Climbing up and down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes

Michael Heyne; Jason Shirian; Itay Cohen; Yoav Peleg; Evette S. Radisky; Niv Papo; Julia M. Shifman

doi:10.1021/jacs.1c08707

Climbing up and down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes

Michael Heyne, Jason Shirian, Itay Cohen, Yoav Peleg, Evette S. Radisky, Niv Papo^*, Julia M. Shifman^*

^*Corresponding author for this work

Department of Biological Chemistry

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

Original language	American English
Pages (from-to)	17261-17275
Number of pages	15
Journal	Journal of the American Chemical Society
Volume	143
Issue number	41
DOIs	https://doi.org/10.1021/jacs.1c08707
State	Published - 20 Oct 2021

Bibliographical note

Funding Information:
We thank U. Hadad (BGU) for help with FACS experiments. This work was supported by the Israel Science Foundation (ISF) grant 1873/15 and 2335/20 (J.M.S.), by the European Research Council (ERC) grant 336041, and by the ISF grant 1615/19 (N.P.). N.P. and E.S.R. acknowledge support from the US-Israel Binational Science Foundation (BSF). J.S.M. acknowledges support from the US-Israel Binational Science Foundation (BSF) and the Israel Cancer Research Foundation (ICRF). E.S.R. acknowledges support from U.S. National Institutes of Health grants R01 CA154387 and R01 GM132100.

Publisher Copyright:
© 2021 The Authors. Published by American Chemical Society.

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title = "Climbing up and down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes",

abstract = "Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.",

author = "Michael Heyne and Jason Shirian and Itay Cohen and Yoav Peleg and Radisky, {Evette S.} and Niv Papo and Shifman, {Julia M.}",

note = "Funding Information: We thank U. Hadad (BGU) for help with FACS experiments. This work was supported by the Israel Science Foundation (ISF) grant 1873/15 and 2335/20 (J.M.S.), by the European Research Council (ERC) grant 336041, and by the ISF grant 1615/19 (N.P.). N.P. and E.S.R. acknowledge support from the US-Israel Binational Science Foundation (BSF). J.S.M. acknowledges support from the US-Israel Binational Science Foundation (BSF) and the Israel Cancer Research Foundation (ICRF). E.S.R. acknowledges support from U.S. National Institutes of Health grants R01 CA154387 and R01 GM132100. Publisher Copyright: {\textcopyright} 2021 The Authors. Published by American Chemical Society.",

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AU - Shifman, Julia M.

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PY - 2021/10/20

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N2 - Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

AB - Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

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Climbing up and down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes

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