Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information

C.M. Buslje, J. Santos, J.M. Delfino, Morten Nielsen

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    Motivation: Mutual information (MI) theory is often applied to predict positional correlations in a multiple sequence alignment (MSA) to make possible the analysis of those positions structurally or functionally important in a given fold or protein family. Accurate identification of coevolving positions in protein sequences is difficult due to the high background signal imposed by phylogeny and noise. Several methods have been proposed using MI to identify coevolving amino acids in protein families. Results: After evaluating two current methods, we demonstrate how the use of sequence-weighting techniques to reduce sequence redundancy and low-count corrections to account for small number of observations in limited size sequence families, can significantly improve the predictability of MI. The evaluation is made on large sets of both in silico-generated alignments as well as on biological sequence data. The methods included in the analysis are the APC (average product correction) and RCW (row-column weighting) methods. The best performing method was APC including sequence-weighting and low-count corrections. The use of sequence-permutations to calculate a MI rescaling is shown to significantly improve the prediction accuracy and allows for direct comparison of information values across protein families. Finally, we demonstrate how a lower bound of 400 sequences
    Original languageEnglish
    JournalBioinformatics
    Volume25
    Issue number9
    Pages (from-to)1125-1131
    ISSN1367-4803
    DOIs
    Publication statusPublished - 2009

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