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.
J Microbiol Methods. 2011 Jan;84(1):67-70. doi: 10.1016/j.mimet.2010.10.013. Epub 2010 Oct 31.
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.
蛋白质的热稳定性对于酶工程尤为重要。开发一种能够识别中温蛋白的计算方法将有助于蛋白质工程和设计。在这项工作中，我们开发了基于支持向量机的方法，利用氨基酸分布和选择的氨基酸对信息来预测嗜热蛋白。为了训练和测试所提出的模型，构建了一个可靠的基准数据集，其中包含 915 个嗜热蛋白和 793 个非嗜热蛋白。结果表明，使用交叉验证法可以正确预测 93.8%的嗜热蛋白和 92.7%的非嗜热蛋白。高预测成功率表明，该模型可用于设计稳定的蛋白质。
