Department of Chemistry, Indian Institute of Technology Kanpur, Kanpur, India.
Department of Chemistry, Manipal University Jaipur, Jaipur, Rajasthan, India.
J Comput Chem. 2022 Jun 30;43(17):1186-1200. doi: 10.1002/jcc.26882. Epub 2022 May 5.
Temperature-accelerated sliced sampling (TASS) is an enhanced sampling method for achieving accelerated and controlled exploration of high-dimensional free energy landscapes in molecular dynamics simulations. With the aid of umbrella bias potentials, the TASS method realizes a controlled exploration and divide-and-conquer strategy for computing high-dimensional free energy surfaces. In TASS, diffusion of the system in the collective variable (CV) space is enhanced with the help of metadynamics bias and elevated-temperature of the auxiliary degrees of freedom (DOF) that are coupled to the CVs. Usually, a low-dimensional metadynamics bias is applied in TASS. In order to further improve the performance of TASS, we propose here to use a highdimensional metadynamics bias, in the same form as in a parallel bias metadynamics scheme. Here, a modified reweighting scheme, in combination with artificial neural network is used for computing unbiased probability distribution of CVs and projections of high-dimensional free energy surfaces. We first validate the accuracy and efficiency of our method in computing the four-dimensional free energy landscape for alanine tripeptide in vacuo. Subsequently, we employ the approach to calculate the eight-dimensional free energy landscape of alanine pentapeptide in vacuo. Finally, the method is applied to a more realistic problem wherein we compute the broad four-dimensional free energy surface corresponding to the deacylation of a drug molecule which is covalently complexed with a β-lactamase enzyme. We demonstrate that using parallel bias in TASS improves the efficiency of exploration of high-dimensional free energy landscapes.
温度加速切片采样（TASS）是一种增强采样方法，可在分子动力学模拟中实现对高维自由能景观的加速和控制探索。借助伞形偏压势，TASS 方法实现了对计算高维自由能表面的受控探索和分而治之策略。在 TASS 中，借助元动力学偏差和与 CV 耦合的辅助自由度（DOF）的高温，增强了系统在协变量（CV）空间中的扩散。通常，TASS 中应用低维元动力学偏差。为了进一步提高 TASS 的性能，我们在这里提出使用高维元动力学偏差，其形式与平行偏置元动力学方案相同。在这里，修改后的重新加权方案与人工神经网络结合使用，用于计算 CV 的无偏概率分布和高维自由能表面的投影。我们首先在计算丙氨酸三肽真空的四维自由能景观中验证了我们方法的准确性和效率。随后，我们采用该方法计算了丙氨酸五肽真空的八维自由能景观。最后，该方法应用于一个更现实的问题，我们计算了与β-内酰胺酶共价复合的药物分子去酰化的宽四维自由能表面。我们证明了在 TASS 中使用平行偏置可以提高对高维自由能景观的探索效率。
