Authors:
Pierre Thevenet
and
Pierre Tufféry
Affiliation:
INSERM and Univ Paris Diderot, France
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.
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