基于氨基酸组成预测蛋白质热稳定性的v-支持向量机方法(英文)  

Prediction of protein thermostability from amino acid composition with v-support vector machines

作　　者：丁彦蕊[1,2] 蔡宇杰[1,2] 须文波[3] 

机构地区：[1]江南大学生物工程学院生物信息学研究室,江苏无锡214036 [2]江南大学工业生物技术教育部重点实验室 [3]江南大学信息工程学院,江苏无锡214036

出　　处：《计算机与应用化学》2005年第6期459-465,共7页Computers and Applied Chemistry

摘　　要：支持向量机有许多优点:有效防止过拟和,适合大的特征空间,给定数据集的信息压缩。本文首次利用支持向量机从氨基酸组成来预测蛋白质的稳定性。总预测率可以达到80.64%,对嗜热蛋白质的预测率为82.50%,对嗜温蛋白质的预测率为80.29%从预测率可以验证氨基酸组成与蛋白质热稳定性成正相关的关系,支持向量机可以成为基于氨基酸组成预测蛋白质热稳定性的有效工具。Support vector machines have many attractive features, such as effective avoidance of overfitting, the ability to handle large feature spaces, information condensing of the given data set. We firstly use ν-support vector machines to predict protein thermostability from amino acid composition. The total prediction accuracy reaches 80. 64% , the prediction accuracy of thermophilic proteins is 82. 50% , and the prediction accuracy of mesophilic proteins is 80. 29% . From the prediction accuracy, we can conclude that amino acid composition is correlative significantly to protein thermostability, and we regard support vector machines would become a powerful tool for predicting protein thermostability from amino acid composition.

关 键 词：ν-支持向量机 蛋白质热稳定性 嗜热蛋白质 嗜温蛋白质 

分 类 号：Q51[生物学—生物化学]