Seminar by Dr. Jana Shen

University of Maryland School of Pharmacy, Baltimore (USA)

Fueled by computing power, algorithm development, and the rise of AI, we are witnessing a transformation in the way we understand the cellular machines and pharmacology of drugs. In this talk, I will discuss three projects from my lab that harness this transformation: the mechanistic elucidation of 1) ion channels and 2) membrane-dependent pharmacology of drugs; 3) the development of ML models for proteomewide drug discovery. 1) Human Hv1 is a voltageand pH-gated channel exclusively selective for protons. Despite its physiological importance and prototypical role in our understanding of voltage- and pH-gated channels, mechanistic knowledge is very limited due to the lack of experimental structures for the inactive or the active state. I will discuss how we illuminated the channel activation mechanism of Hv1 by leveraging state-of-the-art AI tools and the all-atom particle-mesh Ewald continuous constant pH (CpH) molecular dynamics (MD) method in Amber24. 2) Many drugs, including opioids, are weakly basic; it is poorly understood whether and how they permeate the membrane at physiological pH. For example, fentanyl and morphine both have a pKa around 8.5, and yet experimental evidence suggests that fentanyl (but not morphine) partitions into the membrane and uses it as a reservoir to activate the mu-opioid receptor. I will discuss the weighted-ensemble CpHMD simulations that yield membrane permeation profiles and rates of fentanyl and morphine. 3) Finally, I will discuss our development of ML models leveraging Large Language Models (LLMs) and chemoproteomic data to map the cysteine-directed covalently druggable sites across the human proteome.