Abstract
Characteristic peptides of the protein segments having common secondary folds are obtained for the I-sites library using maximal position specific probability scores. The secondary structures of these peptides are predicted deploying two best-known computational methods. These are validated with significant accuracy against the corresponding motifs. The characteristic peptides also match with those computed using a Bayesian modeling approach with Markov Chain Monte Carlo Simulation. Percentage representation of the characteristic peptides in the protein structural and functional families shows some interesting results with potential applications in protein structural genomics.
Keywords: I-Sites library, peptides, position specific probability, probabilistic characterization, protein secondary structure, sequential motifs
Protein & Peptide Letters
Title: Characteristic Peptides of Protein Secondary Structural Motifs
Volume: 17 Issue: 10
Author(s): Rajani R. Joshi and Sheeba Sekharan
Affiliation:
Keywords: I-Sites library, peptides, position specific probability, probabilistic characterization, protein secondary structure, sequential motifs
Abstract: Characteristic peptides of the protein segments having common secondary folds are obtained for the I-sites library using maximal position specific probability scores. The secondary structures of these peptides are predicted deploying two best-known computational methods. These are validated with significant accuracy against the corresponding motifs. The characteristic peptides also match with those computed using a Bayesian modeling approach with Markov Chain Monte Carlo Simulation. Percentage representation of the characteristic peptides in the protein structural and functional families shows some interesting results with potential applications in protein structural genomics.
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Cite this article as:
R. Joshi Rajani and Sekharan Sheeba, Characteristic Peptides of Protein Secondary Structural Motifs, Protein & Peptide Letters 2010; 17 (10) . https://dx.doi.org/10.2174/092986610792231500
DOI https://dx.doi.org/10.2174/092986610792231500 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |
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