Revolutionizing the Way in Which Proteins Are Studied

By Dirk Hoffman

It’s a lofty goal, but one that may soon be closer to becoming a reality thanks in part to research led by Thomas D. Grant, PhD, assistant professor of structural biology in the Jacobs School of Medicine and Biomedical Sciences.

“The basic idea is that we’re revolutionizing how we study proteins in their natural environment,” says Grant, principal investigator on a new award from the National Institutes of Health.

The five-year grant from the National Institute of General Medical Sciences totals nearly $2.2 million and is titled “Revolutionizing Protein Structure Determination with Advanced SWAXS Analysis.”

“Proteins are the workhorses of biology, but they are like molecular machines and are constantly moving and changing shape,” Grant says. “Current methods often give us static snapshots, but we need to see them in action to really understand how they work and how to design drugs that target them effectively.”

To accomplish this, Grant is employing a technique called SWAXS (small- and wide-angle X-ray scattering) in combination with computational artificial intelligence (AI) tools.

SWAXS is akin to taking a very sophisticated X-ray of proteins in solution, according to Grant.

“When you shine X-rays on a sample of proteins dissolved in water, the X-rays scatter off the proteins in specific patterns,” he says. “By measuring how the X-rays scatter at different angles, both small angles and wide angles, we can extract information about the protein’s size, shape, and internal structure.”

“The beauty of SWAXS is that it works with proteins in their natural environment, dissolved in water at room temperature, just like they are in your cells. You don’t need to crystallize them or freeze them like with other techniques. It’s also fast and can handle proteins of almost any size,” he adds.

“The downside is that the data is more challenging to interpret than something like X-ray crystallography, which is where our computational advances come in.”

Grant’s research aims to address the limitations of the current methods for analyzing and interpreting SWAXS data.

“The biggest problem is that we don’t have good ways to quantify how confident we should be in the models we build from SWAXS data,” he says.

Researchers often rely on crude measures to say whether a model is good or bad, Grant says.

“It’s kind of like having a thermometer that only tells you ‘hot’ or ‘cold’ instead of giving you an actual temperature,” he says. “Our method is like giving you the actual temperature, not only how precisely ‘good’ or ‘bad’ the model is, but also which parts of the model are better resolved than others.”

Grant notes that current computational tools for calculating what a SWAXS pattern should look like from a protein structure aren’t very accurate, especially in the wide-angle region where one gets information about the protein’s detailed internal structure. 

Grant will be using SWAXSFold, an AI model he developed using the computing capacity of Empire AI, the $500 million New York State-based research consortium advancing artificial intelligence for the public good..

“We’re basically taking the AlphaFold approach — which predicts protein structures with AI — and integrating it with experimental data,” Grant says.

AlphaFold can predict what a protein structure might look like based on its amino acid sequence, but proteins are dynamic and can adopt many different shapes.

“AlphaFold doesn’t know which shape the protein actually has under your specific experimental conditions,” Grant says. “SWAXSFold solves this by directly incorporating SWAXS experimental data into the AI prediction process.”

So instead of just giving the AI a protein sequence and asking, “what might this look like?” — the researchers are giving it the sequence plus experimental data and asking, “what does this actually look like under these specific conditions?”

“Nobody’s done this before — integrating experimental structural data directly into the AI training and prediction process,” Grant says.

Empire AI’s computing center, located at the University at Buffalo, is a major resource for the researchers.

“Empire AI is a big part of this, as we wouldn’t be able to do this level of computation without it,” Grant notes.

Grant says the research is very important for drug discovery because drugs need to bind to the actual shape the protein has in a person’s body, not just any possible shape it could have.

“We’ve been working on this for a while now and have gotten some promising preliminary results,” he says. “It’s a challenging computational problem but we think we can make it work.”

“Right now, a lot of drug development fails because we don’t understand the true, dynamic shapes of proteins well enough,” Grant adds. “By giving researchers tools to see how proteins actually move and change shape in conditions similar to what they experience in the human body, we can design much better, more targeted drugs.”

Grant notes this is especially important for “challenging” drug targets — proteins that are involved in diseases but have been hard to develop drugs against because they’re so dynamic or because they don’t have obvious binding pockets for drugs — such as cancer-causing proteins that change shape, or proteins involved in neurological diseases.

“We’re also developing tools that will help researchers understand how disease-causing mutations change protein structure,” he says. “If we can see exactly how a mutation alters a protein’s shape and function, we can design personalized therapies targeted to that specific change.”

The NIH Award is a single-investigator R35 grant so Grant’s lab is the only one on the grant, but he collaborates extensively with groups at UB and at other institutions around the country.

Among collaborators in the Jacobs School are:

• D. Fernando Estrada, PhD, associate professor of biochemistry
  
• Zackary M. Falls, PhD, assistant professor of biomedical informatics
  
• Ram Samudrala, PhD, professor of biomedical informatics and chief of the Division of Bioinformatics
  

“The beauty of this grant mechanism is that it gives me the flexibility to pursue new collaborations as the science develops over the next five years,” Grant says.