ArticleBMC bioinformatics2016
Predicting MoRFs in protein sequences using HMM profiles.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 42 citations in OpenAlex.
- Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.BMC bioinformatics · 2026Article
- Protein language model identifies disordered, conserved motifs implicated in phase separation.eLife · 2025Article
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Advanced computational approaches to understand protein aggregation.Biophysics reviews · 2024Review
- Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions.Genes · 2023Review
- Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023Review
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Intrinsically disordered proteins play diverse roles in cell signaling.Cell communication and signaling : CCS · 2022Review
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Predicting Protein Conformational Disorder and Disordered Binding Sites.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Prediction of protein-protein interaction sites in intrinsically disordered proteins.Frontiers in molecular biosciences · 2022Review
- Protein Secondary Structure Prediction With a Reductive Deep Learning Method.Frontiers in bioengineering and biotechnology · 2021Article
- APOD: accurate sequence-based predictor of disordered flexible linkers.Bioinformatics (Oxford, England) · 2020Article
- Presence and structure-activity relationship of intrinsically disordered regions across mucins.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2020Article
- Brain wave classification using long short-term memory network based OPTICAL predictor.Scientific reports · 2019Article
- Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions.Computational and structural biotechnology journal · 2019Review
- An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.BMC bioinformatics · 2017Article
- Bioinformatics and systems biology research update from the 15BMC bioinformatics · 2016Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.
methodsIn this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others).
resultsTo evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors.
conclusionsUsing HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.