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三维非晶格蛋白质模型中的结构优化

Structure optimization in a three-dimensional off-lattice protein model.

作者信息

Huang Wenqi, Liu Jingfa

机构信息

School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.

出版信息

Biopolymers. 2006 Jun 5;82(2):93-8. doi: 10.1002/bip.20400.

Abstract

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 < or = n < or = 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.

摘要

我们研究了一种具有两种单体(疏水单体A和亲水单体B)的三维非晶格AB模型,并提出了两种优化算法:面心立方(FCC)晶格修剪富集罗森布鲁斯方法(PERM)以及基于“脱阱”策略的后续共轭梯度(PERM++)最小化和启发式共轭梯度(HCG)模拟。在PERM++中,我们将PERM应用于FCC晶格以生成初始构象,然后使用共轭梯度最小化来达到最低能量状态。这两种算法均已在三维AB模型中针对所有长度为13≤n≤55的序列进行了测试。数值结果表明,所提出的方法在寻找蛋白质基态方面非常有前景。在几种情况下,我们更新了假定的基态能量值。

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