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 () and the interaction between interface residues ().
Highlights
Peptide molecule inhibitors have the potential to bind to proteins classified as “undruggable” by small molecules thanks to their flexibility and complementary nature to proteins [1,2]
The results show that the anchoring residues are necessary to adopt the helical conformations associated with good binding to MDM2 but not enough on their own to promote this helical state
We have shown that possible peptide inhibitors do not necessarily bind with the same binding mode, requiring modeling approaches that allow identification of the correct binding pose
Summary
Peptide molecule inhibitors have the potential to bind to proteins classified as “undruggable” by small molecules thanks to their flexibility and complementary nature to proteins [1,2]. Rational drug design of small molecules via computational tools (e.g., docking of virtual libraries) is a common practice in the drug discovery process. These tools are not well suited to handle the flexible nature of peptide molecules, many of which are intrinsically disordered and only adopt stable structures in the presence of their binding partners [3]. Modeling the binding of flexible molecules continues to be a grand challenge in computational structure prediction. With the increase of peptide therapeutics in the market there has been a continuous development and adaptation of docking tools to capture protein-peptide interactions [3,4]. Initial peptide conformations for docking could come from computationally expensive molecular dynamics (MD) simulations of the free peptide
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