Increasing the precision of comparative models with YASARA NOVA - a self-parameterizing force field
Increasing the precision of comparative models with YASARA NOVA - a self-parameterizing force field
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DOI：
10.1002/prot.10104
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发表时间：
2002-05-15
影响因子：
2.9
通讯作者：
Vriend, G
中科院分区：
文献类型：
--
作者：
Krieger, E;Koraimann, G;Vriend, G
在Asilomar举行的CASP 4会议上得出的结论之一是，在基于模板的模型的改进过程中应用各种力场往往会使预测朝着错误的方向移动，远离实验确定的坐标。我们已经推导出了一个全原子力场，旨在蛋白质和核苷酸的真空优化（NOVA），这是专门设计来避免这个问题。NOVA类似于常见的分子动力学力场，但已自动参数化，有两个主要目标：（i）不使高分辨率X射线结构变差，（ii）改善由WHAT IF建立的同源模型。力场参数不需要物理上正确;相反，它们在力场参数空间中用随机蒙特卡罗移动进行优化，每个参数都通过50个蛋白质优化集的模拟退火运行进行评估。近似力场方程的固有误差可以被力场参数的误差抵消。与优化集相比，力场在独立验证集上表现同样出色，并且显示出在计算机模型中更接近现实。它可以应用于建模应用以及X射线和NMR结构细化。提出了一种基于分子树的力场参数赋值方法。(C)2002 Wiley-Liss，Inc.
One of the conclusions drawn at the CASP4 meeting in Asilomar was that applying various force fields during refinement of template-based models tends to move predictions in the wrong direction, away from the experimentally determined coordinates. We have derived an all-atom force field aimed at protein and nucleotide optimization in vacuo (NOVA), which has been specifically designed to avoid this problem. NOVA resembles common molecular dynamics force fields but has been automatically parameterized with two major goals: (i) not to make high resolution X-ray structures worse and (ii) to improve homology models built by WHAT IF. Force-field parameters were not required to be physically correct; instead, they were optimized with random Monte Carlo moves in force-field parameter space, each one evaluated by simulated annealing runs of a 50-protein optimization set. Errors inherent to the approximate force-field equation could thus be canceled by errors in force-field parameters. Compared with the optimization set, the force field did equally well on an independent validation set and is shown to move in silico models closer to reality. It can be applied to modeling applications as well as X-ray and NMR structure refinement. A new method to assign force-field parameters based on molecular trees is also presented. (C) 2002 Wiley-Liss, Inc.