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Title

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

 

Authors

Seethalakshmi Sakthivel, Habeeb S.K.M*

 

Affiliation

Department of Bioinformatics, School of Bioengineering, Faculty of Engineering & Technology, Kattankulathur Campus, SRM University, Potheri 603203, Tamil Nadu, India

 

Email

habeeb_skm@yahoo.co.in; *Corresponding author

 

Article Type

Webserver

 

Date

Received July 07, 2015; Accepted July 26, 2015; Published August 31, 2015

 

Abstract

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 Q3 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%.

 

Availability

http://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl

Keywords

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

Citation

Sakthivel & Habeeb, Bioinformation 11(8): 416-421 (2015)
 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.