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Knowing exactly where proteins are frustrated could go a long way in making better drugs.
This is one of the findings of a new study by Rice University scientists looking for mechanisms that stabilize or destabilize key sections of biomolecules.
Atomic-scale models by rice theorist Peter Wolynes, lead author and alumnus Mingchen Chen, and their colleagues at the Center for Theoretical Biological Physics show that not only are certain specific frustrated sequences in proteins needed to allow them to function, but localizing them offers also clues to better specificity for drugs.
This knowledge could also help design drugs with fewer side effects, Wolynes said.
The team’s open access studio appears in Nature Communications.
Atomic-scale models focus on interactions within possible binding sites rather than the vast majority of interactions in proteins that drive their folding. The finer resolution models allow for the incorporation of cofactors as chemically active ligands, including drug molecules. The researchers say this ability provides new insight into why ligands are best captured only by specific proteins and not others.
“Unnatural ligands,” aka drugs, tend to bind better with those frustrated pockets of protein that become minimally frustrated once drugs bind, Wolynes said. Having a way to find and then learn the ins and outs of these minimally frustrated sites would help pharmaceutical companies eliminate a lot of trial and error.
“The standard way to design drugs is to try 10,000 binding sites on a protein to find the ones that fit,” Wolynes said. “We are saying that it is not necessary to sample all possible binding sites, but only a reasonably fair number to understand the statistics of what might work in local environments.
“It’s the difference between taking a poll and actually having an election,” he said. “The survey is cheaper, but you’ll still have to check things out.”
Rice researchers are known for their energy landscape theory of how proteins fold. It usually uses coarse-grained models where amino acids are represented by a few sites.
This strategy requires less computing power than trying to determine the positions in time of each atom in each residue, yet it has proven extremely accurate in predicting how proteins fold based on their sequences. But for this study, the researchers modeled proteins and protein-ligand complexes at the atomic level to see if they could find out how frustration gives certain parts of a protein the flexibility to bind to other molecules.
“One of the great advantages of all-atom resolution modeling is that it allows us to assess whether drug molecules fit well at the binding sites or not,” Wolynes said. “This method is able to quickly show whether a binding site for a given drug will be minimally frustrated or remain a frustrated region. If after the molecule binds the site becomes frustrated, the protein may reorganize or the drug may change its status. orientation in such a way that it could cause side effects. “
Modeling the frustrated sites – and sometimes altering them to see what would happen – allows researchers to see how drug specificity correlates with binding pockets. Frustration analysis, they wrote, provides “a pathway to screen for more specific compounds for drug discovery.”
“This concept of frustration was present at the beginning of our work on protein folding,” Wolynes said. “When we applied it to real protein molecules, we found some examples where the folding mechanism violated what we would have predicted from a perfect funnel. Then we found that these deviations from the funnel image occurred where the protein was. in fact, a little frustrated.
“It was like the exception that proves the rule,” he said. “Something that is always true may be trivial. But if it’s not true 1% of the time, it’s a problem to solve and we’ve been able to do it with AWSEM, our facility forecasting software.”
The software can be extended to analyze frustration at the atomic level, as described by the group in another recent article. But the computational cost of tracking every atom in a protein is so high that the researchers needed a way to sample the motions of specific regions where frustration could confuse the folding path.
“Mingchen realized that there was an efficient algorithm to sample local environments at binding sites but maintain atomistic resolution,” said Wolynes, who noted that he and Chen, now in private industry, are using the models to investigate. on possible therapies, including COVID-19 related drugs.
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