Defining the property space for chromatographic ligands from a homologous series of mixed-mode ligands

• Novel ligands were created to identify structural variants with altered selectivity for proteins.
• Hydrophobic interactions were affected by changing solvent exposure and nearby substituents.
• pH gradients were used to demonstrate alternative selectivity of proteins on multimodal resins.
• A chemometric model was created that could predict protein retention on several multimodal resins.

A homologous ligand library based on the commercially-available Nuvia cPrime ligand was generated to systematically explore various features of a multimodal cation-exchange ligand and to identify structural variants that had significantly altered chromatographic selectivity. Substitution of the polar amide bond with more hydrophobic chemistries was found to enhance retention while remaining hydrophobically-selective for aromatic residues. In contrast, increasing the solvent exposure of the aromatic ring was observed to strengthen the ligand affinity for both types of hydrophobic residues. An optimal linker length between the charged and hydrophobic moieties was also observed to enhance retention, balancing the steric accessibility of the hydrophobic moiety with its ability to interact independently of the charged group. The weak pKa of the carboxylate charge group was found to have a notable impact on protein retention on Nuvia cPrime at lower pH, increasing hydrophobic interactions with the protein. Substituting the charged group with a sulfonic acid allowed this strong MM ligand to retain its electrostatic-dominant character in this lower pH range. pH gradient experiments were also carried out to further elucidate this pH dependent behavior. A single QSAR model was generated using this accumulated experimental data to predict protein retention across a range of multimodal and ion exchange systems. This model could correctly predict the retention of proteins on resins that were not included in the original model and could prove quite powerful as an in silico approach toward designing more effective and differentiated multimodal ligands.