Atomistic simulations are becoming increasingly useful, as they
have the potential to investigate physical processes with a
resolution which cannot be achieved by experiments.
Still, interesting events such as chemical reactions, protein folding, phase transitions, etc., happen on a time scale that is enormously long for computer simulations.
have been developed to cope with this problem, for example
thermodynamic integration, free energy perturbation,
parallel tempering, Jarzynski's identity-based methods,
steered MD, etc.
In our group we work at improving and extending these techniques in order to make them suitable for studyng realistic processes. Particular attention is devoted to the metadynamics method, that is very suited for studying complex reactions. The algorithm has been successfully applied in several different fields, ranging from chemistry to crystal structure prediction to biophysics.
We are presently applying this methodology for studying protein folding and protein-protein interaction. In these cases the number of "interesting" variables that one has to sample and explore is intrinsically very large. This has led to develop a new method, bias-exchange metadynamics, that allows the simultaneous reconstruction of a free energy in several variables.
This approach allows predicting the folded state and the folding time of small proteins (up to 40 amino acids) described with an accurate potential, in which the water is described explicitly (see Foding for more details).
The same approach can be used for studying, also with a very accurate potential, the binding process of drugs to their target protein, predicting with great accuracy the binding affinity (see Docking for more details).