Dataset Information

CARd: Carbon distribution analysis program for protein sequences.

ABSTRACT: Carbon distribution is responsible for stability and structure of proteins. Arrangement of carbon along the protein sequence is depends on how the amino acids are organized and is guided by mRNAs. An atomic level revision is important for understanding these codes. This will ultimately help in identification of disorders and suggest mutations. For this purpose a carbon distribution analysis program has been developed. This program captures the hydrophobic / hydrophilic / disordered regions in a protein. The program gives accurate results. The calculations are precise and sensitive to single amino acid resolution. This program is to help in mutational studies leading to protein stabilisation.

SUBMITTER: Rajasekaran E

PROVIDER: S-EPMC3398768 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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CARd: Carbon distribution analysis program for protein sequences.

Rajasekaran Ekambaram E

Bioinformation 20120616 11

Carbon distribution is responsible for stability and structure of proteins. Arrangement of carbon along the protein sequence is depends on how the amino acids are organized and is guided by mRNAs. An atomic level revision is important for understanding these codes. This will ultimately help in identification of disorders and suggest mutations. For this purpose a carbon distribution analysis program has been developed. This program captures the hydrophobic / hydrophilic / disordered regions in a ...[more]

PMID: 22829720

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Project description:BACKGROUND: Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods. RESULTS: This study proposes a novel scoring card method (SCM) by using dipeptide composition only to estimate solubility scores of sequences for predicting protein solubility. SCM calculates the propensities of 400 individual dipeptides to be soluble using statistic discrimination between soluble and insoluble proteins of a training data set. Consequently, the propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The solubility score of a sequence is determined by the weighted sum of all propensity scores and dipeptide composition. To evaluate SCM by performance comparisons, four data sets with different sizes and variation degrees of experimental conditions were used. The results show that the simple method SCM with interpretable propensities of dipeptides has promising performance, compared with existing SVM-based ensemble methods with a number of feature types. Furthermore, the propensities of dipeptides and solubility scores of sequences can provide insights to protein solubility. For example, the analysis of dipeptide scores shows high propensity of ?-helix structure and thermophilic proteins to be soluble. CONCLUSIONS: The propensities of individual dipeptides to be soluble are varied for proteins under altered experimental conditions. For accurately predicting protein solubility using SCM, it is better to customize the score card of dipeptide propensities by using a training data set under the same specified experimental conditions. The proposed method SCM with solubility scores and dipeptide propensities can be easily applied to the protein function prediction problems that dipeptide composition features play an important role. AVAILABILITY: The used datasets, source codes of SCM, and supplementary files are available at http://iclab.life.nctu.edu.tw/SCM/.

Dataset Information

CARd: Carbon distribution analysis program for protein sequences.

Publications

CARd: Carbon distribution analysis program for protein sequences.

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