Identifies thermophilic proteins based on the sequence information. ThermoPred is a web server and a support vector machine (SVM) based method to predict thermophilic proteins using the information of amino acid distribution and selected amino acid pairs. High predictive successful rate exhibits that this model can be applied for designing stable proteins. The 93.8% thermophilic proteins and 92.7% non-thermophilic proteins can be correctly predicted by use of jack-knife cross-validation.
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Department of Physics, School of Basic Medical Sciences, Hebei United University, Tangshan, China
ThermoPred funding source(s)
Supported by the Fundamental Research Funds for the Central Universities (ZYGX2009J081).