title: NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation.
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%.
  
  
  BACKGROUND
  http://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl.
authors.affiliation: Department of Bioinformatics, School of Bioengineering, Faculty of Engineering & Technology, Kattankulathur Campus, SRM University, Potheri - 603203, Tamil Nadu, India.
authors.forename: Seethalakshmi
authors.initials: S
authors.lastname: Sakthivel
authors.affiliation: Department of Bioinformatics, School of Bioengineering, Faculty of Engineering & Technology, Kattankulathur Campus, SRM University, Potheri - 603203, Tamil Nadu, India.
authors.forename: Habeeb
authors.initials: H
authors.lastname: S K M
keywords: automatic validation; neural network; online server; protein secondary structure