Dr. Bianxiao Cui

Membrane-curvature mediated integrin activation and drug discovery

Membrane curvature in the range of tens to hundreds of nanometers is involved in many essential cellular processes. Membrane curvatures in living cells are often below optical resolution and are highly dynamic, making it a technical challenge to explore curvature-initiated signaling events. We use nanofabrication to engineer vertical nanostructures to precisely manipulate the location, degree, and sign (positive or negative) of the interface curvature in live cells. We found that these membrane curvatures drastically affect intracellular signaling on the plasma membrane. Very recently, we found that membrane curvature promotes the formation of a new type of integrin ɑVβ5-mediated cell adhesions – curved adhesions. Curved adhesions are molecularly distinct from focal adhesions and clathrin lattices and are prevalent in soft fiber matrices in 3D. The findings illustrate the molecular basis for the strong molecular connection between membrane topography and intracellular signaling. It also opens up new therapeutic potentials for integrin inhibition. 

Dr. Song Lin

Electrochemistry as an Enabling Tool for Organic Reaction Discovery

Owing to its many distinct characteristics, electrochemistry represents an attractive approach to discovering new reactions and meeting the prevailing trends in organic synthesis. In the past several years, we have showcased a new reaction approach that combines electrochemistry and redox-metal catalysis for the functionalization of alkenes to access a diverse array of vicinally functionalized structures. Moving beyond alkene difunctionalization, we recently expanded the scope of our electrochemical reaction discovery to two-component and three-component cross electrophile coupling reactions for the formation of C–C, C–Si, and C–B bonds. In addition, using either electrooxidation or electroreduction, we achieved the site-selective functionalization of aliphatic and aromatic C–H bonds, respectively. This talk details our design principle underpinning the development of these new electrochemical transformations with a focus on applications in the synthesis of medicinally relevant compounds. In addition, this talk will discuss a parallel effort in the development of new electrochemical high-throughput reactors that can drastically improve the efficiency of reaction discovery and optimization.

Dr. Evan Miller

Fiat Lux! Using Chemistry to Measure and Monitor Cellular Physiology with Light

Cells spend a substantial portion of their energy budget to maintain an electrochemical potential difference across the plasma membrane. Optical methods to measure changes in cellular membrane potential provide a powerful complement to traditional, electrode-based methods by providing spatial resolution. My lab has been developing synthetic, voltage-sensitive fluorescent indicators that respond to changes in membrane potential via a photoinduced electron transfer mechanism. In this presentation, I will discuss my group’s recent efforts to synthesize and apply organic fluorescent indicators to living cells to interrogate cellular physiology in the context of the brain, heart, and beyond.

Dr. Mark Levin

Replacing Atoms

Transformations that allow for the replacement of one atom for another in a ring system will be presented. Key takeaways include the strategies and concepts that enable site-selective replacements without perturbation of the remaining molecular skeleton. Though the chemical modalities employed to accomplish such transformations are diverse, photochemistry and reagent design are a significant focus. 

Dr. Tim Newhouse

Computationally Augmented Synthesis

Efficient syntheses of complex small molecules often involve speculative experimental approaches. The central challenge of such plans is that experimental evaluation of high-risk strategies is resource intensive, as it entails iterative attempts at unsuccessful strategies. This presentation describes a complementary strategy that combines creative human-generated synthetic plans with robust computational prediction of synthetic feasibility. Computational modeling density functional theory and machine learning. This work defines how machine learning models can drive complex molecule synthesis.