Theory and Computationally Guided Molecular Recognition Design for Single-Cell Population Chemical Analysis: Renewal

About This Grant

Antibodies in the human immune system recognize and grab specific molecules by changing their molecular structure to present a lock-and-key site tailored for the target. If this could be duplicated in sensors and medical assays, it could lead to new types of medical and environmental tests for human benefit. This project will study how the structures of synthetic molecules, adsorbed on the surface of a sensing nanoparticle to form what is called a corona, can be changed to grab onto specific molecules of importance, such as signaling hormones in humans or toxins in the environment, that indicate stress or diseases. The methods developed in this project will allow scientists and engineers to predict how to synthesize the molecules forming the corona so that they recognize a target for detection. This research has the potential to lead to new types of biosensors and even therapeutics once an understanding of how structure can be tailored to a target molecule. The project will also train the next generation of scientists and engineers in state-of-the-art methods in nanotechnology, and will involve the mentorship of high school students through the HIP-SAT program at MIT to actively encourage increased student participation in STEM. This project investigates the fundamental mechanisms driving interactions between nanoparticle corona phases (CPs) and specific molecular targets. A corona phase is a layer of adsorbed molecules at the surface of a nanoparticle that prevents its aggregation. The project seeks the solution to the longstanding ‘inverse problem’ or how to reverse design the phase so that it recognizes a specific target molecule. Aim 1 focuses on computational methods to predict CP surface coverage and binding constants for specific analytes, leveraging thermodynamic models and Hamiltonian-based approaches to correlate polymer properties with analyte interactions. These new techniques will complement experimental data that are time consuming to generate. Aim 2 seeks to apply molecular dynamics simulations to examine structural and energetic dynamics of analyte binding, providing insights into binding free energy, site availability, and the influence of single-walled carbon nanotube (SWCNT) geometry. This work will produce the first comprehensive modeling approach and workflow for simulating SWCNT CP systems, predicting molecular binding computationally. Lastly, Aim 3 uses these results in a novel droplet-based microfluidic platform for high-throughput validation, enabling precise measurements of cytokine release from T-cells for single-cell phenotyping as a high impact application that builds upon the previous NSF project results. This integrated framework will significantly narrow the design space for molecular recognition sensors, providing practical computational workflows, datasets, and methodologies that will be shared publicly to advance research collaboration and toolkit development for SWCNT CP tailoring. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.