Institute of Applied Dynamics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany.
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, 94720 San Francisco, California, United States.
J Chem Inf Model. 2022 Jun 13;62(11):2869-2879. doi: 10.1021/acs.jcim.2c00126. Epub 2022 May 20.
The three-dimensional conformations of a protein influence its function and select for the ligands it can interact with. The total free energy change during protein-ligand complex formation includes enthalphic and entropic components, which together report on the binding affinity and conformational states of the complex. However, determining the entropic contribution is computationally burdensome. Here, we apply kinematic flexibility analysis (KFA) to efficiently estimate vibrational frequencies from static protein and protein-ligand structures. The vibrational frequencies, in turn, determine the vibrational entropies of the structures and their complexes. Our estimates of the vibrational entropy change caused by ligand binding compare favorably to values obtained from a dynamic Normal Mode Analysis (NMA). Higher correlation factors can be achieved by increasing the distance cutoff in the potential energy model. Furthermore, we apply our new method to analyze the entropy changes of the SARS CoV-2 main protease when binding with different ligand inhibitors, which is relevant for the design of potential drugs.
蛋白质的三维构象影响其功能，并决定其能够与之相互作用的配体。蛋白质-配体复合物形成过程中的总自由能变化包括焓变和熵变分量，它们共同反映了复合物的结合亲和力和构象状态。然而，确定熵变贡献在计算上是很繁琐的。在这里，我们应用运动学柔性分析（KFA）从静态蛋白质和蛋白质-配体结构中有效地估计振动频率。振动频率反过来又决定了结构及其复合物的振动熵。我们对配体结合引起的振动熵变化的估计与从动态正常模式分析（NMA）获得的值相当吻合。通过增加势能模型中的距离截止值，可以实现更高的相关系数。此外，我们应用新方法分析 SARS CoV-2 主蛋白酶与不同配体抑制剂结合时的熵变，这对于潜在药物的设计具有重要意义。
