Mapping the Ligand Binding Landscape.
Mapping the Ligand Binding Landscape.
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DOI：
10.1016/j.bpj.2018.09.021
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发表时间：
2018-11-06
影响因子：
3.4
通讯作者：
Dickson A
中科院分区：
文献类型：
--
作者：
Dickson A
配体和蛋白质之间的相互作用涉及多种构象状态。为了达到一个特定的深度约束的姿态，配体必须在一个粗糙的自由能景观与许多亚稳态极小值搜索。创建配体结合景观的地图是一个巨大的挑战，因为结合和释放事件通常发生在分子模拟无法达到的时间尺度上。WExplore增强的采样方法非常适合构建这些地图，因为它旨在广泛探索自由能景观，并且能够模拟在长达几分钟的时间尺度上发生的配体释放途径。WExplore还只使用无偏轨迹段，允许构建马尔可夫状态模型（MSM）和构象空间网络，这些网络将多个模拟的结果联合收割机结合起来。在这里，我们使用WExplore研究两个溴结构域抑制剂系统，使用多个对接的起始姿势（Brd 4-MS 436和Baz 2B-ICR 7），并使用一系列MSM使用时滞独立成分分析合成我们的结果。在这两种情况下，通过退出率对起始姿势进行排序与晶体结构姿势一致。我们还预测了最稳定的姿势使用的平衡种群从MSM，但发现，作为MSM参数的函数的预测是不稳健的。模拟的轨迹被合成到网络模型中，可视化每个系统的整个绑定景观，我们研究深度绑定稳定状态之间的过渡路径。我们发现，平均而言，深度绑定状态之间的转换转换通过未绑定状态的81%的时间，这意味着一个试错法配体结合。最后，我们讨论了这一结果对基于动力学的药物发现和虚拟筛选管道，将分子动力学的影响。
The interaction between a ligand and a protein involves a multitude of conformational states. To achieve a particular deeply bound pose, the ligand must search across a rough free-energy landscape with many metastable minima. Creating maps of the ligand binding landscape is a great challenge, as binding and release events typically occur on timescales that are beyond the reach of molecular simulation. The WExplore enhanced sampling method is well suited to build these maps because it is designed to broadly explore free-energy landscapes and is capable of simulating ligand release pathways that occur on timescales as long as minutes. WExplore also uses only unbiased trajectory segments, allowing for the construction of Markov state models (MSMs) and conformation space networks that combine the results of multiple simulations. Here, we use WExplore to study two bromodomain-inhibitor systems using multiple docked starting poses (Brd4-MS436 and Baz2B-ICR7) and synthesize our results using a series of MSMs using time-lagged independent component analysis. Ranking the starting poses by exit rate agrees with the crystal structure pose in both cases. We also predict the most stable pose using the equilibrium populations from the MSM but find that the prediction is not robust as a function of MSM parameters. The simulated trajectories are synthesized into network models that visualize the entire binding landscape for each system, and we examine transition paths between deeply bound stable states. We find that, on average, transitions between deeply bound states convert through the unbound state 81% of the time, implying a trial-and-error approach to ligand binding. We conclude with a discussion of the implications of this result for both kinetics-based drug discovery and virtual screening pipelines that incorporate molecular dynamics.
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选择性抑制BET溴结构域。
影响因子：
64.8
作者：
通讯作者：
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影响因子：
4.8
作者：
Fox D 3rd;Burgin AB;Gurney ME
通讯作者：
Gurney ME
影响因子：
7.3
作者：
Crawford, Terry D.;Tsui, Vickie;Cochran, Andrea G.
通讯作者：
Cochran, Andrea G.
影响因子：
3.3
作者：
Dickson, Alex;Lotz, Samuel D.
通讯作者：
Lotz, Samuel D.
DOI：
10.1021/jp411479c
发表时间：
2014-04-03
期刊：
The journal of physical chemistry. B
影响因子：
--
作者：
Dickson A;Brooks CL 3rd
通讯作者：
Brooks CL 3rd