Molecular docking programs play a crucial role in drug design and development. In recent years, much attention has been devoted to the protein-peptide docking problem in which docking of a flexible peptide with a know...Molecular docking programs play a crucial role in drug design and development. In recent years, much attention has been devoted to the protein-peptide docking problem in which docking of a flexible peptide with a known protein is sought. In this work we present a new docking algorithm which is based on the use of a filling function method for continuos constrained global optimization. Indeed, the protein-peptide docking position is sought by minimizing the conformational potential energy subject to constraints necessary to maintain the primary sequence of the given peptide. The resulting global optimization problem is difficult mainly for two reasons. First, the problem is large scale in constrained global optimization;second, the energy function is multivariate non-convex so that it has many local minima. The method is based on the device of modifying the original objective function once a local minimum has been attained by adding to it a filling term. This allows the overall algorithm to escape from local minima thus, ultimately, giving the algorithm ability to explore large regions in the peptide conformational space. We present numerical results on a set of benchmark docking pairs and comparison with the well-known software package for molecular docking PacthDock.展开更多
文摘Molecular docking programs play a crucial role in drug design and development. In recent years, much attention has been devoted to the protein-peptide docking problem in which docking of a flexible peptide with a known protein is sought. In this work we present a new docking algorithm which is based on the use of a filling function method for continuos constrained global optimization. Indeed, the protein-peptide docking position is sought by minimizing the conformational potential energy subject to constraints necessary to maintain the primary sequence of the given peptide. The resulting global optimization problem is difficult mainly for two reasons. First, the problem is large scale in constrained global optimization;second, the energy function is multivariate non-convex so that it has many local minima. The method is based on the device of modifying the original objective function once a local minimum has been attained by adding to it a filling term. This allows the overall algorithm to escape from local minima thus, ultimately, giving the algorithm ability to explore large regions in the peptide conformational space. We present numerical results on a set of benchmark docking pairs and comparison with the well-known software package for molecular docking PacthDock.
