Asst Prof Alexander VINOGRADOV

Peptide drug discovery:

1. Li, Y. Sun, A. Vinogradov,^✉ and H. Suga.^✉ De novo discovery of bicyclic cysteine-rich peptides targeting gasdermin D. Proc. Natl. Acad. Sci. U. S. A., 2026, 123, e2516051123.
2. Ohno, A. Vinogradov,^✉ and H. Suga.^✉ Discovery of Ultrapotent Heterodimeric Peptide Ligands using Library-vs-Library RaPID Selections. J. Am. Chem. Soc. 2026, 148, 13174–13185.

Late-stage peptide modification:

3. Vinogradov,^✉ S. Pan, and H. Suga^✉. Ligand-Enabled Selective Coupling of MIDA Boronates to Dehydroalanine-Containing Peptides and Proteins. J. Am. Chem. Soc. 2025, 147, 7533–7544.
4. Chan and A. Vinogradov.^✉ ΔAlaAz cleavable linkers: efficient peptide cleavage in the presence of aromatic thiols and air oxygen. RSC Chem. Biol. 2026, ASAP: https://doi.org/10.1039/D6CB00161K.

Ultra-high-throughput enzymology:

5. Vinogradov^✉ and H. Suga.^✉ Measuring k_cat/K_M values for over 200,000 enzymatic substrates with mRNA display. Chem, 2026, 12, 102737.
6. Vinogradov,^✉ J. Chang, H. Onaka, Y. Goto, and H. Suga.^✉ Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning. ACS Cent. Sci. 2022, 8, 814−824.

We are broadly interested in advancing macrocyclic peptides as a therapeutic modality. Our research interests revolve around developing innovative drug discovery platforms to enable the rapid and reliable identification of macrocyclic peptides targeting therapeutic proteins of interest. We are particularly interested in establishing integrated experimental and computational methods to discover drug-like peptides characterized by potency, target selectivity, high metabolic stability, and cell permeability: i.e., the properties important for drug development. We develop chemical and enzymatic approaches to install such drug-like features into peptides at a late-stage, in a manner compatible with modern drug discovery approaches such as mRNA display. Our interdisciplinary research borrows techniques from biochemistry, bioorganic and peptide chemistry, synthetic biology, and deep learning.

A second focus of our research is ultra-high-throughput enzymology and enzyme engineering. We have developed several techniques that allow us to study substrate preferences of catalytically promiscuous enzymes and to measure kinetics of hundreds of thousands of enzymatic reactions in a single experiment. We use deep learning and other statistical methods to make sense of resulting data with the goal of understanding how enzymes recognize and discriminate between different substrates and how their catalytic properties arise. We then apply these insights in synthetic biology and drug discovery, where catalytically promiscuous enzymes can be harnessed to create molecular diversity.