PROTEIN-FOLDING AND PEPTIDE DOCKING - A MOLECULAR MODELING AND GLOBALOPTIMIZATION APPROACH

Global optimization approaches are proposed for addressing both the pr otein folding and peptide docking problems. In the protein folding pro blem, the ultimate goal involves predicting the native protein conform ation. A common approach,based on the thermodynamic hypothesis, assume s that this conformation corresponds to the structure exhibiting the g lobal minimum free energy. However, molecular modeling of these system s results in highly nonconvex energy hypersurfaces. In order to locate the global minimum energy structure on this surface, a powerful globa l optimization method, alpha BB, is applied. The approach is shown to be extremely effective in locating global minimum energy structures of solvated oligopeptides. A challenging problem related to protein fold ing is peptide docking. In addressing the peptide docking problem, the task is not only to predict a macromolecular-ligand structure but to also rank the binding; affinities of a set of potential ligands. Many methods have used qualitative descriptions of the macromolecular-ligan d complexes in order to avoid the need to perform a global search on t he nonconvex energy hypersurface. In this work, a novel decomposition based approach that incorporates quantitative, atomistic-level energy modeling and global optimization is proposed. This approach employs th e alpha BB global optimization method and is applied to the prediction of peptide docking to the MHC HLA-DR1 protein. (C) 1998 Elsevier Scie nce Ltd. All rights reserved.