Support Vector Machine for Discrimination of Thermophilic and Mesophilic Proteins Based on Amino Acid Composition

Title: Support Vector Machine for Discrimination of Thermophilic and Mesophilic Proteins Based on Amino Acid Composition

Volume: 13 Issue: 10

Author(s): Guangya Zhang and Baishan Fang

Affiliation:

Keywords: Support vector machines, Discrimination, Protein thermostability, Computational screening

Abstract: The identification of the thermostability from the amino acid sequence information would be helpful in computational screening for thermostable proteins. We have developed a method to discriminate thermophilic and mesophilic proteins based on support vector machines. Using self-consistency validation, 5-fold cross-validation and independent testing procedure with other datasets, this module achieved overall accuracy of 94.2%, 90.5% and 92.4%, respectively. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees, when evaluated using these three validation methods. The influence of protein size on prediction accuracy was also addressed.

Cite this article as:

Zhang Guangya and Fang Baishan, Support Vector Machine for Discrimination of Thermophilic and Mesophilic Proteins Based on Amino Acid Composition, Protein & Peptide Letters 2006; 13 (10) . https://dx.doi.org/10.2174/092986606778777560