Kinetics-Driven Drug Discovery Using Persistent Homology, Rare-Event Molecular Dynamics and Experimental Data

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In this project a team of investigators from mathematics, molecular biology and medicinal chemistry will develop mathematical and computational tools to predict the efficacy of compounds that may help treat neuropathic pain in diabetics. Pharmaceutical drugs are mostly made up of very small molecules, which take their effect by binding to target biomolecules in our bodies and perturbing their functions. A major challenge in designing new drugs - such as treatments for cancer, diabetes and Alzheimer's disease - is figuring out how to bind a particular target both accurately (with little off-target binding), and effectively (a high percentage of targets occupied by drug molecules). A key quantity to maximize the effectiveness of a drug is its "residence time," the average amount of time the drug will remain in the binding site after each binding event. However, little is known about how the structure of a drug molecule determines its residence time, and this hinders our ability to incorporate residence time predictions in the drug design process. This research will predict the residence times of compounds binding to a pharmaceutical target molecule that affects diabetic neuropathic pain, as well as test those predictions experimentally. This study will potentially result in new treatments for diabetic neuropathic pain and also serve as a blueprint for future drug discovery efforts focused on residence time. To facilitate adoption of this approach the team of investigators will disseminate their results via a dedicated website, online servers and participation in world-wide competitions for predicting drug binding properties. This project also involves the training of graduate students with unique interdisciplinary backgrounds, and will inform the development of graduate courses at the intersection of mathematics and biological sciences. This project will develop a pipeline of mathematical and computational tools to enable kinetics-based drug discovery. The studies will be conducted on soluble epoxide hydrolase (sEH), an established pharmaceutical target for diabetic neuropathic pain for which only limited drugs have yet been approved. This project will use an integrated approach that encompasses topological modeling, machine learning, virtual screening, molecular simulation, as well as in vitro and in vivo assessment of compound efficacy. In PI Wei's laboratory, persistent homology will be used together with deep learning to abstract topological information from protein-ligand complexes and predict stable binding poses, binding affinities, and binding kinetics. It is believed that the combination of topological analysis and deep learning will be transformative: it will bring a surge in similar approaches in 3D biomolecular data predictions in the near future, as well as applications to other fields, such as chemistry, and material science. PI Dickson will use rare-event techniques in molecular modeling to simulate ligand release events, and identify the rate-limiting transition states of the ligand binding and release process. This project will also examine the robustness of ligand binding transition states for the first time, which is the key quantity to enable kinetics-based drug design. Thirdly, PI Lee will continually assess the binding affinity and residence times of the predicted compounds. Selected compounds will be tested in vivo with a novel mouse model, to determine the limits of the benefits of long in vitro residence times. This collaborative project will achieve synergistic benefits by bringing together expertise from advanced mathematics, computational biophysics, and molecular pharmacology. The collaborative tools for sampling and prediction resulting from this work can then be applied to the discovery of novel long residence time compounds for other targets of interest.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.