通过能量弛豫的真实反应坐标增强蛋白质构象变化采样
Enhanced Sampling of Protein Conformational Changes via True Reaction Coordinates from Energy Relaxation
AI总结：
针对增强采样中难以找到有效集体变量的问题，该研究利用广义功函数方法从能量弛豫模拟中获取控制构象变化和能量弛豫的真实反应坐标，将相关过程加速10^5至10^15倍，拓宽了分子动力学模拟的应用范围。
摘要：
增强采样的瓶颈在于找到能有效加速蛋白质构象变化的集体变量（CVs）。可预测承诺因子的真实反应坐标（tRCs）被视为最优CVs，但识别它们需要无偏自然反应轨迹，而这矛盾地依赖于有效的增强采样。我们使用广义功函数方法发现，tRCs同时控制构象变化和能量弛豫，使我们能从能量弛豫模拟中计算tRCs。对tRCs施加偏置将HIV-1蛋白酶和PDZ2结构域中的构象变化和配体解离加速了10^5至10^15倍。所得轨迹遵循自然过渡路径，能高效生成自然反应轨迹。相比之下，经验CVs产生的偏置轨迹常显示非物理特征。此外，通过从单个蛋白质结构计算tRCs，我们的方法可预测性地采样构象变化。这些发现显著拓宽了分子动力学模拟可访问的蛋白质功能过程范围。
英文摘要：
The bottleneck in enhanced sampling lies in finding collective variables (CVs) that can effectively accelerate protein conformational changes. True reaction coordinates (tRCs) that can predict the committor are considered the optimal CVs, but identifying them requires unbiased natural reactive trajectories, which, paradoxically, depend on effective enhanced sampling. Using the generalized work functional method, we found that tRCs control both conformational changes and energy relaxation, enabling us to compute tRCs from energy relaxation simulations. Applying bias to tRCs accelerated conformational changes and ligand dissociation in HIV-1 protease and the PDZ2 domain by 10^5 to 10^15-fold. The resulting trajectories follow natural transition pathways, enabling efficient generation of natural reactive trajectories. In contrast, biased trajectories from empirical CVs often display non-physical features. Furthermore, by computing tRCs from a single protein structure, our method enables predictive sampling of conformational changes. These findings significantly broaden the range of protein functional processes accessible to molecular dynamics simulations.
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