(565c) Computational Discovery of Peptide Inhibitors That Neutralize C. Diff. Toxin a in Jejunum and Colon Epithelial Cells | AIChE

(565c) Computational Discovery of Peptide Inhibitors That Neutralize C. Diff. Toxin a in Jejunum and Colon Epithelial Cells


Sarma, S. - Presenter, North Carolina State University
Xiao, X., North Carolina State University
Menegatti, S., North Carolina State University
Crook, N., North Carolina State University
Magness, S., UNC Chapel Hill
Clostridium difficile infection is the leading cause of diarrhea and colitis (inflammation of the colon) in North America and Europe. The fraction of the population infected by the disease is increasing as new strains associated with significant morbidity and mortality have appeared. There is a growing concern about the failure of the first line of treatment for C. diff. infection (metronidazole and vancomycin) and thus, current attention is focused on the need for alternative treatment options like biologic drugs which are safer and more efficacious. A viable and cost-effective strategy is the development of targeted peptide-based therapeutics that prevent and treat C. diff. infections by deactivating the pathogen while leaving the resident gut microbiota unharmed.

During infection, the C. diff. pathogen produces two large virulent toxins (toxins A and B) that share 71% sequence homology. The glucosyltransferase domain (GTD) of these toxins acts by binding Uridine diphosphate (UDP)-glucose, hydrolyzing it into glucose and UDP, and attaching the glucose monomer to the human Rho family of GTPases. Glycosylation of the GTPases by toxin A and toxin B GTDs leads to disruption of the cytoskeleton, apoptosis, and death of the colon epithelial mammalian cells.

The objective of this project is to computationally design peptide inhibitors that bind with high affinity and specificity to C. diff. toxin A GTD to inhibit its activity. We use an automated Peptide Binding Design (PepBD) algorithm developed in our lab to design peptide sequences that bind to C. diff. Toxin A. Our peptide search algorithm employs Monte Carlo methods, self-consistent mean-field theory, and the concerted rotation (CONROT) technique to search for peptides in sequence and conformation space that bind to a target protein. The starting input structure for the algorithm requires a reference ligand, which is usually an experimentally determined peptide sequence that has a good binding affinity to the target protein.

We initially discovered a 10-mer peptide inhibitor, NPA, using EGWHAHTGGG (Kd = 100 nM) as the reference ligand, a peptide identified by the Feig lab using phage display. The peptide showed good neutralization against toxin A when tested in-vitro in jejunum (intestinal) cells but showed no effect in colon cells. Based on feedback from experiments and further computational analysis we designed shorter peptide inhibitors. These peptides neutralize toxin A in both jejunum and colon cells. Current work is focused on furthering our understanding of the interaction of these peptides with the toxins, and discovering peptides that can bind to Toxin B.