Bridging The Physical Scales In Evolutionary Biology: From .

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Available online at www.sciencedirect.comScienceDirectBridging the physical scales in evolutionary biology:from protein sequence space to fitness of organismsand populationsShimon Bershtein1, Adrian WR Serohijos2 andEugene I Shakhnovich3Bridging the gap between the molecular properties of proteinsand organismal/population fitness is essential forunderstanding evolutionary processes. This task requires theintegration of the several physical scales of biologicalorganization, each defined by a distinct set of mechanisms andconstraints, into a single unifying model. The molecular scale isdominated by the constraints imposed by the physicochemical properties of proteins and their substrates, which giverise to trade-offs and epistatic (non-additive) effects ofmutations. At the systems scale, biological networks modulateprotein expression and can either buffer or enhance the fitnesseffects of mutations. The population scale is influenced by themutational input, selection regimes, and stochastic changesaffecting the size and structure of populations, which eventuallydetermine the evolutionary fate of mutations. Here, wesummarize the recent advances in theory, computersimulations, and experiments that advance our understandingof the links between various physical scales in biology.Addresses1Department of Life Sciences, Ben-Gurion University of the Negev,Beer-Sheva 84501, Israel2Département de Biochimie, Centre Robert-Cedergren enBioinformatique & Génomique, Université de Montréal, Montréal,QC H3T 1J4, Canada3Department of Chemistry and Chemical Biology, Harvard University,12 Oxford Street, Cambridge, MA 02138, United StatesCorresponding author: Shakhnovich, Eugene I([email protected])Current Opinion in Structural Biology 2017, 42:31–40This review comes from a themed issue on Folding and bindingEdited by Jane Clarke and Rohit V ructure, function and intracellular abundances, selection operates directly on the phenotypes and its outcomedepends on fitness of organisms and populations. Understanding the link between the molecular effect of mutations and their fitness effects, the genotype-phenotypegap, is a major problem in biology. The relationshipbetween the biophysical properties of macromoleculesand their fitness effects is complex [1] and is influencedby various mechanisms operating at the molecular,systems, organismal, and, finally, population scales[2,3,4 ,5,6,7 ,8 ,9–14]. Although each scale of biologicalorganization is usually studied independently by traditional scientific disciplines, it is becoming clear thatmore accurate understanding of evolutionary processeswill depend on integrating these scales into a unifiedmodel [10].A useful concept to visualize the linkage between genotype and phenotype in biology is fitness landscape. Theconcept was originally introduced by Sewall Wright inthe early 1930s [15]. Wright’s landscape can be easilyimagined in three dimensions (in reality it is highlymultidimensional) where axes x and y represent allelefrequencies at loci 1 and 2, while axis z (the height)represents the mean fitness of a population. In such arepresentation, peaks and valleys describe, respectively,high and low mean fitness of a population. Selection isviewed as a force driving the allele frequencies to increasea population’s mean fitness over time. However, if thelandscape is rugged, that is, characterized by multiplepeaks, selection is most likely to push a population to thenearest peak (local optimum), thus, effectively, locking itin a suboptimal fitness [16]. Climbing to the highest peakof the landscape (the global optimum) would requirepassing through a fitness valley that is disfavoured byselection.0959-440/# 2016 Elsevier Ltd. All rights reserved.IntroductionThe direction of evolution is shaped by the interplay ofmutations and selection. These two factors operate onvastly different scales: while mutations perturb the biophysical properties of proteins, potentially altering theirwww.sciencedirect.comThe common use of the ‘fitness landscape’ metaphoroften involves discrete values of fitness that correspondto specific alleles [16–18], or, more generally, discretesequence variants [17,18]. However, such representations are limited to known variants, and their predictivepower of the effects and evolutionary consequences ofde novo mutations is limited. Conversely, full representation of the fitness landscape in sequence space is stillvery challenging due to high dimensionality of suchmapping.Current Opinion in Structural Biology 2017, 42:31–40

32 Folding and bindingIt is important to note that the term ‘fitness’ is often usedin a variety of contexts that might be different from thetraditional use of the term by Wright who defined fitnessas a quantity that is proportional to the mean number ofproduced viable and fertile progeny [19]. For example,‘fitness’ can stand in the literature for an organismalphenotype, systems level response, biological functionof a protein, and, in case of sequence space-based landscapes, a molecular property that directly affects a protein’s biological function [20–22]. Thus, the concept of‘fitness’ is often interpreted to mean functional ratherthan reproductive capacity. However, in our view, such abroad definition of fitness detracts from the main conundrum in evolution that mutations and selection are separated by several levels of biological organization. To thatend, we will exclusively use the term ‘fitness’ in thetraditional sense close to Wright’s as a phenotypic traitthat is under selection and, as such, determines theoutcome of a competitive evolutionary scenario in thewild, or in laboratory experiments. In the subsequentdiscussion we focus on organism-based definition of fitness as a reproductive capacity or related phenotypictraits of an organism linked to its specific genotype ratherthan a more traditional Wright’s view of fitness as meanreproductive capacity of a population. We also note thatspecific phenotypic traits that are most relevant for theoutcome of a competition might depend on the environment and ecological scenarios under which the competition occurs.Another challenge that limits our ability to predict thedirection and outcomes of evolution is that structure of apopulation has a crucial effect on how it evolves on thefitness landscape. Theoretical population genetics predicts that population size controls the balance betweenthe forces of selection and random genetic drift; thiseventually determines the direction of evolution on thefitness landscape. Earlier studies appreciated the role ofpopulation size, deriving an ‘effective population size’ tofit the genetics or the evolutionary data to existing population genetics models [23]. Nevertheless, direct experimental evidence for this fundamental concept is veryscarce due to the inherent difficulty of simultaneouslycontrolling the population size and obtaining tractablemolecular readouts of evolutionary processes in a configurable and reproducible environment of a laboratoryexperiment, or in multiscale organism-based simulations.To address these challenges, several authors have recently introduced the concept of a ‘biophysical fitness landscape’. The key premise of a biophysical fitness landscapeis that the gap between genotype and fitness is bridgedthrough the intermediate phenotype-molecular biophysical properties of proteins (Figure 1). In this approach thesequence-fitness gap might be overcome in two (or more)steps: (A) from sequence variation to variation of themolecular and systems-level properties of proteinsCurrent Opinion in Structural Biology 2017, 42:31–40(panels a–c in Figure 1) and (B) from variation of themolecular (stability, activity, etc.) and systems-level properties (e.g. abundances) to the organismal fitness effectsreflected in a biophysical fitness landscape (panel d inFigure 1) to the fate of a mutation in a population (fixationor loss, panel e in Figure 1). The key assumption of thisapproach is that the complexity of mapping sequencevariation to changes in the molecular properties (step Aabove, from panel a to panels b,c in Figure 1)) is at the rootof the complexity of the sequence-fitness relationship(‘ruggedness of fitness landscape’). Molecular effects ofmutations are complex, degenerate and epistatic: similarmolecular effects can be caused by different mutationsand they strongly depend on the molecular background[9,22,24,25 ]. However, at the next level(s) (step B), therelationship between changes in the molecular properties(stability, activity, aggregation propensity), cellular (intracellular abundance), and the ensuing phenotypiceffects might be much less degenerate and predictive,giving rise to a smooth fitness landscape in the space ofmolecular properties of proteins (Figure 1). It is alsoimportant to note that pleiotropy, when mutations haveconflicting effects on different molecular traits, is anothermajor source of epistasis. In terms of the biophysicalfitness landscape this means that projection of individualmutational effects could lead to complex trajectories inthe space of biophysical parameters. In this review wepresent recent theoretical, computational and experimental studies that elaborate on and further develop theseideas.From protein sequence space to proteinbiophysicsMaynard Smith was first to suggest that evolution can beviewed as a walk through a protein sequence space [26].Assuming an L-residue long protein sequence, its sequence space will constitute a network of 20L sequencesinterconnected through edges, each representing achange in a single residue. Each of the sequences isassigned a value (a molecular property such as thermodynamic stability, or function), thus forming a discretelandscape of the sequence space. Because mutationsare rare, evolutionary trajectories are thought to traversethe sequence space by single mutation steps, withoutpassing through non-functional intermediates. Theresulting accessible fraction of the interconnected functional sequences constitutes the (nearly) neutral proteinnetwork [27–30]. If the effect of each mutation on fitnessis independent on the genetic background on which itoccurs, the resulting fitness landscape will contain a singlepeak populated by optimal sequences with multipleevolutionary trajectories leading to it. Conversely, shouldepistasis (non-additivity of mutations) be prevalent, fitness effect of a mutation might be beneficial or detrimental depending on the genetic background [31,32 ].Epistasis can severely constrain the accessible evolutionary pathways, and create a rugged fitness landscape withwww.sciencedirect.com

From sequences to fitness Bershtein, Serohijos and Shakhnovich 33Figure 1(a)(b)Sequence scale(c)Molecular scaleWildtype1Folded fractionSystems scale0.80.60.4mutant0.20–202468WildtypeBound fraction1sequence to structure& function of proteinsWildtype0.8modulation & regulationby biological netwroks0.6mutant0.40.2010 –4mutantFunctional capacityStability, kcal/molmutant10.80.6Wildtype0.40.20010 –210 010 2246810cytoplasmic abundance10 4Product turnoverAffinity, 810Enzyme Efficiency, k cat/K m(d)(e)Organismal scalePopulation scaleFitness effect of a mutation:s Fitnessconsequence onreproductive capacityevolutionary successof mutationsbiophysicWildtypeal trait,rait,XtsicaliophybYfitnessmutant fitnesswildtypefitnesswildtypeProbability of fixation:Pfix 1 exp(–2s)1 exp(–2Ne s)Current Opinion in Structural BiologyBiophysics as a stepping stone between sequence and phenotype. Closing the genotype-phenotype gap is facilitated by an intermediateprojection of organismal fitness to biophysical properties of macromolecules. While the effect of sequence variation (panel a) on molecular traits(e.g. folding stability, binding affinity, catalytic activity, panel b) might be complex, the ensuing relation between variation of the observablebiophysical traits (vertical axes in panel b) and their fitness effect on the organism might be simple and predictable in some cases (panel d andFigure 2). The effects of mutations are also modulated by a regulation of biological networks (panel c) that might also have simple integrativeeffect on fitness. On the level of populations, the probability of fixing a specific mutation is a function of the effect of a mutation (selectioncoefficient) and an effective population size (Ne). Note a different interpretation of fitness landscape in (panel d) from classical Wright’s wheremean fitness of the population versus allele frequencies is usually plotted.multiple local optima separated from each other and theglobal fitness peak by trajectories passing through nonfunctional sequence states [33 ,34,35]. Kaltenbachet al. [33 ] examined the evolutionary reversibility ofphosphotriesterase that was evolved by directed evolution to arylesterase and back, and found that many of theoriginal amino acid exchanges between the new and theoriginal function could not be tolerated, and an alternativewww.sciencedirect.comset of mutations was needed to restore the phosphodiesterase activity. Extensive epistasis made the adaptivefitness landscape in sequence space highly rugged, and,although the evolutionary trajectories were phenotypically (at the level of protein phenotype, function) reversible,the genotypic trajectory became irreversible, suggestingthat the new and the original activities constitute separatefitness peaks [36].Current Opinion in Structural Biology 2017, 42:31–40

34 Folding and bindingWhich biophysical properties determine the epistatic interaction between mutations? Various mechanisms wereproposed, including protein-ligand or protein-DNA binding [25 ,37,38 ], protein conformation [33 ,39], and allostery [40]. However, protein stability appears to be themost prevalent mechanism of intramolecular epistasis[7 ,41 ,42,43]. Since most mutations destabilize proteinstructures [14,44], it was asserted that protein evolution isoften accompanied by stability-activity trade-offs [45].Indeed, Gong et al. [41 ] demonstrated that evolutionof influenza nucleoprotein is constrained by a stabilityrelated epistasis: acquisition of stabilizing mutations wasrequired before obtaining the adaptive substitutions,which, on their own, caused adverse effects due to destabilization of the nucleoprotein and, therefore, were evolutionary inaccessible. Stability activity trade-off is oftencast as the effect of stabilizing mutations on ‘flexibility’ ofthe protein which is detrimental to ‘functional proteindynamics’ [46,47]. Experimental studies indeed showedthat mutations introduced in the active site of a proteinstabilize the protein yet make it less or completely inactive[48]. However, more comprehensive analyses wheremutations were introduced throughout the protein, andnot just in the active site, showed no inverse relationbetween stability and activity [36,49]. Moreover, a weakpositive correlation between activity and stability wasobserved for multiple variants of dihydrofolate reductase(DHFR) from Escherichia coli [36]. Nonetheless, a highlystabilizing mutation in the active site of DHFR, D27F,completely eliminated its function [36], similar to whatwas observed for another enzyme in [48]. The apparentdisparity of the conclusions from substitutions in/near theactive site and elsewhere in the protein can be rationalizedby the observation that most ‘random’ mutations in proteins are destabilizing [13,44,50]. Carving an active site onan enzyme requires several functional substitutions,which, from the point of view of an independent trait– stability – could be random or even detrimental (e.g.,placing charged residues in partly buried areas of theprotein). Thus, activity-stability trade-off could be realfor substitutions in the active sites. However, it disappearsor reverses itself for substitutions outside the active sites.Two possibilities can be raised to explain this finding: (1)that global stability of a protein against unfolding mightnot be relevant for its global dynamics in the native stateand (2) that global dynamics might not determine functionality, as was indeed argued in [51]. To illustrate thesearguments, the following analogy with building a housemight be offered. In order for a house to be livable, it musthave windows. Windows certainly diminish structuralintegrity of the building (decreased ‘stability’). However,that does not mean that a building must be structurallyshaky to be livable. What it does imply, though, is thatother parts of the building must be reinforced to allow forwindows to be carved in the walls without the buildingfalling apart completely — the analogy with the needto make stabilizing mutations elsewhere in the proteinCurrent Opinion in Structural Biology 2017, 42:31–40to evolve functional variants [49,52]. A strong evidencethat overall stabilization of proteins does not come at adetriment to its function comes from long term evolutionary experiments with E. coli [53]. Most lines that evolvedat 20 8C lost fitness relative to the ancestor when theycompeted at 40 8C and above [88], whereas most lines thatevolved at 41.5 8C did not lose fitness at 20 8C and below[54].The prevalence of epistasis, and its impact on evolution isstill debated. Some describe it as rampant [38 ] andpervasive [55], or even consider it a determining factorof the molecular evolution [22,56], whereas others view itas less important [57,58]. We return to the discussion ofprevalence and mechanisms of epistasis later, after introducing the theoretical foundation of protein biophysicspopulation genetics mapping.From protein biophysics to organismal fitnessAlthough insightful and informative, evolutionary studiesthat focus entirely on the protein sequence space landscapes have one major disadvantage — being purelyphenomenological, they are oblivious to the biophysicalmechanisms of the molecular and systems scales thatdetermine fitness effects of mutations [59]. Indeed, biological networks are responsible for the striking capacityof organisms, on one hand, to limit the enormous fitnesscost of the mutational load [60,61], and, on the other hand,to harness its evolutionary potential [62]. A major role inmodulating the mutational effects at a systems scale isplayed by protein quality control (PQC). In general,molecular chaperones constituting the PQC can serveas a buffer against the deleterious effects of mutations,thus they are less likely to be purged by purifying selection [12,63]. This lead to the view that certain elements ofthe PQC act as ‘capacitors’ of genetic variability thatbroadly reshape the biophysical properties — organismalfitness map [12,64–67] and control the rate of proteinevolution [63,68–72]. Recently, PQC was also identifiedas imposing a global barrier to functional integration ofhorizontally transferred genes in bacteria, because thenewly acquired genes were found to be incompatible withthe elements of PQC of the host [73 ].Rodrigues et al. [4 ] have recently demonstrated that it ispossible to incorporate the pleiotropic effects of mutations originating in the PQC interaction with the mutantproteins into a model that correctly predicts organismalfitness entirely from the biophysical characteristics. Themodel is based on the relationship between fitness andmetabolic flux [74,75]:fitness flux ¼a E;BþE(1)where E is the functional capacity of a protein in theenzymatic chain, a denotes maximal fitness at the highestflux, and B is the constant related to the effect of all otherwww.sciencedirect.com

From sequences to fitness Bershtein, Serohijos and Shakhnovich 35PQC [12]. Rodrigues et al. [4 ] showed that this latterquantity can be accurately predicted from the in vitromeasurements of the population of the molten-globulelike state of the mutant proteins, in agreement withtheoretical predictions from the dynamic turnover model[12]. Using the model, Rodrigues et al. [4 ] success

Enzyme Efficiency, k cat /K m 0 0.2 0.4 0.6 0.8 1 Product turnover 0102 4 6 8 cytoplasmic abundance 0 0.2 0.4 0.6 0.8 Functional capacity 1 Fitness Probability of fixation: s fitness mutant fitness wildtype fitness wildtype Fitness effect of a mutation: P fix 1 exp(–2s) 1 (a) (

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