To facilitate investigations of protein–protein interactions (PPIs), we developed a novel platform for quantitative mapping of protein binding specificity landscapes, which combines the multi-target screening of a mutagenesis library into high- and low-affinity populations with sophisticated next-generation sequencing analysis. Importantly, this method generates accurate models to predict affinity and specificity values for any mutation within a protein complex, and requires only a few experimental binding affinity measurements using purified proteins for calibration. We demonstrated the utility of the approach by mapping quantitative landscapes for interactions between the N-terminal domain of the tissue inhibitor of metalloproteinase 2 (N-TIMP2) and three matrix metalloproteinases (MMPs) having homologous structures but different affinities (MMP-1, MMP-3, and MMP-14). The binding landscapes for N-TIMP2/MMP-1 and N-TIMP2/MMP-3 showed the PPIs to be almost fully optimized, with most single mutations giving a loss of affinity. In contrast, the non-optimized PPI for N-TIMP2/MMP-14 was reflected in a wide range of binding affinities, where single mutations exhibited a far more attenuated effect on the PPI. Our new platform reliably and comprehensively identified not only hot- and cold-spot residues, but also specificity-switch mutations that shape target affinity and specificity. Thus, our approach provides a methodology giving an unprecedentedly rich quantitative analysis of the binding specificity landscape, which will broaden the understanding of the mechanisms and evolutionary origins of specific PPIs and facilitate the rational design of specific inhibitors for structurally similar target proteins.
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May 2020
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Cover Image
Cover Image
Hot and cold spots in N-TIMP2 interacting with MMP-1 (green), MMP-3 (purple) and MMP-14 (blue). Each couple present 180° rotation with respect to each other. Hot spots (red) and cold spots (blue) are shown on the interface of N-TIMP2. To learn more about this, see the article by Aharon and colleagues (pp. 1701–1719) in this issue. The image was provided by Niv Papo.
Research Article|
May 11 2020
Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method
Lidan Aharon;
Lidan Aharon
1Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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Shay-Lee Aharoni;
Shay-Lee Aharoni
1Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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Evette S. Radisky;
Evette S. Radisky
2Department of Cancer Biology, Mayo Clinic Comprehensive Cancer Center, Jacksonville 32224, Florida, U.S.A
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Niv Papo
1Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
Correspondence: Niv Papo (papo@bgu.ac.il)
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Publisher: Portland Press Ltd
Received:
March 03 2020
Revision Received:
April 11 2020
Accepted:
April 14 2020
Accepted Manuscript online:
April 15 2020
Online ISSN: 1470-8728
Print ISSN: 0264-6021
© 2020 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society
2020
Biochem J (2020) 477 (9): 1701–1719.
Article history
Received:
March 03 2020
Revision Received:
April 11 2020
Accepted:
April 14 2020
Accepted Manuscript online:
April 15 2020
Citation
Lidan Aharon, Shay-Lee Aharoni, Evette S. Radisky, Niv Papo; Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method. Biochem J 15 May 2020; 477 (9): 1701–1719. doi: https://doi.org/10.1042/BCJ20200188
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