title: Knowledge base and neural network approach for protein secondary structure prediction.
abstract: Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods.
authors.affiliation: Department of Computer Engineering, G H Patel College of Engineering & Technology, Vallabh Vidyanagar, Gujarat, India. Electronic address: maulika.sandip@gmail.com.
authors.forename: Maulika S
authors.initials: MS
authors.lastname: Patel
authors.affiliation: Research & Development Center, Dharmsinh Desai University, Nadiad, Gujarat, India. Electronic address: hsmazumdar@hotmail.com.
authors.forename: Himanshu S
authors.initials: HS
authors.lastname: Mazumdar
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein / Q000379:methods*
keywords: 5-Residue words; Backpropogation neural network; Hierarchical validation; Knowledge base; Lateral association and validation