ArticleBioinformatics (Oxford, England)2016
DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences.
Article in Bioinformatics (Oxford, England), 2016. 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.
- Phase separation drives NLRP3 inflammasome activation independently of Golgi localization.Cell death & disease · 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
- Prediction of Intrinsically Disordered Regions That Bind RNA with DisoRDPbind.Methods in molecular biology (Clifton, N.J.) · 2026Article
- 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
- Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER.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
- Prediction of Intrinsic Disorder Functions with DEPICTER2.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- Systematic identification of 20S proteasome substrates.Molecular systems biology · 2024Article
- Assessment of Disordered Linker Predictions in the CAID2 Experiment.Biomolecules · 2024Article
- DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.Nucleic acids research · 2024Article
- DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model.BMC biology · 2024Article
- RNet: a network strategy to predict RNA binding preferences.Briefings in bioinformatics · 2023Article
- Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins.Nature protocols · 2023Review
- IDP-LM: Prediction of protein intrinsic disorder and disorder functions based on language models.PLoS computational biology · 2023Article
- The Origin of Discrepancies between Predictions and Annotations in Intrinsically Disordered Proteins.Biomolecules · 2023Article
- DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction.Nucleic acids research · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationDisordered flexible linkers (DFLs) are disordered regions that serve as flexible linkers/spacers in multi-domain proteins or between structured constituents in domains. They are different from flexible linkers/residues because they are disordered and longer. Availability of experimentally annotated DFLs provides an opportunity to build high-throughput computational predictors of these regions from protein sequences. To date, there are no computational methods that directly predict DFLs and they can be found only indirectly by filtering predicted flexible residues with predictions of disorder.
resultsWe conceptualized, developed and empirically assessed a first-of-its-kind sequence-based predictor of DFLs, DFLpred. This method outputs propensity to form DFLs for each residue in the input sequence. DFLpred uses a small set of empirically selected features that quantify propensities to form certain secondary structures, disordered regions and structured regions, which are processed by a fast linear model. Our high-throughput predictor can be used on the whole-proteome scale; it needs <1 h to predict entire proteome on a single CPU. When assessed on an independent test dataset with low sequence-identity proteins, it secures area under the receiver operating characteristic curve equal 0.715 and outperforms existing alternatives that include methods for the prediction of flexible linkers, flexible residues, intrinsically disordered residues and various combinations of these methods. Prediction on the complete human proteome reveals that about 10% of proteins have a large content of over 30% DFL residues. We also estimate that about 6000 DFL regions are long with ≥30 consecutive residues. AVAILABILITY AND IMPLEMENTATION: http://biomine.ece.ualberta.ca/DFLpred/ CONTACT: lkurgan@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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