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Authors: Pierre Thevenet and Pierre Tufféry

Affiliation: INSERM and Univ Paris Diderot, France

ISBN: 978-989-758-012-3

Keyword(s): Structure Prediction, Peptide, Structural Alphabet, Hidden Markov Models.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Biostatistics and Stochastic Models ; Model Design and Evaluation ; Structural Bioinformatics ; Structure Prediction

Abstract: Peptides have, in the recent years, become plausible candidate therapeutics. However, their structural characterization at a large scale, necessary for their identification and optimization, still remains an open in silico challenge. We introduce a new procedure to the rapid generation of 3D models of peptides. It is based on the concept of Hidden Markov Model derived structural alphabet, a generalization of the secondary structure. Based on this concept we have previously setup an approach to the de novo modeling of peptide structure based on a greedy algorithm. Here, we explore a new strategy that relies on the sampling of the sub-optimal sequences of states in the terms of a Hidden Markov Model derived structural alphabet. Our results suggest such procedure is able to identify the native conformation of peptides at a very low algorithmic complexity, while having a performance similar to the former greedy approach. On average peptide models approximate the experimental structure at less than 3°A RMSD, for a processing cost of only few minutes on a workstation. As a result, peptide de novo modeling becomes tractable at a large scale. (More)

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Paper citation in several formats:
Thevenet P. and Tufféry P. (2014). Exploring a Sub-optimal Hidden Markov Model Sampling Approach for De Novo Peptide Structure Modeling.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 24-30. DOI: 10.5220/0004750000240030

@conference{bioinformatics14,
author={Pierre Thevenet and Pierre Tufféry},
title={Exploring a Sub-optimal Hidden Markov Model Sampling Approach for De Novo Peptide Structure Modeling},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},
year={2014},
pages={24-30},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004750000240030},
isbn={978-989-758-012-3},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - Exploring a Sub-optimal Hidden Markov Model Sampling Approach for De Novo Peptide Structure Modeling
SN - 978-989-758-012-3
AU - Thevenet P.
AU - Tufféry P.
PY - 2014
SP - 24
EP - 30
DO - 10.5220/0004750000240030

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