Mapping the energetic and allosteric landscapes of protein binding domains

• Published in: Nature (London) 2022-04, Vol.604 (7904), p.175-183
• Main Authors: Faure, Andre J., Domingo, Júlia, Schmiedel, Jörn M., Hidalgo-Carcedo, Cristina, Diss, Guillaume, Lehner, Ben
• Format: Article
• Language: English
• Subjects: 45; 45/23; 45/47; 45/70; 631/1647; 631/181/735; 631/208; 631/208/212; 631/553; 64; Allosteric properties; Allosteric Regulation - genetics; Allosteric Site; Binding; Communication; Design of experiments; Dihydrofolate reductase; Enzymes; Experimental design; Gene mapping; Glycine; Humanities and Social Sciences; Interfaces; Methods; multidisciplinary; Mutagenesis; Mutation; Neural networks; PDZ Domains - genetics; Peptide mapping; Phenotypes; Physiology; Protein Binding - genetics; Proteins; Proteins - chemistry; Residues; Science; Science (multidisciplinary); Signal transduction; Thermodynamic models; Thermodynamics
• ISSN: 0028-0836; 1476-4687
• DOI: 10.1038/s41586-022-04586-4

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development 1 – 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. An approach that combines deep mutational scanning with neural network-based thermodynamic modelling is used to provide comprehensive maps of the energetic and allosteric effects of mutations in two common protein domains.