Peptides are important biomarkers for a range of diseases, however distinguishing different amino-acid sequences using artificial receptors remains a major challenge in biomedical sensing. Here we present a new approach to creating highly selective recognition surfaces using phage display biopanning against metal-organic nanosheets (MONs) and demonstrate their use as the next-generation of biomolecular recognition surfaces. Three MONs (ZIF-7, ZIF-7-NH2 and Hf-BTB-NH2) were chosen as initial targets to demonstrate how simple synthetic modifications can enhance selectivity towards specific amino acid sequences. Each MON system was added to a solution containing every possible combination of 7-residue peptides attached to bacteriophage hosts and the highest affinity binding peptides for each system was identified via successive biopanning rounds. In each case only a single peptide sequence was isolated (YNYRNLL – ZIF-7, NNWWAPA – ZIF-7-NH2 and FTVRDLS – Hf-BTB-NH2). This indicates that these MONs are highly selective, which is attributed to their 2D nanosheet structure. Zeta potential and contact angle measurements were conducted on each MON and combined with calculated properties for the peptide sequences and binding studies to provide insights into the relative importance of electrostatic, hydrophobic and co-ordination bonding interactions. A quartz crystal microbalance (QCM) was used to model phage binding and the Hf-BTB-NH2 MON coated QCM produced a 5-fold higher signal for FTVRDLS functionalised phage compared to phage with generic peptide sequences. Further studies focusing on Hf-BTB-NH2 confirmed that the VRDL sequence was highly conserved, and on-target binding exhibited equilibrium dissociation constants that are comparable to natural recognition materials. Surface plasmon resonance (SPR) studies indicated a 4600-fold higher equilibrium dissociation constant (KD) for FTVRDLS compared to those obtained for off-target sequences, comparable to those of antibodies (KD = 4 x10-10). We anticipate that the highly tunable nature of MONs will enhance our understanding of binding interactions and enable molecular recognition of biomedically important peptides.
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