Theoretical Molecular Biophysics Laboratory, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20814, United States.
J Chem Theory Comput. 2021 Nov 9;17(11):6775-6788. doi: 10.1021/acs.jctc.1c00586. Epub 2021 Oct 20.
A methodology is proposed for the calculation of multidimensional free-energy landscapes of molecular systems, based on analysis of multiple molecular dynamics trajectories wherein adaptive biases have been applied to enhance the sampling of different collective variables. In this approach, which we refer to as the Force-Correction Analysis Method (FCAM), local averages of the total and biasing forces are evaluated post hoc, and the latter are subtracted from the former to obtain unbiased estimates of the mean force across collective-variable space. Multidimensional free-energy surfaces and minimum free-energy pathways are then derived by integrating the mean-force landscape with a kinetic Monte Carlo algorithm. To evaluate the proposed method, a series of numerical tests and comparisons with existing approaches were carried out for small molecules, peptides, and proteins, based on all-atom trajectories generated with standard, concurrent, and replica-exchange metadynamics in collective-variable spaces ranging from one to six dimensional. The tests confirm the correctness of the FCAM formulation and demonstrate that calculated mean forces and free energies converge rapidly and accurately, outperforming other methods used to unbias this kind of simulation data.
提出了一种基于分析施加自适应偏差以增强不同集体变量采样的多个分子动力学轨迹来计算分子系统多维自由能景观的方法。在这种我们称之为力校正分析方法（FCAM）的方法中，在事后评估总力和偏置力的局部平均值，并从前者中减去后者，以获得跨集体变量空间的无偏平均力估计值。然后通过将平均力景观与动力学蒙特卡罗算法集成来推导出多维自由能表面和最小自由能途径。为了评估所提出的方法，针对小分子、肽和蛋白质进行了一系列数值测试和与现有方法的比较，这些测试是基于在一到六个维度的集体变量空间中使用标准、并发和 replica-exchange 元动力学生成的全原子轨迹进行的。测试证实了 FCAM 公式的正确性，并表明计算出的平均力和自由能迅速而准确地收敛，优于用于消除这种模拟数据偏差的其他方法。
