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Trends in Bioinformatics
  Year: 2014 | Volume: 7 | Issue: 1 | Page No.: 1-6
DOI: 10.3923/tb.2014.1.6
 
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Novel Method of Protein Structure Prediction (NPSPM) based on Short Range Interactions between Amino Acids
Arul Mugilan, Sherlyn Jemimah and Preethi Jennifer

Abstract:
Many methods have been developed to predict the secondary structure of protein sequences. Most methods predict the protein model as α helix-sheet-random coil structure, which is a simplistic view of protein structure. Very few predict other structural elements such as β-turns, 3/10 helix, bends etc. Currently, most approaches rely on neural networks to predict secondary structure. We propose a Novel Protein Structure Prediction Method (NPSPM), which uses DSSP (Dictionary of Protein Secondary Structure) and statistical techniques to generate Secondary Structure Prediction Parameters (SSPP) for single amino acids, all possible amino acid pairs and all possible amino acid triplets. These parameters can be used to predict the secondary structure elements present in the protein sequence. Our method shows better sensitivity and accuracy than other methods.
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How to cite this article:

Arul Mugilan, Sherlyn Jemimah and Preethi Jennifer, 2014. Novel Method of Protein Structure Prediction (NPSPM) based on Short Range Interactions between Amino Acids. Trends in Bioinformatics, 7: 1-6.

DOI: 10.3923/tb.2014.1.6

URL: https://scialert.net/abstract/?doi=tb.2014.1.6

 
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