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Exploiting heterogeneous sequence properties improves prediction of protein disorder

Obradovic, Zoran, Peng, Kang, Vucetic, Slobodan, Radivojac, Predrag, Dunker, A. Keith·2005·Proteins: Structure, Function, and Bioinformatics

Abstract

During the past few years we have investigated methods to improve predictors of intrinsically disordered regions longer than 30 consecutive residues. Experimental evidence, however, showed that these predictors were less successful on short disordered regions, as observed two years ago during the fifth Critical Assessment of Techniques for Protein Structure Prediction (CASP5). To address this shortcoming, we developed a two-level model called VSL1 (CASP6 id: 193-1). At the first level, VSL1 consists of two specialized predictors, one of which was optimized for long disordered regions (>30 residues) and the other for short disordered regions (< or =30 residues). At the second level, a meta-predictor was built to assign weights for combining the two first-level predictors. As the results of the CASP6 experiment showed, this new predictor has achieved the highest accuracy yet and significantly improved performance on short disordered regions, while maintaining high performance on long disordered regions.

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Metadata

Authors
Obradovic, Z., Peng, K., Vucetic, S., Radivojac, P., & Dunker, A. K.
Venue
Proteins: Structure, Function, and Bioinformatics
Publisher
Wiley
Published
2005
Volume
61
Issue
S7
Pages
176-182
ISSN
  • 0887-3585 (ISSN-L)
  • 1097-0134
OpenAlex
W2128413613
Cited by
555
References
4
FWCI
7.49
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Exploiting heterogeneous sequence properties improves prediction of protein disorder. https://app.cochat.ai/papers/10.1002/prot.20735
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Obradovic, Z., Peng, K., Vucetic, S., Radivojac, P., & Dunker, A. K. (2005). Exploiting heterogeneous sequence properties improves prediction of protein disorder. Proteins: Structure, Function, and Bioinformatics. https://doi.org/10.1002/prot.20735

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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