Variational Method for Networkwide Analysis of Relative Ligand Binding Free Energies with Loop Closure and Experimental Constraints.
Variational Method for Networkwide Analysis of Relative Ligand Binding Free Energies with Loop Closure and Experimental Constraints.
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DOI：
10.1021/acs.jctc.0c01219
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发表时间：
2021-03-09
影响因子：
5.5
通讯作者：
York DM
中科院分区：
文献类型：
--
作者：
Giese TJ;York DM
我们描述了一个有效的方法，同时解决所有的自由能内的相对结合自由能（RBFE）网络与循环封闭和实验/参考约束条件，使用班尼特接受比（BAR）和多状态BAR（MBAR）分析。而不是解决BAR或MBAR方程的每个变换独立，所有的变换的同时解决方案，通过执行一个约束最小化的全球目标函数。目标函数的非线性优化受到耦合网络边缘之间的自由能的仿射线性约束。这些约束用于强制封闭RBFE网络内的热力学循环，并强制执行一组附加的线性约束条件，这些约束条件在这里被证明是（1或2）个实验值的子集。我们描述的网络BAR/MBAR程序的实际实施的细节，包括使用广义坐标的自由能目标函数的最小化，从这些坐标的引导错误的传播，以及性能和内存优化。在某些情况下，发现在优化中使用约束比使用广义坐标强制约束条件更实用。快速BARnet和MBARnet方法被用来分析6个原型蛋白质-配体系统的RBFE，它表明，循环封闭条件的实施减少了预测中的误差只有适度，和进一步减少误差时，可以实现一个或两个实验RBFE包括在优化过程中。这些方法已被实现到FE-ToolKit，一个新的自由能分析工具包。本文介绍的BARnet/MBARnet框架为新的、更有效和更强大的自由能分析打开了大门，并增强了药物发现应用的预测能力。
We describe an efficient method for the simultaneous solution of all free energies within a relative binding free energy (RBFE) network with cycle closure and experimental/reference constraint conditions using Bennett Acceptance Ratio (BAR) and Multistate BAR (MBAR) analysis. Rather than solving the BAR or MBAR equations for each transformation independently, the simultaneous solution of all transformations are obtained by performing a constrained minimization of a global objective function. The nonlinear optimization of the objective function is subjected to affine linear constraints that couple the free energies between the network edges. The constraints are used to enforce the closure of thermodynamic cycles within the RBFE network, and to enforce an additional set of linear constraint conditions demonstrated here to be subsets of (1 or 2) experimental values. We describe details of the practical implementation of the network BAR/MBAR procedure, including use of generalized coordinates in the minimization of the free energy objective function, propagation of bootstrap errors from those coordinates, and performance and memory optimization. In some cases it is found that use of restraints in the optimization is more practical than use of generalized coordinates for enforcing constraint conditions. The fast BARnet and MBARnet methods are used to analyze the RBFEs of 6 prototypical protein-ligand systems, and it is shown that enforcement of cycle closure conditions reduces the error in the predictions only modestly, and further reduction in errors can be achieved when one or two experimental RBFEs are included in the optimization procedure. These methods have been implemented into FE-ToolKit, a new free energy analysis toolkit. The BARnet/MBARnet framework presented here opens the door to new, more efficient and robust free energy analysis with enhanced predictive capability for drug discovery applications.
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影响因子：
4.1
作者：
BENNETT, CH
通讯作者：
BENNETT, CH
影响因子：
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作者：
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5.5
作者：
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通讯作者：
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