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

SeeHaBITaT: A server on bioinformatics applications for Tospoviruses and other species

“…Two mostly employed methods of secondary structure prediction Chou Fasman algorithm (CFvPDB) ( Ashok, 2013 ) and Neural Networks ( Qian and Sejnowski, 1988 ) (NNvPDB) ( Sakthivel, 2015 ) based secondary structure prediction can be done at SS with PDB. The most distinguishing part of these two applications is automated validation of the predictions made by these applications with experimentally solved structures in PDB is done.…”

Application of Bioinformatics on Protein Structure Prediction

“…The average accuracy for helix is 76%, beta sheet is 71% and overall (helix, sheet and coil) is 66% (Sakthivel et al,2015).…”