New Developments on Generalized Simulated Annealing Applied to ab-initio Protein Structure Prediction

Abstract

Proteins are the building blocks of cells and the executioners of nearly all cellular functions. Their activity directly depends on their specific three dimensional structure, determined by the folding of its amino acid chain. The folding process ultimately creates a stable structure balancing internal contacts between amino acids and their occlusion to create the protein surface and the hydrophobic core. In this work we explore a new design for applying Generalized Simulated Annealing (GSA) on protein structure prediction, based on previous software developed by our group. The GSA is a stochastic search algorithm employed in energy minimization and used in global optimization problems, such as gravity models, fitting of numerical data and conformation optimization of small molecules. The software deploys a new way of updating the protein structure at each step of the simulation, a different potential energy calculation function based on NAMD and parallel execution of simulations, granting a new take on ab-initio protein structure prediction. The design of the software also allows for the inclusion of data derived from large scale analysis of protein structures from the PDB, as the Solvation Free Energy, allowing us to use information already gathered by experimental structure determinations. We present results on the 20 residue trp-cage mini protein and mastoparan-X, a 13 amino acid peptide. Both chains fold with RMSD of 0,2 nm and 0,1 nm respectively after 10000 GSA steps and a molecular dinamics optimization of 1 ms for trp-cage and 200 ns for mastoparan-X. Structure prediction softwares allow us to study protein structures that cannot be experimentally determined, by using data on chemical bonds, non-bonded interactions and protein solvation. Once approximately predicted, the three dimensional structure can be refined by molecular dynamics simulations.