Chemistry Department, University of Florida, Gainesville, FL 32611, USA.
Molecules. 2021 Jan 2;26(1):198. doi: 10.3390/molecules26010198.
Designing peptide inhibitors of the -MDM2 interaction against cancer is of wide interest. Computational modeling and virtual screening are a well established step in the rational design of small molecules. But they face challenges for binding flexible peptide molecules that fold upon binding. We look at the ability of five different peptides, three of which are intrinsically disordered, to bind to MDM2 with a new Bayesian inference approach (MELD × MD). The method is able to capture the folding upon binding mechanism and differentiate binding preferences between the five peptides. Processing the ensembles with statistical mechanics tools depicts the most likely bound conformations and hints at differences in the binding mechanism. Finally, the study shows the importance of capturing two driving forces to binding in this system: the ability of peptides to adopt bound conformations (ΔGconformation) and the interaction between interface residues (ΔGinteraction).
设计与 -MDM2 相互作用的肽抑制剂来对抗癌症是一个备受关注的领域。计算建模和虚拟筛选是小分子合理设计的一个成熟步骤。但它们在结合具有折叠结构的柔性肽分子时面临挑战。我们使用一种新的贝叶斯推断方法 (MELD × MD) 来研究五种不同的肽，其中三种是固有无序的，与 MDM2 结合的能力。该方法能够捕捉结合过程中的折叠机制，并区分这五种肽之间的结合偏好。使用统计力学工具处理这些集合可以描绘出最可能的结合构象，并暗示结合机制的差异。最后，该研究表明在这个体系中捕捉两个结合驱动力的重要性：肽能够采用结合构象的能力（ΔGconformation）和界面残基之间的相互作用（ΔGinteraction）。
