Predicting peptide structures in native proteins from physical simulations of fragments

• Published in: PLoS computational biology 2009-02, Vol.5 (2), p.e1000281-e1000281
• Main Authors: Voelz, Vincent A, Shell, M Scott, Dill, Ken A
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Bayes Theorem; Biophysics/Protein Folding; Computational biology; Computational Biology/Molecular Dynamics; Computational Biology/Protein Structure Prediction; Computer Simulation; Computer-generated environments; Hypothesis testing; Logistic Models; Logistics; Methods; Models, Chemical; Models, Molecular; Peptides; Physics; Physiological aspects; Protein Conformation; Protein Folding; Proteins; Proteins - chemistry; Proteins - ultrastructure; Proteomics - methods; Sample variance; Simulation; Solvents - chemistry; Structure; Studies; Success; Thermodynamics; Water - chemistry; Weights and Measures
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000281

It has long been proposed that much of the information encoding how a protein folds is contained locally in the peptide chain. Here we present a large-scale simulation study designed to examine the extent to which conformations of peptide fragments in water predict native conformations in proteins. We perform replica exchange molecular dynamics (REMD) simulations of 872 8-mer, 12-mer, and 16-mer peptide fragments from 13 proteins using the AMBER 96 force field and the OBC implicit solvent model. To analyze the simulations, we compute various contact-based metrics, such as contact probability, and then apply Bayesian classifier methods to infer which metastable contacts are likely to be native vs. non-native. We find that a simple measure, the observed contact probability, is largely more predictive of a peptide's native structure in the protein than combinations of metrics or multi-body components. Our best classification model is a logistic regression model that can achieve up to 63% correct classifications for 8-mers, 71% for 12-mers, and 76% for 16-mers. We validate these results on fragments of a protein outside our training set. We conclude that local structure provides information to solve some but not all of the conformational search problem. These results help improve our understanding of folding mechanisms, and have implications for improving physics-based conformational sampling and structure prediction using all-atom molecular simulations.