Bioinformatics Advance Access published online on December 6, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti810
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1 School of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India
* To whom correspondence should be addressed.
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 E. 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 SVM-based classifier to predict the over-expression status in Escherichia coli. The prediction accuracy is Availability: On request from the authors. Supplementary Information: Available at the journal's web site.
Received April 30, 2005
Revised November 26, 2005
Accepted December 1, 2005
Article
A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on over-expression in Escherichia coli
Susan Idicula-Thomas 1 #,
Abhijit J. Kulkarni 2 #,
Bhaskar D. Kulkarni 2,
Valadi K. Jayaraman 2,
and
Petety V. Balaji 1 *
2 Chemical Engineering and Process Development Division, National Chemical Laboratory, Dr. Homi Bhabha Road, Pune 411 008, India
Petety V. Balaji, E-mail: balaji{at}iitb.ac.in
Abstract 
72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or 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.
#These authors have contributed equally to this work.
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