ReviewComputational and structural biotechnology journal2019
Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions.
Review in Computational and structural biotechnology journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- How Well Do Molecular Dynamics Force Fields Model Peptides: A Systematic Benchmark across Diverse Folding Behaviors.The journal of physical chemistry. B · 2026Article
- Prediction of Intrinsically Disordered Regions That Bind RNA with DisoRDPbind.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Comparative assessment of binding residue predictions in intrinsically disordered regions.Protein science : a publication of the Protein Society · 2025Article
- MoRFs_TransFuse: a MoRFs predictor based on multimodal feature fusion and the lightweight Transformer network.BioData mining · 2025Article
- Current perspectives in drug targeting intrinsically disordered proteins and biomolecular condensates.BMC biology · 2025Review
- Flexible iron: disorder in the ironome brings order to protein structure and function.Frontiers in molecular biosciences · 2025Article
- Prediction of Disordered Linear Interacting Peptides with CLIP.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Disordered Linkers Using APOD.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn.Methods in molecular biology (Clifton, N.J.) · 2025Article
- DescribePROT Database of Residue-Level Protein Structure and Function Annotations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Evaluation of predictions of disordered binding regions in the CAID2 experiment.Computational and structural biotechnology journal · 2025Review
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- Inhibitors of the transactivation domain of androgen receptor as a therapy for prostate cancer.Steroids · 2024Review
- Article
- A complete map of specificity encoding for a partially fuzzy protein interaction.bioRxiv : the preprint server for biology · 2024Article
- Systematic identification of 20S proteasome substrates.Molecular systems biology · 2024Article
- Assessment of Disordered Linker Predictions in the CAID2 Experiment.Biomolecules · 2024Article
- Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins.Nature protocols · 2023Review
- Intrinsic Disorder in the Human Tear Proteome.Investigative ophthalmology & visual science · 2023Article
- DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction.Nucleic acids research · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular recognition features (MoRFs) are short protein-binding regions that undergo disorder-to-order transitions (induced folding) upon binding protein partners. These regions are abundant in nature and can be predicted from protein sequences based on their distinctive sequence signatures. This first-of-its-kind survey covers 14 MoRF predictors and six related methods for the prediction of short protein-binding linear motifs, disordered protein-binding regions and semi-disordered regions. We show that the development of MoRF predictors has accelerated in the recent years. These predictors depend on machine learning-derived models that were generated using training datasets where MoRFs are annotated using putative disorder. Our analysis reveals that they generate accurate predictions. We identified eight methods that offer area under the ROC curve (AUC) ≥ 0.7 on experimentally-validated test datasets. We show that modern MoRF predictors accurately find experimentally annotated MoRFs even though they were trained using the putative disorder annotations. They are relatively highly-cited, particularly the methods available as webservers that on average secure three times more citations than methods without this option. MoRF predictions contribute to the experimental discovery of protein-protein interactions, annotation of protein functions and computational analysis of a variety of proteomes, protein families, and pathways. We outline future development and application directions for these tools, stressing the importance to develop novel tools that would target interactions of disordered regions with other types of partners.
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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.