Over the past few decades, very few new antibiotics have been developed, largely because current methods for screening potential drugs are prohibitively expensive and time-consuming. One promising new strategy is to use computational models, which offer a potentially faster and cheaper way to identify new drugs.
A new study from MIT reveals the potential and limitations of one such computational approach. Using protein structures generated by an artificial intelligence program called AlphaFold, the researchers explored whether existing models could accurately predict the interactions between bacterial proteins and antibacterial compounds. If so, then researchers could begin to use this type of modeling to do large-scale screens for new compounds that target previously untargeted proteins. This would enable the development of antibiotics with unprecedented mechanisms of action, a task essential to addressing the antibiotic resistance crisis.
However, the researchers, led by James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, found that these existing models did not perform well for this purpose. In fact, their predictions performed little better than chance.
“Breakthroughs such as AlphaFold are expanding the possibilities for in silico drug discovery efforts, but these developments need to be coupled with additional advances in other aspects of modeling that are part of drug discovery efforts,” Collins says. “Our study speaks to both the current abilities and the current limitations of computational platforms for drug discovery.”
In their new study, the researchers were able to improve the performance of these types of models, known as molecular docking simulations, by applying machine-learning techniques to refine the results. However, more improvement will be necessary to fully take advantage of the protein structures provided by AlphaFold, the researchers say.
Collins is the senior author of the study, which appears today in the journal Molecular Systems Biology. MIT postdocs Felix Wong and Aarti Krishnan are the lead authors of the paper.
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