Abstract: Measurement of interaction forces and binding kinetics of ligand‐receptor interactions on live cells at single molecular level is extremely crucial for gaining novel biological insights, evaluating potential therapeutic agents for cancer and other diseases as well as for developing new gene and drug delivery approaches. Atomic Force Microscopy (AFM) is a unique tool to investigate protein‐protein interactions including receptor‐ligand binding. In this talk, I will discuss how high-resolution imaging and nanomechanical property characterization of both plant and animal proteins, biomolecules, and live cells using AFM are empowering us to discover structural and topographical, and nanomechanical property details yet to be discovered. I will talk about developing a biophysical method to isolate and measure specific interactions between Discoidin domain receptors (DDRs) which are overexpressed in cancer, fibrosis and other diseases and collagen (ligands of DDRs) in live cells at the single molecule level using atomic force microscopy (AFM). Another commonly over‐expressed receptor in diseases which can be targeted in drug delivery by ligand‐conjugated nanoparticles is the folate receptor alpha (FRα). In this regard, I will talk about measuring the binding dynamics of this receptor with self‐assembled nanoparticles decorated with a folic acid (FA) ligand using AFM and proving that multivalent micelleplexes bind to FRα with a higher binding probability and binding force than monovalent FA. In a statistically significant data set of 1000 force distance curves obtained with AFM, we may observe no rupture, single, double and multiple bond rupture events. In this regard, measuring rupture force for each force-distance curve manually is extremely time consuming for experimentalists, making this method low throughput as well as vulnerable to human biases. Therefore, fast, and robust analytics techniques are essential for AFM data analytics to make the process high throughput as well as free of human biases. I propose a new deep learning-based AFM data analytics technique that can cut down the data selection and analysis time by more than 10 times. Prediction of 3D structures of protein complexes using a hybrid method (combining AFM based 2.5D high-resolution imaging and geometry aware machine learning approaches) is one of the newest directions that I will summarize.
Short bio: Dr. Anwesha Sarkar received her undergraduate degree (B.Sc.) in Physics from St. Xavier’s College, Kolkata, India in 2008, her Master of Science (M.Sc.) form Indian Institute of Technology (IIT) Madras, India in 2010 and a Master of Philosophy (M.Phil.) degree from Institute of Physics (IOP) Bhubaneswar, India in 2011. She received her Ph.D. in Physics from Wayne State University, Michigan in 2015. She did her postdoc from the Department of Physics and Astronomy, Iowa State University (2016-2020). She joined as an Adjunct Assistant Professor at Department of Electrical and Computer Engineering, Iowa State University in Fall 2020. She joined Department of Electrical and Computer Engineering, Iowa State University as an Assistant Professor in Fall 2022. She became Harpole-Pentair Assistant Professor in Fall 2023. Her main research interests are high resolution imaging, and nanomechanical property characterization of biomolecules, proteins, DNA, RNA, and live cells using Atomic Force Microscopy (AFM). She also specializes in the isolation and quantification of single molecule interaction forces and binding kinetics of ligand-receptor systems on live using Atomic Force Microscopy. She is exploring machine learning approaches for fast atomic force microscopy (AFM) data analytics.
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