Abstract
Enzymes play a fundamental role in almost all biological processes and identification of catalytic residues is a crucial step for deciphering the biological functions and understanding the underlying catalytic mechanisms. In this work, we developed a novel structural feature called MEDscore to identify catalytic residues, which integrated the microenvironment (ME) and geometrical properties of amino acid residues. Firstly, we converted a residue's ME into a series of spatially neighboring residue pairs, whose likelihood of being located in a catalytic ME was deduced from a benchmark enzyme dataset. We then calculated an ME-based score, termed as MEscore, by summing up the likelihood of all residue pairs. Secondly, we defined a parameter called Dscore to measure the relative distance of a residue to the center of the protein, provided that catalytic residues are typically located in the center of the protein structure. Finally, we defined the MEDscore feature based on an effective nonlinear integration of MEscore and Dscore. When evaluated on a well-prepared benchmark dataset using five-fold cross-validation tests, MEDscore achieved a robust performance in identifying catalytic residues with an AUC1.0 of 0.889. At a ≤10% false positive rate control, MEDscore correctly identified approximately 70% of the catalytic residues. Remarkably, MEDscore achieved a competitive performance compared with the residue conservation score (e.g. CONscore), the most informative singular feature predominantly employed to identify catalytic residues. To the best of our knowledge, MEDscore is the first singular structural feature exhibiting such an advantage. More importantly, we found that MEDscore is complementary with CONscore and a significantly improved performance can be achieved by combining CONscore with MEDscore in a linear manner. As an implementation of this work, MEDscore has been made freely accessible at http://protein.cau.edu.cn/mepi/.
Highlights
Enzymes play a fundamental role in fulfilling diverse biochemical functions and are essentially required for almost all cellular processes
In this work we develop a novel promising structural feature termed as MEDscore for the identification of catalytic residues
It allows the ME of a residue to be converted into a series of spatially neighboring residue pairs such that the likelihood of belonging to the catalytic ME could be deduced from a pre-existing enzyme dataset
Summary
Enzymes play a fundamental role in fulfilling diverse biochemical functions and are essentially required for almost all cellular processes. Owing to structural genomics efforts [2,3], a considerable number of protein structures have been determined. It is still a challenging task to establish the linkage between the given protein structures and their catalytic mechanisms, reflected by the vast number of functionally uncharacterized enzyme structures generated from the structural genomics projects [4]. As catalytic residues are directly involved in catalytic processes, their identification is the first crucial step to characterize the catalytic mechanism and function of an enzyme. Since experimental determination of catalytic residues from large-scale proteome data is a costly and daunting task, computational methods that are capable of identifying catalytic residues from enzyme sequence and/or structure information play an increasingly important role in complementing the experimental efforts and supporting the functional annotation. Apart from providing critical insights into the rules that govern enzymatic catalysis, the identification of catalytic residues has important applications in the areas of drug design [5], protein engineering, metabolic pathway analysis and synthetic biology [6]
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