Molecular dynamics (MD) simulations generate valuable all-atom resolution trajectories of complex systems, but analyzing this high-dimensional data as well as reaching practical timescales, even with powerful supercomputers, remain open problems. As such, many specialized sampling and reaction coordinate construction methods exist that alleviate these problems. However, these methods typically don't work directly on all atomic coordinates, and still require previous knowledge of the important distinguishing features of the system, known as order parameters (OPs). Here we present AMINO, an automated method that generates such OPs by screening through a very large dictionary of OPs, such as all heavy atom contacts in a biomolecule. AMINO uses ideas from information theory to learn OPs that can then serve as an input for designing a reaction coordinate which can then be used in many enhanced sampling methods. Here we outline its key theoretical underpinnings, and apply it to systems of increasing complexity. Our applications include a problem of tremendous pharmaceutical and engineering relevance, namely, calculating the binding affinity of a protein–ligand system when all that is known is the structure of the bound system. Our calculations are performed in a human-free fashion, obtaining very accurate results compared to long unbiased MD simulations on the Anton supercomputer, but in orders of magnitude less computer time. We thus expect AMINO to be useful for the calculation of thermodynamics and kinetics in the study of diverse molecular systems.

中文翻译：

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自动互信息噪声遗漏（AMINO）：为分子系统生成有序参数

分子动力学（MD）模拟生成复杂系统的有价值的全原子分辨率轨迹，但是即使使用强大的超级计算机来分析这种高维数据以及达到实际的时间尺度，仍然是一个悬而未决的问题。这样，存在许多减轻这些问题的专门采样和反应坐标构造方法。但是，这些方法通常不能直接在所有原子坐标上工作，并且仍然需要事先了解系统的重要区别特征（称为顺序参数（OP））。在这里，我们介绍了AMINO，这是一种自动化方法，可通过对OP的超大型词典（例如生物分子中的所有重原子接触）进行筛选来生成此类OP。AMINO使用信息论中的思想来学习OP，然后可以将其用作设计反应坐标的输入，然后将其用于许多增强的采样方法中。在这里，我们概述了其关键的理论基础，并将其应用于日益复杂的系统。我们的应用程序存在着巨大的制药和工程相关性问题，即，当仅知道结合系统的结构时，计算蛋白质-配体系统的结合亲和力。与在Anton超级计算机上进行长时间的无偏MD仿真相比，我们的计算以无人的方式进行，获得了非常准确的结果，但是计算机时间却减少了几个数量级。因此，我们希望AMINO可用于各种分子系统研究中的热力学和动力学计算。