Research

Computational Protein/Peptide Design

Short peptides that bind to specific bio-molecules have many uses, including as biosensors, diagnostics, therapeutics, and affinity ligands for bio-separations. We have been developing a computational search algorithm to design peptide sequences that can bind to specific RNA- or protein-based targets. The peptide design algorithm works on stochastic predictions based on the Monte Carlo methods. Our algorithm performs two kinds of moves to discover peptides: sequence mutation and backbone conformation change. We apply several other mathematical and first principle techniques to our design strategy. We extensively employ atomistic molecular dynamics simulations in local and national computing clusters to test our designed peptides in silico. We are continuously working on improving the code to increase the efficiency of our computational predictions.

Peptide Inhibitors for Clostridoides difficile (C. diff) toxins 

C. diff. is the leading cause of hospital acquired infections which causes symptoms of diarrhea to life threatening inflammation of the colon cells. Clostridium difficile (C. diff.) infection is mediated by two major exotoxins: toxins A (TcdA) and B (TcdB). Inhibiting the biocatalytic activities of these toxins with targeted peptide-based drugs can reduce the risk of C. diff. infection. The glucosyltransferase domain (GTD) of these toxins act by binding UDP (Uridine diphosphate)-glucose, hydrolyzing it into glucose and UDP, and attaching the glucose monomer to human Rho family of GTPases.  Glycosylation of the GTPases by TcdA and TcdB GTDs leads to disruption of the cytoskeleton, apoptosis and cell death. We discover peptides that block the activity of the GTD by using our computational peptide design strategy. Our experimental collaborators synthesize these peptides, and test their efficacy in primary derived human jejunum and colon cells. This work is in collaboration with the Magness lab (UNC-Chapel Hill), Crook lab (NC State) and Menegatti lab (NC State).

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Peptide Inhibitors for SARS-COVID-19 virus

The recent SARS-COVID19 pandemic has brought our attention on the need of disruptive peptide based therapeutics that can block the entry of the virus into out body. We have recognized that one way of doing that is to design peptide inhibitors that will have a high affinity to the Receptor Binding Domain (RBD) of the SARS-COV2 Spike Protein which will prevent the binding of the RBD to the ACE2 (Angiotensin converting enzyme 2) receptor in the human epithelial cells. The RBD-ACE2 binding has a Kd value of 15 nM which is very good. We are trying to discover peptides that bind to the SARS-CoV-2 RBD by redesining the peptidase domain of the ACE2 receptor.  This work is in collaboration with the Hudalla lab at University of Florida.

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Computational design of peptides to form self-assembling supramolecular structures

Self-assembling peptides that can form different supramolecular architectures can be useful in various drug delivery and biomedical engineering applications. The discovery of peptides that can self-assemble to form amyloid-like structures lacks a systematic approach and there has been limited work by researchers to search for peptide sequences that are predicted to form cross-β spines. We have developed a computational peptide assembly design (PepAD) algorithm, that enables the discovery of amyloid-forming peptides. Discontinuous molecular dynamics (DMD) simulation with the PRIME20 force field combined with the FoldAmyloid tool is used to examine the fibrilization kinetics of PepAD-generated peptides. The self-assembling tendencies of the peptides are characterized in biophysical experiments. This work is in collaboration with the Paravastu lab at Georgia Tech University. 

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