Protein Folding Pathways Revealed by Essential Dynamics Sampling.
Daniele Narzi, I. Daidone, A. Amadei
Oct 21, 2008
Citations
12
Citations
Journal
Journal of chemical theory and computation
Full text
Semantic Scholar
Key Takeaway Key Takeaway: Essential dynamics sampling (EDS) effectively identifies protein folding pathways by focusing on the most rigid motions in the native state, rather than essential eigenvectors.
Abstract
The characterization of the protein folding process represents one of the major challenges in molecular biology. Here, a method to simulate the folding process of a protein to its native state is reported, the essential dynamics sampling (EDS) method, and is successfully applied to detecting the correct folding pathways of two small proteins, the all-β SH3 domain of Src tyrosine kinase transforming protein (SH3) and the α/β B1 domain of streptococcal protein G (GB1). The main idea of the method is that a subset of the natural modes of fluctuation in the native state is key in directing the folding process. A biased molecular dynamics simulation is performed, in which the restrained degrees of freedom are chosen among those obtained by a principal component, or essential dynamics, analysis of the positional fluctuations of the Cα atoms in the native state. Successful folding is obtained if the restraints are applied only to the eigenvectors with lowest eigenvalues, representing the most rigid quasi-constraint motions. If the essential eigenvectors, the ones accounting for most of the variance, are used, folding is not successful. These results clearly show that the eigenvectors with lowest eigenvalues contain the main mechanical information necessary to drive the folding process, while the essential eigenvectors represent the large concerted motions which can occur without folding/unfolding the protein.