Application of multiple sequence alignment profiles to improve protein secondary structure prediction

James A. Cuff, Geoffrey J. Barton (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

670 Citations (Scopus)


The effect of training a neural network secondary structure prediction algorithm with different types of multiple sequence alignment profiles derived from the same sequences, is shown to provide a range of accuracy from 70.5% to 76.4%. The best accuracy of 76.4% (standard deviation 8.4%), is 3.1% (Q(3)) and 4.4% (SOV2) better than the PHD algorithm run on the same set of 406 sequence non-redundant proteins that were not used to train either method. Residues predicted by the new method with a confidence value of 5 or greater, have an average Q(3) accuracy of 84%, and cover 68% of the residues. Relative solvent accessibility based on a two state model, for 25, 5, and 0% accessibility are predicted at 76.2, 79.8, and 86. 6% accuracy respectively. The source of the improvements obtained from training with different representations of the same alignment data are described in detail. The new Jnet prediction method resulting from this study is available in the Jpred secondary structure prediction server, and as a stand-alone computer program from: Proteins 2000;40:502-511.

Original languageEnglish
Pages (from-to)502-511
Number of pages10
JournalProteins: Structure, Function, and Bioinformatics
Issue number3
Early online date14 Jun 2000
Publication statusPublished - 15 Aug 2000


  • Algorithms
  • Amino acid sequence
  • Databases, Factual
  • Molecular sequence data
  • Neural networks (Computer)
  • Protein structure, Secondary
  • Reproducibility of Results
  • Sequence alignment
  • Sequence analysis, Protein
  • Software
  • Solvents


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