Automatic classification of highly related Malate Dehydrogenase and L-Lactate Dehydrogenase based on 3D-pattern of active sites

Abstract

Accurate protein function prediction is an important subject in bioinformatics, especially wheresequentially and structurally similar proteins have different functions. Malate dehydrogenaseand L-lactate dehydrogenase are two evolutionary related enzymes, which exist in a widevariety of organisms. These enzymes are sequentially and structurally similar and sharecommon active site residues, spatial patterns and molecular mechanisms. Here, we studyvarious features of the active site cavity of 229 PDB chain entries and try to classify themautomatically by various classifiers including the support vector machine, k nearest neighbourand random forest methods. The results show that the support vector machine yields the highestpredictive performance among mentioned classifiers. Despite very close and conserved patternsamong Malate dehydrogenases and L-lactate dehydrogenases, the SVM predicts the functionefficiently and achieves 0.973 Matthew’s correlation coefficient and 0.987 F-score. The sameapproach can be used in other enzyme families for automatic discrimination betweenhomologous enzymes with common active site elements, however, acting on differentsubstrates.