The Radhakrishnan Lab

Matchmaking Molecules and Catalyzing Cross-Disciplinary Collaboration

Carla_final_sim1_crowded_movie.mov

(video above is an MD simulation of the antimicrobial peptide buforin-2 in complex with a short stretch of DNA in an environment crowded with lysozyme protein.) Simulation by Carla Perez '19, in collaboration with Professor Don Elmore

The Radhakrishnan Lab develops, analyzes, and applies computational tools to better understand how biological molecules interact and to design novel interactions. We like to think of ourselves as molecule "matchmakers"; in the same way that matchmakers seek to understand the fundamental determinants that cause people to interact, we seek to understand the structural determinants of molecular recognition. Doing so can help in the design of novel therapeutic molecules to treat disease. As electrostatic interactions play a crucial role in molecular recognition, we are especially interested in the accurate modeling of electrostatic interactions in biological systems.

(image above is a "static" analysis of the optimality of electrostatic binding between CML drug imatinib and its Abl-kinase protein target (wild-type and T315I mutant; analyses carried out by Aliyah Audil '20 and Chi Trinh '20)

Ongoing projects include:

Design and analyses of membrane translocating antimicrobial peptides (in collaboration with Professor Don Elmore.)
An analysis of drug/target interactions in the chronic myeloid leukemia system
An analysis of the effects of macromolecular crowding on electrostatic interactions in biomolecular systems (in collaboration with Don Elmore)
Development and assessment of case-based, interdisciplinary molecular modeling and visualization activities to engage high school students (in collaboration with Don Elmore and Martin Berryman)
Understanding how the incorporation of poetry-writing into an undergraduate STEM curriculum can affect students and inform educators (in collaboration with Dr. Sam Illingworth)

As these examples show, the work in our group is highly interdisciplinary, involving various connections between physics, chemistry, biology, math, computer science, education, qualitative analysis, and the humanities.