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.
abstract: BACKGROUND
  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 approximately 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.
authors.affiliation: School of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India.
authors.forename: Susan
authors.initials: S
authors.lastname: Idicula-Thomas
authors.forename: Abhijit J
authors.initials: AJ
authors.lastname: Kulkarni
authors.forename: Bhaskar D
authors.initials: BD
authors.lastname: Kulkarni
authors.forename: Valadi K
authors.initials: VK
authors.lastname: Jayaraman
authors.forename: Petety V
authors.initials: PV
authors.lastname: Balaji
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D030562:Databases, Protein; D004926:Escherichia coli / Q000737:chemistry* / Q000378:metabolism*; D029968:Escherichia coli Proteins / Q000737:chemistry* / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods; D015964:Gene Expression Regulation, Bacterial / Q000502:physiology; D002479:Inclusion Bodies / Q000737:chemistry* / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012995:Solubility
chemical_list: D029968:Escherichia coli Proteins