Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

中文翻译：

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通过端点结合自由能计算预测蛋白质-配体相互作用的突变效应：策略和分析

蛋白质突变在生物系统中经常发生，这可能会影响药物与其靶标的结合，例如通过破坏关键的氢键、改变疏水相互作用等。因此，准确预测突变对生物系统的影响是对各个领域都产生了浓厚的兴趣。不幸的是，由于无法负担的实验时间和财务成本，仍然无法进行大规模的湿实验室突变实验。或者，计算机计算可以作为指导实验的先驱。事实上，从计算成本较低的机器学习 (ML) 方法到更昂贵的炼金术方法，已经进行了许多开创性工作，目的是准确预测突变效应。然而，这些方法通常要么不能产生物理上可理解的模型（基于 ML 的方法），要么使用巨大的计算资源（炼金术方法）。因此，期望具有良好物理特性和高计算效率的折衷方法。因此，在这里，我们以MM/GBSA和MM/PBSA为代表的著名的端点结合自由能计算方法对生物系统的突变问题进行了全面的研究。研究了考虑不同长度的 MD 模拟、不同介电常数值以及是否将熵效应纳入预测的总结合亲和力的不同计算策略，以提供更准确的方法来预测蛋白质突变时的能量变化。全面的，我们的结果表明，相对较长的 MD 模拟（例如 100 ns）有利于 MM/GBSA 和 MM/PBSA 的预测精度（预测的 ΔΔG 和实验数据之间的最佳 Pearson 相关系数约为 0.44数据集）。进一步的分析表明，涉及大扰动的系统（例如多个突变和突变位点的大量原子变化）更容易被准确预测，因为该算法对系统的大变化更敏感。此外，系统特定的研究表明，需要进行构象调整来优化手动突变系统的微环境，从而使人们理解为什么需要更长的 MD 模拟来改善预测结果。