Seminar: Sukrit Singh

The Simons Center for Computational Physical Chemistry at NYU regularly hosts visiting scholars to discuss their work.  Join us on April 28th for a presentation by Sukrit Singh, a Postdoctoral Research Fellow at Memorial Sloan-Kettering Cancer Center working jointly with John Chodera and Markus Seeliger:

Mechanism-based modeling of drug resistant mutations in cancer

Abstract:
My work at Memorial Sloan Kettering Cancer Center focuses on quantitatively modeling kinase activation, inhibitor resistance, and ligand sensitivity. Protein kinases are important signaling enzymes often dysregulated in cancer; their pharmacological value as drug targets exemplified by the clinical use of over 80 FDA-approved inhibitors. Unfortunately, intrinsic and acquired mutations have been clinically observed to confer inhibitor resistance, drastically reducing patient survival rates. Precision oncology approaches, matching patient tumor profiles to optimal therapies, have proven somewhat useful with the advent of tumor sequencing. However, it remains challenging to identify drug-resistant mutants prior to treatment and develop regimens to circumvent them. In fact, the NCI-MATCH trial showed that only 6% of clinical mutations can be successfully paired with patients to improve clinical outcomes. This gap in improved clinical outcomes is due to a lack of mechanistic information describing the impact of mutations upon ligand binding. Kinase mutations may decrease drug-binding affinity, increase kinase activity, tune inhibitor sensitivity profiles, or any combination of these and other mechanisms. However, describing inhibitor selectivity and resistance remains difficult due to a lack of biophysical data characterizing a mutation’s impact on ligand binding. Computational approaches offer a promising launchpad to generate these datasets and predict the impact of mutations to inform inhibitor efficacy. Integrating biophysical experiments with the distributed Folding@home platform to run molecular dynamics (MD) simulations and free energy calculations (FECs), I describe our efforts to characterize the mechanistic basis of drug-resistant mutations. I mechanistically describe whether a mutation perturbs ligand binding affinity, reorganizes the kinase conformational landscape, or increases kinase activity. I demonstrate a prototype approach by modeling potential drug-resistant mutations in Menin, a target in leukemias that develops resistance to the clinical inhibitor revumeninb. By integrating computational pipelines and experimental approaches, we characterize drug-resistant Menin mutants and demonstrate the conformational basis of mutation-induced drug-resistance. I then demonstrate this approach’s predictive power by prospectively estimating the impact of mutations in Abelson (Abl) kinase against the inhibitors imatinib and dasatinib.

All Simons Center seminars are held in Waverly 540.  Refreshments will be served at 10:45, and the seminar begins promptly at 11:00 AM ET.      

Or join via Zoom:  https://nyu.zoom.us/j/99318701420?pwd=eGVvSzlKWFRlV0ZldnJJbjhYVUtEQT09