Protein Folding Pathways Revealed by Essential Dynamics Sampling



Daniele Narzi§, Isabella Daidone* , Andrea Amadei and Alfredo Di Nola

Department of Chemistry, University of Rome ‘La Sapienza’, P.le Aldo Moro 5, 00185 Rome, Italy, and Dipartimento di Scienze e Tecnologie Chimiche, University of Rome ‘Tor Vergata’, via della Ricerca Scientifica 1, I-00133 Rome, Italy

J. Chem. Theory Comput., 2008, 4 (11), pp 1940–1948

DOI: 10.1021/ct800157v

Publication Date (Web): October 20, 2008

Copyright © 2008 American Chemical Society


University of Rome ‘La Sapienza’.,

*Current address: Theoretical & Computational Membrane Biology, Center for Bioinformatics Saar, Universitt des Saarlandes, D-66041 Saarbrcken, Germany.,

* Corresponding author e-mail: Isabella.Daidone@iwr.uni-heidelberg.de.

,

§Current address: Interdisciplinary Center for Scientific Computing, University of Heidelberg, Im Neuenheimer Feld 368, 69120 Heidelberg, Germany.

‡ University of Rome ‘Tor Vergata’.

FULL TEXT

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.