Sudeep Sarma, Ph. D.
Computational Protein Design | Biomolecular Simulations | Protein Aggregation | Chemical Engineer
Hi.
I am Sudeep Sarma, a Postdoctoral Fellow in Jeffrey Gray's lab at Johns Hopkins University. Previously, I completed my Ph.D. in Chemical and Biomolecular Engineering from Carol Hall's lab at North Carolina State University.
My Research Interests are:
Computational Peptide Design: Write and implement algorithms based on Markov-Chain Monte-Carlo search and Molecular Dynamics simulations to design peptides that bind to protein interfaces of biomolecular targets and form self-assembling structures.
Peptide inhibitors for C. diff Infection: The pathogenicity of C. difficile toxins is derived from two major exotoxins: Toxin A and Toxin B. Short peptides that bind to the catalytic site of these toxins and inactivate their glucosyltransferase activity is a promising therapeutic strategy. We are working to design peptide inhibitors for C. diff toxin A and B. Our peptides are experimentally synthesized and tested in cell-based assays. This work is in collaboration with the Menegatti lab and Crook lab at NC State, and the Magness lab at UNC-Chapel Hill.
Peptide inhibitors for SARS-COVID19 virus: Cellular entry of SARS-CoV-2 into the human body is mediated via binding of the Receptor Binding Domain (RBD) on the viral Spike protein (SARS-CoV-2 RBD) to Angiotensin-Converting Enzyme 2 (ACE2) expressed on host cells. Molecules that can disrupt ACE2:RBD interactions are attractive therapeutic candidates to prevent virus entry into human cells. We have redesigned the peptidase domain of ACE2 that bind to the SARS-CoV-2 RBD. This work is in collaboration with the Hudalla lab at University of Florida.
Computational discovery of peptide-based biomaterials: Peptide self-assembly into amyloid fibrils provides numerous applications in drug delivery and biomedical engineering applications. The discovery of peptides that form specific supramolecular structures lacks a sytematic approach and most research methodologies are limited to searching for amyloid-forming peptides in nature that self-assemble to amyloid-like structures. We developed a computational peptide assembly design (PepAD) algorithm, that enables the discovery of amyloid-forming peptides. The aggregation kinetics are studies in DMD/PRIME20 simulations, a fast coarse-grained approach to traditional molecular dynamics. This work is in collaboration with the Paravastu lab at Georgia Tech.
