Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.

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 <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.