Dynamic signature of activity-stability tradeoff in lactamase evolution

Domain: chemistry
Published: 2026-01-21
Editorial outcome: major_revision

Abstract
Our ability to understand protein evolution hinges on understanding how evolutionary landscapes are shaped at the fundamental protein level. Using TEM-1 β-lactamase we show that molecular traits related to the statistical ensemble nature of protein structure contribute to broader substrate specificity, active site-scaffold communication, and the selection of stabilizing substitutions. During the evolution of cefotaxime resistance, the initial mutation reorganizes the active site, introducing a new function conformation. Secondary substitutions improve catalytic efficiency by redistributing the ensemble and restoring a significant population of the original conformation, rather than by stabilizing the new conformation. Stability defects associated with initial mutations are not evenly disseminated but are clustered at specific distal scaffold elements. The capacity of mutants to independently modulate the populations of individual active site walls and scaffold regions through narrow residue networks, produces conformational epistasis and a combinatorial set of cefotaximase states, which enables local compensation of scaffold defects.

Introduction
Adaptive protein evolution occurs through successive mutation steps that define an evolutionary landscape towards increased fitness. Understanding the sequence-structure-function relationship at the protein level is critical for predicting evolution, designing distinct functions in existing enzymes, and engineer effective biologics. During the evolution of TEM-1 from a bona fide penicillinase to an extended-spectrum β-lactamase (ESBL) with broad activity against third generation β-lactam cephalosporins, such as cefotaxime and ceftazidime, a set of highly beneficial mutations is initially fixed at loops or termini of secondary structure elements flanking the active site1. These mutations typically have a strong impact on the enzyme’s catalytic properties. This is followed by secondary mutations distributed throughout the protein fold, at either distal or interacting sites relative to the initial mutation1. Secondary mutations have weak to no effect on catalysis but alter the enzyme’s biophysical properties, reversing detrimental pleiotropic effects caused by the initial mutations and optimizing fitness. This temporal and spatial pattern of evolutionary steps2–4, forms the basis of activity-stability tradeoff, a pervasive mechanistic constraint in protein evolution5,6 that reflects the balance between active site functional innovation and scaffold robustness7.

Initial mutations provide functional innovation by acting upon the underlying enzymatic promiscuity, increasing fitness by affecting a functional property, such as the kcat/KM. They do so by shifting the conformational manifold of the active site to sparsely populated conformers or by allowing for de novo sampling of conformers that confer new specificities and functions8–15. However, random mutations are destabilizing by a ΔΔG factor of 0.5−5.0 kcal/mol, which is of the same order as ΔGinitial, while “new function” mutations at the active site result in even higher ΔΔG values5,16–24. Evidently, for protein sequences with low robustness or when successive evolutionary steps produce large positive ΔΔG, or even when non-native intermediate states are populated, secondary compensatory25–29 or ancestor mutations16,25,29–33 are fixed to restore scaffold stability and avoid exhausting the ΔΔG robustness window. These secondary mutations typically act in an epistatic manner, being neutral in the initial background but beneficial in the mutant background25,34.

Although the tradeoff model forms the biophysical basis for the two opposing selective pressures, it does not provide a complete molecular view of how the mechanism-based observables ΔΔG and kcat/KM render specific evolutionary trajectories accessible. For TEM-1, for example, ΔGN-U has no explanatory power in models using minimal inhibitory concentration (MIC) as a fitness proxy, in contrast to kcat/KM, which drives the evolution of cefotaxime resistance35. Additionally, thermal unfolding may not capture all fundamental scaffold properties that act as selective pressure for secondary mutations, including non-native states6,36 or kinetic stability effects37,38. Importantly, neither thermal nor chemical unfolding can resolve the energetic coupling between local and remote sites, which imposes spatial contingencies of energy propagation and explains how secondary substitutions are fixed at a particular site in a given genetic background39,40. Finally, structural information alone, without insights into conformational dynamics, fails to provide the complete set of molecular traits behind kcat/KM-driven selection and the acquisition of initial beneficial mutations39–41.

Here, we investigate a set of TEM-1 evolutionary intermediates involved in acquiring the ESBL phenotype. TEM-1 is an ideal model system to characterize enzyme evolution since there is ample biochemical and biophysical data available providing a detailed map of its adaptive landscape (Supplementary Table S1). Importantly, it has a clear functional context, free of metabolic cross-effects, which allows for a direct link between genetic changes, phenotypic outcomes and molecular properties, including ΔGN-U or kcat/KM. We find that along an evolutionary landscape the active site walls of TEM-1 participate in a dynamic two-state population shift between penicillinase and cefotaximase optimized states. Individual mutations control the population of each active site wall conformation, giving rise to conformational epistasis and a combinatorial set of states. The new function conformation induced by the prominent G238S substitution is not progressively stabilized relative to the original penicillinase conformation, but secondary substitutions optimize the equilibrium populations of the two conformations. The compromised stability associated with G238S is not evenly disseminated but instead is clustered at specific distal scaffold sites. Secondary substitutions fixed within those regions locally correct these defects through narrow networks. Overall, we show that molecular proxies relevant to the statistical ensemble nature of protein structure explain how adaptive substitutions are selected within the activity-stability tradeoff constraint.

Results
G238S has a global effect on the conformational properties of the β-lactamase fold

The active site walls of class A β-lactamases are formed by residues located at the termini of secondary structure elements, including helix α2 (69-73), strand β3 (230-237), and helix α12 (272, 275, 276), as well as loops 103-107 (105-loop), 130-132 (SDN-loop), 164-178 (Ω-loop), 213-220 (216-loop), 238-243 (238-loop) and 267-272 (270-loop), according to the Ambler numbering scheme42 (Supplementary Fig. 1). Mutation of any of the core residues S70, K73, S130/N132, E166/N170 has a detrimental effect on function, hence adaptive benefits are realized by initial mutations in other positions within these segments. Despite the vast sequence space available, the most prominent pathways for the evolution of TEM-1 ESBL phenotype include the G238S substitution1,43 (Fig. 1a). G238S (TEM-19) causes a ~100-fold increase in the kcat/KM for cefotaxime hydrolysis, while maintaining high levels of the native penicillinase activity, albeit with a ~10-fold reduction in kcat/KM for ampicillin6,44.

To understand the molecular mechanism by which 238-loop substitutions modulate hydrolysis of bulky cephalosporins, we used NMR spectroscopy. The backbone amide signals in a 1H-15N-HSQC spectrum are site-specific probes sensitive to local conformational properties, reporting on both structural and dynamic features. Comparison of the TEM-1 and G238S spectra (Fig. 1b) reveals widespread chemical shift perturbations (CSPs) and signal attenuation (Fig. 1c), suggesting that the mutation elicits conformational changes that reach well beyond the mutation site. The regions showing prominent changes (Fig. 1d) include residues flanking the substitution site at the 238-loop, as well as the entire Ω-loop sequence, which is directly implicated in catalysis by providing the catalytic E166 and N170. Most 238-loop residues remain unassigned due to severe exchange broadening, while three Ω-loop residues are tentatively assigned based on signal proximity, and one remained unassigned. This is consistent with the dynamic structural rearrangements observed in the G238S crystal structures6,45–47, where the 238-loop samples two states: a low-populated TEM-1-like “closed” conformation, competent for catalysis, and an “open” conformation, with the conserved E240(NH)-N170(O) hydrogen bond that pins the Ω- and 238-loops disrupted, resulting in an expanded active site (Fig. 1e). Significant perturbation is also observed for the catalytic S130 and S70, the latter of which covalently binds β-lactams in the acylation state and is tentatively assigned in G238S. Furthermore, signals from residues comprising other active site walls, including helix α2, the SDN-, 216- and 270-loops, show significant attenuation due to exchange broadening (Fig. 1c, d). Thus, in addition to the 238-loop dynamics revealed by crystallographic studies, that evidently affect even subtly the Ω-loop conformation, the spectral properties of G238S show that the acquired cefotaximase activity is associated with enhanced conformational dynamics throughout the active site occurring at the fast-to-intermediate NMR timescale. Notably, CSPs and line broadening effects are propagated beyond the active site. Changes are observed for residues located at strands β1, and β3-β5, which make up the core of the α/β subdomain, the C-terminal end of helix α1, a large part of helix α12, the corresponding loops, and even residues 27 Å away from the mutation site (Fig. 1c, d), at positions with very low RMSD between TEM-1 and G238S structures (Fig. 1c).

Therefore, in addition to the well documented difference observed in the architecture of the G238-loop, solution-based techniques reveal that the G238S induced conformational changes are propagated to all active site walls and remote scaffold sites. These changes are not captured in the available crystal structures, either because structures were determined in the presence of designed or secondary stabilizing substitutions, or because they reflect subtle dynamic changes that are only captured by solution-based techniques.

G238S enhances the microsecond-millisecond timescale dynamics of TEM-1

To date, the molecular features that convert G238S or other initial substitutions into extended spectrum β-lactamases are not fully understood, mainly because the structures of these evolutionary intermediates have not been determined in a wild-type background. Instead, the effect of G238S on the conformational properties of TEM-1 has been studied in clinical isolates with acquired secondary substitutions, including TEM-52 (E104K/M182T/G238S)45 and TEM-72 (Q39K/M182T/G238S/E240K)47, or in the presence of multiple laboratory-evolved stabilizing substitutions, which facilitate recombinant expression and crystallization of the enzyme (G238S-TEM-v.13)29,46. The consensus of these studies is that the ESBL phenotype is linked to an enlarged active site, caused by alternative loop conformations, consistent with the structures of other extended-spectrum enzymes like Toho-148. However, no distinct loop conformation has been identified (Fig. 1e), suggesting that broadened substrate specificity and acquired cefotaximase activity result from conformational heterogeneity and enhanced active site dynamics. Specifically, TEM-72 and the Ala-substituted enzyme, G238A, cause only marginal changes to the 238- or Ω-loop (Fig. 1e). In TEM-52, however, G238S expands the active site by displacing the tip of the 238-loop, while in G238S-TEM-v.13, it causes the dynamic sampling of two conformations, resulting in an even larger displacement of the 238-loop and a more expanded active site (Fig. 1e).

To identify the dynamic traits that accompany acquisition of the ESBL phenotype, we investigated the underlying protein motions of G238S. To this end, we used NMR to measure backbone 15N R1 and R2 relaxation rates, as well as [1H]-15N heteronuclear NOEs at two magnetic field strengths (Supplementary Fig. 2a). Optimization of the rotational diffusion tensor using these observables yields an ellipsoid model with a global correlation time τm = 12.7 ns and an anisotropy D||/D⊥ = 1.16 (Supplementary Fig. 2b, c). These values are very close to the parameters determined previously for TEM-1 and its variants49–51, suggesting that the global properties of the two proteins are similar. To describe the effective local motions and identify changes in protein flexibility induced by G238S at the ps-ns timescale, the relaxation data were analyzed using the model-free approach and interpreted based on the squared order parameter, S2. The order parameter describes the amplitude of the internal motion of an N-H bond vector in a cone and ranges from S2=1 to S2=0 for fully restricted and unrestricted motions, respectively. Similar to other β-lactamases studied by NMR, including TEM-1, PSE-4, Toho-1, CTX-M, and a number of their mutants49–55, the S2 values of G238S reveal that the enzyme retains the rigidity observed for TEM-1, with an average S2 of 0.84 ± 0.01 (Fig. 2a and Supplementary Table S2) and only small |ΔS2| values (<0.2) (Supplementary Fig. 2d). As expected, the only two positions showing large |ΔS2| values (>0.2) are not essential for catalysis and include K32 and Q278 at helices α1 and α12, respectively (Supplementary Fig. 2e). The sidechains of these residues form a hydrogen bond that connects the two terminal helices, while the neighboring N276 links helix α12 to both the active site and the mutation site through a hydrogen bond with R244, at the C-terminus of the 238-loop, highlighting how active site substitutions may induce ps-ns timescale motions at remote regions of the scaffold (Supplementary Fig. 2e).

For many residues, proper fitting of the relaxation data required an additional term, Rex, to account for the contribution of slower timescale motions to R2 (μs-ms), as evident by the local model-free model selected for each site (Supplementary Table S2). In order to characterize the exchange properties at this timescale we acquired a series of Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion experiments, wherein in the presence of conformational exchange the effective relaxation rate, R2,eff, changes as a function of repetition frequency (νcpmg) of a series of π pulses within a constant time period (Fig. 2b). The set of G238S residues undergoing μs-ms conformational exchange (R2,eff(50Hz) - R2,eff(2000Hz) ≥ 4 s-1) cluster primarily at the α/β subdomain of the lactamase fold (Fig. 2c, d and Supplementary Table S2). When signals missing due to excessive broadening are considered in this timescale regime, a total of 64 G238S residues undergo slow dynamic conformational fluctuations (Fig. 2d). Except for the dispersion curves of M129, G269, A280, A284 and I287 that were best fitted with the general Carver-Richards equation (Supplementary Table S2), the dispersion data of all other residues were best described by the 2-state fast exchange Luz-Meiboom equation, where the apparent exchange rate, kex, is accurately determined, but the populations of the two states are not. Although the kex for different G238S sites cover a wide range of values (Supplementary Table S2), when mapped on the structure, an asymmetric spatial distribution is evident (Fig. 2d). Active site residues undergo a slow exchange transition (<500 s-1) which is of the same order as the kcat of cefotaxime hydrolysis (15-85 s-1), while scaffold residues experience significantly faster exchange transitions (>2500 s-1). Exchanging residues are distributed across all the active site walls (Fig. 2d). These include T71 and F72 at helix α2, which are adjacent to the catalytic S70, the complete 238-loop, as well as R275 from helix α12, which is one of the residues forming the oxyanion hole. In addition, residue Y105, which is involved in substrate recognition and binding, and contributes to active site expansion via alternate rotamer sampling, all residues from the SDN-loop, including the catalytic S130, the entire 216-loop, featuring the invariant V216, which stabilizes the position of large substrates56, as well as all residues in the 270-loop, which is connected to the 238-loop via hydrogen bonding. Notably, only two residues in the Ω-loop, E168 (R2,eff) and A172 (broadened), exhibit μs-ms conformational exchange, while the catalytically important residues E166 and N170 remain rigid. For E166, even the observed chemical shift change is minimal, suggesting that the mutation has little effect on the local conformation in this region of the Ω-loop. Compared to TEM-1 and a set of engineered TEM-1 enzymes studied previously by NMR57,58 (Fig. 2d), the slow active site dynamics induced by G238S indicate that an optimized window of motions provides access to the new function, as neither excessive dynamics that include the Ω-loop wall and the catalytic E166 nor limited dynamics where the other active site walls are rigid, result in efficient cefotaximase activity.

In summary, G238S has a profound impact on the dynamic landscape of TEM-1. It induces extensive μs-ms dynamics at the active site walls and a hierarchic profile of motions at the scaffold, with enhanced ps-ns timescale dynamics at key sites linking secondary structure elements, causing widespread μs-ms dynamics.

Dynamic population shift of TEM-1 conformers along cefotaximase evolution

To assess how secondary substitutions modulate the conformational landscape of TEM-1, we investigated the spectral properties of multiple mutants. These span the first three steps of the well-studied evolutionary pathway of TEM-1 to TEM-1* (TEM-1→G238S→E104K→A42G→M182T), a model cefotaximase that increases cefotaxime MIC by a factor of ~32,00059–61. We omitted the last step (M182T), as it acts as a global suppressor62,63 and does not impact the catalytic properties of TEM-16,35. Overall, we examined the single mutants G238S, E104K and A42G, as well as the double mutant E104K/G238S (TEM-15) and the triple mutant A42G/E104K/G238S. E104Κ occurs in clinical isolates (TEM-17) and is located at the 105-loop, which shares a dense interaction network with other primary substitution sites. A42G, identified in laboratory evolution experiments29,30,36,59,64, is found at the α-β loop, which packs against the 270-loop.

Similarly to G238S, E104K elicits large changes at the active site. These include effects on the N-terminus of helix α2, the SDN- and 216-loops, strands β3 and β4, with smaller perturbations in the Ω- and 238-loops (Supplementary Fig. 3a). However, no significant perturbations are observed for scaffold residues. In contrast, A42G produces large perturbations throughout the termini of TEM-1, including the scaffold α/β and the associated loop, as well as the β/α region, both of which are significantly affected by the initial G238S substitution, in addition to smaller perturbations at the Ω- and 238-loops (Supplementary Fig. 3b).

Examining the effect of A42G and E104K single mutants on TEM-1, together with the effects of introducing E104K in the G238S background and A42G in the E104K/G238S background to generate the second and third steps of the pathway, respectively, provides key insights on how the conformational properties of the enzyme are optimized along a model evolutionary landscape (Fig. 3a–c and Supplementary Fig. 3c). When the signal perturbations of these evolutionary intermediates are plotted together, a linear relationship is apparent (Fig. 3a, b and Supplementary Fig. 3d), irrespective of whether they belong to the active site walls or remote scaffold sites. This suggests that each of these sites exists in an equilibrium between two states that interconvert in the fast exchange regime, with the position of a signal along the line marking the population weighted average of each state (Fig. 3b). G238S, which affects all active site walls except the SDN loop, where the catalytic S130 is significantly affected (Fig. 1c and Supplementary Fig. 3d), represents the evolutionary intermediate that brings the highest increase in cefotaximase activity, with a ~100 fold higher kcat/KM compared to TEM-16,35,44, and a significantly compromised penicillinase activity. Therefore, its relative position marks the population shift of a loop towards a cefotaximase-optimized state (Fig. 3a–c). In contrast, the relative position of TEM-1, a bona fide penicillinase, marks the population shift towards a penicillinase optimized state (Fig. 3a–c).

For E104K/G238S, the residues most affected by G238S, including the active site walls formed by the Ω- and 216-loops, strand β3 and helix α2, as well as scaffold sites such as the two terminal helices, α1 and α12, and residues of the α/β subdomain, show a chemical shift close to that of G238S in the double mutant (Fig. 3 and Supplementary Fig. 3). Thus, even in the presence of E104K, the initial mutation utilizes a broad network of residues to modulate and control the relative population of the two states sampled by multiple TEM-1 sites (Fig. 3c). In contrast, only residues at or adjacent the SDN-loop (T128, S130 and N132) shift towards the position of E104K in the double mutant, but not other core residues like K73 at helix α2 (Supplementary Fig. 3d). Notably, this population shift is towards the penicillinase state (Fig. 3a and Supplementary Fig. 3d). Hence, in the G238S background, E104K utilizes a narrow residue network to control the population of only the SDN-loop conformers. Inversely, the effect of G238S on the population of the SDN loop is different in the E104K background compared to the TEM-1 background. This indicates the presence of epistatic interactions (Fig. 3c) and that the single, double and triple mutants will form a combinatorial set of cefotaximase states differing in the relative populations of active site walls and scaffold sites participating in the two-state exchange. In agreement with the critical role of the SDN-loop in substrate binding and the acylation and diacylation steps, the conformational epistasis between E104K and G238S observed for the SDN-loop correlates with a significant increase in kcat/KM 6,29,35,44,46 (Fig. 3d and Supplementary Table 1). When A42G is introduced in E104K/G238S, residues significantly perturbed by the single mutant, primarily in helices α1 and α12 and the adjacent loops, shift to the same position in the triple mutant (Supplementary Fig. 3d), indicating that A42G acts through a narrow network to exert control over the population of scaffold conformers sampled by the two terminal helices.

Of note, through a broad residue network, A42G also affects active site walls and non α1 and α12 scaffold residues in the vicinity of the active site, partially reversing the shift toward the penicillinase state or completely restoring the position of triple mutant signals to that of TEM-1 (Fig. 3a and Supplementary Fig. 3d). For all active site walls, including helix α2, strand β3, the Ω-, SDN- and 216-loops, the population shift to the penicillinase conformation is similar, and in the range of ~40-50% (Fig. 3c), suggesting that short-range cooperative interactions within the active site lead to a concerted transition. The large subglobal population shift back to the penicillinase state caused by A42G/E104K/G238S and the local population shift of the SDN-loop caused by E104K/G238S, however, occurs despite a marked increase in catalytic efficiency for cefotaxime35 (Fig. 3d and Supplementary Table 1). Thus, while the new function, G238S state, is essential for acquiring cefotaximase activity, sampling a threshold level of the TEM-1 penicillinase conformation is mechanistically still required for improving kcat/KM. Therefore, for the G238S trajectory, secondary substitutions A42G and E104K do not modulate kcat/KM by further stabilizing the G238S state, but by recovering a significant TEM-1 population, and therefore by optimizing the relative population ratio of G238S and TEM-1 states (Fig. 3c, d).

In summary, the six TEM states considered here, distinctly modulate the populations of active site walls and scaffold conformers through either broad, but subglobal, or narrow and therefore local interaction networks. This results in a combinatorial set of cefotaximases differing in the relative population of active site wall conformers. Importantly, along the TEM-1→G238S→E104K/G238S→A42G/E104K/G238S trajectory, cefotaximase activity is not enhanced simply by progressively increasing the population of the G238S-derived active site conformation. Instead, catalytic efficiency is optimized by fine-tuning the relative population of the original TEM-1 and the G238S active site conformations.

Local stability changes reveal G238S-induced pleiotropic defects

While G238S shifts the population of TEM-1 active site walls to conformers that render it an efficient cefotaximase, new-function mutations are generally destabilizing (ΔΔG>0), resulting in a tradeoff between activity and stability. However, given the large robustness of TEM-1 (ΔGNative-Unfolded - ΔGN-U, ΔG > -7.5 kcal/mol) and the small ΔΔG associated with G238S (~2.0 kcal/mol)6,35,36, a significant population of the unfolded state cannot account for the acquisition of stabilizing substitutions. Indeed, the explanatory power of ΔG n-u for models that use MIC as a fitness proxy is weak, in contrast to kcat/KM, which drives evolution of cefotaxime resistance35. Moreover, G238S lowers the robustness threshold between the native state and a low-populated intermediate (2%), providing a pathway to proteolytic degradation65. Disturbing the system with denaturing agents or through thermal unfolding provides insight into the stability of species populated along the unfolded-native transition. However, the global nature of the derived observables does not provide information on how stability changes are distributed throughout the three-dimensional structure of a protein in its native state. To probe local stability perturbations caused by G238S at the residue level and resolve the thermodynamic contingencies imposed by an initial substitution on subsequent evolutionary steps, we employed hydrogen/deuterium-exchange coupled to mass spectrometry (HDX-MS). HDX provides a distinct advantage in that, under appropriate conditions, changes in the exchange properties between evolutionary intermediates yield direct thermodynamic information, while MS affords measurements even for amide groups that are broadened beyond detection in the NMR spectra (Fig. 1). In our analysis we measured differential deuterium uptake as a proxy of local stability changes between successive evolutionary intermediates.

For all peptides, a single isotopic envelop with continuous increase in deuterium incorporation is observed as a function of time, for both TEM-1 and G238S. This behavior is consistent with the EX2 exchange limit, where exchange at each peptide site is uncorrelated with exchange at other sites within the peptide (Supplementary Fig. 4a). Hence, differences in the deuterium uptake between evolutionary intermediates reflect variations in the thermodynamics of their corresponding local fluctuations. TEM-1 exhibits slow but high deuterium incorporation (>50%) after 4 h of labeling, primarily in a few exposed loops (Supplementary Fig. 4b, c), as expected for an overall stable protein. In contrast, introduction of G238S results in a significant increase in the final level or rate of deuterium incorporation (>15% relative to TEM-1) across a set of peptides covering ~40% of the sequence (Fig. 4a and Supplementary Fig. 4b, c). Several peptides that correspond to the active site walls were identified, including 163-182 (Ω-loop), 213-218 (216-loop), 230-243 (strand β3 and 238-loop), and 265-271 (270-loop) (Fig. 4b and Supplementary Fig. 4b–d). We note that for multiple residues in these peptides, particularly those located in loops, significant broadening is observed in the 2D 15N-HSQC of G238S (Fig. 1), indicating that acquisition of μs-ms timescale motions is accompanied by reduced local stability. Additionally, a second set of peptides, comprising long stretches of scaffold residues, displayed enhanced deuterium uptake in G238S. This includes 26-39 and 273-289, spanning the N- and C-terminal helices (α1 and α12), 40-46 and 244-265, covering most of the antiparallel β-sheet core (strands β1, β4, β5 and α/β loop), and 220-229, following the 216-loop (helix α11 and α11-β3 loop) (Fig. 4b and Supplementary Fig. 4b, c). The complete set of G238S peptides exhibiting reduced local stability shows spatial and sequence proximity, forming a continuous network or set of local networks that energetically couples the substitution site to remote scaffold sites (Fig. 4c).

The interface between helices α11 and α12, identified here as destabilized, along with the associated loops, forms a cryptic allosteric site, which emerges after the rotation of α12 towards α1, displacement of helix α11 and separation of the α11/α12 interface. This exposes non-polar side chains and leaves the central β-sheet partially unprotected (Supplementary Fig. 5a)66. It is observed as a sparsely populated conformation in TEM-167–70 and is stabilized in an open state by ligands acting as allosteric inhibitors66, which effectively disrupt the hydrophobic core of the enzyme (Fig. 5a). Two such core disrupting ligands have been identified for TEM-166, CBT and FTA, that each bind to two sites resulting in four binding sites (Fig. 5a): site I, adjacent to the orthosteric site and therefore accessible, partially disrupts the α11/α12 and β4/α12 contacts, sites II and III, which are highly overlapping and require large structural rearrangements that disrupt the α11/α12 interface, further extending site I toward the core and exposing the β3-β5 core strands, and site IV, which is remote, forms along the exposed α/β interface, and is not expected to contribute in disrupting the hydrophobic core. To further explore the mechanistic consequences of G238S on the properties of TEM-1 we monitored the interaction of FTA with TEM-1 and G238S using NMR (Fig. 5b). In agreement with the HDX data, addition of FTA to TEM-1 results in only small perturbations, limited to a region that covers the orthosteric site and site I, indicating that the allosteric site maintains a stable hydrophobic core that is not disrupted by the ligand (Fig. 5c). Consistent with this observation, we found that TEM-1 exhibits a relatively high Ki (203 ± 38 μM) for nitrocefin hydrolysis (Fig. 5d and Supplementary Fig. 5b), as previously reported66. For G238S on the other hand, addition of FTA causes prominent changes in chemical shifts as well as extensive line broadening to multiple signals throughout the α/β subdomain (Fig. 5b), indicating global changes, reminiscent of ligand binding at the core of the protein. Mapping this effect on the structure of TEM-1 shows that the most affected residues are found at the interface between the β-sheet (β3-β5) and helices α1, α11 and α12, suggesting that the ligand occupies sites I and/or II/III, which are only accessible when the core of the protein is partially disrupted (Fig. 5c). Therefore, in support of the HDX data, the reduced local stability observed at these structural elements for G238S allow ligand binding and partial opening of the allosteric site (Supplementary Fig. 5c, d). In agreement with this, G238S increases the potency of FTA-mediated allosteric inhibition, resulting in a 5-fold drop in Ki (40.4 ± 4.0 μM) compared to TEM-1 (Fig. 5d and Supplementary Fig. 5b). Thus, while G238S optimizes the orthosteric site for efficient cefotaxime hydrolysis, it partially stabilizes the open conformation of the allosteric site that functions as an autoinhibitory element and therefore prevent the initial substitution from conferring an optimal fitness advantage.

Overall, G238S alters the equilibrium ensemble of TEM-1, enabling it to populate states with clustered distribution of local stability defects. This redistribution defines the network of energy propagation between distal sites and sets the spatial contingencies for the acquisition of secondary substitutions. Importantly, the requirement for secondary substitutions may reach beyond pleiotropic defects associated with ΔΔG6,35,65, to partially stabilize the network of structural elements that acquire conformations of non-native packing and have weak autoinhibitory effects66–68 or suboptimal catalytic properties.

Secondary substitutions restore scaffold stability while maintaining active-site dynamics

The dynamic population shift resulting from substitutions in the G238S pathway provides insight into how these changes fine-tune the population of conformers. However, it does not reveal how the thermodynamic stability of these evolutionary intermediates is altered at the residue level throughout the pathway. To investigate this, we compared the changes in local thermodynamic stability across the evolutionary steps leading to the E104K/G238S double mutant and the A42G/E104K/G238S triple mutant. At a global level, it has been shown that E104K marginally impacts the stability of TEM-135. However, when combined with G238S, E104K further decreases the ΔGN-U of E104K/G238S by ~1.0 kcal/mol6,35. On a local level, the incorporation of E104K into the G238S background causes a significant decrease in deuterium incorporation for scaffold peptides at the termini of the lactamase fold, including the entire helix α1 and the C-terminal half of helix α12, as well as for peptides covering helix α11 (Supplementary Fig. 6a–c). Additionally, E104K/G238S exhibits a small increase in the deuterium uptake compared to G238S for the peptides covering the 105-loop, at the site of the first secondary substitution, and the N-terminal half of the Ω-loop, including the catalytic E166 site (Supplementary Fig. 6a-c). In contrast, the exchange properties of peptides covering the SDN-loop are unaffected by the E104K substitution, suggesting that the population shift of the SDN-loop to a cefotaximase conformation detected by NMR (Fig. 3 and Supplementary Fig. 3) is not accompanied by changes in local stability. On the other hand, A42G has been shown to have a strong impact on the stability of TEM-1 (ΔΔGN-U ~ -3.5 kcal/mol) and restores the ΔGN-U of A42G/E104K/G238S to the level of G238S35. Compared to E104K/G238S, the most prominent change observed upon incorporating A42G is an additional large decrease in the deuterium uptake for helix α1 (>30%) (Supplementary Fig. 6d–f). This is accompanied by a further, though smaller, decrease in the uptake of the peptides covering the 216-loop and the C-terminal helix α12 (Supplementary Fig. 6d–f). Overall, the effect of the two secondary substitutions from the initial G238S step to the A42G/E104K/G238S triple mutant is additive and stabilizing (Fig. 6a, b). The scaffold regions of the TEM-1 fold that progressively show a gain in local stability form a spatially contiguous network of secondary structure elements, including helices α1, α11, α12, and the associated loops (Fig. 6c). The stabilizing contributions of E104K and A42G restore the stability of helix α1 to a level that exceeds that of TEM-1, while partially buffering the stability of helix α12 and to a lower extend that of α11 (Fig. 6b, c). Consistent with an only partial stabilizing effect, FTA can still bind to the triple mutant and inhibit its catalytic activity (Supplementary. Figure 5b). Among the peptides comprising active site elements, the Ω-loop is the only region that becomes further destabilized compared to G238S and hence TEM-1. In contrast, for all other active site peptides, there is a cumulative stabilizing effect by A42G and E104K, although this does not fully compensate for the destabilizing effect of G238S. This agrees with the population shift back to the penicillinase state observed by NMR (Fig. 3), which is not complete, but occurs to the relatively more stable TEM-1 state.

In summary, the secondary substitutions E104K and A42G compensate pleiotropic scaffold stability defects caused by the initial substitutions, primarily at the terminal secondary structure elements α1 and α12, and helix α11. Despite this gain in scaffold local stability, the functionally relevant enhanced dynamics induced by G238S are preserved, as expected for a stabilized, but extended spectrum enzyme.

Methods
Sample preparations

TEM-1 was expressed from a modified pET-GST vector using its signal sequence for secretion to the periplasmic place, yielding a sample with a native N-terminal sequence. The same TEM-1 construct was used as a template for mutagenesis using the QuikChange Lightning Site Directed Mutagenesis Kit to prepare G238S, R164K, E104K, A42G, E104K/G238S and A42G/E104K/G238S. Mutations were confirmed by sequencing. Protein expression was performed in E. Coli BL21 (DE3) grown in LB media for preparing unlabeled samples for HDX and kinetic studies. For the preparation of isotopically labeled samples used for NMR analysis TEM-1 proteins were expressed in M9 minimal media supplemented with either 15NH4Cl or 15NH4Cl/13C-glucose for preparing 15N- and 13C/15N-labeled samples, respectively. Cells were grown in the presence of 50 μg/mL kanamycin and incubated at 37 °C to an OD600 ~0.6. Expression was induced with 0.5 mM IPTG at 22 °C for 18 h. Cell cultures were centrifuged at 4 °C at 5000 × g for 30 minutes and pellets were resuspended in 25 mM Tris-HCl pH 8.4. Resuspended cells were lysed by sonication with 1 mM PMSF and 0.1 μM of each of Leupeptin, ABSF, Bestatin, and Pepstatin protease inhibitors. Lysed cells were centrifuged for 1 h at 35,000 × g. The supernatant was diluted and loaded on an anion exchange column (Q Sepharose) equilibrated with 25 mM Tris-HCl pH 8.4 and eluted using an isocratic gradient of 0-100 mM NaCl, followed by a wash with 100 mM NaCl and a second isocratic gradient of 100-500 mM NaCl. TEM-1 containing fractions were pooled, exchanged to 25 mM Tris-HCl pH 8.4, and further purified through a second anion exchange column (HiTrap Q HP 5 mL) run with the same elution scheme as the Q Sepharose. TEM-1 fractions were concentrated to 5 mL using a stirred cell (10 kDa cutoff) and further purified via a size exclusion column (HiLoad 26/600 SuperDex 200 prep grade) in 25 mM Tris-HCl pH 8.4, 150 mM NaCl. Purity of samples was confirmed by SDS-PAGE. Samples were aliquoted, snap-frozen in liquid nitrogen and stored in -80 oC.

NMR spectroscopy

All NMR experiments were performed in 25 mM PIPES pH 6.4 with 7.5% D2O, at 30 oC and at a concentration ranging from 0.4 to 0.8 mM. The available TEM-1 assignment50 was used to assign single mutants of the G238S pathway based on signal proximity and was further aided by a 3D HNCA/HN(CO)CA pair of triple resonance spectra acquired on a Varian direct drive 800-MHz instrument equipped with a cryoprobe. The double and triple mutants were assigned by transferring the assignment from the single mutants and aided by an HNCA/HN(CO)CA pair. For G238S only, a 15N-edited 3D NOESY was also acquired. Backbone 15N R1 and R2 relaxation rates, 15N-(1H) heteronuclear NOEs and 15N CPMG relaxation dispersion experiments were acquired on Bruker Avance Neo 600 and Avance III 850 MHz at 30 oC with 15N-HSQC-based interleaved pseudo-3Ds. 15N R1 rates were measured using the standard Bruker pulse sequence and relaxation delays of 0.025, 0.05, 0.10, 0.20 (x2), 0.30, 0.40, 0.50, 0.60, 0.75, 1.00 (x2), 1.25, 1.50 and 1.75 s. 15N R2 rates were measured using a CPMG length of 16.5 ms and relaxation delays of 0.017, 0.034, 0.051, 0.068, 0.102 (x2), 0.136, 0.170, 0.204, 0.237 and 0.305 s. 15N-hNOEs were obtained from signal intensities of spectra acquired in the presence and absence of a 4-s proton saturation pulse, and errors were calculated from the S/N of the spectra. The exchange contribution to the transverse relaxation rate (R2,eff) was determined using a modified CPMG sequence that reduces off-resonance effects111, with a total CPMG time of 40 ms and pulse frequencies of 0, 50, 100 (x2), 150, 200, 250, 300, 350, 400, 450, 500 (x2), 600, 700, 800, 900, 1000 (x2), 1200, 1600 and 2000 s-1. All spectra were processed using NMRpipe112 and analyzed using sparky (University of California, San Francisco).

Backbone 15N R1, R2 and hNOEs were interpreted with the model-free formalism113 using scripts integrated in relax114,115. The D’Auvergne protocol was used114, which optimizes the model-free parameters without global parameters and then refines the diffusion tensor models, mitigating issues related to the initial tensor estimate. Briefly, for tensor determination spins are excluded based on three criteria: (1) lack of clear secondary structure, as determined by the DSSP algorithm, (2) spectral overlap, and (3) NOE values lower than 0.8. The optimal tensor is selected using the Akaike Information Criterion (AIC)116,117. Once the diffusion model is established, multiple model-free models (m1 to m5) are fitted and optimized for each spin, except those exhibiting overlap. The best model was selected based on AIC. CPMG data were analyzed using relax. When relaxation dispersion is identified (ΔR2,eff > 4 s-1) curves were fitted to the Luz–Meiboom118 or Carver–Richards119 models for extracting kex, Δδ and populations. AIC was used for model selection. Errors in both model-free and relaxation dispersion fitting were determined via 500 Monte Carlo simulations in relax. Chemical shift perturbations were calculated as 1H-15N combined chemical shift, Δδ = sqrt(ΔδH^2 + ΔδN^2).

The TEM-1 and G238S sample concentration used to monitor FTA binding was 0.2 mM, while the FTA concentration was 1.2 mM. Both reference and FTA-bound spectra were acquired in the presence of 4% DMSO to keep the ligand soluble.

Hydrogen/deuterium exchange

HDX experiments were initiated by diluting TEM-1 and its mutants from stock solutions prepared in 25 mM MES, pH = 6.4 and H2O (60-100 μM), 11-fold into buffer prepared in D2O (pD = 7.1) at 15 °C. At a designated exchange time (30, 100, 1000, 3000, 9000, and 15000 sec), the exchange was quenched by diluting the samples with an equal volume solution of cold quenching solution (0.8% formic acid, 3.0% acetonitrile in H2O). The mixture was injected by the LEAP HDX liquid handling robotic system to Enzymate BEH Pepsin column (Waters) for digestion and further analysis. The digestion was performed at a flow rate of 0.15 ml/min using 0.15% formic acid/3% acetonitrile as the mobile phase. The resulting peptides were collected and desalted with an inline 4-μl C18-Opti-lynx II trap cartridge (Optimize Technologies) and then eluted through a C-18 column (Thermo Fisher Scientific, 50 × 1 mm Hypersil Gold C-18) using a rapid gradient from 2 to 90% acetonitrile containing 0.15% formic acid and a flow rate of 0.04 ml/min, leading directly into a maXis-II ETD ESI-QqTOF mass spectrometer. The total time for the digest and desalting was 3 min, and all peptides had eluted from the C-18 column by 15 min. The peptide fragments were identified using Bruker Compass and Biotools software packages. The level of deuterium incorporation was assessed using the commercial software HDExaminer-3 (Trajan Scientific). The standard deviation for deuterium incorporation from triplicate technical measurements at the representative time points of 30 and 300 seconds did not exceed 0.15 Dalton. Experiments were performed in the absence of any reducing agent to maintain the native disulfide bond between C77 and C123. Representative peptides that included the two cysteines were identified during the analysis and confirmed that no exchange occurred in this region of the proteins.

Constructs used in this study produce periplasmic TEM proteins with native N-terminus starting at position H26. Residue numbering is according to the Ambler scheme for class A β-lactamases, which places the active site serine at number 70. For TEM-1, this numbering includes gaps between residues G238-E240 and P252-D254, therefore positions 239 and 253 are not included in the sequence.

The HDX summary table is included in the supplementary information (Supplementary Table 3).

Enzymatic assays

The catalytic activity of TEM-1 and mutants was measured using cefotaxime and nitrocefin as substrates. Cefotaxime hydrolysis was monitored at 260 nm using a Biotek Cytation multimode plate reader at 25 oC in 50 mM sodium phosphate buffer (pH 7.0). FTA inhibition was assessed using serial dilutions of inhibitor (starting at 1.2 mM, 800 μM, or 500 μM), with cefotaxime concentration ranging from 500 to 100 μM, and enzyme concentrations between 1 μM and 2 nM, depending on catalytic activity. Nitrocefin hydrolysis was measured at 486 nm at 37 oC in 50 mM Tris buffer (pH 7.0), using inhibitor dilutions from 800 μM, with 100 μM to 10 μM nitrocefin and 0.5 nM enzyme.

Kinetic parameters (Vmax and KM) for cefotaxime and nitrocefin were determined by nonlinear regression fitting to the Michaelis-Menten equation using SigmaPlot 12.5. IC50 values were obtained from sigmoidal concentration response curves, and Ki values were calculated using the Cheng-Prusoff equation: Ki = IC50 / (1 + [S]/KM), where [S] is the substrate concentration (cefotaxime or nitrocefin), and KM is the Michaelis constant of each enzyme.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Arcia and coworkers provides a compelling and detailed NMR-based study of the dynamic features in a\nkey evolutionary pathway of the β-lactamase TEM-1 towards cefotaxime resistance. This enzyme has been thoroughly\nstudied as a model for protein evolution. In this regard, the authors rely on a large amount of clinical evidence, biochemical\nand structural studies over three decades. The initial substitution, G238S, induces chemical shift perturbations all over the\nstructure. CPMG relaxation dispersion experiments on this variant can be described by a two-state model, that the authors\ninterpret as the two conformations adopted by the 238-loop upon this mutation (open and closed) in fast exchange in the µs-\nms timescale. Instead, R164S elicits an exchange phenomenon slow in the NMR timescale, as witnessed by the duplication of\nseveral signals. Other mutants in the evolutionary pathway TEM-1 → G238S → E104K → A42G → M182T are analyzed in\nterms of chemical shift perturbations. The authors conclude that a linear relationship of the CSPs with these\nperturbations reveal that these variants are sampling two extreme conformations, one favoring hydrolysis of penicillin, and the evolved\none, favoring cefotaxime hydrolysis. Then, they explore stability perturbations by HDX-MS, disclosing the pleiotropic effects\nof mutation G238S. Finally, they assess the compensatory role of mutations E104K and A42G.\nOverall, this manuscript assesses directly the role of protein dynamics in the evolution of a well-studied system,\nmostly by NMR spectroscopy. One main challenge of this manuscript is to convince the readership that the authors are not “reinventing\nthe wheel”. In this regard, I am convinced that the information is novel, but there are some sections in the manuscript that\nheavily rely in already published information. The authors should better work some of these sections.\nThere are some specific aspects that the authors could address to further improve the quality of their contribution:\n1. The authors only report CPMG experiments for the G238S variants, while the rest of the interpretation mostly relies in CSP\nanalysis. Given that the main conclusions of the paper are regarding protein dynamics, some results should be supported by\nadditional relaxation measurements, or the impact of temperature in the proposed equilibria.\n2. Several papers have shown that the evolutionary pathway here studied is not only dominated by an activity-stability\ntradeoff, but by strong epistatic interactions (see papers from Weinreich, Palzkill and Shoichet cited). There are no\ncomments on this regard. The authors have studied different combinations of mutations that can reflect the impact of epistatic\neffects (for example, mutation G238S in the wt background, versus the E104K background).\n3. The authors conclude that “In summary, mutations that define the evolution of TEM-1 to TEM-1* do not cause a global\npopulation shift between two unique conformations, a penicinillase and a cefotaximase. Instead, mutations independently\noptimize the population of different regions, including active site walls and scaffold sites, resulting in a combinatorial set of\nTEM-1 conformers from which a potent cefotaximase conformation is optimized along the pathway” Could this be interpreted\nas reflecting epistatic interactions?\n4. The manuscript clearly describes the dynamic effects of individual mutations, and relies in a large amount of previously\npublished activity and MIC data. A recompilation of this information from the literature in a Supplementary table to illustrate\nquantitatively the impact of each mutation in activity and fitness would help the reader who is not acquainted with the details\nof evolution of TEM lactamases. Additionally, if feasible, including a column summarizing the overall dynamic impact of each\nmutation (e.g., ΔΔG). The authors choose MS instead of NMR for studying the impact of HXD based on the fact that MS\naffords measurements even for amide groups broadened due to exchange phenomena. NMR was used to interrogate the\ndynamics of lactamases from the CTX-M family. Did they give it a try?", "reviewer_2": "Arcia et al. present a comprehensive study on the role of protein dynamics in the activity-stability tradeoff of TEM-1 β-\nlactamase and its evolutionary adaptations toward cefotaxime resistance. Employing a combination of NMR, HDX, the\nauthors systematically dissect the dynamic behavior of several TEM-1 variants, focusing on key mutations (G238S, E104K,\nA42G) and the combined effects of these mutations along the evolutionary trajectory. This approach allows for a detailed\nassessment of how primary and secondary mutations differentially modulate dynamics and local stability.\nThe study emphasizes the significance of local and global dynamics in shaping the functional landscape of TEM-1 and\nidentifies the mutations propagate to modulate many sites around active site wall as well as remote scaffold sites. While the\nstudy provides a methodologically robust analysis of TEM-1 dynamics and the evolutionary tradeoff between activity and\nlocal stability, emphasizing the conceptual novelty and organizing key findings more clearly would significantly enhance its\nimpact on the fields of protein engineering and antibiotic resistance research.\nIt would be beneficial for the authors to address the following concerns and provide clarifications prior to publication:\n1. The authors extensively analyze the dynamics of single and combined mutations along the evolutionary trajectory. The\nWeinreich group (Ref. 35) characterized 16 variants comprising all combinations of the four mutations and discussed\npleiotropic effects and epistasis, measuring catalytic efficiency and thermostability (ΔG of folding). They concluded that\ncefotaxime resistance in TEM-1 β-lactamase is primarily driven by enhancements in catalytic activity rather than changes in\nthermostability. Furthermore, they noted that subsequent mutations primarily reinforce local stability or kinetic stability rather\nthan global stability. The manuscript uses the terms local stability and global thermodynamic stability interchangeably, which\ncould be misleading. The authors should clearly define these terms and distinguish their roles in\npleiotropy and evolutionary compensation. Additionally, clarifying how acquired mutations specifically\ncompensate for deleterious effects would align the current findings with prior work and highlight the\nnovel contributions of the study.\n2. Studies by Bowman et al. using thiol labeling and NMR addressed the dynamic interplay between closed and open\nconformations of the Ω-loop in β-lactamases. Beta-lactamases with a higher population of the open Ω-loop conformation\nexhibit increased catalytic efficiency against benzylpenicillin, a smaller β-lactam substrate, whereas the closed conformation\nis more favorable for the hydrolysis of larger substrates like cefotaxime. The findings in Bowman et al. appear to align with\nthe current work, yet the manuscript does not explicitly connect these studies. Incorporating a discussion of how the\nobserved dynamic shifts in the Ω-loop correlate with substrate specificity in the current work would strengthen\nthe contextual framework and provide a more integrated perspective.\n3. The linear chemical shift analysis presented in Figure 4, illustrating the introduction of E104K in the G238S background\nand A42G in the E104K/G238S background, is compelling. However, the text is somewhat ambiguous. While the authors\nstate that mutations independently optimize distinct regions without inducing a global population shift between\ncefotaximase- and penicillinase-optimized conformations, the analysis simultaneously implies that such states exist. This\ndual framing is conceptually inconsistent. It would be beneficial to clearly define what constitutes a cefotaximase-\noptimized conformation versus a penicillinase-optimized conformation. Additionally, providing quantitative data, such as\ninteratomic distances, dihedral angles, or root-mean-square fluctuations, would concretely delineate how these states differ. Further,\na clarification is needed on whether the observed shifts represent distinct, discrete states or a continuous ensemble of\nconformations. Statistical validation of the linearity of the chemical shift data should also be provided to ensure that the\nobserved trend is not an artifact of selective data reporting or overfitting.\n4. The authors effectively utilize HDX data to assess how G238S induces local destabilization and how E104K and A42G\nsubsequently mitigate these effects. However, the manuscript predominantly discusses changes in folding stability (ΔΔG\nvalues), which do not directly correlate with HDX data reflecting localized stability perturbations. The manuscript would be\nstrengthened by integrating additional analyses that specifically quantify changes in local stability, potentially including\nentropy-enthalpy compensation to capture how different regions are variably affected by each mutation. Additionally,\nmolecular dynamics simulations, as reported in Yu et al. (Proc. Natl. Acad. Sci. USA 2018) and Ose et al. (Biophys. J. 2023),\nprovide supporting evidence for the findings in Figures 5 and 6. Citing these studies and discussing how they align with the\ncurrent work would provide a more comprehensive contextual framework.\n5. The identification of a cryptic allosteric site formed by the α11-α12 interface is briefly mentioned but not sufficiently\nanalyzed mechanistically. The cited works also do not provide a clear mechanistic explanation. How this site influences\ncatalytic efficiency and inhibition remains unclear. Is the formation of this cryptic site a compensatory mechanism or an\nunintended structural consequence of the mutations? More direct evidence is needed to substantiate the proposed role of\nthis site, potentially including data on how ligand binding, mutational analysis, or dynamic perturbations influence its\nfunctional role.\n6. The authors assert that their findings offer actionable strategies for enhancing enzyme function while\nmitigating stability tradeoffs. However, the broader applicability of this framework beyond TEM-1 is not clearly articulated.\nExplicitly discussing how the dynamic tradeoff framework developed here could be applied to other enzymes,\nparticularly those with known allosteric networks or cryptic binding sites, would broaden the impact of the study and\nreinforce its relevance to protein engineering and evolutionary biology.", "reviewer_3": "Review for Arcia et al.,\n“Dynamic signature of activity-stability tradeoff in lactamase evolution”\nArcia et al. describe how mutations in the early evolution of antibiotic resistance in TEM-1 beta lactamase influence the\nprotein’s substrate specificity and overall conformational ensemble properties. They use a combination of NMR\nspectroscopy and HDX/MS to achieve high spatiotemporal resolution in their study of the allosteric effects in the protein\nensemble.\nIt’s a very interesting paper, but I focus here only on the HDX/MS data analysis. There are some errors that affect the\ninterpretation of the data. Some of them might come from errors in offsetting the residue indexing in the analysis. One would\nassume that the authors meant to maintain indexing that places G238S at position 238, but the indexing is inconsistent no\nmatter what the reference is. Apart from the ambiguity in the annotation, in several cases the authors actually have the wrong\nnumber of residues in a peptide, which means that their reported %D is very likely incorrect, if the data themselves are\ncorrect. Additionally, this means that some of the peptides are not possible to correctly identify, because if the residue\nnumbering is correct, then the data would correspond to the reported peptide, but if the residue numbering is off by -3 to +3\nas in some of the other peptides, then the reported data correspond to a different peptide altogether.\nIn Fig. 5b:\n- Peptide 32-40 should be 31-40: VKVKDAEDQL\n- Peptide 163-182: DRWEPELNEAIPNDERDTTM is correct\n- Peptide 211-221 should be 214-221: DKVAGPLL\n- Peptide 222-230: RSALPAGW is correct\n- Peptide 230-248 is ambiguous: it is possible that it should be 230-247: FIADKSGAGERGSRGIIA given the indexing\nproblem, or alternatively, it might be correctly identified as 230-248: FIADKSGASERGSRGIIAA (it’s not clear which peptide\nthese data correspond to)\n- Peptide 263-269 should be 261-267: IYTTGSQ\n- Peptide 273-283 should be 271-281: DERNRQIAEIG\n- Peptide 286-290 should be 284-288: LIKHW\nAlso in Fig. 5c, the authors might want to add the yellow sphere mentioned in the caption to the figure.\nThere are also mistakes in Fig. 6b:\n- Peptide 32-40 should be 31-40\n- Peptide 163-182 is correct\n- Peptide 211-221 should be 214-221\n- Peptide 222-230 is correct\n- Peptide 230-248 is ambiguous: if the peptide sequence is identified correctly, then it should be 230-247, but if the indexing\nis correct, it should likely correspond to peptide 230-248: FIADKSGAGERGSRGIIAA (also, it is not proper to compare the\ndeuterium uptake for two peptides where there is a difference in the sequence on the same graph, because the k_int for the\nmutated residue will be different and we cannot account for the magnitude of this difference in the %D – the authors should\nreally find another way to show the data for these peptides)\n- Peptide 263-269 should be 261-267\n- Peptide 273-283 should be 271-281\n- Peptide 286-290 should be 284-288.\nDid not look at the supplement but the authors should double-check all their peptides."}, "author_response": "Point-to-point response to reviewers’ comments.\nReviewer #1: The manuscript by Arcia and coworkers provides a compelling and detailed NMR-based study\nof the dynamic features in a key evolutionary pathway of the β-lactamase TEM-1 towards cefotaxime resistance.\nThis enzyme has been thoroughly studied as a model for protein evolution. In this regard, the authors rely on a\nlarge amount of clinical evidence, biochemical and structural studies over three decades. The initial substitution,\nG238S, induces chemical shift perturbations all over the structure. CPMG relaxation dispersion experiments on\nthis variant can be described by a two-state model, that the authors interpret as the two conformations adopted\nby the 238-loop upon this mutation (open and closed) in fast exchange in the µs-ms timescale. Instead,\nR164S elicits an exchange phenomenon slow in the NMR timescale, as witnessed by the duplication of several signals.\nOther mutants in the evolutionary pathway TEM-1 → G238S → E104K → A42G → M182T are analyzed in terms\nof chemical shift perturbations. The authors conclude that a linear relationship of the CSPs with these\nperturbations reveal that these variants are sampling two extreme conformations, one favoring hydrolysis of\npenicillin, and the evolved one, favoring cefotaxime hydrolysis. Then, they explore stability perturbations by HDX-\nMS, disclosing the pleiotropic effects of mutation G238S. Finally, they assess the compensatory role of mutations\nE104K and A42G.\nOverall, this manuscript assesses directly the role of protein dynamics in the evolution of a well-studied system,\nmostly by NMR spectroscopy. One main challenge of this manuscript is to convince the readership that the\nauthors are not “reinventing the wheel”. In this regard, I am convinced that the information is novel, but there are\nsome sections in the manuscript that heavily rely in already published information. The authors should better\nwork some of these sections.\nThere are some specific aspects that the authors could address to further improve the quality of their contribution:\n1. The authors only report CPMG experiments for the G238S variants, while the rest of the interpretation\nmostly relies in CSP analysis. Given that the main conclusions of the paper are regarding protein\ndynamics, some results should be supported by additional relaxation measurements, or the impact of\ntemperature in the proposed equilibria.\nResponse: We fully agree that this approach would provide robust evidence, but we would like to clarify the\ntechnical challenges we encountered. The primary challenge in collecting a full set of CPMG data for all mutants\nwas the profound line broadening for the G238S-containing mutants. Many resonances are broadened beyond\ndetection, while others are broadened to the extent that reliable fitting and extraction of relaxation parameters is\nnot possible. As detailed in Supplementary Table 1, even residues that did show a dispersion curve -such as\nS130, V216, K234, and D254- yield poor fits, making the data unreliable for quantitative analysis (e.g., see the\ndispersion curve for residue L220 in Fig. 1, below).\nGiven these limitations, we relied on the analysis of chemical shifts, which are very sensitive to conformational\nexchange. Our observation of a strong linear correlation in the signal position across the series of TEM mutants\nprovides compelling evidence for a two-state exchange process. The agreement between CPMG and CSP for\nthe presence of a two-state exchange provides confidence that this approach is the most robust and informative\ngiven the spectral characteristics of these proteins.\nFig. 1: Representative example of a CPMG dispersion curve showing non-zero DR2,eff, but resulting in poor fitting.\nRegarding the impact of temperature, we did conduct temperature-dependent NMR experiments. While\nscreening for an optimal temperature to perform the assignment and dynamic studies of G238S, we recorded\nan incomplete CPMG series at 12 oC (n points of 50 and 2000Hz) and found that the same set of residues\nCPMG\nshows a significant DR . We thus used 30 °C, which yielded the best quality spectra for a complete CPMG\n2,eff\nseries and allowed performing the analysis reported in the manuscript.\nEDITORIAL NOTE: REDEACTED DUE TO UNPUBLISHED MATERIAL\n2. Several papers have shown that the evolutionary pathway here studied is not only dominated by an\nactivity-stability tradeoff, but by strong epistatic interactions (see papers from Weinreich, Palzkill, Palzkill and\nShoichet cited). There are no comments on this regard. The authors have studied different combinations\nof mutations that can reflect the impact of epistatic effects (for example, mutation G238S in the wt\nbackground, versus the E104K background).\nResponse: See comment #3, below.\n3. The authors conclude that “In summary, mutations that define the evolution of TEM-1 to TEM-1* do\nnot cause a global population shift between two unique conformations, a penicinillase and a\ncefotaximase. Instead, mutations independently optimize the population of different regions, including\nactive site walls and scaffold sites, resulting in a combinatorial set of TEM-1 conformers from which a\npotent cefotaximase conformation is optimized along the pathway” Could this be interpreted as\nreflecting epistatic interactions?\nResponse: Yes, this is indeed the case. In the revised version, we explicitly mention the conformational\nepistasis between E104K and G238S and how this reflects in the two-site exchange of active-site walls.\nHowever, the current NMR dataset lacks sufficient information, as it comprises only 6 of the 16 evolutionary\nintermediates investigated by Weinreich and other groups. Even for the three mutations considered here, we\nhave analyzed only one of the three possible double mutants. Therefore, we cannot generalize how\nconformational epistasis and epistasis in k /K observed across the landscape correlate.\ncat M\nInstead, we have highlighted that mutations have unique control over the positions of the individual equilibria\nthrough narrow networks. For G238S and E104K, the position of the equilibrium in the context of the\nE104K/G238S double mutant is determined by G238S for all active-site walls and scaffold elements, except for\nthe SDN loop, where the position is determined by E104K. Similarly, for scaffold elements (e.g. helices a and\n1\na ), the position of the equilibrium in the A42G/E104K/G238S triple mutant is determined by A42G. Fig. 3 is now\n12\nupdated and in the relevant free energy diagram we have included E104K/G238S to better illustrate the epistatic\neffect.\nFor the same reason, our HDX data cannot provide insights into possible epistasis in local stability. In this\nrespect, E104 and A42G do stabilize specific secondary structure elements in the G238S and E104K/G238S\nbackgrounds, respectively, but epistatic interactions cannot be assessed unless HDX profiles are determined in\nadditional genetic backgrounds, including E104K and A42G. As noted below, we do seek ways of obtaining local\n(residue resolved) DDGs from which we hope to obtain information on epistasis in stability.\n4. The manuscript clearly describes the dynamic effects of individual mutations, and relies in a large\namount of previously published activity and MIC data. A recompilation of this information from the\nliterature in a Supplementary table to illustrate quantitatively the impact of each mutation in activity and\nfitness would help the reader who is not acquainted with the details of evolution of TEM lactamases.\nAdditionally, if feasible, including a column summarizing the overall dynamic impact of each mutation\n(e.g., ΔΔG).\nResponse: The table with references has been included and cited in the Introduction section.\nThe authors choose MS instead of NMR for studying the impact of HXD based on the fact that MS affords\nmeasurements even for amide groups broadened due to exchange phenomena. NMR was used to\ninterrogate the dynamics of lactamases from the CTX-M family. Did they give it a try?\nResponse: Yes, we did attempt to use NMR to measure HDX for the TEM variants; however, we ultimately\nchose to proceed with mass spectrometry (MS) for the following reasons:\n(1) Increased Coverage: As noted in the main text, MS allows us to obtain HDX information for a broader\nrange of amide groups, including those that are broadened beyond detection due to conformational\nexchange phenomena and therefore not detectable by NMR. Thus, MS provides a more comprehensive\nview of the system's dynamics.\n(2) Structural Rearrangements: When attempted to monitor HDX by NMR, we observed significant chemical\nshift changes and, for multiple signals, peak doubling in the HSQC spectra upon rehydration of lyophilized\nsamples in D O. This was the case for both the G238S and E104K/G238S variants. These observations\n2\nstrongly suggest that structural rearrangements occur during the rehydration process, which would\ncomplicate interpretation of an NMR-based HDX experiment. While TEM-1 did not show peak doubling,\nit did exhibit similar chemical shift changes (See Fig. 3, below).\n(3) Complex Exchange Profile: A subset of signals showed a rapid initial deuterium incorporation of\napproximately 20% in the first 15N HMQC experiment, followed by a plateau with no further exchange.\nThis rapid, non-progressive exchange, combined with the observed structural changes, further\nsubstantiates that lyophilization and subsequent rehydration in D O alter the folding properties of the\n2\nprotein and reinforced our decision to use MS for reliable HDX measurements.\nDespite of these challenges, we do seek ways to accurately obtain local DDGs via either NMR or HDX.\nFig. 3: Comparison of the 1H-15N HSQCs of G238S (left) and\nTEM-1 (right) acquired in H2O and in D2O after lyophilization.\nThe spectra acquired in D2O correspond to the first exchange\ntime point (includes a deadtime of ~10 minutes) and are\nplotted at a lower threshold compared to the reference\nspectra. Multiple G238S signals show peak doubling,\ncorresponding to two different conformations. Only small\nchemical shift perturbations were observed for TEM-1.\nReviewer #2: Arcia et al. present a comprehensive study on the role of protein dynamics in the activity-stability\ntradeoff of TEM-1 β-lactamase and its evolutionary adaptations toward cefotaxime resistance. Employing a\ncombination of NMR, HDX, the authors systematically dissect the dynamic behavior of several TEM-1 variants,\nfocusing on key mutations (G238S, E104K, A42G) and the combined effects of these mutations along the\nevolutionary trajectory. This approach allows for a detailed assessment of how primary and secondary mutations\ndifferentially modulate dynamics and local stability.\nThe study emphasizes the significance of local and global dynamics in shaping the functional landscape of TEM-\n1 and identifies the mutations propagate to modulate many sites around active site wall as well as remote scaffold\nsites. While the study provides a methodologically robust analysis of TEM-1 dynamics and the evolutionary\ntradeoff between activity and local stability, emphasizing the conceptual novelty and organizing key findings\nmore clearly would significantly enhance its impact on the fields of protein engineering and antibiotic resistance\nresearch.It would be beneficial for the authors to address the following concerns and provide clarifications prior\nto publication:\n1. The authors extensively analyze the dynamics of single and combined mutations along the\nevolutionary trajectory. The Weinreich group (Ref. 35) characterized 16 variants comprising all\ncombinations of the four mutations and discussed pleiotropic effects and epistasis, measuring catalytic\nefficiency and thermostability (ΔG of folding). They concluded that cefotaxime resistance in TEM-1 β-\nlactamase is primarily driven by enhancements in catalytic activity rather than changes in\nthermostability. Furthermore, they noted that subsequent mutations primarily reinforce local stability or\nkinetic stability rather than global stability.\nThe manuscript uses the terms local stability and global thermodynamic stability interchangeably, which\ncould be misleading. The authors should clearly define these terms and distinguish their roles in\npleiotropy and evolutionary compensation. Additionally, clarifying how acquired mutations specifically\ncompensate for deleterious effects would align the current findings with prior work and highlight the\nnovel contributions of the study.\nResponse: Thank you for the comment.\n(Global vs. Local stability) We agree that a clear distinction between local and global stability is needed to better\ndeliver the importance of this work. The original version of the manuscript includes, in the Introduction, a section\non global stability, which remains as is. A direct comparison between global and local stability is now provided\nin the Discussion section. As in the original manuscript, in the revised version we still use ΔG and ΔΔG only to\nrefer to global thermodynamic stability (measured by thermal or chemical denaturation). We have removed the\nterm non-native when referring to the open allosteric state to avoid any misconception with a largely unfolded\nstate.\n(Pleiotropy and Compensation) We have now included connections between the local stability changes reported\nhere for the a /a /a network and prior work on mutant or engineered TEM-1 constructs in the same region.\n1 11 12\nThese studies have shown that perturbations of this network result in (global) stability defects.\nIn the original manuscript we have discussed our data in the context of pleiotropic defects relating to misfolding,\naggregation and degradation, mainly because these have been characterized in the past for TEM-1 and other\nb-lactamases. In the revised version, we now recognize that multiple other pleiotropic effects may operate\ndifferentially along the trajectory (Discussion section), including kinetic stability, the in vivo environment\n(crowding) and the interaction with other cellular factors. Still, their impact on fitness provided by TEM-1 remains\nunexplored, and we have not expanded our discussion further. In addition, in the revised Discussion, we cite the\ninfluential work by the Villa group, where, using HDX, it was shown that Zn2+ and naturally occurring variants\nmodulate the dynamics of a long C-terminal segment of NDM metallo-β-lactamases, causing differential\naccessibility to proteolytic degradation and, therefore, distinct in-cell kinetic stability profiles.\nMore on the pleiotropy: We have significantly revised the presentation of pleiotropic effects caused by\nsecondary mutations, that was heavily stability-oriented in the original manuscript. In the revised version we also\nhighlight how pleiotropic effects related to activity aid to TEM-1 evolution (see the paragraph Dynamic population\nshift of TEM-1 conformers along cefotaximase evolution and the Discussion section). This is very clear for A42G.\nThis mutation is considered a stabilizing substitution, as it is selected for enhancing stability. However, it is also\npresent in resurrected b-lactamase sequences that behave as generalists (see work from Ozkan and Sanchez-\nRuiz groups doi:10.1093/molbev/msu281 & https://doi.org/10.1021/ja311630a) and in CTX-Ms that are bona fide\ncefotaximases. Moreover, the Weinreich group reports a tenfold improved catalytic efficiency for the\nincorporation of A42G in the G238S or E104K/G238S backgrounds.\nUnder the “Dynamic population shift” paragraph we have included a series of our own activity assays covering\nthe TEM-1 → G238S → E104K/G238S → A42G/E104K/G238S trajectory and highlight that the concerted\npopulation shift of active site walls by A42G back to the original penicillinase state is indeed accompanied by\nimproved catalytic efficiency. The same is true for the local effect of E104K on the two-state exchange of the\nSDN-loop. We conclude that the role of secondary substitutions like A42G in pleiotropy goes beyond correcting\nlocal stability defects, and directly affects catalytic efficiency. Importantly, it is also becoming apparent that both\nstates are required for efficient catalysis. The model we propose for this TEM-1 trajectory is: G238S introduces\nand strongly biases the conformational ensemble toward the new function conformation, while E104K and A42G\nprogressively restore a significant population of the penicillinase conformation. The new Fig. 3 and the revised\ntext better convey the pleiotropic effects on activity, while our HDX data on stability.\n2. Studies by Bowman et al. using thiol labeling and NMR addressed the dynamic interplay between\nclosed and open conformations of the Ω-loop in β-lactamases. Beta-lactamases with a higher population\nof the open Ω-loop conformation exhibit increased catalytic efficiency against benzylpenicillin, a smaller\nβ-lactam substrate, whereas the closed conformation is more favorable for the hydrolysis of larger\nsubstrates like cefotaxime. The findings in Bowman et al. appear to align with the current work, yet the\nmanuscript does not explicitly connect these studies. Incorporating a discussion of how the\nobserved dynamic shifts in the Ω-loop correlate with substrate specificity in the current work would strengthen\nthe contextual framework and provide a more integrated perspective.\nResponse: Our current dataset agrees with the work from the Bowman group in that there is conformational\nexchange at the Ω- and 238-loop interface, at the C-terminal segment of the Ω-loop. We do cite and note the\noriginal thiol labeling work (Nat Commun 7, 12965, doi:10.1038/ncomms12965 (2016)) as well as the subsequent\nand highly relevant NMR work from the same group (Proc Natl Acad Sci U S A 118,\ndoi:10.1073/pnas.2106473118 (2021)). Consistent with the MST data for a TEM-1-like conformation, i.e. a state\nwhere the Ω-loop samples predominantly a penicillinase state, also termed the “closed state”, we specifically\nnote that the E166 signal of G238S HSQC shows a minimal perturbation, to emphasize that this is the preferred\nΩ-loop geometry for cefotaxime hydrolysis.\nEDITORIAL NOTE: REDEACTED DUE T O UNPUB LISHED MATERIAL\n3. The linear chemical shift analysis presented in Figure 4, illustrating the introduction of E104K in the\nG238S background and A42G in the E104K/G238S background, is compelling. However, the text is\nsomewhat ambiguous. While the authors state that mutations independently optimize distinct regions\nwithout inducing a global population shift between cefotaximase- and penicillinase-optimized\nconformations, the analysis simultaneously implies that such states exist. This dual framing is\nconceptually inconsistent. It would be beneficial to clearly define what constitutes a cefotaximase-\noptimized conformation versus a penicillinase-optimized conformation.\nResponse: We have now modified the text in the “Dynamic population shift of TEM-1 conformers along\ncefotaximase evolution” paragraph and throughout the manuscript to better reflect our findings and clarify that\nmultiple penicinilase/cefotaximase states are formed by this series of mutants. They result by altered population\nratios of individual active site walls undergoing two-state exchange and they are subject to epistatic effects. We\nwere therefore cautious to use the terms like a penicillinase state.\nWe also find that the term “state” more accurately reflects the mechanistic details of this system. Mutations act\nas rheostats of the two-state exchange processes, and the new Fig. 3, describes better this continuum between\npenicillinase and cefotaximase states. A42G, for example, affects all active site wall conformers and alters the\nrelative population ratio between the new-function conformation and the penicillinase conformation to improve\ncatalytic efficiency.\nAdditionally, providing quantitative data, such as interatomic distances, dihedral angles, or root-mean-\nsquare fluctuations, would concretely delineate how these states differ.\nResponse: The NMR data presented here do not yield structural information. Still, in the revised manuscript we\nincluded that the structural rearrangement observed in the available structures, primarily those containing G238S\nand E104K/G238S, are found at the b\n3\nstrand/238-loop.\nEDITORIAL NOTE: REDEACTED DUE TO UNPUBLISHED MATERIAL\nFurther, a clarification is needed on whether the observed shifts represent distinct, discrete states or a\ncontinuous ensemble of conformations.\nResponse: In the revised manuscript we have kept the original statement “This suggests that each of these\nsites exists in an equilibrium between two states that interconvert in the fast exchange regime, with the position\nof a signal along the line marking the population weighted average of each state.”. This best describes our\nobservations and agrees with the CPMG data acquired with G238S, showing a two-state exchange. We cannot\nexclude the possibility that in addition to the two states observed here, other low populated states are also\nsampled. This is however the minimal set of conformers that can explain the dataset and the biochemistry of the\nsystem.\nStatistical validation of the linearity of the chemical shift data should also be provided to ensure that the\nobserved trend is not an artifact of selective data reporting or overfitting.\nResponse: We have a standard digital resolution for the two dimensions of the HSQC experiments (~4 Hz/point\nfor 1H and ~8 Hz/point for 15N), so the position of a signal in the spectrum is very accurately determined. The\nbiggest source of error in this kind of analysis typically arises from different samples and is related to small\ntemperature or pH variations between runs. For TEM-1, G238S and E104K, that multiple spectra have been\nacquired as both biological and technical replicates during our studies, an RMSD of ~ 1 Hz is obtained for both\nthe 1H and 15N dimensions. This is significantly lower compared to the CSPs reported in here as significant,\nwhich typically is in the order of > 10 Hz. Although not common in this type of analysis, we have now included\nthe range of R2 values obtained for multiple signals, which is from 0.82 to 0.98. We note that deviations from\nlinearity are expected when nuclei are in the vicinity of a mutation site and therefore there is a direct impact on\nthe chemical shift. We have tried not to select such residues.\n4. The authors effectively utilize HDX data to assess how G238S induces local destabilization and how\nE104K and A42G subsequently mitigate these effects. However, the manuscript predominantly\ndiscusses changes in folding stability (ΔΔG values), which do not directly correlate with HDX data\nreflecting localized stability perturbations.\nResponse: The data presented in this study report differential deuterium incorporation (Δ% uptake), which is\ncertainly not local ΔΔG, but directly reflects on changes in differential local stability. As noted above, here we\nhave used ΔG and ΔΔG only when referring to global stability.\nIn general, HDX requires an unfolding event that breaks H-bonding and allows exposure to solvent. Hence,\nrates depend on the equilibrium between folded and locally unfolded states. While the set of our HDX data does\nnot allow extraction of residue level ΔG values, we still can use our peptic peptide level resolution to assess local\nstability. This is further supported by the available structures of the evolutionary intermediates studied in here\n, where the structural rearrangements observed cannot account for enhanced\ndeuterium incorporation due to changes in secondary structure or major structural rearrangements, but truly\nreflect changes in local stability and dynamics. We now explicitly mention in the manuscript that ΔD% uptake is\nused as a proxy of local stability (see paragraph Local stability changes reveal unique G238S-induced pleiotropic\ndefects).\nThe manuscript would be strengthened by integrating additional analyses that specifically quantify\nchanges in local stability, potentially including entropy-enthalpy compensation to capture how different\nregions are variably affected by each mutation.\nResponse: The current set of HDX data does not allow accurate determination of local ΔG values at the residue\nlevel. This would require a significantly larger pool of fragments than is currently available using only pepsin. We\nwill agree that enthalpy-entropy compensation will be apparent in the differential stability distribution between\nmutants -as is in many other biophysical measurements- but assessing it would require HDX datasets acquired\nat different temperatures, and this is beyond the purpose of this work.\nAdditionally, molecular dynamics simulations, as reported in Yu et al. (Proc. Natl. Acad. Sci. USA 2018)\nand Ose et al. (Biophys. J. 2023), provide supporting evidence for the findings in Figures 5 and 6. Citing\nthese studies and discussing how they align with the current work would provide a more comprehensive\ncontextual framework.\nResponse: These are indeed very relevant papers and are now cited in both the Introduction and the Discussion.\n5. The identification of a cryptic allosteric site formed by the α11-α12 interface is briefly mentioned but\nnot sufficiently analyzed mechanistically. The cited works also do not provide a clear mechanistic\nexplanation. How this site influences catalytic efficiency and inhibition remains unclear. Is the formation\nof this cryptic site a compensatory mechanism or an unintended structural consequence of the\nmutations? More direct evidence is needed to substantiate the proposed role of this site, potentially\nincluding data on how ligand binding, mutational analysis, or dynamic perturbations influence its\nfunctional role.\nResponse: The cited papers are on the work demonstrating that the TEM-1 allosteric site pre-exists as a\nsparsely populated excited state. It also includes the structural work from the Shoichet lab, where the different\nmodes of binding for core disruptive ligands were identified. The HDX data presented in here indicate that G238S\ndestabilizes helices a , a and a , as well as strands b and b (Fig. 4 and Supplementary Fig. 5). Therefore,\n1 11 12 3 4\nwe hypothesized that this would lead to partial shift of the allosteric site from the closed to the open state.\nIn the revised manuscript we include NMR and kinetic data acquired with FTA, one of the core disruptive ligands\n(allosteric inhibitors), previously identified (Fig. 5). Consistent with our hypothesis, we find that FTA causes\nminimal perturbations in the NMR spectrum of TEM-1 and exhibits weak inhibition against nitrocefin hydrolysis\nindicating that the equilibrium is strongly biased towards the closed state. G238S, however, shows global and\nlarge changes in chemical shifts that localize at the predicted opening of the fold, suggesting that the mutation\nacts by partially destabilizing the closed state, and therefore is significantly more susceptible to FTA inhibition.\nStill, the strong stabilizing effect of E104K and A42G on helices a and a is not enough to reverse this\n12 1\n(apparently small) population shift. We speculate that this is because strands b /b and helix a are not stabilized\n3 4 11\nby the secondary substitutions. Therefore, the double and triple mutant show lower albeit comparable IC to\n50\nthat of G238S (Supplementary Fig. 5b). These data are now incorporated in the revised version. Certainly, more\neffort (structural studies with TEM-1 mutants) is required to fully understand its function and exploit it\npharmacologically. We did not manage to achieve this during the revision cycle.\n6. The authors assert that their findings offer actionable strategies for enhancing enzyme function while\nmitigating stability tradeoffs. However, the broader applicability of this framework beyond TEM-1 is not\nclearly articulated. Explicitly discussing how the dynamic tradeoff framework developed here could be\napplied to other enzymes, particularly those with known allosteric networks or cryptic binding sites,\nwould broaden the impact of the study and reinforce its relevance to protein engineering and\nevolutionary biology.\nResponse: As noted, the general impact in (protein) evolution biology, is that the use of solution techniques that\nprovide adequate resolution can inform on the molecular basis of the activity-stability tradeoff in other systems.\nWe expect that the dynamic properties and extent of population shifts between states, as observed here for\nTEM-1, to differ significantly from system to system. However, we believe that by integrating tools to identify the\nscaffold residue networks that are subject to stability defects, such as HDX, can guide the thermostability problem\nof engineered or designed proteins.\nReviewer #3: Review for Arcia et al., “Dynamic signature of activity-stability tradeoff in lactamase\nevolution”\nArcia et al. describe how mutations in the early evolution of antibiotic resistance in TEM-1 beta lactamase\ninfluence the protein’s substrate specificity and overall conformational ensemble properties. They use\na combination of NMR spectroscopy and HDX/MS to achieve high spatiotemporal resolution in their\nstudy of the allosteric effects in the protein ensemble.\nIt’s a very interesting paper, but I focus here only on the HDX/MS data analysis. There are some errors\nthat affect the interpretation of the data. Some of them might come from errors in offsetting the residue\nindexing in the analysis. One would assume that the authors meant to maintain indexing that places\nG238S at position 238, but the indexing is inconsistent no matter what the reference is. Apart from the\nambiguity in the annotation, in several cases the authors actually have the wrong number of residues in\na peptide, which means that their reported %D is very likely incorrect, if the data themselves are\ncorrect. Additionally, this means that some of the peptides are not possible to correctly identify, because if the\nresidue numbering is correct, then the data would correspond to the reported peptide, but if the\nindexing is off by -3 to +3 as in some of the other peptides, then the reported data correspond to a\ndifferent peptide altogether.\nResponse: We first note that the constructs used in this study produce periplasmic TEM proteins with native N-\nterminus starting at position H26. Throughout this work residue numbering is according to the Ambler\nscheme for class A β-lactamases (PMID: 2039479, Ambler et al., Biochem. J. 276, 269-272, 1991), which places the\nactive site serine at number 70. For TEM-1, this numbering includes gaps between residues G238-E240 and\nP252-D254, i.e. there are no residues 239 and 253. This important information is now provided early in the\nmanuscript (first paragraph), as well as under the HDX methods paragraph.\nWe have now checked all the peptides reported in the manuscript and identified two peptides presented in Fig.\n5 and Fig. 6 (31-40 and 214-221), and one presented in the Supplementary Fig. 5 (31-40), for which the\nnumbering given is not correct, although the sequence is and it was correctly identified (see below in detail).\nIn addition, (a) we have corrected the sequence of peptide #45 in Supplementary Fig. 5 (a T was missing at the\nC-terminus of the sequence) and (b) corrected the legend for Supplementary Fig. 5b, as G238S is shown in blue.\nIn Fig. 5b:\n- Peptide 32-40 should be 31-40: VKVKDAEDQL corrected\n- Peptide 163-182: DRWEPELNEAIPNDERDTTM is correct no change\n- Peptide 211-221 should be 214-221: DKVAGPLL corrected\n- Peptide 222-230: RSALPAGW is correct no change\n- Peptide 230-248 is ambiguous: it is possible that it should be 230-247: FIADKSGAGERGSRGIIA given the\nindexing problem, or alternatively, it might be correctly identified as 230-248: FIADKSGASERGSRGIIAA (it’s not\nclear which peptide these data correspond to) 230-248 is correct (one gap is included)\n- Peptide 263-269 should be 261-267: IYTTGSQ 263-269 is correct (two gaps are included)\n- Peptide 273-283 should be 271-281: DERNRQIAEIG 273-283 is correct (two gaps are included)\n- Peptide 286-290 should be 284-288: LIKHW 286-290 is correct (two gaps are included)\nAlso in Fig. 5c, the authors might want to add the yellow sphere mentioned in the caption to the figure. The\nopacity of the surface representation is now adjusted to make the position of the mutation site apparent.\nThere are also mistakes in Fig. 6b:\n- Peptide 32-40 should be 31-40 corrected\n- Peptide 163-182 is correct no change\n- Peptide 211-221 should be 214-221 corrected\n- Peptide 222-230 is correct no change\n- Peptide 230-248 is ambiguous: if the peptide sequence is identified correctly, then it should be 230-247, but if\nthe indexing is correct, it should likely correspond to peptide 230-248: FIADKSGAGERGSRGIIAA 230-248 is\ncorrect (one gap included)\nalso, it is not proper to compare the deuterium uptake for two peptides where there is a difference in the\nsequence on the same graph, because the k_int for the mutated residue will be different and we cannot\naccount for the magnitude of this difference in the %D – the authors should really find another way to\nshow the data for these peptides\nYes, the k(int) of the mutated residue will differ. In fact, the mutation will also affect the k(int) of the adjacent\nresidue on its C-terminal side. According to Englander’s predictive formulas, the k(int) of the mutated residue is\nexpected to increase by approximately 2.5x, while that of its C-terminal neighbor may increase by about 1.5x.\nAs a result, we agree that the variations in k(int) could introduce a measurable change in deuterium uptake that\nis unrelated to changes in local structural stability or dynamics.\nHowever, this change in deuterium uptake should not exceed ~1.5 Da, even under the most unfavorable (yet\nunlikely) scenario in which the backbone amide hydrogens of both affected residues are fully solvent-exposed\nand not engaged in hydrogen bonding (assuming an average back-exchange of 25%, too). Still, we observed\nthat the mutation leads to an increase of ~ 3.3 Da in deuterium uptake after 1000 seconds of exchange and\nnever less than 2.5 Da across all measured HDX time points. Therefore, we believe that it remains appropriate\nto include the ΔD% for these two or other peptides corresponding to this region, given the magnitude of the\nobserved differences in deuterium uptake among these evolutionary intermediates.\n- Peptide 263-269 should be 261-267 263-269 is correct (two gaps included)\n- Peptide 273-283 should be 271-281 273-283 is correct (two gaps included)\n- Peptide 286-290 should be 284-288. 286-290 is correct (two gaps included)\nDid not look at the supplement but the authors should double-check all their peptides. As noted above, the\nsupplementary information is also corrected."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed all my concerns, providing new experiments, and appropriate explaining the\nexperimental limitations to perform some assays.\nOverall, this resulted in a great manuscript that will be impactful in the field.", "reviewer_2": "The authors addressed all the questions I raised.", "reviewer_3": "Thank you to the authors for correcting the mistakes in their uptake plot annotations. But the authors still did not correct the\nuptake plots showing peptides of different sequences together on the same axes. This is incredibly improper, and there are\na lot of problems with their handwaving explanation about why this is OK - it is not OK, and it sets a bad precedent in the\nliterature for our field. The biggest problems with their explanation: 1) mutations affect both N- and C-terminal residues'\nexchange rates (see recent work from Hamuro showing this in a tabular format, JASMS); 2) even single mutations often\nchange the local/global structure of the protein, which commonly manifest as changes in forward AND back exchange. The\nauthors do not take back-exchange into account in this work. So it would be great if the authors could please treat the data\nproperly and put the uptake for peptides of different sequences in different plots. Thanks."}, "author_response": "Response: The following edits were made:\nWe have removed the panel showing the comparison of deuterium uptake for the peptide that contains\nthe G238S mutation and an adjacent peptide was included instead. We now compare the uptake of the\nwild-type and mutant peptide of the G238-loop in different plots (side-by-side) in a new panel of\nSupplementary Fig. 4d. Similarly, the peptide with another mutation site (E104K) was removed from\nSupplementary Fig. 6b."}]

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