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

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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.





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

Document Type: Research Article

DOI: http://dx.doi.org/10.2174/092986606778777560

Affiliations: Institute of Industrial Biotechnology,Huaqiao University, Quanzhou 362021, Fujiang, PR China.

Publication date: October 1, 2006

More about this publication?
  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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