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

By Nawar Malhis, Matthew Jacobson, Jörg Gsponer. 2016

147 citations recorded by the source corpus.

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/

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

Critical assessment of protein intrinsic disorder prediction (CAID) ‐ Results of round 2 (2023, background): “MoRFchibi-web 20 and DeepDRPBind-protein seem to capture the reference definition better, as they are in the top-three in terms of both APS and AUC.”
Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions (2017, methodology): “There are several predictors of MoRFs including alpha-MoRFpred [124,125], MoRFpred [126], MFSPSSMpred [127], MoRFChiBi [128], MoRFChiBiWeb [129], fMoRFpred [130], retro-MoRF [131] and DISOPRED3 [61].”