期刊检索

  • 2019年第17卷
  • 2018年第16卷
  • 2017年第15卷
  • 2016年第14卷
  • 2015年第13卷
  • 2014年第12卷
  • 2013年第11卷
  • 第1期
  • 第2期

主管单位 工业和信息化部 主办单位 哈尔滨工业大学 主编 任南琪 国际刊号ISSN 1672-5565 国内刊号CN 23-1513/Q

期刊网站二维码
微信公众号二维码
引用本文:朱树平,刘毅慧.蛋白质二级结构在线服务器预测评估[J].生物信息学,2019,17(1):53-60.
ZHU Shuping,LIU Yihui.Protein secondary structure online server predictive evaluation[J].Chinese Journal of Bioinformatics,2019,17(1):53-60.
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  下载PDF阅读器  关闭
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 341次   下载 298 本文二维码信息
码上扫一扫!
分享到: 微信 更多
蛋白质二级结构在线服务器预测评估
朱树平,刘毅慧
(齐鲁工业大学(山东省科学院) 计算机科学与技术学院,济南 250353)
摘要:
蛋白质二级结构的预测,对于研究蛋白质的功能和人类生命科学意义非凡。1951年开始提出预测蛋白质二级结构,1983年对于二级结构的预测只有50%的准确率。经过多年的发展,预测方式不断的改进和完善,到如今准确率已经超过80%。但目前预测在线服务器繁多,连续自动模型评估(CAMEO)也只给出服务器三级结构的预测评估,二级结构评估还未实现。针对上述问题,选取了以下6个服务器:PSRSM、MUFOLD、SPIDER、RAPTORX、JPRED和PSIPRED,对其预测的二级结构进行评估。并且为保证测试集不在训练集内,实验数据选取蛋白质结构数据库(Protein Data Bank,PDB)最新发布的蛋白质。在基于蛋白质同源性30%、50%和70%的实验中,PSRSM取得Q3的准确率分别为91.44%、88.12%和90.17%,比其他预测服务器中最高的MUFOLD分别高出3.19%、1.33%和2.19%,证明在同一类同源性数据中PSRSM比其他服务器有更好的预测效果。除此之外实验也得到其预测的Sov准确度也比其他服务器要高。比较各类服务器的方法与结果,得出今后蛋白质二级结构预测应当重点从大数据、模板和深度学习的角度进行研究。
关键词:  蛋白质二级结构  预测  在线服务器  准确率  评估
DOI:10.12113/j.issn.1672-5565.201808002
分类号:Q518.1
文献标识码:A
基金项目:
Protein secondary structure online server predictive evaluation
ZHU Shuping, LIU Yihui
(School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)
Abstract:
The prediction of protein secondary structure is of great significance for studying the function of proteins and human life sciences. The prediction of protein secondary structure was put forward in 1951, but the accuracy rate was only 50% in 1983. During years of development, the prediction method has been continuously optimized, and the accuracy rate has already exceeded 80%. However, there are many online servers, and Continuous Automate Model EvaluatiOn (CAMEO) can only provide predictive evaluation of the servers three-level structure, while the secondary structure evaluation has not been realized. Aiming to solve the above problems, PSRSM, MUFOLD, SPIDER, RAPTORX, JPRED, and PSIPRED were selected to evaluate their predicted secondary structure. The latest released protein from the Protein Data Bank (PDB) was applied to ensure that the test set is not included in the training set. In the experiments where the protein homology was 30%, 50% and 70%, the obtained accuracy of PSRSM for Q3 were 91.44%, 88.12%, and 90.17%, respectively. The accuracy was higher than the best prediction server MUFOLD by 3.19%, 1.33%, and 2.19% correspondingly, which proved that PSRSM has better prediction accuracy than other servers for the same kind of homology data and for the Sov.This paper focuses on analyzing the operating methods and corresponding results of various servers, thus it is concluded that the prediction of protein secondary structure should be studied from the perspectives of big data, templates, and in-depth learning.
Key words:  Protein secondary structure  Prediction  Online server  Accuracy  Evaluation

友情链接LINKS