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Structure optimization in a three‐dimensional off‐lattice protein model
Author(s) -
Huang Wenqi,
Liu Jingfa
Publication year - 2005
Publication title -
biopolymers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 125
eISSN - 1097-0282
pISSN - 0006-3525
DOI - 10.1002/bip.20400
Subject(s) - conjugate gradient method , conjugate , lattice (music) , energy minimization , minification , ground state , heuristic , chemistry , nonlinear conjugate gradient method , algorithm , statistical physics , physics , mathematical optimization , computational chemistry , computer science , mathematics , gradient descent , atomic physics , mathematical analysis , artificial intelligence , artificial neural network , acoustics
We studied a three‐dimensional off‐lattice AB model with two species of monomers, hydrophobic (A) and hydrophilic (B), and present two optimization algorithms: face‐centered‐cubic (FCC)‐lattice pruned‐enriched‐Rosenbluth method (PERM) and subsequent conjugate gradient (PERM++) minimization and heuristic conjugate gradient (HCG) simulation based on “off‐trap” strategy. In PERM++, we apply the PERM to the FCC‐lattice to produce the initial conformation, and conjugate gradient minimization is then used to reach the minimum energy state. Both algorithms have been tested in the three‐dimensional AB model for all sequences with lengths 13 ≤ n ≤ 55. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. In several cases, we renew the putative ground states energy values. © 2005 Wiley Periodicals, Inc. Biopolymers 82: 93–98, 2006 This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com