Prediction of thermophilic proteins using feature selection technique.
The thermostability of proteins is particularly relevant for enzyme engineering. Developing a computational method to identify mesophilic proteins would be helpful for protein engineering and design. In this work, we developed support vector machine based method to predict thermophilic proteins using the information of amino acid distribution and selected amino acid pairs. A reliable benchmark dataset including 915 thermophilic proteins and 793 non-thermophilic proteins was constructed for training and testing the proposed models. Results showed that 93.8% thermophilic proteins and 92.7% non-thermophilic proteins could be correctly predicted by using jackknife cross-validation. High predictive successful rate exhibits that this model can be applied for designing stable proteins.
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China. hlin@uestc.edu.cn
Hao
H
Lin
Wei
W
Chen
D000465:Algorithms; D003198:Computer Simulation; D006358:Hot Temperature; D015202:Protein Engineering / Q000379:methods*; D055550:Protein Stability; D011506:Proteins / Q000737:chemistry*
D011506:Proteins