Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atoms. For larger systems, efficient, but much less reliable empirical force fields are used. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations, offering similar accuracy as ab initio methods at orders-of-magnitude speedup. Until now, MLFFs mainly capture short-range interactions in small molecules or periodic materials, due to the increased complexity of constructing models and obtaining reliable reference data for large molecules, where long-ranged many-body effects become important. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations (GEMS) by training on "bottom-up" and "top-down" molecular fragments of varying size, from which the relevant physicochemical interactions can be learned. GEMS is applied to study the dynamics of alanine-based peptides and the 46-residue protein crambin in aqueous solution, allowing nanosecond-scale MD simulations of >25k atoms at essentially ab initio quality. Our findings suggest that structural motifs in peptides and proteins are more flexible than previously thought, indicating that simulations at ab initio accuracy might be necessary to understand dynamic biomolecular processes such as protein (mis)folding, drug-protein binding, or allosteric regulation.
翻译:分子动态( MD) 模拟可以对化学和生物过程进行原子学洞察。 精确的 MD 模拟需要计算要求量子机械计算, 几乎限于短时间尺度和少数原子。 对于更大的系统, 使用高效但远不那么可靠的实验力场。 最近, 机器学习的力场( MLFFs) 出现作为执行MD模拟的替代手段, 提供了类似的精度, 类似于在微量级加速时的初始分子碎片。 到目前为止, MLFF 主要是捕捉小分子或定期材料的短距离互动, 这是因为构建模型的复杂程度更高, 并且获得大型分子的可靠生物参考数据, 长期远距离多体效应变得重要。 这项工作提出了一个总体方法, 构建精确的 MLFFs, 用于大规模分子模拟( GEMS), 进行“ 自下而 ” 和“ 上下调” 的分子碎片, 从中可以了解相关的物理化学互动( GEMS ) 用于研究基于直线型分子分子分子的精度和定期材料的精度, 的精确度模型精确度数据数据数据, 数据精确的精确度将显示, 在二等的分子模拟模型中, 质化模型中, 质化中, 显示我们的质质质质化 质化 质化 质化 质化 质化 质化 质变的解的 质解的 质解 质解 质 质 质 质 质解, 。