ReviewComputational and structural biotechnology journal2023
Computational prediction of disordered binding regions.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- AnThe Journal of general virology · 2026Article
- SPPIDER-seq: sequence-based partner-aware predictor of protein-protein interaction sites.Bioinformatics (Oxford, England) · 2026Article
- SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites.bioRxiv : the preprint server for biology · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- 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
- Article
- Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn.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
- DescribePROT Database of Residue-Level Protein Structure and Function Annotations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Disordered Linkers Using APOD.Methods in molecular biology (Clifton, N.J.) · 2025Article
- IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions.Frontiers in bioinformatics · 2025Article
- Prediction of Intrinsic Disorder Functions with DEPICTER2.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Disordered Linear Interacting Peptides with CLIP.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Intrinsically Disordered Lipid Binding Residues with DisoLipPred.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation.Orphanet journal of rare diseases · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.
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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.