ArticleBMC biology2024
DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model.
Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- DNAreader: accurate prediction of DNA-binding residues in structured and disordered proteins using transformers and contrastive learning.Nucleic acids research · 2026Article
- Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.BMC bioinformatics · 2026Article
- Hybrid deep learning with protein language models and dual-path architecture for predicting IDP functions.Briefings in bioinformatics · 2026Article
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- ESMDisPred: A Structure-Aware CNN-Transformer Architecture for Intrinsically Disordered Protein Prediction.bioRxiv : the preprint server for biology · 2026Article
- Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models.Proteins · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Decoding intrinsically disordered regions in biomolecular condensates.Fundamental research · 2026Review
- Candidate genes for kernel ratio in maize identified via combined GWAS and QTL mapping across multiple environments.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Impact of intrinsically disordered regions and functional disorder hotspots in the human kinome.Briefings in bioinformatics · 2025Article
- Prediction of liquid-liquid phase separation proteins based on protein language model.Briefings in bioinformatics · 2025Article
- Article
- Prediction of protein-protein interaction based on interaction-specific learning and hierarchical information.BMC biology · 2025Article
- FusionEncoder: identification of intrinsically disordered regions based on multi-feature fusion.Bioinformatics (Oxford, England) · 2025Article
- The lincRNA Pantr1 is a FOXG1 target gene conferring site-specific chromatin binding of FOXG1.Nucleic acids research · 2025Article
- Membrane Association of Intrinsically Disordered Proteins.Annual review of biophysics · 2025Review
- IDP-EDL: enhancing intrinsically disordered protein prediction by combining protein language model and ensemble deep learning.Briefings in bioinformatics · 2025Article
- MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.BMC biology · 2025Article
- Article
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2 authors.
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Abstract
Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.
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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.