ArticlePloS one2015
DisPredict: A Predictor of Disordered Protein Using Optimized RBF Kernel.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A Proteomic Atlas of Human Milk: Uncovering Milk Functional Components.Research square · 2026Article
- Expose flexible conformations for intrinsically disordered protein.Current research in structural biology · 2025Article
- Prediction of folding patterns for intrinsic disordered protein.Scientific reports · 2023Article
- Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins.Nature protocols · 2023Review
- Article
- ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers.NAR genomics and bioinformatics · 2023Article
- HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines.International journal of molecular sciences · 2023Article
- Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions.Genes · 2023Review
- Characterization of intrinsically disordered regions in proteins informed by human genetic diversity.PLoS computational biology · 2022Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features.Applied bionics and biomechanics · 2022Article
- Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks.Computational intelligence and neuroscience · 2021Article
- Estimation of Position Specific Energy as a Feature of Protein Residues from Sequence Alone for Structural Classification.PloS one · 2016Article
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Authors and funding
2 authors.
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Abstract
Intrinsically disordered proteins or, regions perform important biological functions through their dynamic conformations during binding. Thus accurate identification of these disordered regions have significant implications in proper annotation of function, induced fold prediction and drug design to combat critical diseases. We introduce DisPredict, a disorder predictor that employs a single support vector machine with RBF kernel and novel features for reliable characterization of protein structure. DisPredict yields effective performance. In addition to 10-fold cross validation, training and testing of DisPredict was conducted with independent test datasets. The results were consistent with both the training and test error minimal. The use of multiple data sources, makes the predictor generic. The datasets used in developing the model include disordered regions of various length which are categorized as short and long having different compositions, different types of disorder, ranging from fully to partially disordered regions as well as completely ordered regions. Through comparison with other state of the art approaches and case studies, DisPredict is found to be a useful tool with competitive performance. DisPredict is available at https://github.com/tamjidul/DisPredict_v1.0.
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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.