Predicting protein stability changes from sequences using support vector machines
Predicting protein stability changes from sequences using support vector machines
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DOI：
10.1093/bioinformatics/bti1109
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发表时间：
2005-09-01
期刊：
影响因子：
5.8
通讯作者：
Casadio, R
中科院分区：
文献类型：
--
作者：
Capriotti, E;Fariselli, P;Casadio, R
动机：预测突变后蛋白质稳定性的变化是理解蛋白质折叠和错误折叠的关键。目前，只有当蛋白质的原子结构可用时，才有方法预测稳定性的变化。结果：我们提出了一种基于支持向量机的方法，该方法从蛋白质序列出发，预测单点突变时自由能稳定性变化的符号和数值。在预测与相应蛋白质稳定性相关的Delta Delta G符号的具体任务中，我们的预测器的准确率高达77%。当预测Delta G值时，也发现与实验数据有令人满意的关联。作为最终的盲基准，该预测器被应用于具有一组与疾病相关的SNPs的蛋白质，对于这些SNP，热力学数据也是已知的。我们发现，我们的预测证实了与疾病相关的突变对应于蛋白质稳定性下降的观点。可用性：http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant2.0/I-Mutant2.0.cgi.
Motivation: The prediction of protein stability change upon mutations is key to understanding protein folding and misfolding. At present, methods are available to predict stability changes only when the atomic structure of the protein is available. Methods addressing the same task starting from the protein sequence are, however, necessary in order to complete genome annotation, especially in relation to single nucleotide polymorphisms (SNPs) and related diseases.Results: We develop a method based on support vector machines that, starting from the protein sequence, predicts the sign and the value of free energy stability change upon single point mutation. We show that the accuracy of our predictor is as high as 77% in the specific task of predicting the Delta Delta G sign related to the corresponding protein stability. When predicting the Delta Delta G values, a satisfactory correlation agreement with the experimental data is also found. As a final blind benchmark, the predictor is applied to proteins with a set of disease-related SNPs, for which thermodynamic data are also known. We found that our predictions corroborate the view that disease-related mutations correspond to a decrease in protein stability.Availability: http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant2.0/I-Mutant2.0.cgi.