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A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli

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Title A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli
 
Creator IDICULA-THOMAS, S
KULKARNI, AJ
JAYARAMAN, VK
BALAJI, PV
 
Subject recombinant proteins
structural genomics
eukaryotic proteins
directed evolution
catalytic domain
fold recognition
expression data
solubility
classification
mutations
 
Description Motivation: Inclusion body formation has been a major deterrent for overexpression studies since a large number of proteins form insoluble inclusion bodies when overexpressed in Escherichia coli. The formation of inclusion bodies is known to be an outcome of improper protein folding; thus the composition and arrangement of amino acids in the proteins would be a major influencing factor in deciding its aggregation propensity. There is a significant need for a prediction algorithm that would enable the rational identification of both mutants and also the ideal protein candidates for mutations that would confer higher solubility-on-overexpression instead of the presently used trial-and-error procedures. Results: Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli. The prediction accuracy is similar to 72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies. The algorithm could also correctly predict the change in solubility for most of the point mutations reported in literature. This algorithm can be a useful tool in screening protein libraries to identify soluble variants of proteins.
 
Publisher OXFORD UNIV PRESS
 
Date 2011-08-19T11:44:19Z
2011-12-26T12:56:13Z
2011-12-27T05:44:35Z
2011-08-19T11:44:19Z
2011-12-26T12:56:13Z
2011-12-27T05:44:35Z
2006
 
Type Article
 
Identifier BIOINFORMATICS, 22(3), 278-284
1367-4803
http://dx.doi.org/10.1093/bioinformatics/bti810
http://dspace.library.iitb.ac.in/xmlui/handle/10054/10340
http://hdl.handle.net/10054/10340
 
Language en