Generalized simulated annealing applied to protein folding studies

During the last few years, computational simulations based on the atomic description of biological molecules have resulted in significant advances in the comprehension of biological processes. It is well known, however, that a molecular system may have a great number of conformations due to the large number of rotation of degrees of freedom around chemical bonds, leading to several local minima on the energy hypersurface. It has been proposed though, that proteins express their biological function when their structure is close to a conformation with energy global minimum. To help solve the protein-folding problem, we use a new strategy based on Simulated Annealing methods. These methods have been well suited for a large extent of optimization problems, especially those containing many local minima. In fact, this work applies the Generalized Simulated Annealing method (GSA) coupled to the GROMOS96 Molecular Force Field to research the minimum energy conformation of 18-alanine. We show that the q(T) GSA parameter can be used to control the freezing process during the annealing procedure, and to avoid polypeptide chains to be trapped in energy local minima. We scanned the q-values for visiting (q(V)) and accepting (q(A)) functions for q(T) values ranging from 1 to 3, and found the best values to obtain an alpha-helix conformation for the polyalanine peptide, which is the conformation with energy global minimum. Global optimization methods also exemplify a class of applications that requires a large amount of computational resources, being suitable for Grid computing. To implement a Grid computing platform, we developed and tested a Grid environment based on MYGRID middleware, which is a technology that can employ all machines accessed by the user to run the application.