Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

Bioinformatics. 2006 Jan 15;22(2):164-71. doi: 10.1093/bioinformatics/bti766. Epub 2005 Nov 8.


Motivation: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results.

Results: We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein alpha subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures.

Availability: SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Binding Sites
  • Computer Simulation
  • Conserved Sequence
  • Likelihood Functions
  • Models, Biological
  • Models, Chemical*
  • Models, Molecular*
  • Models, Statistical
  • Protein Binding
  • Protein Conformation
  • Proteins / chemistry*
  • Proteins / classification*
  • Proteins / metabolism
  • Sensitivity and Specificity
  • Sequence Alignment / methods*
  • Sequence Analysis, Protein / methods*
  • Sequence Homology, Amino Acid
  • Software


  • Proteins