Abstract:Although the primary protein sequence of ubiquitin (Ub) is extremely stable over evolutionary time, it is highly tolerant to mutation during selection experiments performed in the laboratory. We have proposed that this discrepancy results from the difference between fitness under laboratory culture conditions and the selective pressures in changing environments over evolutionary timescales. Building on our previous work (Mavor et al., 2016), we used deep mutational scanning to determine how twelve new chemical… Show more
“…In previous deep mutational scanning experiments, stringent selection typically revealed many disadvantageous mutations ( Garst et al, 2017 ; Jiang et al, 2013 ; Mavor et al, 2016 ; Mavor et al, 2018 ; Stiffler et al, 2015 ). In contrast, the most striking observation under our conditions is the large fraction of advantageous mutations (red, Figure 1D ): 736 of 3161 possible variants were advantageous (23.3%), and wild-type DHFR only ranked 1203 rd (although 467 of the 1202 higher-ranking variants fall into the WT-like interval).…”
Protein mutational landscapes are shaped by the cellular environment, but key factors and their quantitative effects are often unknown. Here we show that Lon, a quality control protease naturally absent in common E. coli expression strains, drastically reshapes the mutational landscape of the metabolic enzyme dihydrofolate reductase (DHFR). Selection under conditions that resolve highly active mutants reveals that 23.3% of all single point mutations in DHFR are advantageous in the absence of Lon, but advantageous mutations are largely suppressed when Lon is reintroduced. Protein stability measurements demonstrate extensive activity-stability tradeoffs for the advantageous mutants and provide a mechanistic explanation for Lon’s widespread impact. Our findings suggest possibilities for tuning mutational landscapes by modulating the cellular environment, with implications for protein design and combatting antibiotic resistance.
“…In previous deep mutational scanning experiments, stringent selection typically revealed many disadvantageous mutations ( Garst et al, 2017 ; Jiang et al, 2013 ; Mavor et al, 2016 ; Mavor et al, 2018 ; Stiffler et al, 2015 ). In contrast, the most striking observation under our conditions is the large fraction of advantageous mutations (red, Figure 1D ): 736 of 3161 possible variants were advantageous (23.3%), and wild-type DHFR only ranked 1203 rd (although 467 of the 1202 higher-ranking variants fall into the WT-like interval).…”
Protein mutational landscapes are shaped by the cellular environment, but key factors and their quantitative effects are often unknown. Here we show that Lon, a quality control protease naturally absent in common E. coli expression strains, drastically reshapes the mutational landscape of the metabolic enzyme dihydrofolate reductase (DHFR). Selection under conditions that resolve highly active mutants reveals that 23.3% of all single point mutations in DHFR are advantageous in the absence of Lon, but advantageous mutations are largely suppressed when Lon is reintroduced. Protein stability measurements demonstrate extensive activity-stability tradeoffs for the advantageous mutants and provide a mechanistic explanation for Lon’s widespread impact. Our findings suggest possibilities for tuning mutational landscapes by modulating the cellular environment, with implications for protein design and combatting antibiotic resistance.
“…DMS also screens proteins for improved drug binding, antibody affinity, using non-native chemical stresses, or non-proteinogenic amino acids, and on synthetic proteins [19][20][21][22][23][24][25][26]. Finally, DMS share objectives with directed evolution, benefiting protein engineering [14].…”
Background: Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variantsmissense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs. Results: On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions. Conclusions: DMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.
“…By conducting screens in the presence of different chemical additives, however, Fraser and co-workers showed that virtually every residue in ubiquitin, with the exception of two, can be sensitized to mutation under a particular selection condition (Figure 2C). These results demonstrate that the evolutionary trajectory of ubiquitin is shaped by a necessity to function under different environmental conditions, in which ubiquitin may engage in distinct sets of interactions 69,70 .…”
Section: Interaction Specificity During Cell Signalingmentioning
confidence: 78%
“…The diversity of interactions made by ubiquitin is one likely explanation for its strict conservation. This hypothesis was tested in a series of studies using deep mutational scans of ubiquitin in yeast 68–70 . Under particular selection conditions, most positions in ubiquitin are remarkably tolerant to amino acid substitution.…”
Section: Interaction Specificity During Cell Signalingmentioning
The functionally-tolerated sequence space of proteins can now be explored in an unprecedented way, due to the expansion of genomic databases and the development of high-throughput methods to interrogate protein function. For signaling proteins, several recent studies have shown how the analysis of sequence variation leverages the available protein structure information to provide new insights into specificity and allosteric regulation. In this review, we discuss recent work that illustrates how this emerging approach is providing a deeper understanding of signal transduction mechanisms.
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