Multiscale Optimization of a Truncated Newton Minimization Algorithm and Application to Proteins and Protein-Ligand Complexes.
Multiscale Optimization of a Truncated Newton Minimization Algorithm and Application to Proteins and Protein-Ligand Complexes.
复制标题
截断牛顿最小化算法的多尺度优化及其在蛋白质和蛋白质-配体复合物中的应用。
DOI：
10.1021/ct600129f
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发表时间：
2007
影响因子：
5.5
通讯作者：
Jacobson,MatthewP
中科院分区：
文献类型：
--
作者：
Zhu,Kai;Shirts,MichaelR;Friesner,RichardA;Jacobson,MatthewP
We optimize a truncated Newton (TN) minimization algorithm and computer package, TNPACK, developed for macromolecular minimizations by applying multiscale methods, analogous to those used in molecular dynamics (e.g., r-RESPA). The molecular mechanics forces are divided into short- and long-range components, with the long-range forces updated only intermittently in the iterative evaluations. This algorithm, which we refer to as MSTN, is implemented as a modification to the TNPACK package and is tested on energy minimizations of protein loops, entire proteins, and protein−ligand complexes and compared with the unmodified truncated Newton algorithm, a quasi-Newton algorithm (LBFGS), and a conjugate gradient algorithm (CG+). In vacuum minimizations, the speedup of MSTN relative to the unmodified TN algorithm (TNPACK) depends on system size and the distance cutoffs used for defining the short- and long-range interactions and the long-range force updating frequency, but it is 4 to 5 times greater in the work reported here. This algorithm works best for the minimization of small portions of a protein and shows some degradation (speedup factor of 2−3) for the minimization of entire proteins. The MSTN algorithm is faster than the quasi-Newton and conjugate gradient algorithms by approximately 1 order of magnitude. We also present a modification of the algorithm which permits minimizations with a generalized Born implicit solvent model, using a self-consistent procedure that increases the computational expense, relative to a vacuum, by only a small factor (∼3−4).
