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 identified eight methods that offer area under the ROC curve (AUC) ≥ 0.7 on experimentally-validated test datasets. We show that modern MoRF predictors accurately find experimentally annotated MoRFs even though they were trained using the putative disorder annotations. They are relatively highly-cited, particularly the methods available as webservers that on average secure three times more citations than methods without this option. MoRF predictions contribute to the experimental discovery of protein-protein interactions, annotation of protein functions and computational analysis of a variety of proteomes, protein families, and pathways. We outline future development and application directions for these tools, stressing the importance to develop novel tools that would target interactions of disordered regions with other types of partners.

DescribePROT: database of amino acid-level protein structure and function predictions

“…MoRFs are short disordered protein regions (between 5 and 25 AAs in length) that undergo disorder-to-order transition upon binding the protein partner(s). A significant majority of functional predictors that address disordered AAs focus on this type interaction ( 16 , 18 ). We use a fast and accurate predictor, MoRFchibi ( 58 ), which outputs numeric propensities for MoRFs and binary labels (MoRF versus non-MoRF).…”

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

“…We use ANCHOR (Dosztányi et al, 2009) to predict the disordered protein-binding residues and we aggregate this information to compute the content of disordered protein binding residues for the proteins in our datasets. The selection of this method is motivated by the fact that it is accurate and popular, and provides fast predictions (i.e., is capable of processing our large datasets) (Meng et al, 2017; Katuwawala et al, 2019).…”

Identifying molecular features that are associated with biological function of intrinsically disordered protein regions

“…Intrinsically disordered regions (IDRs), which are protein regions that lack stable tertiary structure, are increasingly appreciated as playing key roles in diverse aspects of cell biology (Forman-Kay and Mittag, 2013). Bioinformatics methods identify thousands of IDRs in eukaryotic proteomes (Dosztányi et al, 2005;Uversky, 2002) and methods have been developed to predict biophysical or structural behavior for specific subsets of IDRs, such as those that fold upon binding (Katuwawala et al, 2019), contain specific N-or C-terminal motifs (e.g., (Chen et al, 2008;Chuang et al, 2012)), or phase separate (e.g. , reviewed in (Vernon and Forman-Kay, 2019)).…”