*Article* **Binding Ensembles of** *p53***-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations**

**Lijun Lang and Alberto Perez \***

Chemistry Department, University of Florida, Gainesville, FL 32611, USA; lijunlang@chem.ufl.edu

**\*** Correspondence: perez@chem.ufl.edu; Tel.: +1-352 3927009

**Abstract:** Designing peptide inhibitors of the *p53*-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 (∆*Gcon f ormation*) and the interaction between interface residues (∆*Ginteraction*).

**Keywords:** IDP 1; binding 2; molecular dynamics 3; MELD×MD 4; advanced sampling 5; *p53* 6; MDM2 7

**Citation:** Lang, L.; Perez, A. Binding Ensembles of *p53*-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations. *Molecules* **2021**, *26*, 198. https:// doi.org/10.3390/molecules26010198

Academic Editor: Marilisa Leone Received: 13 November 2020 Accepted: 28 December 2020 Published: 2 January 2021

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