Literature DB >> 18206645

Identification of catalytic residues from protein structure using support vector machine with sequence and structural features.

Ganesan Pugalenthi1, K Krishna Kumar, P N Suganthan, Rajeev Gangal.   

Abstract

Identification of catalytic residues can provide valuable insights into protein function. With the increasing number of protein 3D structures having been solved by X-ray crystallography and NMR techniques, it is highly desirable to develop an efficient method to identify their catalytic sites. In this paper, we present an SVM method for the identification of catalytic residues using sequence and structural features. The algorithm was applied to the 2096 catalytic residues derived from Catalytic Site Atlas database. We obtained overall prediction accuracy of 88.6% from 10-fold cross validation and 95.76% from resubstitution test. Testing on the 254 catalytic residues shows our method can correctly predict all 254 residues. This result suggests the usefulness of our approach for facilitating the identification of catalytic residues from protein structures.

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Year:  2008        PMID: 18206645     DOI: 10.1016/j.bbrc.2008.01.038

Source DB:  PubMed          Journal:  Biochem Biophys Res Commun        ISSN: 0006-291X            Impact factor:   3.575


  13 in total

1.  Structure-based identification of catalytic residues.

Authors:  Ran Yahalom; Dan Reshef; Ayana Wiener; Sagiv Frankel; Nir Kalisman; Boaz Lerner; Chen Keasar
Journal:  Proteins       Date:  2011-04-12

2.  Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction.

Authors:  Katarzyna Prymula; Tomasz Jadczyk; Irena Roterman
Journal:  J Comput Aided Mol Des       Date:  2010-11-21       Impact factor: 3.686

3.  Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach.

Authors:  Khader Shameer; Ganesan Pugalenthi; Krishna Kumar Kandaswamy; Ponnuthurai N Suganthan; Govindaraju Archunan; Ramanathan Sowdhamini
Journal:  Bioinform Biol Insights       Date:  2010-06-17

4.  Prediction of functionally important sites from protein sequences using sparse kernel least squares classifiers.

Authors:  Ke Tang; Ganesan Pugalenthi; P N Suganthan; Christopher J Lanczycki; Saikat Chakrabarti
Journal:  Biochem Biophys Res Commun       Date:  2009-04-24       Impact factor: 3.575

5.  Novel feature for catalytic protein residues reflecting interactions with other residues.

Authors:  Yizhou Li; Gongbing Li; Zhining Wen; Hui Yin; Mei Hu; Jiamin Xiao; Menglong Li
Journal:  PLoS One       Date:  2011-03-29       Impact factor: 3.240

6.  BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection.

Authors:  Krishna Kumar Kandaswamy; Ganesan Pugalenthi; Mehrnaz Khodam Hazrati; Kai-Uwe Kalies; Thomas Martinetz
Journal:  BMC Bioinformatics       Date:  2011-08-17       Impact factor: 3.169

7.  An ensemble method with hybrid features to identify extracellular matrix proteins.

Authors:  Runtao Yang; Chengjin Zhang; Rui Gao; Lina Zhang
Journal:  PLoS One       Date:  2015-02-13       Impact factor: 3.240

8.  ResBoost: characterizing and predicting catalytic residues in enzymes.

Authors:  Ron Alterovitz; Aaron Arvey; Sriram Sankararaman; Carolina Dallett; Yoav Freund; Kimmen Sjölander
Journal:  BMC Bioinformatics       Date:  2009-06-27       Impact factor: 3.169

9.  Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble.

Authors:  Ganesan Pugalenthi; Ke Tang; P N Suganthan; Saikat Chakrabarti
Journal:  Bioinformatics       Date:  2008-11-27       Impact factor: 6.937

10.  Protein meta-functional signatures from combining sequence, structure, evolution, and amino acid property information.

Authors:  Kai Wang; Jeremy A Horst; Gong Cheng; David C Nickle; Ram Samudrala
Journal:  PLoS Comput Biol       Date:  2008-09-26       Impact factor: 4.475

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