DISOPRED3: precise disordered region predictions with annotated protein-binding activity
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Abstract
Abstract Motivation: A sizeable fraction of eukaryotic proteins contain intrinsically disordered regions (IDRs), which act in unfolded states or by undergoing transitions between structured and unstructured conformations. Over time, sequence-based classifiers of IDRs have become fairly accurate and currently a major challenge is linking IDRs to their biological roles from the molecular to the systems level. Results: We describe DISOPRED3, which extends its predecessor with new modules to predict IDRs and protein-binding sites within them. Based on recent CASP evaluation results, DISOPRED3 can be regarded as state of the art in the identification of IDRs, and our self-assessment shows that it significantly…
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Topics
Keywords
- Intrinsically disordered proteins
- Computer science
- Classifier (UML)
- Sequence (biology)
- Computational biology
- CASP
- Protein methods
- Support vector machine
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