Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions.

ABSTRACT: Molecular recognition features (MoRFs) are short protein-binding regions that undergo disorder-to-order transitions (induced folding) upon binding protein partners. These regions are abundant in nature and can be predicted from protein sequences based on their distinctive sequence signatures. This first-of-its-kind survey covers 14 MoRF predictors and six related methods for the prediction of short protein-binding linear motifs, disordered protein-binding regions and semi-disordered regions. We show that the development of MoRF predictors has accelerated in the recent years. These predictors depend on machine learning-derived models that were generated using training datasets where MoRFs are annotated using putative disorder. Our analysis reveals that they generate accurate predictions. We

SUBMITTER: Katuwawala A 

PROVIDER: S-EPMC6453775 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature