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

Abstract

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 <62% identical is needed in an MSA in order to achieve meaningful predictive performances. With our contribution, we achieve a noteworthy improvement on the current procedures to determine coevolution and residue contacts, and we believe that this will have potential impacts on the understanding of protein structure, function and folding.