Computational modulator design and machine learning to target protein-protein interactions

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Abstract The overall goal of my research program is to develop and apply computational tools to facilitate the rational design of modulators of important cellular pathways for therapeutic use. Protein-protein interactions (PPIs) are central factors in cellular signaling and gene regulation networks. Their misregulation is associated with a variety of diseases, including cancer, neurodegenerative disease, autoimmune disease, and diabetes. Inevitably, many PPIs are biologically compelling targets for drug discovery. But despite a few notable successes, most PPIs have not been successfully targeted and remain undruggable. The fundamental challenge derives from their intrinsic structural features: the binding surfaces of many PPIs are generally large in area, flat, and dynamic. PPIs are often transient and involve multivalent contacts. Currently, one most promising PPI inhibitor discovery strategy is to use miniature protein domain mimetics (PDMs) to reproduce the key interface contacts utilized by nature. PDMs are advantageous as medium-sized molecules with high surface complementarity and a broader set of contact points than typical small molecules, but are still limited because—by definition—only a portion of the total PPI binding energy is captured in the interaction. The binding affinity of the synthetic domains is often lower than the cognate full-length proteins. On the other hand, targeted covalent inhibition is an orthogonal therapeutic approach fit to overcome the fundamental binding limitations at PPIs, but has a well-known drawback: the high reactivity of typical covalent warheads leads to nonspecific inhibition, and toxicity. Here we aim to develop computational methods for a new design strategy that will leverage the strengths of these two methods—PDMs and covalent inhibition—while simultaneously mitigating their respective limitations. The focus of the effort is to rationally discover potent inhibitors that will non-covalently recognize and then covalently target protein-protein binding interfaces with exquisite specificity. Furthermore, our development of robust scoring functions by integrating multitask machine learning and molecular modeling would significantly accelerate the rational drug discovery process. The planned work builds on our recent advances in three state-of-the-art computational approaches: AlphaSpace for fragment- centric topographical mapping of PPI interfaces; ab initio QM/MM molecular dynamics for modeling covalent inhibition; and a novel delta-machine learning strategy to simultaneously improve scoring, docking and screening performance of a protein-ligand scoring function. Our design efforts will result in highly specific and potent modulators of a variety of therapeutically important but previously undruggable PPI interfaces, providing new leads for drug development.