Phage Display Against 2D Metal–Organic Nanosheets as a New Route to Highly Selective Biomolecular Recognition Surfaces

Peptides are important biomarkers for various diseases, however distinguishing specific amino-acid sequences using artificial receptors remains a major challenge in biomedical sensing. This study introduces a new approach for creating highly selective recognition surfaces using phage display biopanning against metal-organic nanosheets (MONs). Three MONs (ZIF-7, ZIF-7-NH<sub>2,</sub> and Hf-BTB-NH<sub>2</sub>) are added to a solution containing every possible combination of seven-residue peptides attached to bacteriophage hosts. The highest affinity peptides for each MON are isolated through successive bio-panning rounds. Comparison of the surface properties of the MONs and high-affinity peptides provide useful insights into the relative importance of electrostatic, hydrophobic, and co-ordination bonding interactions in each system, aiding the design of future MONs. Coating of the Hf-BTB-NH<sub>2</sub> MONs onto a quartz crystal microbalance (QCM) produced a five-fold higher signal for phage with the on-target peptide sequence compared to those with generic sequences. Surface plasmon resonance (SPR) studies produce a 4600-fold higher equilibrium dissociation constant (K<sub>D</sub>) for on-target sequences and are comparable to those of antibodies (K<sub>D</sub> = 4 x 10<sup>-10</sup> m). It is anticipated that insights from the biopanning approach, combined with the highly tunable nature of MONs, will lead to a new generation of highly selective recognition surfaces for use in biomedical sensors.

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Publication Details

Journal
Small
Published
2024-11-13
DOI
https://doi.org/10.1002/smll.202406339
Citations
4
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
0.94

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article

Phage Display Against 2D Metal–Organic Nanosheets as a New Route to Highly Selective Biomolecular Recognition Surfaces

Nicholas W. Turner, Jonathan A. Foster, Edwin C. Johnson, Sarah S. Staniland et al.
4 citations
Small
Advanced biosensing and bioanalysis techniques
0.94
article

Phage Display Against 2D Metal–Organic Nanosheets as a New Route to Highly Selective Biomolecular Recognition Surfaces

Nicholas W. Turner, Jonathan A. Foster, Edwin C. Johnson, Sarah S. Staniland, Ram R. R. Prasad, Steven P. Armes, Mark V. Sullivan, Amelia C. Wood
article en
4 citations

Abstract

Peptides are important biomarkers for various diseases, however distinguishing specific amino-acid sequences using artificial receptors remains a major challenge in biomedical sensing. This study introduces a new approach for creating highly selective recognition surfaces using phage display biopanning against metal-organic nanosheets (MONs). Three MONs (ZIF-7, ZIF-7-NH<sub>2,</sub> and Hf-BTB-NH<sub>2</sub>) are added to a solution containing every possible combination of seven-residue peptides attached to bacteriophage hosts. The highest affinity peptides for each MON are isolated through successive bio-panning rounds. Comparison of the surface properties of the MONs and high-affinity peptides provide useful insights into the relative importance of electrostatic, hydrophobic, and co-ordination bonding interactions in each system, aiding the design of future MONs. Coating of the Hf-BTB-NH<sub>2</sub> MONs onto a quartz crystal microbalance (QCM) produced a five-fold higher signal for phage with the on-target peptide sequence compared to those with generic sequences. Surface plasmon resonance (SPR) studies produce a 4600-fold higher equilibrium dissociation constant (K<sub>D</sub>) for on-target sequences and are comparable to those of antibodies (K<sub>D</sub> = 4 x 10<sup>-10</sup> m). It is anticipated that insights from the biopanning approach, combined with the highly tunable nature of MONs, will lead to a new generation of highly selective recognition surfaces for use in biomedical sensors.

SmallVol. 21(29)
University of Sheffield (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 25%
Advanced biosensing and bioanalysis techniques
0.94
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