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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
ABSTRACT - Internet Electron. J. Mol. Des. June 2003, Volume 2, Number 6, 392-402

Support Vector Machines for Predicting Protein Homo-Oligomers by Incorporating Pseudo-Amino Acid Composition
Shao-Wu Zhang, Quan Pan, Hong-Cai Zhang, Yong-Hong Wu, and Jian-Yu Shi
Internet Electron. J. Mol. Des. 2003, 2, 392-402

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Abstract:
Following the success of human genome project, the gap between sharply increasing the number of protein sequences entering into data bank and slow accumulation of know structure is becoming large. Developing a fast and accurate method to predict the protein properties based on the primary sequences becomes indispensable. In general, the performance of the predictive system can be improved by selecting appropriate algorithm and the fitting method of extracting feature. Thus a new method of extracting feature (the weighting pseudo-amino acid composition) from the sequences has been introduced to predict the protein homo-oligomers, which is a combination of a set of weighting discrete sequence correlation factors computed with the amino acid index profile and the 20 components of the conventional amino acid composition. We extract four attribute parameter datasets (COMP, PLIV, FAUJ and MAXF) from the primary sequences as examples to investigate this problem. The COMP attribute dataset is composed of amino acid composition, and the PLIV, FAUJ and MAXF attribute datasets are composed of the amino acid composition and a set of weighting discrete sequence correlation factors of corresponding amino acid residue index. The total accuracies of PLIV, FAUJ and MAXF using support vector machines (SVM) algorithm are 80.36%, 79.34% and 79.02% respectively in 10 fold cross-validation (10CV) test, which are 4.59%, 3.57% and 3.25% respectively higher than that of COMP. Based on the same COMP and PLIV attribute datasets, the total accuracies of SVM are 33.87% and 18.05% respectively higher than that of covariant discriminant algorithm in the jackknife test. These results show that the method of extracting feature from the protein sequences is effective and feasible for predicting homo-oligomers, and implies that the primary sequences of homo-oligomeric proteins contain quaternary structure information, and also indicates that the performance of SVM is superior to the covariant discriminant algorithm for classifying protein homo-oligomers.

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