A coarse-grained protein force field for folding and structure prediction
A coarse-grained protein force field for folding and structure prediction
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DOI：
10.1002/prot.21505
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发表时间：
2007-11-01
影响因子：
2.9
通讯作者：
Derreumaux, Philippe
中科院分区：
文献类型：
--
作者：
Maupetit, Julien;Tuffery, P.;Derreumaux, Philippe
我们重新审视了蛋白质粗粒度优化的高效结构预测（OPEP）潜力。训练集和验证集包含 13 和 16 个蛋白质目标。由于优化取决于如何对诱饵集合进行采样的详细信息，因此试验构象是通过分子和蒙特卡罗模拟生成的，或者从公开可用的数据库中获取的。 OPEP参数通过遗传算法使用评分函数来改变，该评分函数要求天然结构具有最低能量，并且类天然结构具有高于天然结构但低于远程构象的能量。总体而言，我们发现 OPEP 正确识别了 29 个目标的 24 个天然或类天然状态，并且具有与全原子离散优化蛋白质能量模型 (DOPE) 非常相似的能力，最近发现该模型优于目前使用的五种能量模型。
We have revisited the protein coarse-grained optimized potential for efficient structure prediction (OPEP). The training and validation sets consist of 13 and 16 protein targets. Because optimization depends on details of how the ensemble of decoys is sampled, trial conformations are generated by molecular and Monte Carlo simulations, or taken from publicly available databases. The OPEP parameters are varied by a genetic algorithm using a scoring function which requires that the native structure has the lowest energy, and the native-like structures have energy higher than the native structure but lower than the remote conformations. Overall, we find that OPEP correctly identifies 24 native or native-like state, for 29 targets and has very similar capability to the all-atom discrete optimized protein energy model (DOPE), found recently to outperform five currently used energy models.