Experimental assessment of first-generation HsbA-derived peptide antibodies and independent computational prioritization of candidate epitopes with predicted discriminatory potential for Lichtheimia corymbifera.

Hydrophobic surface-binding protein A (HsbA), a secreted fungal protein, was evaluated as a promising diagnostic target for Mucormycosis, a serious fungal infection. We tested (i) whether HsbA-derived antibodies show preferential binding to Lichtheimia corymbifera and (ii) identify HsbA-derived peptide regions as candidates for discriminating L. corymbifera from other species. Experimental assays against a panel of five Mucorales comparators, A. fumigatus, C. neoformans, five Candida species and three bacterial species showed that five of seven rabbit polyclonal antibody preparations raised against HsbA-derived peptides bound preferentially to L. corymbifera spores on the reference strain, and four of seven on the attenuated strain, within the tested organism panel. This supports the possibility that HsbA contains epitopes with preliminary discriminatory potential within the tested panel. Distinct from this, computational analyses identified a small set of prioritized HsbA peptide regions, with the peptide NSWEPSLDGYAGALK showing predicted sequence-discriminatory potential, consistent surface exposure, and predicted antigenicity. The novel computationally prioritized candidates have not been experimentally validated. Separately and independently of the experimental work, a rational, computation-driven framework combining phylogenetic analysis, sensitivity-specificity profiling, surface accessibility prediction, and linear B-cell epitope prediction was established for identifying novel computationally prioritized peptide candidates Mu_DP2 and Mu_DP3 for future experimental evaluation comparing HsbA from 10 Mucorales and 6 Aspergillus species. We identify computationally prioritized HsbA-derived peptide candidates with predicted discriminatory potential and follow-up studies will aim to experimentally evaluate the top computationally-identified peptides in a serological assay with patient samples.