Protein Melting Temperature Cannot Fully Assess Whether Protein Folding Free Energy Underlies the Universal Abundance–Evolutionary Rate Correlation Seen in Proteins

Razban, Rostam M

doi:10.1093/molbev/msz119

Cited by 22 publications

(30 citation statements)

References 54 publications

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“…a prediction of the misfolding avoidance hypothesis is that highly expressed proteins should be more thermodynamically stable). Possible explanations for the lack of correlation have been proposed (60), including that melting temperature is an imperfect proxy for thermodynamic stability. An additional reason could be that the formation of aggregates depends on how the mutation affects aggregation pathway kinetics more than how much it shifts the balance to the globally unfolded state (i.e.…”

Section: Discussionmentioning

confidence: 99%

Collateral fitness effects of mutations

Mehlhoff

Stearns

Rohm

et al. 2019

Preprint

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AbstractThe distribution of fitness effects (DFE) of mutation plays a central role in constraining protein evolution. The underlying mechanisms by which mutations lead to fitness effects are typically attributed to changes in protein specific activity or abundance. Here, we reveal the importance of a mutation’s collateral fitness effects, which we define as effects that do not derive from changes in the protein’s ability to perform its physiological function. We comprehensively measured the collateral fitness effects of missense mutations in the E. coli TEM-1 β-lactamase antibiotic resistance gene using growth competition experiments in the absence of antibiotic. At least 42% of missense mutations in TEM-1 were deleterious, indicating that for some proteins, collateral fitness effects occur as frequently as effects on protein activity and abundance. Deleterious mutations caused improper post-translational processing, incorrect disulfide-bond formation, protein aggregation, changes in gene expression, and pleiotropic effects on cell phenotype. Deleterious collateral fitness effects occurred more frequently in TEM-1 than deleterious effects on antibiotic resistance in environments with low concentrations of the antibiotic. The surprising prevalence of deleterious collateral fitness effects suggests they may play a role in constraining protein evolution, particularly for highly-expressed proteins, for proteins under intermittent selection for their physiological function, and for proteins whose contribution to fitness is buffered against mutations with deleterious effects on protein activity and protein abundance.Significance StatementMutations provide the source of genetic variability upon which evolution acts. Deleterious protein mutations are commonly thought of in terms of how they compromise the protein’s ability to perform its physiological function. However, mutations might also be deleterious if they cause negative effects on one of the countless other cellular processes. The frequency and magnitude of such collateral fitness effects is unknown. Our systematic study of mutations in a bacterial protein finds widespread collateral fitness effects that were associated with protein aggregation, improper protein processing, incomplete protein transport across membranes, incorrect disulfide-bond formation, induction of stress-response pathways, and unexpected changes in cell properties. Our results suggest that deleterious collateral fitness effects may be an important constraint on protein evolution.

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Section: Discussionmentioning

confidence: 99%

Collateral fitness effects of mutations

Mehlhoff

Stearns

Rohm

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…These universal correlations may emerge from a number of different, and non-mutually exclusive phenomena, so many explanations have been proposed (Razban 2019). These include the protein misfolding avoidance hypothesis (Yang, et al 2010), the protein misinteraction avoidance hypothesis (Levy, et al 2012;Yang, et al 2012), the mRNA folding hypothesis (Park, et al 2013), purifying selection on protein function (Konaté, et al 2019), and others (Tartaglia, et al 2007;Plata, et al 2010;Kepp and Dasmeh 2014) Differentiating among these possibilities is difficult, because it is challenging to study the causes of patterns that span millions of years of protein evolution.…”

Section: Discussionmentioning

confidence: 99%

Universal constraints on protein evolution in the long-term evolution experiment withEscherichia coli

Maddamsetti

2020

Preprint

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Although it is well known that highly expressed and highly interacting proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that highly abundant and highly interacting proteins evolve slowly in the hypermutator populations of Lenski’s long-term evolution experiment with E. coli (LTEE). Specifically, the density of observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, strongly anti-correlates with mRNA abundance, protein abundance, and degree of protein-protein interaction. Weaker positive correlations between protein thermostability and mutation density are observed in the hypermutator populations, counterbalanced by negative correlations between protein thermostability and mRNA and protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why highly expressed and highly interacting proteins evolve slowly.

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“…Alternatively, the biophysical model considered here might be too restrictive. Recent empirical studies have provided evidence against the hypothesis that the rate of sequence evolution is driven solely by the toxicity effect of unfolded proteins (Plata and Vitkup, 2017;Razban, 2019;Biesiadecka et al, 2020).…”

Section: Adequacy To Empirical Datamentioning

confidence: 99%

A theoretical approach for quantifying the impact of changes in effective population size and expression level on the rate of coding sequence evolution

Latrille

Lartillot

2021

Preprint

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Molecular sequences are shaped by selection, where the strength of selection relative to drift is determined by effective population size (Ne). Populations with high Ne are expected to undergo stronger purifying selection, and consequently to show a lower substitution rate for selected mutations relative to the substitution rate for neutral mutations (ω). However, computational models based on biophysics of protein stability have suggested that ω can also be independent of Ne, a result proven under general conditions. Together, the response of ω to changes in Ne depends on the specific mapping from sequence to fitness. Importantly, an increase in protein expression level has been found empirically to result in decrease of ω, an observation predicted by theoretical models assuming selection for protein stability. Here, we derive a theoretical approximation for the response of ω to changes in Ne and expression level, under an explicit genotype-phenotype-fitness map. The method is generally valid for additive traits and log-concave fitness functions. We applied these results to protein undergoing selection for their conformational stability and corroborate out findings with simulations under more complex models. We predict a weak response of ω to changes in either Ne or expression level, which are interchangeable. Based on empirical data, we propose that fitness based on the conformational stability may not be a sufficient mechanism to explain the empirically observed variation in ω across species. Other aspects of protein biophysics might be explored, such as protein-protein interactions, which can lead to a stronger response of ω to changes in Ne.

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Protein Melting Temperature Cannot Fully Assess Whether Protein Folding Free Energy Underlies the Universal Abundance–Evolutionary Rate Correlation Seen in Proteins

Cited by 22 publications

References 54 publications

Collateral fitness effects of mutations

Collateral fitness effects of mutations

Universal constraints on protein evolution in the long-term evolution experiment withEscherichia coli

A theoretical approach for quantifying the impact of changes in effective population size and expression level on the rate of coding sequence evolution

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