<br>用于计算平均力和标准结合自由能潜力的机器学习和增强采样模拟,Journal of Chemical Theory and Computation

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用于计算平均力和标准结合自由能潜力的机器学习和增强采样模拟
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2021-07-14 , DOI: 10.1021/acs.jctc.1c00177
Martina Bertazzo _{1,

2} , Dorothea Gobbo ₁ , Sergio Decherchi _{1,

3} , Andrea Cavalli _{1,

2}

Affiliation

计算能力正在迅速增强，这主要是因为基于 GPU 的架构的可用性。这为热力学可观测值（包括药物与靶标结合的自由能）的系统性和鲁棒性计算创造了前所未有的模拟可能性。与目前广泛用于药物发现的相对结合自由能的计算相比，我们在这里突破了计算结合自由能和平均力潜力的界限。我们引入了一种新颖的协议，它利用增强的采样、机器学习和临时算法来限制自由能计算中的人为干预、计算时间和自由参数。我们首先在主客体系统上验证该方法，然后将该方案应用于糖原合酶激酶 3 beta（一种具有药理学意义的蛋白激酶）。总体而言，我们在相对和绝对方面与实验值具有良好的相关性。虽然我们专注于蛋白质-配体结合，但该策略广泛适用于可以用路径集体变量描述的任何复杂事件。我们系统地讨论影响最终结果的关键细节。 PLUMED-NEST 提供参数和模拟设置，以实现完全的可重复性。

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Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy

Computational capabilities are rapidly increasing, primarily because of the availability of GPU-based architectures. This creates unprecedented simulative possibilities for the systematic and robust computation of thermodynamic observables, including the free energy of a drug binding to a target. In contrast to calculations of relative binding free energy, which are nowadays widely exploited for drug discovery, we here push the boundary of computing the binding free energy and the potential of mean force. We introduce a novel protocol that leverages enhanced sampling, machine learning, and ad hoc algorithms to limit human intervention, computing time, and free parameters in free energy calculations. We first validate the method on a host–guest system, and then we apply the protocol to glycogen synthase kinase 3 beta, a protein kinase of pharmacological interest. Overall, we obtain a good correlation with experimental values in relative and absolute terms. While we focus on protein–ligand binding, the strategy is of broad applicability to any complex event that can be described with a path collective variable. We systematically discuss key details that influence the final result. The parameters and simulation settings are available at PLUMED-NEST to allow full reproducibility.

更新日期：2021-08-10

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