ArticleBioinformatics (Oxford, England)2015
Computational identification of MoRFs in protein sequences.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.BMC bioinformatics · 2026Article
- FuzDrop: sequence-based prediction of the propensity of proteins for liquid-liquid phase separation and aggregation.Nature protocols · 2026Review
- Comparative assessment of binding residue predictions in intrinsically disordered regions.Protein science : a publication of the Protein Society · 2025Article
- IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions.Frontiers in bioinformatics · 2025Article
- Computational Prediction of Linear Interacting Peptides.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Disordered Linear Interacting Peptides with CLIP.Methods in molecular biology (Clifton, N.J.) · 2025Article
- HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins.Nucleic acids research · 2024Article
- Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions.Genes · 2023Review
- Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins.Computational and structural biotechnology journal · 2023Article
- Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023Review
- Portability of a Small-Molecule Binding Site between Disordered Proteins.Biomolecules · 2022Article
- Intrinsically disordered proteins play diverse roles in cell signaling.Cell communication and signaling : CCS · 2022Review
- Prediction of protein-protein interaction sites in intrinsically disordered proteins.Frontiers in molecular biosciences · 2022Review
- Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Subunit cooperation in the Get1/2 receptor promotes tail-anchored membrane protein insertion.The Journal of cell biology · 2021Article
- Prediction of MoRFs based on sequence properties and convolutional neural networks.BioData mining · 2021Article
- APOD: accurate sequence-based predictor of disordered flexible linkers.Bioinformatics (Oxford, England) · 2020Article
- New technologies to analyse protein function: an intrinsic disorder perspective.F1000Research · 2020Review
- Interactions by Disorder - A Matter of Context.Frontiers in molecular biosciences · 2020Review
- Computational prediction of MoRFs based on protein sequences and minimax probability machine.BMC bioinformatics · 2019Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationIntrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is the binding of molecular recognition features (MoRFs) to globular protein domains in a process known as a disorder-to-order transition. Predicting the location of MoRFs in protein sequences with high accuracy remains an important computational challenge.
methodIn this study, we introduce MoRFCHiBi, a new computational approach for fast and accurate prediction of MoRFs in protein sequences. MoRFCHiBi combines the outcomes of two support vector machine (SVM) models that take advantage of two different kernels with high noise tolerance. The first, SVMS, is designed to extract maximal information from the general contrast in amino acid compositions between MoRFs, their surrounding regions (Flanks), and the remainders of the sequences. The second, SVMT, is used to identify similarities between regions in a query sequence and MoRFs of the training set.
resultsWe evaluated the performance of our predictor by comparing its results with those of two currently available MoRF predictors, MoRFpred and ANCHOR. Using three test sets that have previously been collected and used to evaluate MoRFpred and ANCHOR, we demonstrate that MoRFCHiBi outperforms the other predictors with respect to different evaluation metrics. In addition, MoRFCHiBi is downloadable and fast, which makes it useful as a component in other computational prediction tools. AVAILABILITY AND IMPLEMENTATION: http://www.chibi.ubc.ca/morf/.
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