Systematic Evaluation of Macromolecular Carbohydrate-Lectin Recognition Using Precision Glycopolymers.

The precise modulation of protein-carbohydrate interactions is critical in glycobiology, where multivalent binding governs key cellular processes. As such, synthetic glycopolymers are useful for probing these interactions. Herein, we developed precision glycopolymers (PGPs) with unambiguous local chemical composition and well-defined global structure and systematically evaluated the effect of polymer length, hydrophobicity, and backbone hybridization as well as glycan density and identity on the binding to both mammalian and plant lectins. Our studies identified glycan density as a critical factor, with PGPs below 50% grafting density showing significantly weaker lectin interactions. Coarse-grained molecular dynamics simulations suggest that the observed phenomena may be due to a decrease in carbohydrate-carbohydrate interactions in fully grafted PGPs, leading to improved solvent accessibility. In functional assays, these PGPs reduced the cell viability and migration in 4T1 breast cancer cells. Our findings establish a structure-activity relationship in glycopolymers, providing new strategies for designing synthetic glycomacromolecules for a myriad of applications.

Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios.

Emerging pathogens are a historic threat to public health and economic stability. Current trial-and-error approaches to identify new therapeutics are often ineffective due to their inefficient exploration of the enormous small molecule design space. Here, we present a data-driven computational framework composed of hybrid evolutionary algorithms for evolving functional groups on existing drugs to improve their binding affinity toward the main protease (Mpro) of SARS-CoV-2. We show that combinations of functional groups and sites are critical to design drugs with improved binding affinity, which can be easily achieved using our framework by exploring a fraction of the available search space. Atomistic simulations and experimental validation elucidate that enhanced and prolonged interactions between functionalized drugs and Mpro residues result in their improved therapeutic value over that of the parental compound. Overall, this novel framework is extremely flexible and has the potential to rapidly design inhibitors for any protein with available crystal structures.