Abstract:Genetic sensors with unique combinations of DNA recognition and allosteric response can be created by hybridizing DNA-binding modules (DBMs) and ligand-binding modules (LBMs) from distinct transcriptional repressors. This module swapping approach is limited by incompatibility between DBMs and LBMs from different proteins, due to the loss of critical module-module interactions after hybridization. We determine a design strategy for restoring key interactions between DBMs and LBMs by using a computational model … Show more
“…These domains of correlated residues that co-evolve over long evolutionary times are reminiscent of protein sectors 23 . They are also consistent with recent experimental work showing that DCA couplings can capture global phenomena such as allosteric communication between DNA-binding and ligand-binding modules in a protein 24 . Fig.…”
Characterizing the effect of mutations is key to understand the evolution of protein sequences and to separate neutral amino-acid changes from deleterious ones. Epistatic interactions between residues can lead to a context dependence of mutation effects. Context dependence constrains the amino-acid changes that can contribute to polymorphism in the short term, and the ones that can accumulate between species in the long term. We use computational approaches to accurately predict the polymorphisms segregating in a panel of 61,157 Escherichia coli genomes from the analysis of distant homologues. By comparing a context-aware Direct-Coupling Analysis modelling to a non-epistatic approach, we show that the genetic context strongly constrains the tolerable amino acids in 30% to 50% of amino-acid sites. The study of more distant species suggests the gradual build-up of genetic context over long evolutionary timescales by the accumulation of small epistatic contributions.
“…These domains of correlated residues that co-evolve over long evolutionary times are reminiscent of protein sectors 23 . They are also consistent with recent experimental work showing that DCA couplings can capture global phenomena such as allosteric communication between DNA-binding and ligand-binding modules in a protein 24 . Fig.…”
Characterizing the effect of mutations is key to understand the evolution of protein sequences and to separate neutral amino-acid changes from deleterious ones. Epistatic interactions between residues can lead to a context dependence of mutation effects. Context dependence constrains the amino-acid changes that can contribute to polymorphism in the short term, and the ones that can accumulate between species in the long term. We use computational approaches to accurately predict the polymorphisms segregating in a panel of 61,157 Escherichia coli genomes from the analysis of distant homologues. By comparing a context-aware Direct-Coupling Analysis modelling to a non-epistatic approach, we show that the genetic context strongly constrains the tolerable amino acids in 30% to 50% of amino-acid sites. The study of more distant species suggests the gradual build-up of genetic context over long evolutionary timescales by the accumulation of small epistatic contributions.
“…Notably, the non-specific suppressors we identified were relatively rare and not predictable based on naturally occurring homologs of ParE and ParD, which exhibit strong covariation. Models based on natural sequence homologs and covariation are powerfully able to predict protein and protein complex structures 1 – 4 , 7 – 9 , and can help in protein design 9 , 60 , 61 . However, our results indicate that such unsupervised models are currently insufficient to accurately predict the immediate mutational trajectories of our proteins.…”
Summary
Proteins often accumulate neutral mutations that do not affect current functions but can profoundly influence future mutational possibilities and functions. Understanding such hidden potential has major implications for protein design and evolutionary forecasting, but has been limited by a lack of systematic efforts to identify potentiating mutations. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact, and promote tolerance non-specifically to, many different antitoxin mutations, despite covariation in homologs occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets.
“…Transcription factors regulate gene expression through two types of binding interactions: binding to a specific DNA sequence, and binding to a set of small molecule or protein partners that control DNA binding. Domain-swapping (Jiang et al, 2021;Tungtur et al, 2010), chimeric fusions (Juárez et al, 2018), directed evolution (Richards et al, 2017;Taylor et al, 2016), saturation mutagenesis (Suckow et al, 1996;Wu et al, 2017), and deep mutational scanning (Tack et al, 2021) have resulted in LacI variants that control gene transcription in unexpected and interesting ways that depart from WT behavior, or in response to other sugars. However, none of these approaches are predictive with respect to tuning ligand response or allostery.…”
Allostery, broadly defined as a protein’s functional response to distal perturbations, is fundamental to biological regulation. In classical models, allosteric ligand binding produces a defined set of structural changes in the protein, resulting in a different low-energy conformation. Proteins that undergo ligand-induced allostery with few observable structural changes therefore frustrate interpretations by classical models. Here we used hydrogen-deuterium exchange with mass spectrometry (HDX/MS) to map the allosteric effects in a paradigm ligand-responsive allosteric transcription factor, the lac repressor (LacI). X-ray crystal structures of the core domain of LacI bound to different small molecule ligands, or the DNA operator, show less than 1.5 Å difference in the protein all-atom root-mean-square-deviation (RMSD) between any two structures. Despite this high degree of similarity among static structures, our HDX/MS experiments reveal widespread and unexpected differences in the flexibility of secondary structures in the LacI core domain in each functional state. We propose a model in which ligand binding allosterically switches the functional response of the repressor by selectively changing the dynamics of particular secondary structure elements relative to each other, shifting the conformational ensemble of the protein between mutually incompatible DNA-bound and inducer-bound states. Our model also provides a mechanistic context for the altered functions of thousands of documented LacI mutants. Furthermore, our approach provides a platform for characterizing and engineering allosteric responses in proteins.
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