Sequence Conservation in the Prediction of Catalytic Sites

Abstract

Predicting catalytic sites of a given enzyme is an important open problem of Bioinformatics. Recently, many machine learning-based methods have been developed which have the advantage that they can account for many sequential or structural features. We found that although many kinds of features are incorporated, protein sequence conservation is the main part of information they used and should play an important role in the future. So we tested several conservation features in their ability to predict catalytic sites by using the Support Vector Machine classifier. Our results suggest that position specific scoring matrix performs better than other features and incorporating conservation information of sequentially adjacent sites is more effective than that of structurally adjacent ones. Moreover, although conservation information is effective in predicting catalytic sites, it is a difficult problem to optimize the combination of conservation features and other ones.

iCataly-PseAAC: Identification of Enzymes Catalytic Sites Using Sequence Evolution Information with Grey Model GM (2,1)

“…The knowledge of these residues would be very helpful to understand enzyme function because they are closely related to the function of the enzyme as well as its specificity and molecular mechanisms. The catalytic residues are more conserved than other residues during evolution (Dou et al 2011). Various biochemical experiments can identify the catalytic active sites.…”

“…Protein Data Bank (PDB) also provides the catalytic sites annotation for enzymes (Berman et al 2002;Berman et al 2000). The sequence information (Dou et al 2011;Ota et al 2003;Zhang et al 2008;Dou et al 2010;Chea and Livesay 2007;Fischer et al 2008)and the structure information (Bartlett et al 2002;Torrance et al 2005;Zvelebil and Sternberg 1988) have been used in predicting catalytic sites, respectively, or together. Chien et al developed a structure-based method based on residue side chain orientations and backbone flexibility of enzyme structure (2012).…”

CLIPS-1D: analysis of multiple sequence alignments to deduce for residue-positions a role in catalysis, ligand-binding, or protein structure

“…Additionally, some signals seem to be more pronounced in sequence- than in 3D-space [19]. Commonly, these methods depend on a multiple sequence alignment (MSA) composed of a sufficiently large number of homologs.…”

“…A strong argument in favor of sequence-based methods is their broad applicability and their potential to characterize proteins with a novel fold. Additionally, some signals seem to be more pronounced in sequence- than in 3D-space [ 19 ]. Commonly, these methods depend on a multiple sequence alignment (MSA) composed of a sufficiently large number of homologs.…”