广西科学2017，Vol.24Issue(3)：286-291,6.DOI:10.13656/j.cnki.gxkx.20170601.002
基于序列和结构特征的蛋白质自由能预测
Protein Free Energy Prediction based on Sequence and Structure Features
摘要
Abstract
[Objective]Protein free energy not only can accurately reflect the protein interaction,but also can be a great help to drug design and disease treatment. Therefore,it is necessary to establish an accurate regression model of protein free energy.[Methods]In this article,135 proteins complexes were collected and 600 features were calculated. Minimum redundancy maximum relevance algorithm was used to select features which were significantly related to protein free energy and removed redundant features. This was able to obtain the minimum redundancy maximum relevance feature sets. The importance of features was further analyzed by comparing the performance change by removing features. The best model was chosen to predict protein free energy by comparing the result of 10-fold cross validation.[Results]The model had a higher correlation coefficient and lower average absolute error in predicting the performance of 135 pairs of protein complexes compared with other methods.[Conclusion]The experimental results show that our method has better prediction accuracy than other methods.关键词
蛋白质交互/自由能/特征选择/回归模型Key words
protein interaction/free energy/feature selection/regression model分类
信息技术与安全科学引用本文复制引用
鲁帮力,陈庆锋,江家文,罗海琼..基于序列和结构特征的蛋白质自由能预测[J].广西科学,2017,24(3):286-291,6.基金项目
国家自然科学基金项目(61363025)和广西自然科学基金重点项目(2013GXNSFDA019029)资助. （61363025）
