Learned mappings for targeted free energy perturbation between peptide conformations.

Targeted free energy perturbation uses an invertible mapping to promote configuration space overlap and the convergence of free energy estimates. However, developing suitable mappings can be challenging. Wirnsberger et al. [J. Chem. Phys. 153, 144112 (2020)] demonstrated the use of machine learning to train deep neural networks that map between Boltzmann distributions for different thermodynamic states. Here, we adapt their approach to the free energy differences of a flexible bonded molecule, deca-alanine, with harmonic biases and different spring centers. When the neural network is trained until "early stopping"-when the loss value of the test set increases-we calculate accurate free energy differences between thermodynamic states with spring centers separated by 1 Å and sometimes 2 Å. For more distant thermodynamic states, the mapping does not produce structures representative of the target state, and the method does not reproduce reference calculations.

靶向自由能微扰利用可逆映射来促进构象空间重叠以及自由能估计的收敛。然而，开发合适的映射可能具有挑战性。Wirnsberger等人[《化学物理杂志》153卷，144112页（2020年）]展示了利用机器学习来训练深度神经网络，该网络可在不同热力学状态的玻尔兹曼分布之间进行映射。在此，我们将他们的方法应用于一个柔性键合分子——十丙氨酸在具有谐波偏置和不同弹簧中心时的自由能差异。当神经网络训练到“提前停止”——即测试集的损失值增加时——我们能够计算出弹簧中心相隔1埃（有时是2埃）的热力学状态之间准确的自由能差异。对于距离更远的热力学状态，映射无法产生代表目标状态的结构，并且该方法无法重现参考计算结果。