A method for predicting evolved fold switchers exclusively from their sequences

Abstract Although most proteins with known structures conform to the longstanding rule-of-thumb that high levels of aligned sequence identity tend to indicate similar folds and functions, an increasing number of exceptions is emerging. In spite of having highly similar sequences, these “evolved fold switchers” (1) can adopt radically different folds with disparate biological functions. Predictive methods for identifying evolved fold switchers are desirable because some of them are associated with disease and/or can perform different functions in cells. Previously, we showed that inconsistencies between predicted and experimentally determined secondary structures can be used to predict fold switching proteins (2). The usefulness of this approach is limited, however, because it requires experimentally determined protein structures, whose magnitude is dwarfed by the number of genomic proteins. Here, we use secondary structure predictions to identify evolved fold switchers from their amino acid sequences alone. To do this, we looked for inconsistencies between the secondary structure predictions of the alternative conformations of evolved fold switchers. We used three different predictors in this study: JPred4, PSIPRED, and SPIDER3. We find that overall inconsistencies are not a significant predictor of evolved fold switchers for any of the three predictors. Inconsistencies between α-helix and β-strand predictions made by JPred4, however, can discriminate between the different conformations of evolved fold switchers with statistical significance (p < 1.7*10 −13 ). In light of this observation, we used these inconsistencies as a classifier and found that it could robustly discriminate between evolved fold switchers and evolved non-fold-switchers, as evidenced by a Matthews correlation coefficient of 0.90. These results indicate that inconsistencies between secondary structure predictions can indeed be used to identify evolved fold switchers from their genomic sequences alone. Our findings have implications for genomics, structural biology, and human health.