ABSTRACT 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.
Cite this paper
nullLampariello, F. and Liuzzi, G. (2012) Global optimization of protein-peptide docking by a filling function method. Open Journal of Applied Sciences, 2, 26-29. doi: 10.4236/ojapps.2012.24B007.
 F. Lampariello, “A filling function method for continuos unconstrained global optimization: application to morse clusters,” IASI-CNR, Tech. Rep. R.615, 2004.
 R. D. Taylor, P. J. Jewsbury, and J. W. Essex, “FDS:Flexible ligand and receptor docking with a continuum solvent model and soft-core energy function,” Journal of Computational Chemistry, vol. 24, pp. 1637–1656, 2003.
 I. Antes, “Dynadock: A new molecular dynamics-based algorithm for proteinpeptide docking including receptor flexibility,” Proteins: Structure, Function and Bioinformatics, vol. 78, pp. 1084–1104, 2010.
 J. Apostolakis, A. Pluckthun, and A. Caflisch, “Docking small ligands in flexible binding sites,” Journal of Computational Chemistry, vol. 19, pp. 21–37, 1998.
 D. Bertsekas, Nonlinear Programming. Massachusetts, USA: Athena Scientific, 1999.
 R. Andreani, E. Birgin, J. Martinez, and M. Schuverdt, “On augmented lagrangian methods with general lower-level constraints,” SIAM Journal on Optimization, vol. 18, pp. 1286–1309, 2007.
 “Augmented lagrangian methods under the constant positive linear dependence constraint qualification,” Mathematical Programming, vol. 111, pp. 5–32, 2008.
 D. Duhovny, R. Nussinov, and H. Wolfson, “Efficient unbound docking of rigid molecules,” in Proceedings of the 2’nd Workshop on Algorithms in Bioinformatics(WABI) Rome, Italy, ser. Lecture Notes in Computer Science 2452, Gusfield et al., Ed. Springer Verlag, 2002, pp. 185–200.
 D. Schneidman-Duhovny, Y. Inbar, R. Nussinov, and H. Wolfson, “Patchdock and symmdock: servers for rigid and symmetric docking,” Nucleic Acids Research, vol. 33, pp. 363–367, 2005.
 H. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. Bhat, H. Weissig, I. Shindyalov, and P. Bourne, “The protein data bank,” Nucleic Acids Research, vol. 28, pp. 235–242, 2000.
 G. Morris, D. Goodsell, R. Halliday, R. Huey, W. Hart, R. Belew, and A. Olson, “Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function,” Journal of Computational Chemistry, vol. 19, pp. 1639–1662, 1998.