An Improved Statistical Model for Protein Secondary Structure Prognostication
Aswathy. Ravikumar1, Saritha. R2
1Aswathy.Ravikumar, Department of Computer Science and Engineering, College of Engineering, Trivandrum, Kerala, India.
2Saritha.R, Department of Computer Science and Engineering, College of Engineering, Trivandrum, Kerala, India
Manuscript received on Novmber 08, 2012. | Revised Manuscript Received on November 20, 2012. | Manuscript published on November 25, 2012. | PP: 6-9 | Volume-1 Issue-1, November 2012 | Retrieval Number: A0109111112/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Genome sequencing projects continue to provide a flood of new protein sequences. Recently there have been advances in protein structure prognostication which in turn has improved fold recognition algorithms. Predicting the secondary structure of proteins is important in biochemistry because the 3D structure can be determined from the local folds that are found in secondary structures. Moreover, knowing the tertiary structure of proteins can assist in determining their functions. The problem of protein secondary structure prognostication with Hidden Markov Models is addressed here. Sequence family information is integrated via the combination of independent predictions of homologous sequences and a weighting scheme. Hidden Markov models were built for a representative set of just over 1,000 structures from the Protein Data Bank (PDB). The topology of the HMM was restricted to biologically meaningful building blocks.
Keywords: HMM, protein structure, Markov Process