Research

We use a broad range of computational methods—including molecular dynamics (MD) simulations, quantum chemical calculations, and machine learning—to drive complementary experimental work, the results of which, in turn, closely guide the computational work.

Nerve signaling across a chemical synapse relies on vesicular neurotransmitter transporters (VNTs), which harness a proton electrochemical gradient to pump neurotransmitters into synaptic vesicles. Although VNTs are central in neurophysiology and important drug targets, the atomic-level mechanisms by which they pump neurotransmitters into synaptic vesicles remain mysterious. To uncover these mechanisms, we use both all-atom molecular dynamics (MD) simulations and quantum chemical calculations, enabling us to model both large-scale conformational changes as well as proton-transfer reactions. We have a major focus on vesicular monoamine transporter 2 (VMAT2), which transports all monoamine neurotransmitters (serotonin, dopamine, norepinephrine, and histamine) in the brain.

The rates at which a drug binds to and unbinds from its receptor—the drug’s binding kinetics—greatly influence the drug’s safety and efficacy. However, despite the promise of safer, more effective drugs, rationally optimizing a drug’s binding kinetics remains challenging, because doing so requires characterizing not only the bound and unbound states (which determine affinity), but also the binding pathway as well as energetic barriers along the pathway. To achieve the rational design of ligands with desired binding kinetics, we combine all-atom MD simulations—which can uniquely reveal binding pathways and energetics at atomic-level resolution—with efforts in medicinal chemistry and pharmacology. Our main focus is on G protein–coupled receptors (GPCRs), which represent the single largest class of drug targets.