Why do Sequence Signatures Predict Enzyme Mechanism? Homology versus Chemistry

Abstract

First, we identify InterPro sequence signatures representing evolutionary relatedness and, second, signatures identifying specific chemical machinery. Thus, we predict the chemical mechanisms of enzyme-catalyzed reactions from catalytic and non-catalytic subsets of InterPro signatures. We first scanned our 249 sequences using InterProScan and then used the MACiE database to identify those amino acid residues that are important for catalysis. The sequences were mutated in silico to replace these catalytic residues with glycine and then again scanned using InterProScan. Those signature matches from the original scan that disappeared on mutation were called catalytic. Mechanism was predicted using all signatures, only the 78 “catalytic” signatures, or only the 519 “non-catalytic” signatures. The non-catalytic signatures gave indistinguishable results from those for the whole feature set, with precision of 0.991 and sensitivity of 0.970. The catalytic signatures alone gave less impressive predictivity, with precision and sensitivity of 0.791 and 0.735, respectively. These results show that our successful prediction of enzyme mechanism is mostly by homology rather than by identifying catalytic machinery.

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

“…For example, De Ferrari et al obtained 98% prediction accuracy for enzyme function, based on transferring annotations from a query sequence's nearest neighbor of known function (De Ferrari et al, 2012 ). We have recently demonstrated that the majority of the predictive power of such sequence signature-based methods comes from the wealth of evolutionary information contained in the whole sequence, and only a small part of the predictive ability emanates from the many fewer functionally essential conserved residues (Beattie et al, 2015 ). In the context of polypharmacology and off-target interactions, structure-based methods of protein function prediction (Laskowski et al, 2005 ; Pal and Eisenberg, 2005 ; Cuff et al, 2011 ) are also highly relevant.…”

Enzyme function and its evolution

“…The difficulty of function prediction for a particular protein varies greatly, depending on the available sequence and structure information and on the identification of homologues, the available methods being based on one or both of sequence and structure [29,30,31]. The majority of the predictive load is usually carried by sequence [32,33]. Prediction of protein function on a large scale remains a significant challenge.…”

In-Silico Characterization of Glycosyl Hydrolase Family 1 β-Glucosidase from Trichoderma asperellum UPM1

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