Protein folding by atomistic simulations
Proteins are biological molecules formed by 20 types of amino acids and showing very rich and complicated 3D structures, obtained folding the backbone chain. Proteins perform a number of fundamental functions in our body, and the specific function of each protein depends on the structure of the molecule. The knowledge of the structure can help to design drugs against diseases. The sequence of amino acids forming the protein chain, as encoded in the DNA, contains in some way the information about the final 3D structure which the chain assumes spontaneously in water solution. However, understanding how the protein sequence encodes the protein structure (= native fold) is an extremely challenging puzzle, considering the astronomically large number of possible conformations that a given chain could in principle assume.
Atomistic simulations of protein folding using physically-based force fields aim at predicting the protein structure and, at the same time, at obtaining a clear picture of the dynamics of folding. Unfortunately, direct simulation cannot still reach the milliseconds-seconds timescale, that is tipically required to fold a protein, and enhanced sampling techniques are required to observe folding in a short time.
We applied a recently developed a technique, bias-exchange metadynamics, (ref ) that allows exploring the high-dimensional free energy landscape of small proteins, for example the trp-cage, villin and insuline. The results of the simulation were then analized with an approach aimed at extracting a kinetic model from bias-exchange simulations (ref ). This combined approach allows:
1) Predicting the folded state.
2) Predicting the thermodynamic stability of the folded state, of the unfolded state and of the intermediates (if present).
3) Estimating the folding time.
In order to study more complicated proteins, with a nontrivial topology and a large content of beta sheets, we have recently introduced a new kind of reaction coordinate that is specifically designed to drive the formation of of secondary structure elements (see Ref. ). Using this variable in combination with bias-exchange metadynamics allows obtaining almost native structures of GB1 and SH3 starting from a random coil in explicit solvent. Moreover, it allows exploring a large number of misfolded states with a very large secondary content (see Fig).
We are presently working on the development of even more effective reaction coordinates to simulate confromational transitions and on applications to problems of folding, misfolding, and aggregation.
 S Piana, A Laio,
A bias-exchange approach to protein folding
JOURNAL OF PHYSICAL CHEMISTRY B, 111, 4553 (2007)
 F Marinelli, F Pietrucci, A Laio, S Piana,
A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations
PLOS COMPUTATIONAL BIOLOGY, 5, e1000452 (2009)
 F Pietrucci, A Laio,
A Collective Variable for the Efficient Exploration of Protein Beta-Sheet Structures: Application to SH3 and GB1
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 5, 2197 (2009)
 S Piana, A Laio, F Marinelli, Troys, Van D Bourry, C Ampe, JC Martins,
Predicting the effect of a point mutation on a protein fold: The villin and advillin headpieces and their Pro62Ala mutants
JOURNAL OF MOLECULAR BIOLOGY, 375, 460 (2008)
This page has been written by Fabio Pietrucci. Please also refer to his home page for further details.