Hix Mark A, Walker Alice R
Department of Chemistry, Wayne State University, Detroit, Michigan, USA.
J Comput Chem. 2025 May 5;46(12):e70127. doi: 10.1002/jcc.70127.
Quantum mechanical/molecular mechanical geometry optimizations of large-scale biological systems, such as enzymes, proteins, membranes, and solutions, are typically computationally expensive to the point of being cost-prohibitive. By convention, an approximation is made to such calculations that atoms beyond a certain distance from the QM region provide only negligible improvements to the resulting optimization energy and geometry, and as such are restrained to reduce the number of degrees of freedom. These constraints are normally applied beyond a user-defined radius. Here we describe a new method of geometry optimization acceleration and automation which generates adaptive gradient-based restraints for QM/MM optimizations, leading to significantly faster optimizations and generally lower relative energies. The restraints are determined by an algorithm rather than a user, and can adapt to directional optimizations as well as differences in starting geometry. This flexibility is key to finding excited state minima and minimum energy conical intersections (MECIs) in complex protein environments. This algorithm was implemented as an external Python tool for use alongside TeraChem, with a modular interface that can be straightforwardly applied to other QM/MM packages. We tested on a green fluorescent protein (rsEGFP2) and two red fluorescent proteins (FusionRed, mScarlet) in water and a proton-swapping aspartic acid pair in explicit water. We are able to produce a nearly 50% reduction in computational time while maintaining appropriately optimized geometries and relative energies.
对大规模生物系统（如酶、蛋白质、膜和溶液）进行量子力学/分子力学几何优化通常在计算上成本高昂，甚至达到令人望而却步的程度。按照惯例，对此类计算采用一种近似方法，即距离量子力学区域超过一定距离的原子对最终优化能量和几何结构的改善微不足道，因此对其进行约束以减少自由度。这些约束通常应用于用户定义半径之外。在此，我们描述了一种几何优化加速与自动化的新方法，该方法为量子力学/分子力学优化生成基于自适应梯度的约束，从而显著加快优化速度并通常降低相对能量。这些约束由算法而非用户确定，并且能够适应方向优化以及起始几何结构的差异。这种灵活性对于在复杂蛋白质环境中找到激发态极小值和最小能量锥形交叉点（MECIs）至关重要。该算法被实现为一个外部Python工具，与TeraChem一起使用，具有模块化接口，可直接应用于其他量子力学/分子力学软件包。我们在水中的绿色荧光蛋白（rsEGFP2）和两种红色荧光蛋白（FusionRed、mScarlet）以及明确水环境中的一个质子交换天冬氨酸对进行了测试。我们能够在保持适当优化的几何结构和相对能量的同时，将计算时间减少近50%。
