Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.

Authors

Nawar Malhis, Jörg Gsponer

Published in

BMC bioinformatics. Volume 27. Issue 1. Jun 13, 2026. Epub Jun 13, 2026.

Abstract

Molecular Recognition Features (MoRFs) are segments within disordered protein regions (IDRs) that undergo a disorder-to-order transition upon binding to their partners. Identifying MoRFs remains a significant challenge. This paper introduces MoRFchibi 2.0, a specialized prediction tool designed to identify the locations of MoRFs within protein sequences. Our results show that MoRFchibi 2.0 outperforms all existing MoRF and general predictors of protein-binding sites within IDRs, including the top-performing models from the Critical Assessment of protein Intrinsic Disorder (CAID) rounds 1, 2, and 3. Remarkably, MoRFchibi 2.0 surpasses predictors that utilize AlphaFold data and state-of-the-art protein language models, achieving superior ROC and Precision-Recall curves and higher success rates. MoRFchibi 2.0 generates output scores using an ensemble of logistic regression convolutional neural network models normalized for the priors in the training data, making them individually interpretable and compatible with other tools utilizing the same scoring framework.

PMID:
42288727
Bibliographic data and abstract were imported from PubMed on 15 Oct 2026.

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