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Researchers enlist the help of citizen scientists to design new drugs against coronavirus

The best thing most of us can do to fight COVID-19 is stay home, to curb the respiratory disease's spread. But anyone with a computer can pitch in more actively too: by helping scientists design drugs to combat the virus now responsible for hundreds of thousan

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The best thing most of us can do to fight COVID-19 is stay home, to curb the respiratory disease's spread. But anyone with a computer can pitch in more actively too: by helping scientists design drugs to combat the virus now responsible for hundreds of thousands of infections worldwide.

Howard Hughes Medical Institute Investigator David Baker creates new proteins tailor-made for specific tasks. Now, his team at the University of Washington is hunting for proteins that might stop the novel coronavirus (called SARS-CoV-2) from infecting human cells -- and they're enlisting citizen scientists to help via a free computer game called Foldit.

Foldit players around the world compete to solve protein puzzles digitally, designing molecules with certain specs. Scientists are now challenging players to design proteins that can glom onto the new coronavirus and block its entry into cells. Such proteins could potentially be developed into antiviral drugs, to lessen the severity of patients' symptoms.

We find that the creativity of crowdsourcing is really, really useful -- if you ask 100 people to do something, they'll do it in 100 different ways. That's really valuable for us in protein design problems." Brian Koepnick, a research scientist in Baker's lab who helps run Foldit

Protein building blocks called amino acids string together to form long chains. Those chains crumple into more compact 3-D structures -- a protein's finished shape. Researchers can predict how a chain might crumple, or work backwards to figure out what amino acid combinations create a particular protein structure.

Baker's team has a good idea of what kind of protein they're after. Like other coronaviruses, the virus that causes COVID-19 can infect cells via its "spike protein." This protein latches onto certain human cells, letting the virus pass through the external membrane to multiply inside. A protein that grabs the coronavirus's spike protein might be able to run interference, Baker says, preventing the virus from binding to cells.

Finding such a protein is a daunting task, he says. "There's just this massive number of possibilities." Cells build proteins from a toolkit of twenty standard amino acids. Any one protein can have hundreds of amino acids linked together, yielding billions of configurations.

Baker's lab uses custom-made computer algorithms to speed the protein design process. Given a target shape, their program sends back amino acid combinations that, when folded, might yield something similar. The algorithms can also calculate the hypothetical proteins' stability, which reflects how likely they are to hold their proposed shape in real life. The Foldit game works alongside this computer program, but lets humans take the reins. Players can build a protein from the ground up and tweak it as they go, trying to meet design criteria while competing for the highest stability score.

"We're finding ways humans and computers can work together to solve this problem, rather than relying on the strengths of just one or the other," Koepnick says. Humans have ingenuity and a design sense that computers lack. He and his colleagues have posed almost 2,000 different protein design puzzles to hundreds of thousands of Foldit players since 2008, when the game was launched by Baker's lab.

"We've found that Foldit players can really make remarkable designs that fold as predicted," Baker says. "We're very excited to see what Foldit players come up with for COVID-19."

Each protein puzzle is usually open for about a week. The Foldit team has posted several rounds of coronavirus puzzles over the past month, updating players' guidance as new information about the virus comes in.

Already, a few designs seem promising, Baker says. But not every protein that seems good on the computer screen actually works out in real life. The team is running more computational tests, and they plan to study exceptional solutions in the lab. (While UW's labs have mostly shut down, research on COVID-19 is active, with precautions in place for staff.)

His team has had success using a similar strategy to design flu virus inhibitors. "In general, the coronaviruses appear to mutate less than influenza viruses," Baker says. "So that makes them a little bit easier of a target." Still, he cautions, his lab's contribution is just the first step in a long journey toward a drug. They'll send proteins that pass their screening test to other colleagues, who will test them on cells grown in culture dishes, and perhaps eventually in lab animals and humans.

Would-be puzzlers will probably need some practice before tackling open-ended design challenges like the coronavirus puzzles. But ultimately, Foldit players don't need to be biology experts to play the game and design potential drugs, Baker says. Like most puzzles, all it takes is persistence and a little bit of ingenuity.

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Related questions

01With graph neural networks, foundation models, and diffusion models transforming the field, what can we predict reliably today that was difficult a decade ago and are there still any blind spots?

One major example is protein structure. Methods such as AlphaFold and Boltz-2 have transformed how we generate 3D structural data. Accurate protein structure prediction has been a decades-old challenge, and while there is still work to do on underrepresented protein classes, these models have opened an extraordinary pathway. We have also seen progress in multi-output models. Just over a decade ago (circa 2013), toxicology and property prediction models often focused on one endpoint at a time. Now, multi-output models, especially from methods such as graph neural networks (GNNs) and other deep learning methods, can predict multiple endpoints simultaneously. This allows for transfer learning between related properties. That creates opportunities across ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction and toxicology. Another exciting area is machine learning potentials. Molecular simulations traditionally rely on fixed mathematical functions to describe molecular interactions, but neural potentials can now replace or augment those functions. They can, in many cases, provide highly accurate simulations and structures at lower computational cost than quantum chemistry. However, its not just down to model architectures. The biggest blind spot behind all of these advances remains careful data curation, annotation, and collection. Open databases for example ChEMBL, PubChem and Chemspider together with data initiatives such as OpenADEMT and OpenBind to name but a few are making notable progress here. We still see some data for example on formulation chemistry often receiving less attention. Its is not just about data scale, but quality that is critical here to. Accessible high quality data sets are the fuel of AI methods. Architecture matters, but high-quality, accessible and abundant data is also absolutely critical.

Source: www.news-medical.net ↗
02This year at Pittcon, you are presenting ‘Frontiers in Gas Chromatography for Petrochemical Laboratory - From Sampling to Detection.’ Can you tell us a little bit about your research and what you are going to be presenting this year?

I am going to be talking about new columns based on ionic liquids. An ionic liquid is a liquid in which all the components are ions, like salt (sodium chloride), only, instead of being a solid, it is a liquid. These things have very high polarity and can be developed with extremely high thermal stabilities. They are highly useful as GC stationary phases, which I will be talking about. They are really useful as headspace solvents for the analysis of environmental and biological samples. We use it to analyze water and all sorts of materials. That is the thrust of what I will be talking about, new ionic liquids that are highly polar, highly stable and can be used for unusual analyses that ordinary stationary phases and ordinary techniques have difficulty with.

Source: www.news-medical.net ↗
03How are Affimers Made?

Affimers stem from the drive to generate alternative methods of studying protein binding and function, which can complement information provided by antibodies. As a type of affinity reagent, affimers are biophysically stable scaffolds of proteins that can be reused. Affimers can be made from two scaffolds: Adhiron scaffolds and human stefin A scaffolds. The stefin A scaffold is engineered from human stefin A protein, whereas Adhiron is synthetic. The Adhiron synthetic protein was originally made from the sequence of cystatin, and is very thermally stable. The Adhiron and stefin A scaffolds are structurally related. To select an appropriate affimer, screening is done to find suitable reagents for the target protein. The chosen coding regions are then cloned into an expression vector in Escherichia coli, and the recombinant protein was purified over the course of a week. This allows for generation of high quality proteins for diverse applications.

Source: www.news-medical.net ↗
04Your research has focused extensively on axon biology and small non-coding RNAs (sncRNAs). Can you explain why the study of sncRNAs in axons is significant and how it impacts our understanding of neuronal function? What impact does this have on drug discovery?

As an analogy, when the axon is scaled up to be the arm in a human body, you can think of the axon length in relation to the neuron's cell body as the equivalent distance from Nottingham to Paris. This significant length poses a logistical challenge for the neuron. For proteins to be expressed at the axon terminal, which is distant from the cell body, there's a requirement for translation regulation. In other words, there is a need for descentralized gene expression. Here, small non-coding RNAs play a pivotal role. Our work on microRNAs has shown their importance in spatial-temporal control of gene expression. This research aligns with the current interest in RNA biology and offers potential avenues for drug discovery, especially given the advances in RNA technologies post-covid.

Source: www.news-medical.net ↗
05Why measuring CAR expression is important?

The CAR positive rate is a critical metric for evaluating CAR-T product potency, directly influencing target recognition and cytotoxic efficacy. Monitoring CAR expression in peripheral blood or tissues post-infusion provides insights into in vivo expansion and persistence, informing treatment response, durability, and potential relapse risk. Dynamic CAR expression monitoring also helps assess immunogenicity and safety. Thus, a sensitive, reliable, and universal CAR detection system is vital for developing and evaluating CAR-T cell therapies. Currently, approved CAR-T products target CD19 or BCMA, making flow cytometry with recombinant BCMA or CD19 protein the standard detection method. Although highly specific, this approach is limited to single targets and is unsuitable for CAR-T screening and preclinical development. Several CAR detection methods have been developed, including Protein L (which binds immunoglobulins) or Fc-binding polyclonal antibodies. Protein L binds only to kappa light chains and fails to detect CARs containing lambda chains. Polyclonal antibodies may cross-react with patient IgG, causing false positives. Anti-idiotype antibodies offer high specificity and sensitivity by targeting unique idiotopes in the CAR's antigen-binding region, but they are challenging to develop and produce.

Source: www.news-medical.net ↗
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Case Study

Figure 1: Cell-based binding assay of SARS-CoV-2 spike protein RBD with HEK293/ACE2 cells. HEK293/ACE2 cells were incubated with SARS-CoV-2 Spike protein (RBD, His Tag) (Cat. No. Z03479, GenScript) in serial dilution. Then the cells were stained with THE™ His Tag Antibody [iFluor 488], mAb, Mouse (Cat. No. A01800, GenScript) and analyzed with flow cytometer. FACS analysis shows the EC50 of spike protein RBD binding with ACE2 on HEK293 cells is 1.071 ug/ml. Figure 2: Neutralization assay of SARS-CoV-2 spike protein RBD with HEK293/ACE2 stable cell lines. FACS analysis histogram shows ACE2 expression in HEK293/ACE2 stable cell line (Cat. No. M00770) compared with negative control. HEK293/ACE2 cells are plated at 3E5+ cells /well in 100ul PBS in 96-well plate, incubated with 10 µg/ml S-RBD-His (GenScript, Cat. No. Z03479) on ice for 1 hour, or left untreated. Both groups are then washed with PBS, incubated with Anti-His 2nd antibody (GenScript, Cat. No. A01800) at 1:200 on ice for 1 hour, washed with PBS, and then analyzed on flow cytometer.

Source: genscript.com ↗
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