Prediction of protein-binding residues: Dichotomy of sequence-based methods developed using structured complexes versus disordered proteins

Abstract

Motivation There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-Annotated or disorder-Annotated training datasets, potentially creating a dichotomy where the structure-/ disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structuretrained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder-and structure-Trained predictors using a comprehensive benchmark set with the structure-and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid-and small ligand-binding proteins (to study the cross-predictions). Results: Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structuretrained methods make accurate predictions on the structure-Annotated proteins. Similarly, the disorder-Trained predictors predict well on the disorder-Annotated proteins. However, the considered predictors generally fail to crossover, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-Annotated proteins and disoRDPbind for the disorder-Annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder-and structure-Annotated PBRs. HybridPBRpred generates accurate results that cross-over structure-and disorder-Annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs.