Optimizing long intrinsic disorder predictors with protein evolutionary information

Kang Peng, Slobodan Vucetic, Predrag Radivojac, Celeste J. Brown, A. Keith Dunker, Zoran Obradovic

Research output: Contribution to journalArticle

287 Scopus citations

Abstract

Protein existing as an ensemble of structures, called intrinsically disordered, has been shown to be responsible for a wide variety of biological functions and to be common in nature. Here we focus on improving sequence-based predictions of long (>30 amino acid residues) regions lacking specific 3-D structure by means of four new neural-network-based Predictors Of Natural Disordered Regions (PONDRs): VL3, VL3H, VL3P, and VL3E. PONDR VL3 used several features from a previously introduced PONDR VL2, but benefitted from optimized predictor models and a slightly larger (152 vs. 145) set of disordered proteins that were cleaned of mislabeling errors found in the smaller set. PONDR VL3H utilized homologues of the disordered proteins in the training stage, while PONDR VL3P used attributes derived from sequence profiles obtained by PSI-BLAST searches. The measure of accuracy was the average between accuracies on disordered and ordered protein regions. By this measure, the 30-fold cross-validation accuracies of VL3, VL3H, and VL3P were, respectively, 83.6 ± 1.4%, 85.3 ± 1.4%, and 85.2 ± 1.5%. By combining VL3H and VL3P, the resulting PONDR VL3E achieved an accuracy of 86.7 ± 1.4%. This is a significant improvement over our previous PONDRs VLXT (71.6 ± 1.3%) and VL2 (80.9 ± 1.4%). The new disorder predictors with the corresponding datasets are freely accessible through the web server at www.ist.temple.edu/disprot.

Original languageEnglish (US)
Pages (from-to)35-60
Number of pages26
JournalJournal of bioinformatics and computational biology
Volume3
Issue number1
DOIs
StatePublished - Feb 2005

Keywords

  • Evolutionary information
  • Intrinsic protein disorder
  • Neural networks
  • PONDR
  • Prediction
  • PSI-BLAST

ASJC Scopus subject areas

  • Medicine(all)
  • Cell Biology

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