ArticleJournal of molecular biology2023
CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions.
Article in Journal of molecular biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- Comparative assessment of binding residue predictions in intrinsically disordered regions.Protein science : a publication of the Protein Society · 2025Article
- Membrane Association of Intrinsically Disordered Proteins.Annual review of biophysics · 2025Review
- Prediction of Disordered Linear Interacting Peptides with CLIP.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
- Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.Methods in molecular biology (Clifton, N.J.) · 2025Article
- SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Assessment of Disordered Linker Predictions in the CAID2 Experiment.Biomolecules · 2024Article
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3 authors.
Funding
Abstract
Molecular recognition features (MoRFs) are a commonly occurring type of intrinsically disordered regions (IDRs) that undergo disorder-to-order transition upon binding to partner molecules. We focus on recently characterized and functionally important membrane-binding MoRFs (MemMoRFs). Motivated by the lack of computational tools that predict MemMoRFs, we use a dataset of experimentally annotated MemMoRFs to conceptualize, design, evaluate and release an accurate sequence-based predictor. We rely on state-of-the-art tools that predict residues that possess key characteristics of MemMoRFs, such as intrinsic disorder, disorder-to-order transition and lipid-binding. We identify and combine results from three tools that include flDPnn for the disorder prediction, DisoLipPred for the prediction of disordered lipid-binding regions, and MoRFCHiBi
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Registered trials
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.