Exploring a Sub-optimal Hidden Markov Model Sampling Approach for De Novo Peptide Structure Modeling

Pierre Thevenet, Pierre Tufféry

2014

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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