Predictions and experimental validation of fold-switching proteins

National Library Of Medicine

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Abstract

No introductory-level biochemistry textbook is complete without a chapter about how a proteins primary sequence of amino acids determines its fold. So far, nearly all computational work has focused on predicting a single protein structure from the proteins amino acid sequence. Our research challenges the one-sequence-one-structure paradigm. In 2018, we found nearly 100 examples of proteins that can adopt more than one stable fold. The structural heterogeneity of these fold-switching proteins allows them either to perform more than one function or to be highly regulated in cells. These functional changes appear to have significant relevance to human health as several fold-switching proteins are associated with human diseases such as cancer and bacterial infections. Now we are taking this research to the next level by (1) developing computational approaches to predict which amino acid sequences can switch folds and (2) testing our predictions experimentally. This year, we developed ACE, a generalizable method that predicts fold switching from genomic sequences. This method outperforms the powerful algorithms AlphaFold2, RoseTTAFold, and ESMFold in predicting fold switching. It also outperforms SPEACH-AF and AF-Cluster, competing methods for predicting conformational heterogeneity in proteins. The resulting paper, Evolutionary Selection of Proteins with Two Folds, is in press at Nature Communications. We are currently using ACE to identify new fold-switching proteins in the E. coli, yeast, and human genomes. We aim to publish results in 2024. Additionally, we identified the first statistically supported evolutionary pathway between two folds in a large family of bacterial response regulators. This was highlighted in Chemical & Engineering News and published in Nature Communications.