title: Performance of hybrid methods for large-scale unconstrained optimization as applied to models of proteins.
abstract: Energy minimization plays an important role in structure determination and analysis of proteins, peptides, and other organic molecules; therefore, development of efficient minimization algorithms is important. Recently, Morales and Nocedal developed hybrid methods for large-scale unconstrained optimization that interlace iterations of the limited-memory BFGS method (L-BFGS) and the Hessian-free Newton method (Computat Opt Appl 2002, 21, 143-154). We test the performance of this approach as compared to those of the L-BFGS algorithm of Liu and Nocedal and the truncated Newton (TN) with automatic preconditioner of Nash, as applied to the protein bovine pancreatic trypsin inhibitor (BPTI) and a loop of the protein ribonuclease A. These systems are described by the all-atom AMBER force field with a dielectric constant epsilon = 1 and a distance-dependent dielectric function epsilon = 2r, where r is the distance between two atoms. It is shown that for the optimal parameters the hybrid approach is typically two times more efficient in terms of CPU time and function/gradient calculations than the two other methods. The advantage of the hybrid approach increases as the electrostatic interactions become stronger, that is, in going from epsilon = 2r to epsilon = 1, which leads to a more rugged and probably more nonlinear potential energy surface. However, no general rule that defines the optimal parameters has been found and their determination requires a relatively large number of trial-and-error calculations for each problem.
authors.affiliation: Center for Computational Biology and Bioinformatics, University of Pittsburgh School of Medicine, 200 Lothrop Street, BST 1058W, Pittsburgh, Pennsylvania 15261, USA.
authors.forename: B
authors.initials: B
authors.lastname: Das
authors.forename: H
authors.initials: H
authors.lastname: Meirovitch
authors.forename: I M
authors.initials: IM
authors.lastname: Navon
mesh_terms: D000465:Algorithms; D007611:Aprotinin / Q000737:chemistry; D003198:Computer Simulation; D008958:Models, Molecular; D010455:Peptides / Q000737:chemistry; D011506:Proteins / Q000737:chemistry*; D012259:Ribonuclease, Pancreatic / Q000737:chemistry; D013816:Thermodynamics
chemical_list: D010455:Peptides; D011506:Proteins; D007611:Aprotinin; D012259:Ribonuclease, Pancreatic