Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Research article

Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for catalytic residue prediction

Elin Teppa1, Angela D Wilkins2, Morten Nielsen34 and Cristina Marino Buslje1*

Author affiliations

1 Fundación Instituto Leloir, Avda. Patricias Argentinas 435, CABA, C1405BWE, Argentina

2 Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas

3 Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark

4 Instituto de Investigaciones Biotecnológicas, Universidad de San Martín, San Martín, B 1650 HMP, Buenos Aires, Argentina

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2012, 13:235  doi:10.1186/1471-2105-13-235

Published: 14 September 2012

Abstract

Background

A large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to what extent these different methods overlap, and if any of the methods have higher predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content within a multiple sequence alignment to investigate their predictive potential and degree of overlap.

Results

Our results demonstrate that the different methods included in the benchmark in general can be divided into three groups with a limited mutual overlap. One group containing real-value Evolutionary Trace (rvET) methods and conservation, another containing mutual information (MI) methods, and the last containing methods designed explicitly for the identification of specificity determining positions (SDPs): integer-value Evolutionary Trace (ivET), SDPfox, and XDET. In terms of prediction of CR, we find using a proximity score integrating structural information (as the sum of the scores of residues located within a given distance of the residue in question) that only the methods from the first two groups displayed a reliable performance. Next, we investigated to what degree proximity scores for conservation, rvET and cumulative MI (cMI) provide complementary information capable of improving the performance for CR identification. We found that integrating conservation with proximity scores for rvET and cMI achieved the highest performance. The proximity conservation score contained no complementary information when integrated with proximity rvET. Moreover, the signal from rvET provided only a limited gain in predictive performance when integrated with mutual information and conservation proximity scores. Combined, these observations demonstrate that the rvET and cMI scores add complementary information to the prediction system.

Conclusions

This work contributes to the understanding of the different signals of evolution and also shows that it is possible to improve the detection of catalytic residues by integrating structural and higher order sequence evolutionary information with sequence conservation.

Keywords:
Coevolution; Mutual information; Specificity determining position; Catalytic residues; Functional sites; Sequence analysis