H2rs: Deducing evolutionary and functionally important residue positions by means of an entropy and similarity based analysis of multiple sequence alignments

URN zum Zitieren dieses Dokuments:: urn:nbn:de:bvb:355-epub-305490
DOI zum Zitieren dieses Dokuments:: 10.5283/epub.30549

Janda, Jan-Oliver ; Popal, Ajmal ; Bauer, Jochen ; Busch, Markus ; Klocke, Michael ; Spitzer, Wolfgang ; Keller, Jörg ; Merkl, Rainer

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Veröffentlichungsdatum dieses Volltextes: 04 Aug 2014 09:08

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Background
The identification of functionally important residue positions is an important task of computational biology. Methods of correlation analysis allow for the identification of pairs of residue positions, whose occupancy is mutually dependent due to constraints imposed by protein structure or function. A common measure assessing these dependencies is the mutual information, which is based on Shannon’s information theory that utilizes probabilities only. Consequently, such approaches do not consider the similarity of residue pairs, which may degrade the algorithm’s performance. One typical algorithm is H2r, which characterizes each individual residue position k by the conn(k)-value, which is the number of significantly correlated pairs it belongs to.

Results
To improve specificity of H2r, we developed a revised algorithm, named H2rs, which is based on the von Neumann entropy (vNE). To compute the corresponding mutual information, a matrix A is required, which assesses the similarity of residue pairs. We determined A by deducing substitution frequencies from contacting residue pairs observed in the homologs of 35 809 proteins, whose structure is known. In analogy to H2r, the enhanced algorithm computes a normalized conn(k)-value. Within the framework of H2rs, only statistically significant vNE values were considered. To decide on significance, the algorithm calculates a p-value by performing a randomization test for each individual pair of residue positions. The analysis of a large in silico testbed demonstrated that specificity and precision were higher for H2rs than for H2r and two other methods of correlation analysis. The gain in prediction quality is further confirmed by a detailed assessment of five well-studied enzymes. The outcome of H2rs and of a method that predicts contacting residue positions (PSICOV) overlapped only marginally. H2rs can be downloaded from http://www-bioinf.uni-regensburg.de webcite.

Conclusions
Considering substitution frequencies for residue pairs by means of the von Neumann entropy and a p-value improved the success rate in identifying important residue positions. The integration of proven statistical concepts and normalization allows for an easier comparison of results obtained with different proteins. Comparing the outcome of the local method H2rs and of the global method PSICOV indicates that such methods supplement each other and have different scopes of application.

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