ArticleBMC bioinformatics2026
Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.
Article in BMC bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant-effect predictors.HGG advances · 2026Article
- A deep learning method for predicting interactions for intrinsically disordered regions of proteins.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
2 authors.
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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.
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