Mapping the energetic and allosteric landscapes of protein binding domains

Abstract

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterised, limiting understanding, engineering and drug development [1][2][3][4][5][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 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 human protein interaction domains, 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 Glycines and in specific residues connecting to an opposite surface in the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should allow the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

Dominance vs. epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles

“…That is, combining single mutants with the same phenotypic value but different underlying causal biophysical mechanisms results in a different outcome in a double mutant. This is because the non-linear relationships between the phenotype and the free energies of folding or binding are different (Supplementary Figure 3a, b) (Faure et al, 2022; Li and Lehner, 2020; Otwinowski, 2018).…”

“…An important simplification of our approach is that it assumes that free energy changes are additive. Although this is likely to be true for the majority of mutation combinations (Faure et al, 2022; Otwinowski, 2018), specific non-additive changes in free energy, for example between mutations in physically contacting residues (Carter et al, 1984; Horovitz et al, 2019), will generate additional epistasis and dominance.…”

“…Here we address this shortcoming by quantifying the interactions between mutations in statistical thermodynamic models of protein folding and binding. Thermodynamic models have previously proven useful for interpreting and predicting how mutations combine in large-scale experimental datasets (Adams et al, 2016; Domingo et al, 2019; Faure et al, 2022; Kinney et al, 2010; Li et al, 2019; Li and Lehner, 2020; Otwinowski, 2018).…”

Global analysis of multi-mutants to improve protein function

“…1, as long as the data warrants estimation of these. This formulation is similar to previous global models [6–9, 16,17], but here applied with the particular aim of identifying substitutions that enhance the protein in terms of the property that is assayed for.…”

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins

“…In recent years, deep mutational scanning (DMS) has emerged as a powerful tool to understand protein function by measuring the impact of mutational perturbations using highthroughput experiments (25)(26)(27)(28)(29)(30). DMS is particularly useful to study a systemic property like allostery because it permits an unbiased examination of every residue of a protein without a priori assumptions about its functional role (7,(31)(32)(33)(34). Combining DMS with statistical tools allows us to recognize complex underlying patterns describing the molecular rules of allostery.…”