It is challenging to compute structure‐function relationships of proteins using molecular physics. The problem arises from the exponential scaling of the computational searching and sampling of large conformational spaces. This scaling challenge is not met by today's methods, such as Monte Carlo, simulated annealing, genetic algorithms, or molecular dynamics (MD) or its variants such as replica exchange. Such methods of searching for optimal states on complex probabilistic landscapes are referred to more broadly as Explore‐and‐Exploit (EE), including in contexts such as computational learning, games, industrial planning, and modeling military strategies. Here, we describe a Bayesian method, called MELD, that ‘melds’ together EE approaches with externally added information that can be vague, combinatoric, noisy, intuitive, heuristic, or from experimental data. MELD is shown to accelerate physical MD simulations when using experimental data to determine protein structures; for predicting protein structures by using heuristic directives; and when predicting binding affinities of proteins from limited information about the binding site. Such Guided EE approaches might also be useful beyond proteins and beyond molecular science. WIREs Comput Mol Sci 2017, 7:e1309. doi: 10.1002/wcms.1309

中文翻译：

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利用外部知识加速蛋白质的物理模拟

利用分子物理学计算蛋白质的结构-功能关系具有挑战性。问题来自于对大构象空间的计算搜索和采样的指数缩放。当今的方法（例如蒙特卡洛（Monte Carlo），模拟退火，遗传算法或分子动力学（MD）或其变体（例如复制副本））无法解决这种扩展挑战。这种在复杂概率环境中搜索最佳状态的方法被更广泛地称为“探索和利用”（EE），包括在诸如计算学习，游戏，产业规划和军事战略建模等环境中。在这里，我们描述了一种称为MELD的贝叶斯方法，该方法将EE方法与外部添加的信息（可能是模糊的，组合的，嘈杂的，直观的，启发式的或来自实验数据的）“融合”在一起。当使用实验数据确定蛋白质结构时，MELD被证明可以加速物理MD模拟。通过使用启发式指令来预测蛋白质结构；当根据有关结合位点的有限信息预测蛋白质的结合亲和力时。除了蛋白质和分子科学之外，这种Guided EE方法也可能有用。WIRES Comput Mol Sci 2017，7：e1309。土井：10.1002 / wcms.1309