Evolvability as a Function of Purifying Selection in TEM-1 β-Lactamase

Stiffler, Michael A; Hekstra, Doeke R.; Ranganathan, Rama

doi:10.1016/j.cell.2015.01.035

Cited by 293 publications

(408 citation statements)

References 63 publications

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“…However, the findings here are consistent with data in at least one other protein system-TEM-1 b-lactamase, an enzyme that confers resistance to specific antibiotics in bacteria (Salverda et al, 2010). Deep mutational scanning reveals a class of mutations underlying adaptation that shows conditional neutrality; that is, neutral with regard to the existing substrate but gain of function toward a new substrate (Stiffler et al, 2015). As in PSD95 pdz3 , these mutations occur at sites that are distant from the active site, connecting through physically contiguous networks within the protein structure.…”

Section: A Structural Model For Protein Adaptationsupporting

confidence: 89%

“…Recent high-throughput methods for mutagenesis (Fowler and Fields, 2014;McLaughlin et al, 2012;Stiffler et al, 2015) will facilitate testing of the generality of this conclusion. However, the findings here are consistent with data in at least one other protein system-TEM-1 b-lactamase, an enzyme that confers resistance to specific antibiotics in bacteria (Salverda et al, 2010).…”

Section: A Structural Model For Protein Adaptationmentioning

confidence: 91%

See 1 more Smart Citation

Origins of Allostery and Evolvability in Proteins: A Case Study

Raman

White

Ranganathan

2016

Cell

Self Cite

133

170

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Proteins display the capacity for adaptation to new functions, a property critical for evolvability. But what structural principles underlie the capacity for adaptation? Here, we show that adaptation to a physiologically distinct class of ligand specificity in a PSD95, DLG1, ZO-1 (PDZ) domain preferentially occurs through class-bridging intermediate mutations located distant from the ligand-binding site. These mutations provide a functional link between ligand classes and demonstrate the principle of "conditional neutrality" in mediating evolutionary adaptation. Structures show that class-bridging mutations work allosterically to open up conformational plasticity at the active site, permitting novel functions while retaining existing function. More generally, the class-bridging phenotype arises from mutations in an evolutionarily conserved network of coevolving amino acids in the PDZ family (the sector) that connects the active site to distant surface sites. These findings introduce the concept that allostery in proteins could have its origins not in protein function but in the capacity to adapt.

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Section: A Structural Model For Protein Adaptationsupporting

confidence: 89%

Section: A Structural Model For Protein Adaptationmentioning

confidence: 91%

Origins of Allostery and Evolvability in Proteins: A Case Study

Raman

White

Ranganathan

2016

Cell

Self Cite

133

170

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“…However, how the constraining effects of such genetic interactions are affected by environmental variability remains poorly understood. It has been shown that mutational effects (30)(31)(32)(33) and epistasis itself (34,35) can depend on the environment, that bacterial resistance evolution can be contingent on the rate of antibiotic increase (36), and that adaptation in silico can be accelerated by environmental change (37)(38)(39)(40). These observations suggest that the effects of environmental variability may go beyond merely producing variable selective pressures that favor certain phenotypes but also, could be involved in controlling phenotype accessibility and stasis.…”

mentioning

confidence: 87%

Breaking evolutionary constraint with a tradeoff ratchet

Vos

Dawid

Šunderlíková

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Epistatic interactions can frustrate and shape evolutionary change. Indeed, phenotypes may fail to evolve when essential mutations are only accessible through positive selection if they are fixed simultaneously. How environmental variability affects such constraints is poorly understood. Here, we studied genetic constraints in fixed and fluctuating environments using the Escherichia coli lac operon as a model system for genotypeenvironment interactions. We found that, in different fixed environments, all trajectories that were reconstructed by applying point mutations within the transcription factor-operator interface became trapped at suboptima, where no additional improvements were possible. Paradoxically, repeated switching between these same environments allows unconstrained adaptation by continuous improvements. This evolutionary mode is explained by pervasive cross-environmental tradeoffs that reposition the peaks in such a way that trapped genotypes can repeatedly climb ascending slopes and hence, escape adaptive stasis. Using a Markov approach, we developed a mathematical framework to quantify the landscape-crossing rates and show that this ratchet-like adaptive mechanism is robust in a wide spectrum of fluctuating environments. Overall, this study shows that genetic constraints can be overcome by environmental change and that crossenvironmental tradeoffs do not necessarily impede but also, can facilitate adaptive evolution. Because tradeoffs and environmental variability are ubiquitous in nature, we speculate this evolutionary mode to be of general relevance.evolution | environment | epistasis | fitness landscape | constraint I t is widely believed that epistatic interactions can direct evolutionary change (1-7). Epistasis has been implicated in shaping RNA (8) and protein (4, 6, 7, 9) sequences, sensing (5) and translation (10) functions, and developmental programs (11) and speciation (12)(13)(14). Phenotypes may be difficult to evolve not because they are impossible biochemically or physically, but because essential mutations are mutually dependent and must be fixed together to be selected positively (5,(15)(16)(17). How such genetic constraints can be overcome has been considered previously: population expansion or subdivision can limit negative selection and maintain less fit phenotypes (18,19), and large populations and long waiting times can enable the joint fixation of multiple mutations (20), whereas recombination can join mutant alleles (21-23). Other mechanisms include drift (24-26), partial penetrance (27), and nonheritable lifetime plasticity (28, 29). However, how the constraining effects of such genetic interactions are affected by environmental variability remains poorly understood. It has been shown that mutational effects (30-33) and epistasis itself (34, 35) can depend on the environment, that bacterial resistance evolution can be contingent on the rate of antibiotic increase (36), and that adaptation in silico can be accelerated by environmental change (37-40). These observations sugg...

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“…We obtained site-specific fitness parameters for the second simulation data set using Mutation-Selection Model Performance . doi:10.1093/molbev/msw171 MBE amino-acid propensities measured experimentally using deep-mutational scanning (DMS) (Bloom 2014a;Firnberg et al 2014;Stiffler et al 2014;Thyagarajan and Bloom 2014;Doud et al 2015;Kitzman et al 2015). Derivation of simulation parameters is described in depth in Materials and Methods.…”

Section: Simulation and Inference Approachmentioning

confidence: 99%

“…The genes used were influenza H1N1 hemagglutinin (Thyagarajan and Bloom 2014), influenza nucleoprotein (Bloom 2014a;Doud et al 2015), TEM-1 b-lactamase (Firnberg et al 2014;Stiffler et al 2014), and yeast Gal4 (Kitzman et al 2015). We specifically used scaled experimental amino-acid propensities, as given by and described in Bloom (2016).…”

Section: Generation Of Simulated Datamentioning

confidence: 99%

Extensively Parameterized Mutation–Selection Models Reliably Capture Site-Specific Selective Constraint

Spielman

Wilke

2016

Mol Biol Evol

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The mutation-selection model of coding sequence evolution has received renewed attention for its use in estimating sitespecific amino acid propensities and selection coefficient distributions. Two computationally tractable mutationselection inference frameworks have been introduced: One framework employs a fixed-effects, highly parameterized maximum likelihood approach, whereas the other employs a random-effects Bayesian Dirichlet Process approach. While both implementations follow the same model, they appear to make distinct predictions about the distribution of selection coefficients. The fixed-effects framework estimates a large proportion of highly deleterious substitutions, whereas the random-effects framework estimates that all substitutions are either nearly neutral or weakly deleterious. It remains unknown, however, how accurately each method infers evolutionary constraints at individual sites. Indeed, selection coefficient distributions pool all site-specific inferences, thereby obscuring a precise assessment of site-specific estimates. Therefore, in this study, we use a simulation-based strategy to determine how accurately each approach recapitulates the selective constraint at individual sites. We find that the fixed-effects approach, despite its extensive parameterization, consistently and accurately estimates site-specific evolutionary constraint. By contrast, the randomeffects Bayesian approach systematically underestimates the strength of natural selection, particularly for slowly evolving sites. We also find that, despite the strong differences between their inferred selection coefficient distributions, the fixedand random-effects approaches yield surprisingly similar inferences of site-specific selective constraint. We conclude that the fixed-effects mutation-selection framework provides the more reliable software platform for model application and future development.

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Evolvability as a Function of Purifying Selection in TEM-1 β-Lactamase

Cited by 293 publications

References 63 publications

Origins of Allostery and Evolvability in Proteins: A Case Study

Origins of Allostery and Evolvability in Proteins: A Case Study

Breaking evolutionary constraint with a tradeoff ratchet

Extensively Parameterized Mutation–Selection Models Reliably Capture Site-Specific Selective Constraint

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