Large-Scale Validation of Mixed-Solvent Simulations to Assess Hotspots at Protein–Protein Interaction Interfaces

• Published in: Journal of chemical information and modeling Vol. 58; no. 4; pp. 784 - 793
• Main Authors: Ghanakota, Phani, van Vlijmen, Herman, Sherman, Woody, Beuming, Thijs
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 23.04.2018
• Subjects: Acetonitrile; Chemical compounds; Enzymes; Inhibitor drugs; Inhibitors; Interleukins; Molecular chains; Proteins; Receptors; RNA-protein interactions; Simulation; Solvents
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.7b00487

The ability to target protein–protein interactions (PPIs) with small molecule inhibitors offers great promise in expanding the druggable target space and addressing a broad range of untreated diseases. However, due to their nature and function of interacting with protein partners, PPI interfaces tend to extend over large surfaces without the typical pockets of enzymes and receptors. These features present unique challenges for small molecule inhibitor design. As such, determining whether a particular PPI of interest could be pursued with a small molecule discovery strategy requires an understanding of the characteristics of the PPI interface and whether it has hotspots that can be leveraged by small molecules to achieve desired potency. Here, we assess the ability of mixed-solvent molecular dynamic (MSMD) simulations to detect hotspots at PPI interfaces. MSMD simulations using three cosolvents (acetonitrile, isopropanol, and pyrimidine) were performed on a large test set of 21 PPI targets that have been experimentally validated by small molecule inhibitors. We compare MSMD, which includes explicit solvent and full protein flexibility, to a simpler approach that does not include dynamics or explicit solvent (SiteMap) and find that MSMD simulations reveal additional information about the characteristics of these targets and the ability for small molecules to inhibit the PPI interface. In the few cases were MSMD simulations did not detect hotspots, we explore the shortcomings of this technique and propose future improvements. Finally, using Interleukin-2 as an example, we highlight the advantage of the MSMD approach for detecting transient cryptic druggable pockets that exists at PPI interfaces.