2009/06/08 by Gregory R. Bowman, Vijay S. Pande · 9 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · #Protein Structure and Dynamics #Enzyme Structure and Function #Machine Learning in Materials Science #Protein structure prediction #Computer science #Protein folding #Folding (DSP implementation) #Atom (system on chip) #Statistical physics #Kinetics #Entropy (arrow of time) #Work (physics) #Physics #Protein structure #Thermodynamics
paper · pdf · doi:10.1371/journal.pone.0005840
published in PLoS ONE 4(6), e5840 (Public Library of Science)
openalex publication_date 2009/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.