 Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue IdentificationCristina Marino Buslje, Elin Teppa, Tomas Di Doménico, José María Delfino and Morten Nielsen PLOS Computational Biology, 2010, vol. 6, issue 11, 1-8 Abstract: 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.Author Summary: Enzymes are responsible for several critical cellular functions. The so-called catalytic residues are fundamental to attain the enzyme function. Those residues are often highly conserved within protein families sharing similar structure and function. Characterization of catalytic residues is essential for the understanding of enzyme function. However, this is a difficult task because conservation is a poor discriminator of catalytic residues due to the fact that many non-catalytic residues are highly conserved in a given protein family. We anticipate that variations in the structural environment of a catalytic site should be highly restrained in order for the protein to maintain its function along the course of evolution, and hypothesise that catalytic residues, due to these restrains, must carry a particular signature defined by networks of proximity sharing high mutual information (MI). We validated this hypothesis on a large data set of protein sequences with known catalytic residues, and demonstrated that catalytic sites are indeed surrounded by networks of coevolved residues. Such networks should also be present in other classes of proteins and we suggest that MI networks could be a novel feature of general importance beneficial for the prediction of functional residues. Date: 2010 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1000978 DOI: 10.1371/journal.pcbi.1000978 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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