role it plays in the biology of stem cells, has been recognised through prestigious awards and prizes, including being elected as a fellow of the Royal Society of London, a member of the European Molecular Biology Organisation and a fellow of the Academy of Medical Sciences UK.

Prof John Todd

The Jeffrey Cheah fellow in medicine at Brasenose College, Oxford and professor of precision medicine, Prof Todd is a leading pioneer researcher in the fields of genetics, immunology and diabetes. His research areas include Type 1 diabetes genetics and disease mechanisms with the aim of clinical intervention.

In his former role as a professor of human genetics and a Wellcome Trust principal research fellow at Oxford, he helped pioneer genome-wide genetic studies, first in mice and then in humans.

Prof William Swadling

Prof Swadling, a Jeffrey Cheah professorial fellow, is a senior law fellow at Brasenose College, Oxford and Professor in the Law of Property in the Oxford University Law School.

An expert on the Law of Restitution, he is a contributor to Halsburys Laws of England, wrote the section on property in Burrows (ed) English Private Law and is widely cited in the British courts.

Prof William James

A Jeffrey Cheah professorial fellow emeritus and fellow in medicine at Brasenose College, Oxford, Prof James is a virologist with a background in genetics and microbiology.

As the professor of virology with the University of Oxford, he is the principal investigator at the Stem Cell Research Institute of Oxford, running a research lab studying HIV-macrophage biology using stem cell technology.

Prof Mark Wilson

Prof Wilson, the dean of Brasenose College, is a Jeffrey Cheah professorial fellow at the college and the professor of physical chemistry in the University of Oxfords physical and theoretical chemistry department.

The primary focus of his research interest is on the construction, development and application of relatively simple potential models to assess a wide range of systems with potentially unique properties.

Prof Jarlath Ronayne

Appointed in 2010 as the first Jeffrey Cheah distinguished professor, Prof Ronayne is a key member of Sunway Universitys board of directors and has played a pivotal role in establishing links between Sunway, Oxford and Cambridge.

Under his leadership, the Jeffrey Cheah Professorial Fellowships at Gonville and Caius College, Cambridge as well as Brasenose College, Oxford and the Jeffrey Cheah Scholar-in-Residence programmes in both colleges were established, alongside the prestigious Oxford University-Jeffrey Cheah Graduate Scholarship launched by the British High Commissioner in 2018. All these initiatives are in perpetuity.

Prof Sibrandes Poppema

A medical expert on Hodgkins disease, Prof Poppema has published more than 200 articles that have been cited more than 17,000 times.

The Jeffrey Cheah distinguished professor is also the co-owner of 12 patents and the founder of two biotechnology companies, as well as the advisor to the chancellor at Sunway University, especially on the establishment of a new medical school at the university.

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Setting the bar in education - The Star Online

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Site-specific glycan analysis of the SARS-CoV-2 spike – Science Magazine

SARS-CoV-2 spike protein, elaborated

Vaccine development for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is focused on the trimeric spike protein that initiates infection. Each protomer in the trimeric spike has 22 glycosylation sites. How these sites are glycosylated may affect which cells the virus can infect and could shield some epitopes from antibody neutralization. Watanabe et al. expressed and purified recombinant glycosylated spike trimers, proteolysed them to yield glycopeptides containing a single glycan, and determined the composition of the glycan sites by mass spectrometry. The analysis provides a benchmark that can be used to measure antigen quality as vaccines and antibody tests are developed.

Science this issue p. 330

The emergence of the betacoronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), represents a considerable threat to global human health. Vaccine development is focused on the principal target of the humoral immune response, the spike (S) glycoprotein, which mediates cell entry and membrane fusion. The SARS-CoV-2 S gene encodes 22 N-linked glycan sequons per protomer, which likely play a role in protein folding and immune evasion. Here, using a site-specific mass spectrometric approach, we reveal the glycan structures on a recombinant SARS-CoV-2 S immunogen. This analysis enables mapping of the glycan-processing states across the trimeric viral spike. We show how SARS-CoV-2 S glycans differ from typical host glycan processing, which may have implications in viral pathobiology and vaccine design.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of coronavirus 2019 (COVID-19) (1, 2), induces fever, severe respiratory illness, and pneumonia. SARS-CoV-2 uses an extensively glycosylated spike (S) protein that protrudes from the viral surface to bind to angiotensin-converting enzyme 2 (ACE2) to mediate host-cell entry (3). The S protein is a trimeric class I fusion protein, composed of two functional subunits, responsible for receptor binding (S1 subunit) and membrane fusion (S2 subunit) (4, 5). The surface of the envelope spike is dominated by host-derived glycans, with each trimer displaying 66 N-linked glycosylation sites. The S protein is a key target in vaccine design efforts (6), and understanding the glycosylation of recombinant viral spikes can reveal fundamental features of viral biology and guide vaccine design strategies (7, 8).

Viral glycosylation has wide-ranging roles in viral pathobiology, including mediating protein folding and stability and shaping viral tropism (9). Glycosylation sites are under selective pressure as they facilitate immune evasion by shielding specific epitopes from antibody neutralization. However, we note the low mutation rate of SARS-CoV-2 and that as yet, there have been no observed mutations to N-linked glycosylation sites (10). Surfaces with an unusually high density of glycans can also enable immune recognition (9, 11, 12). The role of glycosylation in camouflaging immunogenic protein epitopes has been studied for other coronaviruses (10, 13, 14). Coronaviruses form virions by budding into the lumen of endoplasmic reticulumGolgi intermediate compartments (15, 16). However, observations of complex-type glycans on virally derived material suggests that the viral glycoproteins are subjected to Golgi-resident processing enzymes (13, 17).

High viral glycan density and local protein architecture can sterically impair the glycan maturation pathway. Impaired glycan maturation resulting in the presence of oligomannose-type glycans can be a sensitive reporter of native-like protein architecture (8), and site-specific glycan analysis can be used to compare different immunogens and monitor manufacturing processes (18). Additionally, glycosylation can influence the trafficking of recombinant immunogen to germinal centers (19).

To resolve the site-specific glycosylation of the SARS-CoV-2 S protein and visualize the distribution of glycoforms across the protein surface, we expressed and purified three biological replicates of recombinant soluble material in an identical manner to that which was used to obtain the high-resolution cryoelectron microscopy (cryo-EM) structure, albeit without a glycan-processing blockade using kifunensine (4). This variant of the S protein contains all 22 glycans on the SARS-CoV-2 S protein (Fig. 1A). Stabilization of the trimeric prefusion structure was achieved by using the 2P stabilizing mutations (20) at residues 986 and 987, a GSAS (Gly-Ser-Ala-Ser) substitution at the furin cleavage site (residues 682 to 685), and a C-terminal trimerization motif. This helps to maintain quaternary architecture during glycan processing. Before analysis, supernatant containing the recombinant SARS-CoV-2 S was purified by size exclusion chromatography to ensure that only native-like trimeric protein was analyzed (Fig. 1B and fig. S1). The trimeric conformation of the purified material was validated by using negative-stain EM (Fig. 1C).

(A) Schematic representation of the SARS-CoV-2 S glycoprotein. The positions of N-linked glycosylation sequons (N-X-S/T, where X P) are shown as branches (N, Asn; X, any residue; S, Ser; T, Thr; P, Pro). Protein domains are illustrated: N-terminal domain (NTD), receptor binding domain (RBD), fusion peptide (FP), heptad repeat 1 (HR1), central helix (CH), connector domain (CD), and transmembrane domain (TM). (B) SDSpolyacrylamide gel electrophoresis analysis of the SARS-CoV-2 S protein (indicated by the arrowhead) expressed in human embryonic kidney (HEK) 293F cells. Lane 1: filtered supernatant from transfected cells; lane 2: flow-through from StrepTactin resin; lane 3: wash from StrepTactin resin; lane 4: elution from StrepTactin resin. (C) Negative-stain EM 2D class averages of the SARS-CoV-2 S protein. 2D class averages of the SARS-CoV-2 S protein are shown, confirming that the protein adopts the trimeric prefusion conformation matching the material used to determine the structure (4).

To determine the site-specific glycosylation of SARS-CoV-2 S, we used trypsin, chymotrypsin, and -lytic protease to generate three glycopeptide samples. These proteases were selected to generate glycopeptides that contain a single N-linked glycan sequon. The glycopeptides were analyzed by liquid chromatographymass spectrometry, and the glycan compositions were determined for all 22 N-linked glycan sites (Fig. 2). To convey the main processing features at each site, the abundances of each glycan are summed into oligomannose-type, hybrid-type, and categories of complex-type glycosylation based on branching and fucosylation. The detailed, expanded graphs showing the diverse range of glycan compositions are presented in table S1 and fig. S2.

The schematic illustrates the color code for the principal glycan types that can arise along the maturation pathway from oligomannose- to hybrid- to complex-type glycans. The graphs summarize quantitative mass spectrometric analysis of the glycan population present at individual N-linked glycosylation sites simplified into categories of glycans. The oligomannose-type glycan series (M9 to M5; Man9GlcNAc2 to Man5GlcNAc2) is colored green, afucosylated and fucosylated hybrid-type glycans (hybrid and F hybrid) are dashed pink, and complex glycans are grouped according to the number of antennae and presence of core fucosylation (A1 to FA4) and are colored pink. Unoccupancy of an N-linked glycan site is represented in gray. The pie charts summarize the quantification of these glycans. Glycan sites are colored according to oligomannose-type glycan content, with the glycan sites labeled in green (80 to 100%), orange (30 to 79%), and pink (0 to 29%). An extended version of the site-specific analysis showing the heterogeneity within each category can be found in table S1 and fig. S2. The bar graphs represent the mean quantities of three biological replicates, with error bars representing the standard error of the mean.

Two sites on SARS-CoV-2 S are principally oligomannose-type: N234 and N709. The predominant oligomannose-type glycan structure observed across the protein, with the exception of N234, is Man5GlcNAc2 (Man, mannose; GlcNAc, N-acetylglucosamine), which demonstrates that these sites are largely accessible to -1,2-mannosidases but are poor substrates for GlcNAcT-I, which is the gateway enzyme in the formation of hybrid- and complex-type glycans in the Golgi apparatus. The stage at which processing is impeded is a signature related to the density and presentation of glycans on the viral spike. For example, the more densely glycosylated spikes of HIV-1 Env and Lassa virus (LASV) GPC exhibit numerous sites dominated by Man9GlcNAc2 (2124).

A mixture of oligomannose- and complex-type glycans can be found at sites N61, N122, N603, N717, N801, and N1074 (Fig. 2). Of the 22 sites on the S protein, 8 contain substantial populations of oligomannose-type glycans, highlighting how the processing of the SARS-CoV-2 S glycans is divergent from host glycoproteins (25). The remaining 14 sites are dominated by processed, complex-type glycans.

Although unoccupied glycosylation sites were detected on SARS-CoV-2 S, when quantified they were revealed to form a very minor component of the total peptide pool (table S2). In HIV-1 immunogen research, the holes generated by unoccupied glycan sites have been shown to be immunogenic and potentially give rise to distracting epitopes (26). The high occupancy of N-linked glycan sequons of SARS-CoV-2 S indicates that recombinant immunogens will not require further optimization to enhance site occupancy.

Using the cryo-EM structure of the trimeric SARS-CoV-2 S protein [Protein Data Bank (PDB) ID 6VSB] (4), we mapped the glycosylation status of the coronavirus spike mimetic onto the experimentally determined three-dimensional (3D) structure (Fig. 3). This combined mass spectrometric and cryo-EM analysis reveals how the N-linked glycans occlude distinct regions across the surface of the SARS-CoV-2 spike.

Representative glycans are modeled onto the prefusion structure of the trimeric SARS-CoV-2 S glycoprotein (PDB ID 6VSB) (4), with one RBD in the up conformation and the other two RBDs in the down conformation. The glycans are colored according to oligomannose content as defined by the key. ACE2 receptor binding sites are highlighted in light blue. The S1 and S2 subunits are rendered with translucent surface representation, colored light and dark gray, respectively. The flexible loops on which the N74 and N149 glycan sites reside are represented as gray dashed lines, with glycan sites on the loops mapped at their approximate regions.

Shielding of the receptor binding sites on the SARS-CoV-2 spike by proximal glycosylation sites (N165, N234, N343) can be observed, especially when the receptor binding domain is in the down conformation. The shielding of receptor binding sites by glycans is a common feature of viral glycoproteins, as observed on SARS-CoV-1 S (10, 13), HIV-1 Env (27), influenza hemagglutinin (28, 29), and LASV GPC (24). Given the functional constraints of receptor binding sites and the resulting low mutation rates of these residues, there is likely selective pressure to use N-linked glycans to camouflage one of the most conserved and potentially vulnerable areas of their respective glycoproteins (30, 31).

We note the dispersion of oligomannose-type glycans across both the S1 and S2 subunits. This is in contrast to other viral glycoproteins; for example, the dense glycan clusters in several strains of HIV-1 Env induce oligomannose-type glycans that are recognized by antibodies (32, 33). In SARS-CoV-2 S, the oligomannose-type structures are likely protected by the protein component, as exemplified by the N234 glycan, which is partially sandwiched between the N-terminal and receptor binding domains (Fig. 3).

We characterized the N-linked glycans on extended flexible loop structures (N74 and N149) and at the membrane-proximal C terminus (N1158, N1173, N1194) that were not resolved in the cryo-EM maps (4). These were determined to be complex-type glycans, consistent with steric accessibility of these residues.

Whereas the oligomannose-type glycan content (28%) (table S2) is above that observed on typical host glycoproteins, it is lower than other viral glycoproteins. For example, one of the most densely glycosylated viral spike proteins is HIV-1 Env, which exhibits ~60% oligomannose-type glycans (21, 34). This suggests that the SARS-CoV-2 S protein is less densely glycosylated and that the glycans form less of a shield compared with other viral glycoproteins, including HIV-1 Env and LASV GPC, which may be beneficial for the elicitation of neutralizing antibodies.

Additionally, the processing of complex-type glycans is an important consideration in immunogen engineering, especially considering that epitopes of neutralizing antibodies against SARS-CoV-2 S can contain fucosylated glycans at N343 (35). Across the 22 N-linked glycosylation sites, 52% are fucosylated and 15% of the glycans contain at least one sialic acid residue (table S2 and fig. S3). Our analysis reveals that N343 is highly fucosylated with 98% of detected glycans bearing fucose residues. Glycan modifications can be heavily influenced by the cellular expression system used. We have previously demonstrated for HIV-1 Env glycosylation that the processing of complex-type glycans is driven by the producer cell but that the levels of oligomannose-type glycans were largely independent of the expression system and are much more closely related to the protein structure and glycan density (36).

Highly dense glycan shields, such as those observed on LASV GPC and HIV-1 Env, feature so-called mannose clusters (22, 24) on the protein surface (Fig. 4). Whereas small mannose-type clusters have been characterized on the S1 subunit of Middle East respiratory syndrome (MERS)CoV S (10), no such phenomenon has been observed for the SARS-CoV-1 or SARS-CoV-2 S proteins. The site-specific glycosylation analysis reported here suggests that the glycan shield of SARS-CoV-2 S is consistent with other coronaviruses and similarly exhibits numerous vulnerabilities throughout the glycan shield (10). Last, we detected trace levels of O-linked glycosylation at Thr323/Ser325 (T323/S325), with over 99% of these sites unmodified (fig. S4), suggesting that O-linked glycosylation of this region is minimal when the structure is native-like.

From left to right, MERS-CoV S (10), SARS-CoV-1 S (10), SARS-CoV-2 S, LASV GPC (24), and HIV-1 Env (8, 21). Site-specific N-linked glycan oligomannose quantifications are colored according to the key. All glycoproteins were expressed as soluble trimers in HEK 293F cells apart from LASV GPC, which was derived from virus-like particles from Madin-Darby canine kidney II cells.

Our glycosylation analysis of SARS-CoV-2 offers a detailed benchmark of site-specific glycan signatures characteristic of a natively folded trimeric spike. As an increasing number of glycoprotein-based vaccine candidates are being developed, their detailed glycan analysis offers a route for comparing immunogen integrity and will also be important to monitor as manufacturing processes are scaled for clinical use. Glycan profiling will therefore also be an important measure of antigen quality in the manufacture of serological testing kits. Last, with the advent of nucleotide-based vaccines, it will be important to understand how those delivery mechanisms affect immunogen processing and presentation.

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Site-specific glycan analysis of the SARS-CoV-2 spike - Science Magazine

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Demand for Syndromes of Progressive Ataxia and Weakness Disorders Market to Witness Rapid Surge – Lake Shore Gazette

Ataxia is a neurological condition, characterized by lack of voluntary coordination of muscle movement. Ataxia causes head trauma, stroke, Transient Ischemic Attack (TIA), tumor and toxic reaction. Progressive ataxia and weakness disorders are related to damage, degeneration or loss of neurons of the brain which leads to muscle coordination disability.

The global market for treatments of syndromes of progressive ataxia and weakness disorders is categorized based on various drugs used for treatment of progressive ataxia syndromes, drugs for progressive weakness syndromes and by technology. The progressive ataxia syndrome segment is further sub-segmented into major diseases, such as Friedreichs ataxia, Gertsman-Straussler-Scheinker disease and Machado-Joseph disease. The progressive weakness syndrome segment includes amyotrophic lateral sclerosis, hereditary spastic paraplegia, hereditary neuropathies, progressive bulbar palsy and multiple sclerosis. The technology segment is further sub-segmented into small molecules based therapies and monoclonal antibody.

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In terms of geography, the U.S. and Canada holds major market share of treatments for syndromes of progressive ataxia and weakness disorders market in North America. In Europe, Germany, France and the U.K are major markets for treatments of syndromes of progressive ataxia and weakness disorders.

Globally, treatments for syndromes of progressive ataxia and weakness disorders market are growing due to novel drug development and rapid technological advancement for treatment of progressive ataxia and weakness disorders. Some of the major technological advancement involved in growth of the market are protein mis-folding, gene mutation and stem cell therapy. In addition, increased collaborations between industry players for development of new therapies is a key trend for the market.

However, patent expiries of major drugs hampers growth of the treatments for syndromes of progressive ataxia and weakness disorders market. Moreover, stringent regulations and standard requires for approval process of new drugs impede growth of the treatments for syndromes of progressive ataxia and weakness disorders market. Several government agencies, such as FDA and European Medicines Agency, are responsible for the approval of every drug. In addition, the approval process takes a very long time to approve a specific drug.

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Some of the major companies operating in the treatment for syndromes of progressive ataxia and weakness disorders market are Abbott Laboratories, Acorda Therapeutics Inc., American Regent Inc., Baxter International Inc., Biogen Idec., Bristol-Myers Squibb, Cadila Healthcare Ltd., Eli Lilly and Company, Glaxosmilthkline Plc., Sanofi, Roche Holding Ltd., Pfizer Inc. and Novartis AG.

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Demand for Syndromes of Progressive Ataxia and Weakness Disorders Market to Witness Rapid Surge - Lake Shore Gazette

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