AFM is advantageous over other SPM and electron micron microscopy techniques as it can operate in liquid, ambient air, and vacuum and it permits visualization of three-dimensional surface profiles of biological specimens in the near physiological environment without sacrificing their native structures and functions. We are performing high resolution imaging, characterization for inorganic as well as organic samples especially biomolecules, biosensors, proteins, DNA and live cells (animal as well as plant cells) by combining fluorescence microscopy and bio-AFM platform. The regions/ proteins/ biomolecules of interest can be tagged with fluorescent dye to identify their locations on the sample using the fluorescence microscope and AFM probe will be approached towards that particular location. Overlay of fluorescence and AFM imaging will help us to gain more precise topographical information of the sample or subcellular structures in case of live cells. Nanomechanical property and viscoelastic property measurements of biomolecules, biosensors and live cells will also be my primary focus areas considering it can shed some light on effects of different drugs on normal and cancer cells, and quantify relaxation time and dynamic modulus.

We developed a biophysical method to isolate and measure specific interactions between receptor-ligand pair on live animal cells at the single molecule level using atomic force microscopy. We will pursue applying this method to other protein-protein or ligand-receptor pairs on cancer as well as healthy cells to determine their interaction forces and binding dynamics that are instrumental for the development of novel therapeutic strategies. This method will also help to test the viability and efficacy of different drugs and antibodies on live animal cells. Simultaneously, we will focus on measuring important parameters of binding kinetics between receptor-ligand pairs on live plant cells and cell walls. Determination of the mechanical force, involved in neuron synapsis with AFM will be in my priority list as well.

Determination of sizes, shapes, structural details, roughness of drug delivery carriers (cellular, particle and soluble carriers) are crucial for developing improved targeted drug delivery approaches. We determine those important parameters with AFM as it can provide more accurate measurements of sizes, and shapes when compared to its contemporary techniques like SEM and DLS with the advantage of less time consuming and less costly sample preparation protocols. Nanoparticle based drug delivery carriers are the most widely used drug delivery carriers that can be utilized to selectively deliver their payload to specific cells, while avoiding unwanted delivery to healthy cells. We intend to continue understanding the binding dynamics of the targeted receptor (overexpressed in diseases) with the ligand of the nanoparticles. Using AFM, we will also be able to choose a better drug delivery vehicle based on higher values of binding probability and the most probable unbinding/ rupture force by introducing competitive binding in the flow cell.

AFM is beneficial for measuring interaction forces and binding kinetics for protein-protein or receptor-ligand interactions on live cells at a single-molecule level. However, high-resolution imaging and force measurements performed with AFM and data analytics are time-consuming and require specific skill sets and constant human supervision. It also involves problems such as cantilever tip breakage after prolonged functionalization and damage to the live cell samples due to lack of optimization of the loading forces, making this a low throughput method. Although remarkable progress in the area of AI and machine learning (ML) over the past few years has left its mark on bio-imaging as well, the potential of AI-AFM strategies in a live cell characterization has been mostly unexplored. As a member of the newly formed Translational AI Center (TrAC), We have been collaborating with machine learning groups at Iowa State to build ML framework to perform automatic sample selection for AFM navigation during AFM biomechanical mapping. We also established ML based closed-loop scanner trajectory and force tracking algorithms for precise AFM positioning during sample navigation and biomechanical mapping at high speed. Our innovation will directly address state-of-the-art AFM operation via AI-driven intelligent automation, including intelligent navigation and image data analysis.

Recently, we began exploring the exciting area of predicting the 3D structure of protein complexes from atomic force microscopy (AFM)-based high-resolution 2.5D images using geometry-aware machine learning (ML). There have been recent advances in predicting the structure of proteins from their amino acid sequences using machine learning. We plan to extend this approach to predicting the structure of interacting protein complexes (with several proteins docked) using 3D reconstruction from multiple view 2.5D AFM images using neural networks with geometric constraint. These innovative strategies will revolutionize our basic understanding of protein functions dictated by their structure. Such an approach will ultimately help us understand various biological phenomena such as drug interactions and will help us to develop novel therapeutic and drug delivery strategies. These approaches will also reduce the reliance on tedious, time-consuming experiments that involve expensive equipment such as x-ray crystallography, nuclear magnetic resonance (NMR), electron microscopy (EM), and cryo-electron microscopy (cryo-EM) to understand protein-complex structures.