ArticleInternational journal of molecular sciences2015
An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.
Article in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Modern resources for intrinsic disorder predictions: protein language models, deep learning, meta-servers, and databases.Cellular and molecular life sciences : CMLS · 2026Review
- GloEC: a hierarchical-aware global model for predicting enzyme function.Briefings in bioinformatics · 2024Article
- Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions.Genes · 2023Review
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning.Frontiers in pharmacology · 2022Review
- An Interpretable Double-Scale Attention Model for Enzyme Protein Class Prediction Based on Transformer Encoders and Multi-Scale Convolutions.Frontiers in genetics · 2022Article
- Machine-learning analysis of intrinsically disordered proteins identifies key factors that contribute to neurodegeneration-related aggregation.Frontiers in aging neuroscience · 2022Article
- Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins.Biomolecules · 2020Review
- Identification of Intrinsically Disordered Proteins and Regions by Length-Dependent Predictors Based on Conditional Random Fields.Molecular therapy. Nucleic acids · 2019Article
- The Next Era: Deep Learning in Pharmaceutical Research.Pharmaceutical research · 2016Review
- AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields.Bioinformatics (Oxford, England) · 2016Article
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Authors and funding
5 authors.
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Abstract
Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.
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