Experimentally motivated stepwise protein modeling via IOHMM sampling
Year: 2016
Type: other
Abstract: Motivated by the experimental hypothesis that proteins fold via stepwise assembly of structural units named ‘foldons’ through the process of sequential stabilization, we develop a novel generative, probabilistic Input Output Hidden Markov Model (IOHMM) that simultaneously captures local structural preferences of backbone and side chain conformational space of polypeptide chains in united-residue representation to perform experimentally motivated probabilistic conformational sampling via stepwise synthesis and assembly of foldon units. We demonstrate that (i) the trained IOHMM is able to capture and reproduce united-residue conformational preferences of polypeptide chains reasonably well; (ii) stepwise sampling produces lower energy conformations with higher accuracy than random sampling when everything else remains the same; and (iii) it outperforms two state-of-theart approaches for protein modeling aided by predicted residue-residue contacts on 45 targets from the 10th Critical Assessment of Protein Structure Prediction (CASP) experiment.
Authors: Debswapna Bhattacharya, Jianlin Cheng
Institution: Wichita State University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W2755337870