MoRFchibi SYSTEM: software tools for the identification of MoRFs in protein sequences.

Nawar Malhis,Matthew Jacobson,Jörg Gsponer

doi:10.1093/nar/gkw409

Abstract

Molecular recognition features, MoRFs, are short segments within longer disordered protein regions that bind to globular protein domains in a process known as disorder-to-order transition. MoRFs have been found to play a significant role in signaling and regulatory processes in cells. High-confidence computational identification of MoRFs remains an important challenge. In this work, we introduce MoRFchibi SYSTEM that contains three MoRF predictors: MoRFCHiBi, a basic predictor best suited as a component in other applications, MoRFCHiBi_Light, ideal for high-throughput predictions and MoRFCHiBi_Web, slower than the other two but best for high accuracy predictions. Results show that MoRFchibi SYSTEM provides more than double the precision of other predictors. MoRFchibi SYSTEM is available in three different forms: as HTML web server, RESTful web server and downloadable software at: http://www.chibi.ubc.ca/faculty/joerg-gsponer/gsponer-lab/software/morf_chibi/

Highlights

Protein–protein interactions (PPIs) play essential rolls in most biological processes in cells
intrinsically disordered protein regions (IDRs) binding sites are classified under two overlapping categories: short linear motifs (SLiMs) [2] and molecular recognition features or elements (MoRFs) [3]
We introduce MoRFchibi SYSTEM, a series of MoRF predictors that serve different purposes and users

Summary

Introduction

Protein–protein interactions (PPIs) play essential rolls in most biological processes in cells. While the prediction precisions of the first five general MoRF predictors are about equal, MoRFCHiBi Web provides more than twice that precision.

Results

Conclusion