Detection of unrelated proteins in sequences multiple alignments by using predicted secondary structures

Bioinformatics. 2003 Mar 1;19(4):506-12. doi: 10.1093/bioinformatics/btg016.

Abstract

Motivation: Multiple sequence alignments are essential tools for establishing the homology relations between proteins. Essential amino acids for the function and/or the structure are generally conserved, thus providing key arguments to help in protein characterization. However for distant proteins, it is more difficult to establish, in a reliable way, the homology relations that may exist between them. In this article, we show that secondary structure prediction is a valuable way to validate protein families at low identity rate.

Results: We show that the analysis of the secondary structures compatibility is a reliable way to discard non-related proteins in low identity multiple alignment.

Availability: This validation is possible through our NPS@ server (http://npsa-pbil.ibcp.fr)

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Base Sequence
  • Databases, Protein*
  • Protein Conformation
  • Protein Structure, Secondary
  • Proteins / analysis*
  • Proteins / chemistry*
  • Proteins / classification
  • Sequence Alignment / methods*
  • Sequence Analysis, Protein / methods*
  • Sequence Homology, Amino Acid
  • Serine Proteinase Inhibitors / analysis
  • Serine Proteinase Inhibitors / chemistry

Substances

  • Proteins
  • Serine Proteinase Inhibitors