Department of Biomedical Engineering and Center for Science & Engineering of Living Systems (CSELS) , Washington University in St. Louis , One Brookings Drive , Campus Box 1097, St. Louis , Missouri 63130 , United States.
J Phys Chem B. 2019 Aug 15;123(32):6952-6967. doi: 10.1021/acs.jpcb.9b05206. Epub 2019 Aug 7.
The overall charge content and the patterning of charged residues have a profound impact on the conformational ensembles adopted by intrinsically disordered proteins. These parameters can be altered by charge regulation, which refers to the effects of post-translational modifications, pH-dependent changes to charge, and conformational fluctuations that modify the p values of ionizable residues. Although atomistic simulations have played a prominent role in uncovering the major sequence-ensemble relationships of IDPs, most simulations assume fixed charge states for ionizable residues. This may lead to erroneous estimates for conformational equilibria if they are linked to charge regulation. Here, we report the development of a new method we term -canonical Monte Carlo sampling for modeling the linkage between charge regulation and conformational equilibria. The method, which is designed to be interoperable with the ABSINTH implicit solvation model, operates as follows: For a protein sequence with ionizable residues, we start with all 2 charge microstates and use a criterion based on model compound p values to prune down to a subset of thermodynamically relevant charge microstates. This subset is then grouped into mesostates, where all microstates that belong to a mesostate have the same net charge. Conformational distributions, drawn from a canonical ensemble, are generated for each of the charge microstates that make up a mesostate using a method we designate as . This method combines Metropolis Monte Carlo sampling in conformational space with an auxiliary Markov process that enables interconversions between charge microstates along a mesostate. Proton walk sampling helps identify the most likely charge microstate per mesostate. We then use thermodynamic integration aided by the multistate Bennett acceptance ratio method to estimate the free energies for converting between mesostates. These free energies are then combined with the per-microstate weights along each mesostate to estimate standard state free energies and pH-dependent free energies for all thermodynamically relevant charge microstates. The results provide quantitative estimates of the probabilities and preferred conformations associated with every thermodynamically accessible charge microstate. We showcase the application of -canonical sampling using two model systems. The results establish the soundness of the method and the importance of charge regulation in systems characterized by conformational heterogeneity.
总体电荷含量和荷电残基的模式对无规卷曲蛋白质所采用的构象集合有深远的影响。这些参数可以通过电荷调节来改变，电荷调节是指翻译后修饰、pH 依赖性电荷变化和构象波动对可离子化残基的 p 值的影响。尽管原子模拟在揭示 IDP 的主要序列-集合关系方面发挥了重要作用，但大多数模拟都假设可离子化残基的固定电荷状态。如果它们与电荷调节有关，这可能会导致对构象平衡的错误估计。在这里，我们报告了一种新方法的开发，我们称之为用于模拟电荷调节和构象平衡之间联系的 -正则蒙特卡罗抽样。该方法旨在与 ABSINTH 隐式溶剂模型兼容，其操作如下：对于具有 个可离子化残基的蛋白质序列，我们从所有 2 个电荷微状态开始，并使用基于模型化合物 p 值的标准来修剪到一组热力学相关的电荷微状态。然后将这个子集分组为中态，其中属于一个中态的所有微状态都具有相同的净电荷。使用我们指定为 的方法，从正则系综中为构成中态的每个电荷微状态生成构象分布。该方法将构象空间中的 Metropolis 蒙特卡罗抽样与辅助 Markov 过程相结合，该过程允许在中态内进行电荷微状态之间的转换。质子游走采样有助于确定每个中态最可能的电荷微状态。然后，我们使用热力学积分辅助多态 Bennett 接受比方法来估计从中态转换的自由能。然后，将这些自由能与每个中态的每个微状态的权重结合起来，以估计所有热力学相关电荷微状态的标准状态自由能和 pH 依赖性自由能。结果提供了与每个热力学可及的电荷微状态相关的概率和首选构象的定量估计。我们使用两个模型系统展示了 -正则抽样的应用。结果证明了该方法的正确性和构象异质性系统中电荷调节的重要性。
