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

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.