(361f) Design of Peptide-Based Drugs for Treating Clostridioides Difficile Infection | AIChE

(361f) Design of Peptide-Based Drugs for Treating Clostridioides Difficile Infection


Sarma, S. - Presenter, North Carolina State University
Crook, N., North Carolina State University
Magness, S., UNC Chapel Hill
Durmusoglu, D., North Carolina State University
Menegatti, S., North Carolina State University
Clostridioides 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 to develop 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 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 and toxin B 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 identified an 8-mer peptide, SA1, that binds to the Clostridioides difficile toxin A GTD. The efficacy of peptide SA1 was tested using a trans-epithelial electrical resistance (TEER) assay on monolayers of the human gut epithelial culture model. Peptide SA1 blocks TcdA toxicity in jejunum (small intestine) cells and in colon epithelial cells and exhibits a binding affinity in the nanomolar range. Next, we designed peptides that bind to toxin B GTD. Preliminary experimental data suggest that two of our in-silico identified peptides, SB2 and SB6, are inhibiting glycosylation of toxin B GTD with an IC50 in the micromolar range. Collectively, our results demonstrate the potential of our computational peptide design protocol to identify peptide-based inhibitors that can treat Clostridoides difficile infection and other infectious pathogens in the future.