ArticleBioinformatics (Oxford, England)2020
APOD: accurate sequence-based predictor of disordered flexible linkers.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- 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
- Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models.Proteins · 2026Article
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- Prediction of Disordered Linkers Using APOD.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Prediction of Intrinsic Disorder Functions with DEPICTER2.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Assessment of Disordered Linker Predictions in the CAID2 Experiment.Biomolecules · 2024Article
- DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model.BMC biology · 2024Article
- IDP-LM: Prediction of protein intrinsic disorder and disorder functions based on language models.PLoS computational biology · 2023Article
- CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins.Nucleic acids research · 2023Article
- TransDFL: Identification of Disordered Flexible Linkers in Proteins by Transfer Learning.Genomics, proteomics & bioinformatics · 2023Article
- Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins.Computational and structural biotechnology journal · 2023Article
- DMFpred: Predicting protein disorder molecular functions based on protein cubic language model.PLoS computational biology · 2022Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions.Nature communications · 2021Article
- Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks.Computational intelligence and neuroscience · 2021Article
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3 authors.
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Abstract
motivationDisordered flexible linkers (DFLs) are abundant and functionally important intrinsically disordered regions that connect protein domains and structural elements within domains and which facilitate disorder-based allosteric regulation. Although computational estimates suggest that thousands of proteins have DFLs, they were annotated experimentally in <200 proteins. This substantial annotation gap can be reduced with the help of accurate computational predictors. The sole predictor of DFLs, DFLpred, trade-off accuracy for shorter runtime by excluding relevant but computationally costly predictive inputs. Moreover, it relies on the local/window-based information while lacking to consider useful protein-level characteristics.
resultsWe conceptualize, design and test APOD (Accurate Predictor Of DFLs), the first highly accurate predictor that utilizes both local- and protein-level inputs that quantify propensity for disorder, sequence composition, sequence conservation and selected putative structural properties. Consequently, APOD offers significantly more accurate predictions when compared with its faster predecessor, DFLpred, and several other alternative ways to predict DFLs. These improvements stem from the use of a more comprehensive set of inputs that cover the protein-level information and the application of a more sophisticated predictive model, a well-parametrized support vector machine. APOD achieves area under the curve = 0.82 (28% improvement over DFLpred) and Matthews correlation coefficient = 0.42 (180% increase over DFLpred) when tested on an independent/low-similarity test dataset. Consequently, APOD is a suitable choice for accurate and small-scale prediction of DFLs. AVAILABILITY AND IMPLEMENTATION: https://yanglab.nankai.edu.cn/APOD/.
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