Positional Scanning MUC1 Glycopeptide Library Reveals Importance of PDTR Epitope Glycosylation for Lectin Binding
Authors: Cudic Mare , Singh YashoNandini , Benavente Maria Carolina Rodriguez , Beckwith Donella , Ayyalasomayajula Ramya 
Published: April 2020

ObjectiveTo evaluate the effect of neighboring residue glycosylation, glycan density, and/or the presence of unique patterns of O‐glycan clusters on MUC1 tandem repeat for binding to lectins using a positional scanning synthetic glycopeptide combinatorial library (PS‐SGCL).AbstractOne of the main barriers to explaining the functional significance of glycan‐based changes in cancer is the natural epitope heterogeneity found on the surface of cancer cells. To help address this knowledge gap, we focused on designing synthetic tools to explore the role of tumor‐associated glycans of MUC1 in the formation of metastasis via association with lectins. In this study, we have synthesized for the first time a MUC1‐derived positional scanning synthetic glycopeptide combinatorial library (PS‐SGCL) that vary in number and location of cancer‐associated α‐GalNAc (Tn antigen). This focused combinatorial library with defined structural complexity will allow us to evaluate the effect of neighboring residue glycosylation, glycan density, and/or the presence of unique patterns of O‐glycan clusters on binding to lectins, thus helping us understand the multivalent carbohydrate‐lectin recognition processes at the molecular level. Glycopeptide library was prepared by “tea‐bag” approach using standard Fmoc‐SPPS. This approach creates two sublibraries for each of the five possible glycosylation sites, resulting in a library that consists of 10 sublibraries, and a total of 32 unique individual glycopeptides. The determination of the isokinetic ratios necessary for the equimolar incorporation of (glyco)amino acids mixtures to resin‐bound amino acid was determined using RP‐HPLC and MALDI‐TOF MS. We also explored protocols for on resin deprotection of O‐acetyl groups on MUC1‐derived glycopeptides using basic reagents such as sodium methoxide or hydroxide, ammonia (NH3), and hydrazine, with NH3 (7M) in methanol chosen being the best choice for multi‐glycosylated peptides. Enzyme‐linked lectin assay (ELLA) was used to screen PS‐SGCL against two plant lectins, Glycine max soybean agglutinin (SBA) and Vicia villosa (VVA). Results revealed a carbohydrate density‐dependent affinity trend and site‐specific glycosylation requirements for high affinity binding to these lectins. The Tn antigen on Thr9 in the PDTR epitope of MUC1 showed the highest affinity for SBA, followed by Thr16 and Ser15, and lastly, Ser5 and Thr4, therefore, suggesting that interaction depends not only on the carbohydrate moiety but also on the peptide region surrounding the glycan site of attachment. In conclusion, PS‐SGCLs provide a platform to systematically elucidate MUC1‐lectin binding specificities, which in long term may provide a rational design for novel inhibitors of MUC1‐lectin interactions involved in tumor spread and glycopeptide‐based cancer vaccines.Support or Funding InformationThis work was supported by the National Institutes of Health Grant CA242351 to M. C.