Mapping the energetic and allosteric landscapes of protein binding domains

Andre J. Faure, Júlia Domingo, Jörn M. Schmiedel, Cristina Hidalgo-Carcedo, Guillaume Diss and Ben Lehner ()
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Andre J. Faure: The Barcelona Institute of Science and Technology
Júlia Domingo: The Barcelona Institute of Science and Technology
Jörn M. Schmiedel: The Barcelona Institute of Science and Technology
Cristina Hidalgo-Carcedo: The Barcelona Institute of Science and Technology
Guillaume Diss: The Barcelona Institute of Science and Technology
Ben Lehner: The Barcelona Institute of Science and Technology

Nature, 2022, vol. 604, issue 7904, 175-183

Abstract: Abstract Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development1–6. An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes—here we examine binding and protein abundance—in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering ‘edgetic’ variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

Date: 2022
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DOI: 10.1038/s41586-022-04586-4

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