Binding Ensembles of <i>p53</i>-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations

• Main Authors: Lijun Lang, Alberto Perez
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Molecules
• Subjects: IDP 1; binding 2; molecular dynamics 3; MELD×MD 4; advanced sampling 5; <i>p53</i> 6
• Online Access: https://www.mdpi.com/1420-3049/26/1/198

Designing peptide inhibitors of the <i>p53</i>-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 (<inline-formula><math display="inline"><semantics><mrow><mi>Δ</mi><msub><mi>G</mi><mrow><mi>c</mi><mi>o</mi><mi>n</mi><mi>f</mi><mi>o</mi><mi>r</mi><mi>m</mi><mi>a</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></msub></mrow></semantics></math></inline-formula>) and the interaction between interface residues (<inline-formula><math display="inline"><semantics><mrow><mi>Δ</mi><msub><mi>G</mi><mrow><mi>i</mi><mi>n</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>a</mi><mi>c</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></msub></mrow></semantics></math></inline-formula>).