Probing Multivalent Carbohydrate-Protein Interactions With On-Chip Synthesized Glycopeptides Using Different Functionalized Surfaces

Design and synthesis of multivalent drug delivery system with CA IX inhibitors as ligands

A multivalent ligand delivery system holds tremendous potential in the field of tumor-targeted drug delivery. It addresses the challenges posed by the low affinity between small molecule ligand receptors and the rapid metabolism of small molecule drug conjugates (SMDCs) in vivo. Notably, existing multivalent ligand systems have demonstrated significant anti-tumor activity in various tumor models. In this study, we have developed a novel multivalent ligand delivery system for SN38, utilizing acetazolamide, a carbonic anhydrase IX (CA IX) inhibitor, as the target ligand. Our multivalent ligand delivery systems exhibited superior metabolic stability and enhanced targeting specificity compared to SMDC molecules. Furthermore, they demonstrated improved anti-proliferation activity, addressing the existing challenges associated with the low receptor affinity and rapid metabolism of SMDCs.

Exploring the In situ pairing of human galectins toward synthetic O-mannosylated core M1 glycopeptides of α-dystroglycan

Dystroglycan (DG), which constitutes a part of the dystrophin-glycoprotein complex, connects the extracellular matrix to the cytoskeleton. The matriglycans presented by the extracellular α-DG serve as a contact point with extracellular matrix proteins (ECM) containing laminin G-like domains, providing cellular stability. However, it remains unknown whether core M1 (GlcNAcβ1-2Man) structures can serve as ligands among the various O-Mannosylated glycans. Therefore, based on the presence of N-acetylLactosamine (LacNAc) in this glycan following the core extension, the binding interactions with adhesion/growth-regulatory galectins were explored. To elucidate this process, the interaction between galectin (Gal)-1, -3, -4 and -9 with α-DG fragment ³⁷²TRGAIIQTPTLGPIQPTRV³⁹⁰ core M1-based glycopeptide library were profiled, using glycan microarray and nuclear magnetic resonance studies. The binding of galectins was revealed irrespective of its modular architecture, adding galectins to the list of possible binding partners of α-DG core M1 glycoconjugates by cis-binding (via peptide- and carbohydrate-protein interactions), which can be abrogated by α2,3-sialylation of the LacNAc units. The LacNAc-terminated α-DG glycopeptide interact simultaneously with both the S- and F-faces of Gal-1, thereby inducing oligomerization. Furthermore, Gal-1 can trans-bridge α-DG core M1 structures and laminins, which proposed a possible mechanism by which Gal-1 ameliorates muscular dystrophies; however, this proposal warrants further investigation.

An Automated Approach to Assess Relative Galectin-Glycan Affinity Following Glycan Microarray Analysis

Numerous studies have highlighted the utility of glycan microarray analysis for the elucidation of protein-glycan interactions. However, most current glycan microarray studies analyze glycan binding protein (GBP)-glycan interactions at a single protein concentration. While this approach provides useful information related to a GBP's overall binding capabilities, extrapolation of true glycan binding preferences using this method fails to account for printing variations or other factors that may confound relative binding. To overcome this limitation, we examined glycan array binding of three galectins over a range of concentrations to allow for a more complete assessment of binding preferences. This approach produced a richer data set than single concentration analysis and provided more accurate identification of true glycan binding preferences. However, while this approach can be highly informative, currently available data analysis approaches make it impractical to perform binding isotherms for each glycan present on currently available platforms following GBP evaluation. To overcome this limitation, we developed a method to directly optimize the efficiency of assessing association constants following multi-GBP concentration glycan array analysis. To this end, we developed programs that automatically analyze raw array data (kdMining) to generate output graphics (kaPlotting) following array analysis at multiple doses. These automatic programing methods reduced processing time from 32.8 h to 1.67 min. Taken together, these results demonstrate an effective approach to glycan array analysis that provides improved detail and efficiency when compared to previous methods.