The Roles of Entropy and Kinetics in Structure Prediction
Year: 2009
Type: article
Abstract: BACKGROUND: Here we continue our efforts to use methods developed in the folding mechanism community to both better understand and improve structure prediction. Our previous work demonstrated that Rosetta's coarse-grained potentials may actually impede accurate structure prediction at full-atom resolution. Based on this work we postulated that it may be time to work completely at full-atom resolution but that doing so may require more careful attention to the kinetics of convergence. METHODOLOGY/PRINCIPAL FINDINGS: To explore the possibility of working entirely at full-atom resolution, we apply enhanced sampling algorithms and the free energy theory developed in the folding mechanism community to full-atom protein structure prediction with the prominent Rosetta package. We find that Rosetta's full-atom scoring function is indeed able to recognize diverse protein native states and that there is a strong correlation between score and Calpha RMSD to the native state. However, we also show that there is a huge entropic barrier to folding under this potential and the kinetics of folding are extremely slow. We then exploit this new understanding to suggest ways to improve structure prediction. CONCLUSIONS/SIGNIFICANCE: Based on this work we hypothesize that structure prediction may be improved by taking a more physical approach, i.e. considering the nature of the model thermodynamics and kinetics which result from structure prediction simulations.
Source: PLoS ONE
Authors: Gregory R. Bowman, Vijay S. Pande
Institution: Stanford University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,382
Awards: Collaborative Research: Cyberinfrastructure for Next Generation Biomolecular Modeling, G5520316217, MRI: Acquisition of a Hybrid Shared-Memory / Massively-Parallel Commodity Cluster for Cost-Effective Super-Computing at Stanford, Folding@Home: Simulating Folding on the Millisecond to Second Timescale
OpenAlex ID: W2036755526