Accelerating molecular simulations of proteins using Bayesian inference on weak information
Accelerating molecular simulations of proteins using Bayesian inference on weak information
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DOI：
10.1073/pnas.1515561112
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发表时间：
2015-09-22
影响因子：
11.1
通讯作者：
Dill, Ken A.
中科院分区：
文献类型：
--
作者：
Perez, Alberto;MacCallum, Justin L.;Dill, Ken A.
蛋白质分子的原子分子动力学（MD）模拟计算量太大，无法从氨基酸序列中预测大多数天然结构。在这里，我们将“弱”外部知识整合到折叠模拟中，以预测蛋白质结构，给定它们的序列。例如，我们指示计算机“形成疏水核心”，“形成良好的二级结构”，或“寻求紧凑状态”。“这种信息过于组合，非特异性和模糊，无法帮助指导以前的MD模拟。在原子复制交换分子动力学（REMD），我们开发了一个统计力学框架，建模使用有限的数据与粗糙的物理洞察力（MELD + CPI），利用弱信息。作为测试，我们应用MELD + CPI预测了20个小分子蛋白质的天然结构。MELD + CPI对所有20个样本进行采样，使其与天然结构的距离小于3埃，并正确选择其中15个的天然结构（< 4埃），包括泛素（毫秒折叠）。MELD + CPI比蛮力MD快五个数量级，满足详细的平衡，并且应该很好地扩展到更大的蛋白质。MELD + CPI在需要基于物理的模拟来研究蛋白质机制和种群以及我们对感兴趣的状态有一些启发式或粗略的物理知识的情况下可能是有用的。
Atomistic molecular dynamics (MD) simulations of protein molecules are too computationally expensive to predict most native structures from amino acid sequences. Here, we integrate "weak" external knowledge into folding simulations to predict protein structures, given their sequence. For example, we instruct the computer "to form a hydrophobic core," " to form good secondary structures," or " to seek a compact state." This kind of information has been too combinatoric, nonspecific, and vague to help guide MD simulations before. Within atomistic replica-exchange molecular dynamics (REMD), we develop a statistical mechanical framework, modeling using limited data with coarse physical insight(s) (MELD + CPI), for harnessing weak information. As a test, we apply MELD + CPI to predict the native structures of 20 small proteins. MELD + CPI samples to within less than 3 angstrom from native for all 20 and correctly chooses the native structures (< 4 angstrom) for 15 of them, including ubiquitin, a millisecond folder. MELD + CPI is up to five orders of magnitude faster than brute-force MD, satisfies detailed balance, and should scale well to larger proteins. MELD + CPI may be useful where physics-based simulations are needed to study protein mechanisms and populations and where we have some heuristic or coarse physical knowledge about states of interest.