Events

David Halat, Ph.D Abstract Understanding the fundamental mechanisms of ion transport in energy storage and conversion devices is key to advancing next-generation, beyond-Li technologies less reliant on critical minerals. Novel and emerging magnetic resonance methods, including quadrupolar and electrophoretic NMR (eNMR), are premier local structure probes that can provide sensitive...

Morkos Henen Abstract With the challenges in the drug-discovery field, the need for innovative approaches is indispensable. Multiple NMR techniques have been developed over the years and used by pharmaceutical companies to ensure robust detection of biomolecular binders. During this talk, I will discuss the collaboration between academic institutes and...

Gaetano T. Montelione, Ph.D. Abstract Protein structures are not static objects, but rather sample a conformational energy landscape that underpins their functions. Recent advances in molecular modeling using deep learning can revolutionize our understanding of dynamic protein structures. NMR is particularly well-suited for studying dynamic features of biomolecular structures. The...

Sunil Kumar, Ph.D. Abstract The aberrant protein-protein interactions (aPPIs) lead to numerous pathological conditions, including infectious diseases, cancer, and amyloid diseases. Therefore, modulation of aPPIs could be considered a viable therapeutic intervention for a wide range of pathologies. Two common strategies to modulate aPPIs are small molecules and peptides/antibodies. Both...

Yunsik Kang, Ph.D. Abstract Neuronal remodeling during development produces substantial cellular debris, which glial cells must phagocytose to maintain tissue homeostasis and guide proper circuit formation. In Drosophila, astrocytes transform into phagocytes during the larva-to-adult transition, phagocytosing synaptic, axonal, and dendritic debris. We show that Tweek, a bridge-like lipid transfer...

Tosaporn Sattasathuchana, Ph.D. Abstarct Quantum mechanics (QM) is a valuable tool for investigating chemical and biological phenomena at the molecular level. Two topics from my research group will be presented in this talk. The first topic focuses on our new hybrid QM-statistical mechanics approach, namely protein-ligand QM-VM2 (PLQM-VM2), for accurately...

Fang Liu, Ph.D. Abstract Machine learning (ML) and big data play increasingly critical roles in chemical discovery. However, datasets (both computational and experimental) and ML models remain scarce for solvated molecules and molecule assemblies. My group leverages GPU-accelerated quantum chemistry and machine learning to address these gaps. We developed AutoSolvate,...