NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation

被引：4

作者：

Sakthivel, Seethalakshmi ^{[1
]}

Habeeb, S. K. M. ^{[1
]}

机构：

[1] SRM Univ, Fac Engn & Technol, Dept Bioinformat, Sch Bioengn, Kattankulathur Campus, Potheri 603203, Tamil Nadu, India

来源：

BIOINFORMATION | 2015年 / 11卷 / 08期

关键词：

protein secondary structure; neural network; automatic validation; online server;

D O I：

10.6026/97320630011416

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The predicted secondary structural states are not cross validated by any of the existing servers. Hence, information on the level of accuracy for every sequence is not reported by the existing servers. This was overcome by NNvPDB, which not only reported greater Q(3) but also validates every prediction with the homologous PDB entries. NNvPDB is based on the concept of Neural Network, with a new and different approach of training the network every time with five PDB structures that are similar to query sequence. The average accuracy for helix is 76%, beta sheet is 71% and overall (helix, sheet and coil) is 66%.

引用

页码：416 / 421

页数：6

共 15 条

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