We report an original methodology based on affinity chromatography coupled with mass spectrometry to decipher the complexity of dynamic combinatorial libraries (DCLs) of glycoclusters. Such libraries are intended to boost the design of potential therapeutic anti-infectious agents targeting Pseudomonas aeruginosa, which is responsible for numerous diseases, mostly found in hospitals as major a cause of nosocomial infections. Dynamic combinatorial chemistry provides a rapid access to an equilibrating mixture of glycocluster candidates through the formation of reversible covalent bonds under thermodynamic control. Identifying each molecule in the complex mixture overcomes challenges due to the dynamic process. Selection of glycoclusters candidates was first realized on a model lectin (Concanavalin A, ConA). Home-made affinity nanocolumns, containing covalently immobilized ConA and have volumes in the microliter range, were used to separate DCLs of glycoclusters with respect to their specific lectin binding properties under buffered aqueous conditions. Miniaturization facilitates the inline coupling with MS detection in such purely aqueous and buffered conditions and reduces target protein consumption. Monolithic lectin-affinity columns prepared by immobilization of ConA were first characterized using a known ligand. The amount of active binding immobilized lectin is 61 ± 5 pmol on 8.5-cm length column. We demonstrated the ability of our approach to evaluate individual dissociation constants of species directly in the complex mixture. The concept was then successfully applied to the screening of DCLs of more complex glycoclusters to identify (by mass spectrometry) and rank the ligands (by relative breakthrough curve delay) according to their affinity for the immobilized lectin in a single experiment.
Keywords: Affinity chromatography; Concanavalin A; Dynamic combinatorial chemistry; Glycocluster; Multivalency; Pseudomonas aeruginosa.
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