Abstract：
Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here, we have developed a scoring strategy that predicts the effect of the mutations on the protein-ligand binding affinity. In view of the critical importance of electrostatics in protein-ligand interactions, the charge penetration corrected AMOEBA force field (AMOEBA_CP model) was employed to improve the accuracy of the calculated electrostatic energy. We calculated the electrostatic energy using an energy decomposition analysis scheme based on the generalized Kohn-Sham (GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment-ligand complex data set (Abl236) constructed from the co-crystal structures of the cancer target Abl kinase with six inhibitors. To predict ligand binding affinity changes upon protein mutation of Abl kinase, we used sampling protocol with multistep simulated annealing to search conformations of mutant proteins. The scoring strategy based on AMOEBA_CP model has achieved considerable performance in predicting resistance for 8 kinase inhibitors across 144 clinically identified point mutations. Overall, this study illustrates that the AMOEBA_CP model, which accurately treats electrostatics through penetration correction, enables the accurate prediction of the mutation-induced variation of protein-ligand binding affinity.
摘要：
直接损害药物结合的蛋白质突变与治疗抗性有关，准确预测它们对药物结合的影响将有利于药物设计和临床实践。这里,我们开发了一种评分策略,可以预测突变对蛋白-配体结合亲和力的影响.鉴于静电在蛋白质-配体相互作用中的至关重要性，采用电荷穿透校正的AMOEBA力场（AMOEBA_CP模型）来提高计算静电能量的准确性。我们使用基于广义Kohn-Sham（GKS-EDA）的能量分解分析方案计算了静电能。通过从癌症靶标Abl激酶与六种抑制剂的共晶体结构构建的蛋白质-片段-配体复合物数据集(Abl236)验证AMOEBA_CP模型。预测Abl激酶蛋白突变后配体结合亲和力的变化,我们使用多步模拟退火的采样方案来搜索突变蛋白的构象。基于AMOEBA_CP模型的评分策略在预测144个临床鉴定的点突变中对8种激酶抑制剂的抗性方面取得了相当大的性能。总的来说,这项研究表明，AMOEBA_CP模型，通过渗透校正准确地处理静电，能够准确预测突变诱导的蛋白质-配体结合亲和力的变化。
