Mechanistic snapshots of lipid-linked sugar transfer

Domain: physics
Published: 2025-12-06
Editorial outcome: major_revision

Abstract
Enzymes undergo dynamic conformational changes during catalysis, yet conventional high-resolution structural methods typically capture only the most stable states. Here, we address this gap using rapid UV photolysis of a chemically caged substrate with cryogenic time-resolved electron microscopy (cryo-TREM). We elucidate the catalytic mechanism of GtrB, a membrane-bound glycosyltransferase that transfers glucose from UDP-glucose to the lipid carrier undecaprenyl phosphate. We visualized how GtrB, which has an active site ~15 Å from the membrane, transitions during the catalytic cycle to move each substrate in proximity for catalysis. From a single dataset, we resolved distinct conformational states: the initial substrate-bound state, a catalytically poised intermediate, and the product-bound state. Through molecular dynamics simulations and biochemical analyses, we identify coordinated movements within the active site that drive catalysis. These findings provide a molecular framework for understanding how glycosyltransferases function and highlight a broadly applicable strategy for capturing dynamic enzymatic states in native-like environments.

Introduction
Enzymes are highly specialized machines that adopt distinct conformational states1–4 during the various stages of their catalytic cycle. This is a prerequisite to ensure substrate specificity, efficient product release, and an energetically permissible chemical reaction, with molecular-level precision. Successful approaches for high-resolution structure determination, such as X-ray crystallography and single-particle cryo-electron microscopy (cryo-EM), are often limited by their intrinsic capability to capture only the lowest-energy conformations. Thus, the diverse ensemble of physiological enzymatic states is rarely accounted for in molecular-level structural analysis5–7. While recent studies using microfluidic spray-plunging8,9 or sequential time-point freezing10–12 have shown promise, few if any, molecular-level snapshots of enzymes across their complete catalytic cycle exist. To address this limitation, we focused on the catalysis of lipid glycosylation, one of the most common reactions found on cellular membranes across all domains of life carried out by glycosyltransferases (GTs).

The first step in all extracellular glycan synthesis typically requires the generation of membrane-embedded sugar conjugates containing polyisoprenyl phosphate (PP)- or pyrophosphate-lipid carriers13,14. In Gram-negative bacteria, GTs use a universal PP carrier, undecaprenyl phosphate (UndP), a 55-carbon isoprenoid, to carry sugars across the membrane for synthesis and modification of structures such as lipopolysaccharide and peptidoglycan15,16. In eukaryotes, a similar PP, dolichol phosphate (DolP), carries an oligosaccharide as a donor for N-linked protein glycosylation13,17. PP sugar carriers are synthesized by a class of membrane-attached GTs, referred to as acceptor polyisoprenyl phosphate GTs (PP-GTs)18. These enzymes often function through a metal-dependent inverting displacement reaction, facilitating the transfer of a monosaccharide from a sugar nucleotide onto a PP19. Despite the functional and ubiquitous nature of this process in cellular function, we lack a molecular-level understanding of how PP-GTs function. A handful of currently available structures show that the reaction between the phosphate head group of the polyprenyl and the nucleotide-activated sugar occurs within a soluble glycosyltransferase domain of the GT-A or GT-B fold superfamily, located ~15–20 Å from the membrane leaflet18,20–23. This considerable molecular distance raises an intriguing biophysical question as to how the membrane-embedded lipidic substrate and the distant active site come in contact for catalysis.

Here, we focused on GtrB from Synechocystis sp. PCC 6803, which was the first reported acceptor PP-GT structure20, and shares structural homology to GTs across all kingdoms of life21,22,24. GtrB catalyzes the transfer of glucose (Glc) from UDP-Glc to UndP to form UndP-Glc (Fig. 1a). This product is used as a donor for serotype conversion of the O-antigen in Gram-negative strains, a mechanism of immune evasion in bacteria25,26 (Supplementary Fig. 1a). To capture an ensemble of catalytic states, we synthesized a caged photolabile UDP-Glc derivative, allowing substrate binding without initiating catalysis. Following rapid UV photolysis on the grid, the cage is released and immediately thereafter vitrified for cryo-EM using a ‘flash-and-freeze’ plunge-freeze system, trapping nanodisc-incorporated enzyme with PP in various states along the reaction trajectory. Our cryogenic time-resolved electron microscopy (cryo-TREM) approach resulted in structures with both substrates, of an intermediate state primed for catalysis, and of the product-bound state. Combining structural information, molecular dynamics simulations, and biochemical assays allowed us to decipher the entire catalytic cycle of GtrB in molecular detail.

Results
To establish the model system before cryo-TREM, GtrB from Synechocystis sp. PCC 6803 was overexpressed and purified in detergent, as previously described20, and reconstituted into lipid-filled nanodiscs (Supplementary Fig. 1b–d) for structure determination by cryo-EM. After collecting ~3700 micrographs on a Technai Polara electron microscope with a K3 direct electron detector, particles were sorted by 2D-classification, volumes separated by heterogeneous refinement, and refined to yield a 2.79 Å resolution density map. Into this map, we successfully modeled the structure of apo-GtrB (Supplementary Fig. 1e–h), which closely resembles the previously published UDP-bound GtrB crystal structure20 (Supplementary Fig. 1i). GtrB is a homotetramer in which each protomer consists of a soluble domain, which bears the classical GT-A superfamily fold with two β/α/β Rossmann-like nucleotide-binding domains tightly associated to resemble a single domain (Supplementary Fig. 2a)19,27. This extends into the membrane with two juxtamembrane (JM) helices, a common structural element found in related GTs21, and two transmembrane (TM) helices that form an eight-helix TM bundle (two TM helices per protomer of the tetramer). Both β5-JM1 and β7-JM2 loops lack interpretable density, similar to what was previously observed. The GT-A domain of apo-GtrB is structurally homologous to that of the crystal structure with a root-mean-square-deviation (RMSD) of 1.9 Å among backbone atoms (Supplementary Fig. 2b). The primary difference is within the TM helix domain, where improved density in the periplasmic TM1-TM2 loop of the apo map revises the original assignment of TM helix 2 in the tetrameric assembly (Supplementary Fig. 2c, d). This results in an arrangement where TM helix 2 and the C-terminal domain interact with the adjacent protomer in the tetrameric assembly. Accounting for this difference and comparing over an entire protomer shows an RMSD of 2.3 Å among the backbone between the apo-GtrB and the crystal structure.

While GtrB is a tetramer, we predominantly observe a non-physiological tail-to-tail octamer among the particles collected (Supplementary Fig. 1e). This was previously seen in the crystallographic analysis of GtrB and assumed to be due to crystal-packing20. We find it originates from a stacking interaction mediated by Q58 and R61 between tetramerized particles. Attempts to purify a Q58S-R61T double-mutant tetramer were made, but the majority of the protein aggregated, hampering nanodisc incorporation and limiting the overall yield (Supplementary Fig. 2e, f).

We developed a cryo-TREM approach using a caged substrate in conjunction with a modified UV-LED mounted plunge-freeze system, as previously described28 (Fig. 1b, c). This approach relies on the controlled release just before vitrification of a ‘cage’ from a catalytically inert compound, thereby liberating an active substrate. To this end, we synthesized a caged-derivative of UDP-Glc29, 3-O-(2’-nitrobenzyl)-UDP-Glc (Supplementary Fig. 3a), which exploits the photolabile properties of 2-nitrobenzyl ethers to protect the Glc moiety from nucleophilic attack until UV exposure removes the cage (Fig. 1b). By applying short UV irradiation, it ensures minimal heating to the sample28 while allowing for rapid uncaging of UDP-Glc. To test optimal substrate release at saturating concentrations, a standard curve was determined using the photolysis system combined with HPLC quantification with a photostable internal standard (Supplementary Fig. 3b-d).

Detergent-solubilized GtrB was reconstituted into nanodiscs with 10% w/w UndP to lipid and mixed with caged-UDP-Glc. After application of the sample and blotting, the grids traveled through a focused UV beam for 17 ms, reaching the liquid ethane bath after 55 ms (Fig. 1b), before being vitrified within 1 ms30,31 for subsequent cryo-EM analysis. After collecting ~9200 micrographs on a Titan Krios electron microscope with a K3 direct electron detector, particles were sorted by 2D-classification and subsequent 3D-heterogeneous refinement to yield a 2.54 Å density map of GtrB in C4 symmetry (Fig. 1d, e; Supplementary Fig. 4). Here, we find clear density for both UndP, held within the JM helices, and UDP-Glc bound in the active site. We refer to the resulting structure as the substrate-bound state (Fig. 2a). Through subsequent rounds of 3D-variability analysis (3DVA) and 3D-classification, as implemented in cryoSPARC v4.6.232,33, we observed heterogeneity within the TM domain, JM helices, and the substrate binding pocket (Supplementary Movie 1). Particle sets were further separated by conventional 3D-classification to obtain two distinct states of GtrB along its catalytic cycle. First, in a 2.85 Å structure that we refer to as the pre-catalysis state, UndP is positioned near UDP-Glc, deep inside the active site (Fig. 2b). Second, within the same map, a different protomer contained density for the reaction products, UDP and UndP-Glc, which we consider the product-bound state (Fig. 2c).

Although octameric particles were most prevalent, as in the apo dataset, a small fraction (~4.5%) of GtrB tetramer particles were also observed in this dataset. These were classified, leading to a 3.19 Å cryo-EM map (Supplementary Fig. 4a). This map exhibits a poorer density in the periplasmic TM loop and C-terminal domain but is largely identical (correlation value of 0.86) to the octamer, substrate-bound map (Supplementary Fig. 4e). During 3D-classification, we chose to use D1 symmetry-expanded particles instead of D4, which reduced the dataset size and produced maps with greater clarity, while still accounting for tetramer-wide variations.

In the substrate-bound state of GtrB, we found unambiguous density for UndP in between JM helices 1 and 2 (Supplementary Fig. 5a). The UndP headgroup is bound in a hydrophilic pocket below JM1 containing R122, R125, K132, and K180 (Fig. 2a). R122 is universally conserved among GT-A acceptor PP-GTs18, while R125 appears partially conserved20,23, and it was previously shown that their mutation abolished activity20. The tail of UndP extends upwards into the membrane, though only five isoprenyl units are resolved in the map, making it difficult to interpret if and how the tail associates with the TM domain. In the GT-A domain, there is well-defined density for UDP complexed with a Mg2+-ion coordinated partially by the conserved Asp-X-Asp (DxD, in this case D96) motif (Fig. 2a). Extending from UDP, we model the Glc moiety, albeit with weaker density (Supplementary Fig. 5a) than surrounding regions likely due to a lack of strong coordination of the sugar residue, resulting in considerable rotational freedom within the active site. When comparing the substrate-bound state to the apo structure, both JM1 and JM2 are shifted laterally (Supplementary Fig. 5b). While the density in this region is of poorer quality in the apo map, the JM helices become ordered in the substrate-bound state, forming a hydrophobic tunnel for the polyprenyl chain. We also observed shifts in the TM domains (Supplementary Fig. 5c), suggesting that the acyl chain of bound UndP may slightly alter the arrangement of the TM bundle.

The UndP headgroup is positioned ~9.5 Å from the electrophilic C1 of Glc, and a similar distance from D157, the previously hypothesized catalytic residue20 (Supplementary Fig. 6a). Therefore, significant movement from either or both substrates would be required to allow the reaction to occur. While our use of cryo-TREM in this context makes it difficult to understand the precise temporal placement of this state in the reaction coordinate, it remains the dominant structure encompassing over 80% of the particle dataset, suggesting this is a stable state adopted by GtrB following initial binding of substrates.

Through 3D-classification, we determined a pre-catalysis state where the density for UndP extends deeper into the soluble domain, shifting from the pocket of R125 and R122 onto R197 and just underneath C1 of the Glc (Fig. 2b, Supplementary Movie 2). The phosphate headgroup is located 4.6 Å from the Glc C1, a distance that is permissible for nucleophilic attack. However, it is farther from D157 (~10.3 Å) compared to the substrate-bound state (Supplementary Fig. 6b), suggesting this state could exist after the initial deprotonation of UndP. Near the UDP-Glc, D96 remains coordinated to Mg2+ but is joined by Q98, which flips towards the ion while the Glc moiety is slightly rotated to accommodate the UndP tail (Fig. 2b). The UndP headgroup is shifted downwards ~9 Å from the substrate-bound state, but the UDP moiety has not moved, suggesting that UndP translocation is a key factor in catalysis. Interestingly, the TM bundle in the pre-catalysis state closely matches the TM domain in the apo structure (Supplementary Fig. 5d), whereas the differences in JM1 and JM2 position are comparable to what was observed in the substrate-bound state (Supplementary Fig. 5e). The density for Glc is also well-defined suggesting that at least within this state, Glc may have less freedom to remain flexible in the active site compared to our substrate-bound state. However, the density in the β7-JM2 loop is poor and wasn’t sufficient to fully model.

In the same map as the pre-catalysis state, we observe density in a different protomer that we interpret as UndP-Glc and UDP, the products of the reaction. Interestingly, the product-bound state appears to best match the substrate-bound state in relation to both UndP, located towards R125 and R122 (Fig. 2c), and UDP position, but several key differences do arise (Fig. 2, Supplementary Fig. 6c). In particular, the Glc moiety of UndP is underneath JM2 and appears to interact with W207 along the β7-JM2 loop (Fig. 2c). The density in the β7-JM2 loop after W207 is weak and therefore was n’t modeled, consistent with our pre-catalytic state, limiting our understanding of how this loop contributes to product coordination. However, W207 in both the pre-catalysis and substrate-bound states is flipped inward in front of the UndP, but here, it faces away from the Glc, opening a potential path for the product to exit toward the membrane (Fig. 2, Supplementary Movie 3).

In 3DVA intermediate volumes within the cryo-TREM dataset, the β6-JM1 loop oscillates up and down in conjunction with JM1 (Fig. 3a, Supplementary Movie 1), while JM2 remains largely static. When comparing the modeled pre-catalysis and substrate-bound states, the β6-JM1 loop is ~4.4 Å lower along with the end of JM1 (Fig. 3b). Interestingly, the lowered position of the β6-JM1 loop is also observed in the product-bound state (Supplementary Fig. 6e). This coincides with an ~8.9 Å shift of UndP from the substrate-bound state (Fig. 3b), suggesting that lowering of the β6-JM1 loop toward the cytosol correlates with UndP translocation to the C1 of Glc (Supplementary Movie 2).

Although JM2 remains stationary, the directly connected TM2 also oscillates vertically in 3DVA intermediates (Supplementary Movie 1). Unbiased molecular dynamics (MD) simulations were performed on the GtrB tetramer, with analyses conducted separately for each of its four chains. To ensure the robustness of our findings, the stability of each state was confirmed using two different water models (TIP3P and OPC), which yielded highly congruent results (Supplementary Fig. 7 and 8). In simulations of both the substrate-bound and pre-catalysis state models, root mean square fluctuation (RMSF) analysis indicates greater flexibility in JM1 and JM2 in the pre-catalysis state compared to the substrate-bound state (Supplementary Fig. 9a, b). In the substrate-bound state simulation, K180 and K132 remain proximal to the phosphate of UndP but do not maintain stable coordination over time (Supplementary Fig. 9c, d). As expected, UndP remains stable in this conformation and does not spontaneously transition to the pre-catalysis state within the simulation time (Supplementary Fig. 9e), indicating the presence of a local free energy minimum due to the coordination of the phosphate headgroup in the positively charged pocket. Conversely, in unbiased MD simulations of the pre-catalysis state, R122 and R197 emerge as the dominant coordinating residues, while R125, K132, and K180 are positioned farther from the phosphate headgroup than in the substrate-bound state (Supplementary Fig. 10a–e). UndP remains partially stable in this intermediate conformation, with only one out of four chains reverting to a substrate-bound-like arrangement, persisting near R122 and R197 (Supplementary Fig. 10f).

Steered molecular dynamics (SMD) simulations were conducted to investigate the translocation mechanism of UndP toward the Mg2+ starting from the substrate-bound state. These simulations reveal a gradual descent of UndP within the active site (Fig. 3c, Supplementary Movie 4), with K180, K132, and R125 initially coordinating the phosphate headgroup and facilitating translocation. As UndP progresses, R122 and R197 overtake as the primary coordinators (Fig. 3d). Essentially, K132 and K180 remain closely associated with UndP as it moves downward (t = 60–216 ns), while R125 initially engages (t = 60 ns) but subsequently transfers coordination to R197 and R122 (t = 216 ns, Fig. 3c-d). The center-of-mass (COM) displacement of JM1 in these simulations follows a directed motion along the z-axis, reaching deviations of approximately 6 Å (Supplementary Fig. 9f), similar to what was observed in the pre-catalysis and product-bound states. The work profiles for all SMD replicates were analyzed to estimate the free energy barrier (Supplementary Fig. 11a). The analysis of the lowest-work trajectory, which is the dominant contributor to the Jarzynski average, suggests a maximum barrier lower than 12 kcal/mol (~20 kBT), indicating a physically accessible and rapid transition (Supplementary Fig. 9g).

A major structural difference in the product-bound state lies in W207, which appears swung out on the β7-JM2 loop, opening a path for the lipid carrier to exit the active site into the membrane (Fig. 4a, Supplementary Movie 3). In similar enzymes such as the archaeal DolP Mannose Synthase (DPMS), this loop has been proposed to act as a ‘door’, letting substrates and products enter or exit the active site21. In unbiased MD simulations of the product-bound state, we show the product (UndP-Glc) remains stable without an input of force (Supplementary Fig. 10h). To model the conformational dynamics following product formation, additional SMD simulations were performed using the product-bound state with the full-length β7-JM2 loop modeled using the substrate-bound state as a reference (Supplementary Movie 5). Here, the phosphate head of UndP-Glc was pulled away from the reaction site (specifically from the Cα of K180), towards the β7-JM2 loop. The work profiles for all SMD replicates of the product exit process were analyzed to characterize the transition (Supplementary Fig. 11b). The lowest-work trajectory, which best represents the Jarzynski-weighted ensemble, shows a near-spontaneous shift of UndP-Glc with an initial negative force barrier, followed by additional work needed as the β7 loop accommodates the UndP tail (Supplementary Fig. 10g). At the start of the simulation (t = 0 ns), W207 is in a conformation characteristic of the product-bound state (Fig. 4b), leaving an open pathway towards the membrane. As the simulation progresses, W207 gradually rotates, further widening the exit for UndP-Glc, which eventually translocates beneath the β7-JM2 loop (t = 200 ns; Fig. 4b). Superimposing the initial (t = 0 ns) and final (t = 200 ns) states highlights a lateral displacement of UndP-Glc towards the membrane, coupled with a coordinated rotation of W207 that facilitates its release (Fig. 4b). Throughout this transition, R125 and K180 initially coordinate the phosphate headgroup of UndP-Glc in a similar fashion to the substrate-bound state before the product exits (Fig. 4c).

To understand the kinetics of GtrB independent of cryo-TREM, we performed functional studies of GtrB in a reconstituted setting by measuring equilibrium binding and sugar transfer kinetics. Using a scintillation proximity assay34, we tested binding of radiolabeled UDP-[14C]-Glc to GtrB incorporated in nanodiscs. Our results demonstrate that GtrB binds UDP-Glc with an affinity of 114.4 µM, comparable to that of caged-UDP-Glc (Fig. 4d). Additionally, UDP-galactose (UDP-Gal), as well as Glc or UDP alone, also bind GtrB, albeit with slightly lower affinity. Notably, the addition of 50 µM UndP to the binding assay increased the binding affinity for UDP-[14C]-Glc by 32%, suggesting cooperative interactions between the donor and acceptor substrates (Fig. 4d).

To characterize the kinetics of Glc transfer to membrane-embedded UndP, proteoliposomes containing GtrB and 200 µM UndP (Supplementary Fig. 12a) were subjected to UDP-[14C]-Glc incorporation assays. Expectedly, GtrB-containing proteoliposomes show a progressive increase in [14C]-Glc incorporation over time (Fig. 4e). In contrast, all tested mutants – targeting the catalytic site (D157), Mg2+ coordination site (Q98), and the W207 gate between the β7-JM2 loop – exhibited significantly lower incorporation levels that limited conclusive kinetic analyses.

Lastly, we determined GtrB-mediated [14C]-Glc incorporation to membrane-bound UndP, measured for 10-second periods by varying the concentration of UDP-[14C]-Glc from 1 nM to 2.5 µM, revealing a Michaelis–Menten constant (Km) of 0.23 ± 0.03 μM and a Vmax of 8.99 ± 0.38 mmol mg GtrB−1 min−1, resulting in a catalytic turnover rate (kcat) of 4.77 ± 0.33 s−1 (Supplementary Fig. 9b). Thus, the incorporation affinity of UDP-Glc during catalysis is estimated to be 500-fold higher than the binding affinity of UDP-[14C]-Glc to GtrB alone. Our kinetic assays on proteoliposomes likely favor tetramer formation, but in our previous study20, we performed kinetic assays on crude, solubilized membranes. In both, we yield similar results, particularly that mutation of D157 abolishes catalytic activity despite this not being a conserved residue among membrane-bound GT-A enzymes. Thus, it could suggest that individual GtrB monomers act independently in catalysis, rather than in an allosteric model.

We propose a sequence of events that results in the catalysis of UndP-Glc by GtrB (Fig. 5a, b). First, membrane-embedded UndP binds GtrB through the JM helices. Based on our apo structure and previous findings20, the N-termini of both JM1 and JM2 remain disordered until binding of UndP, which stabilizes the two helices. The UndP headgroup is then stabilized in a pocket lined by K132, K180, R125, and R122. UDP-Glc binds in a conserved pocket lined by Q98, and the DxD motif (D96 and D94). Following the initial binding event, the UndP headgroup interacts primarily with K180, K132, and R125 until it reaches R197. This coincides with a downwards shift in JM1 and the β6-JM1 loop. In this position, UndP is in proximity to the Glc C1, primed for the enzymatic reaction. Product formation is associated with a return of UndP toward the original bound position, with an added Glc moiety. Exit is mediated by an interaction with W207 acting as a gate for the β7-JM2 loop which subsequently opens to allow exit of the product into the membrane.

Methods
Cloning and expression
GtrB from cyanobacterium Synechocystis sp. PCC 6803 (accession #Q55487) was cloned into plasmid expression vector pMCSG7 as previously described20. Briefly, the open reading frame of GtrB was amplified and cloned into pMCSG7 by ligation-independent cloning, which contains a 6xHis-tag and tobacco etch virus (TEV) protease cleavage site at the N-terminus. All point mutations were prepared by site-directed mutagenesis using primers designed with QuikChange Primer Design Tool (Agilent). All plasmid constructs (Supplementary Table 1). were verified by DNA sequencing and whole plasmid sequencing (sequence alignments were performed using Unipro UGENE44).

Protein expression and purification
The GtrB pMCSG7 plasmids were transformed into E. coli strain BL21 (DE3) pLysS for overexpression. Detergent-solubilized protein was purified as previously described20. Briefly, 800 mL of 2xYT media with 100 μg/mL of ampicillin and 50 μg/mL of chloramphenicol was inoculated 1/100 with overnight culture. Cells were grown at 37°C until OD reached 0.8, after which the temperature was reduced to 22°C and allowed to shake at 200 revolutions per minute (rpm) for 15 min. After which, 0.2 mM isopropyl β-D-thiogalactopyranoside (IPTG) was added, and growth proceeded overnight with shaking (240 rpm). Cells were harvested at 5300xg for 20 min, washed with 40 mL of phosphate-buffered saline (PBS), and frozen at −80°C for future use. For structural studies, a single pellet (corresponding to 800 mL of culture) was resuspended into 40 mL of lysis buffer containing 20 mM 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid (HEPES) pH 7.5, 500 mM NaCl, 10 μg ml−1 DNase I, 8 μg ml−1 RNase A, 1 mM tris(2-carboxyethyl)phosphine hydrochloride (TCEP), 1 mM phenylmethanesulfonyl fluoride (PMSF), and Complete Mini EDTA-free protease inhibitor cocktail (Roche) used according to the manufacturer’s instructions, before being sonicated for 20 min. 1% (w/v) n-dodecyl-β-d-maltopyranoside (DDM, Anatrace) was added to the solution to solubilize and rotated at 4°C for 2 h. Solubilized cells were ultracentrifuged (Beckman Coulter) at 90,500xg for 30 min at 4°C, the supernatant was then added to a 1 mL solution of Ni-NTA slurry (500 μL total resin, Qiagen) and rotated overnight. Elutate from metal-affinity chromatography was further purified by size-exclusion chromatography using a 24 mL Superdex 200 Increase 10/300 GL column (Cytiva). Peak fractions were extracted and frozen at −80°C for future use or directly used for nanodisc incorporation.

Preparation of nanodiscs
1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPG) lipids (Avanti) were stored in a stock of 20 mM HEPES pH 7.5, 100 mM NaCl, and 30 mM Sodium Cholate at 10 mM, dried with Argon gas, and frozen at −80°C. Commercial undecaprenyl phosphate (Larodan) was resuspended in chloroform at 11.8 mM and dried with argon before adding 10% w/w to POPG and sonicated to ensure proper incorporation. Eluted GtrB was stored in 20 mM HEPES pH 7.5, 150 mM NaCl, 0.05% DDM, 300 mM imidazole, and 1 mM MgCl2. GtrB was incorporated into lipid nanodiscs with a 1:280:5 molar ratio of protein: POPG mixture (POPG and UndP): 1E3D1 (MSPE3D1) in reconstitution buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 1 mM MgCl2, and 1 mM TCEP). This mixture was incubated at 4°C for 2 h with gentle agitation. Reconstitution was initiated by removing detergent with the addition of Bio-beads (Bio-Rad) at 4°C overnight with constant rotation. The nanodisc reconstitution mixture was bound again to Ni2+-NTA resin at 4°C for 4 h to remove empty nanodiscs. The resin was washed with 10 column volumes of wash buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 20 mM imidazole, and 1 mM MgCl2) followed by 4 column volumes of elution buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 300 mM imidazole, and 1 mM MgCl2). The protein was further purified by a Superdex 200 Increase 10/300 GL size-exclusion column in gel filtration buffer (20 mM HEPES 7.5 pH, 150 mM NaCl, 1 mM MgCl2, and 1 mM TCEP). Protein typically eluted as a sharp peak at ~10.5 mL with a neighboring aggregate peak at 8.5 mL, observed by monitoring the absorbance at 280 nm (Supplementary Fig. 1d). Peak fractions were pooled and frozen at −80°C.

Synthesis of caged-UDP-Glc
All reagents were purchased from commercial sources and used without further purification unless otherwise noted. Solvents used in reactions were purified by passage through alumina and copper columns under argon. Unless otherwise noted, reactions were carried out at room temperature, under argon and were monitored by TLC on silica gel G-25 F254 (0.25 mm). TLC spots were detected under UV light and charring after staining with p-anisaldehyde in ethanol, acetic acid, and H2SO4. Column chromatography was performed on silica gel 60 (40–60 μM) unless otherwise noted. 1H NMR spectra were recorded at 500 MHz and were referenced to the solvent residual proton signal of CDCl3 (7.26 ppm) or D2O (4.79 ppm). 13C(1H) NMR spectra were recorded at 125 MHz and were referenced to CDCl3 (77.06 ppm) or external acetone (31.07 ppm, D2O). Peak assignments were made based on 2D NMR analysis (1H–1H COSY HSQC). High resolution ESI mass spectra were recorded on an Agilent Technologies 6220 spectrometer or a JMS-T100LP AccuTOF LC-plus 4G TOF mass spectrometer (JEOL, Tokyo, Japan) using samples in CH3OH and added NaCl. Spectra were obtained by voltage scan over a narrow range at a resolution of approximately 10,000 (Spectra can be found in the Source Data).

1,2,4,6-Tetra-O-acetyl-3-O-(2’-nitrobenzyl)-β-D-glucopyranose (S2): To a solution of diacetone Glc (S1, 4.0 g, 15.37 mmol) and tetrabutylammonium bromide (991 mg, 3.07 mmol) in CH2Cl2 (20 mL) was added KOH (10 mL, 33% aqueous solution). The resulting mixture was stirred for 10 min and then 2-nitrobenzyl bromide (6.64 g, 30.74 mmol) was added. The solution was stirred for 2 h and then was extracted with CH2Cl2 twice. The combined organic phase was dried over MgSO4, filtered and concentrated. The resulting residue was purified by chromatography (4:1 to 3:1, Hexanes–EtOAc) to afford the product (6.2 g, 93%) as an orange gel. The compound obtained was dissolved in 4:1 AcOH:H2O (40.0 mL), heated at 80°C for 8 h before the reaction mixture was cooled to room temperature and concentrated. The residue was suspended in Ac2O (20 mL) and pyridine (15 mL) in presence of catalytic amount of DMAP (202 mg, 1.57 mmol) and the solution was stirred for 10 h. The reaction mixture was concentrated, diluted with EtOAc (60 mL) and then sequentially washed with 1N HCl, saturated aqueous NaHCO3, and brine. The organic layer was concentrated and the resulting residue was purified by chromatography (2:1, toluene–EtOAc) to yield S2 (4.62 g, 9.52 mmol, 62% over three steps, 2:1 β:α) as an orange gel (Rf 0.29, 2:1, toluene–EtOAc).

Diallyl (2,4,6-tri-O-acetyl-3-O-(2’-nitrobenzyl)-β-D-glucopyranosyl) phosphate (S3): Imidazole (700 mg, 10.31 mmol) was added to compound S2 (4.15 g, 8.59 mmol) in CH3OH (45.0 mL), and the solution was heated at 40°C for 24 h. The reaction mixture was cooled to room temperature, concentrated and then diluted with EtOAc before being washed with brine. The organic layer was dried over MgSO4, filtered, concentrated and the residue purified by chromatography (1:1, Hexane–EtOAc) to obtain the corresponding hemiacetal intermediate (3.26 g, 7.41 mmol) as white solid. Diallyl n-diisopropylphosphoramidite (1.18 ml, 4.6 mmol) and tetrazole (3% in acetonitrile) (26 mL, 13.2 mmol) were added to hemiacetal intermediate (1 g, 2.2 mmol) in anhydrous CH2Cl2 (30 mL) under argon and the mixture was stirred for 2 h. After completion of the reaction, the solution was cooled to –40°C and t-butyl hydroperoxide (30% aq., 790 μL, 8.8 mmol) was added. After 1.5 h, the mixture was concentrated and the residue was purified by chromatography (1:1, Hexane–EtOAc) to give S3 (1.21 g, 2.01 mmol, 91%) as white foam (Rf 0.24, 1:1, Hexane–EtOAc).

3-O-(2’-nitrobenzyl)-uridine-5’-diphosphate (caged-UDP-Glc): To a solution of S3 (130 mg, 0.216 mmol) in CH2Cl2–CH3OH (1:1) was added PdCl2 (19 mg, 0.108 mmol). The reaction mixture was stirred for 8 h, neutralized by the addition of Et3N (100 μL), filtered through Celite, concentrated, dried and taken to next step without further purification. This crude residue was dissolved in pyridine (3 mL), and then UMP-morpholidate (222 mg, 0.324 mmol) and 1H-tetrazole (45 mg, 0.65 mmol) were added. The reaction mixture was stirred for 3 days, concentrated and the resulting residue was purified by chromatography (5:2:1, EtOAc–CH3OH–H2O). The compound obtained was suspended in CH3OH (10 mL) and cooled to 0°C; to this solution 1 M LiOH (1 mL) was added. The reaction mixture was warmed to room temperature and then stirred for 5 h. Excess base present in the reaction mixture was quenched by the addition of IR-120 resin and the solution was filtered and concentrated. The resulting residue was by purified by C18 column chromatography (H2O as eluent) to give caged-UDP-Glc (38 mg, 0.055 mmol, 26% over three steps) as a white foam (Rf 0.15, 4:1.5:0.5:0.5 EtOAc–CH3OH–H2O–AcOH).

Photorelease characterization
Reverse-phase high-performance liquid chromatography (Agilent 1100, C18 column, Eluent A – 95% water, 5% acetonitrile and 0.1% TFA, Eluent B- 95% acetonitrile, 5% water and 0.1% TFA, absorbance at 260 nm) was used to quantify the photorelease of caged-UDP-Glc from the starting material. To generate the standard curve, inosine (100 μM, Sigma-Aldrich) was used as the internal standard due to its stability under UV exposure. Supplementary Fig. 3b shows the plot of the ratio of integrated areas corresponding to the signals from caged-UDP-Glc (no UV) and inosine, versus their concentration ratios. The linear relation between the peak-area ratio and the concentration ratio was used to calculate photoreleased UDP-Glc upon UV exposure. For photorelease quantification, a 75 μL sample containing the caged-UDP-Glc and inosine was deposited in a well of a 96-well plate. The UV delivery optics were aligned with the center of that well, and the distance between the liquid surface and the collimating lens was adjusted to 15 mm, which is approximately the corresponding value in the vitrification instrument. After an exposure of 17 ms, the sample was collected for HPLC analysis. Supplementary Fig. 3c shows the photoreleased UDP-Glc for two concentrations of the starting caged material. The details of the calculation and the theoretical background of the relation between exposure in the wellplate and in the grid are found in the Supplementary Information. Additionally, to validate the photoproduct signature as well as the stability of the caged material, direct injection ESI mass-spectrometry analysis was performed with samples exposed to room temperature for 24 h and no UV, to 5 s UV, and to no UV fresh. The intensity signals corresponding to the photoproduct (mass 565) and the caged material (mass 700) are shown in Supplementary Fig. 3d.

Cryo-TREM sample preparation
Caged-UDP-Glc (final concentration 2.5 mM) was added to the GtrB solution (1.22 mg/mL; 20 mM HEPES pH 7.5, 150 mM NaCl, 1 mM MgCl2, 1 mM TCEP) immediately before vitrification. Photosensitive material was protected from laboratory light by wrapping the tubes with aluminum foil. A Vitrobot Mark IV (Thermo Fisher) was used for freezing the sample EM grids. For the UV (365 nm) activated photorelease of caged-UDP-Glc, the light-guide and collimation optics were installed on the left side of the Vitrobot chamber, as previously described28. A 3.5 μL sample was deposited on the glow-discharged copper R1.2/1.3 300-mesh holey carbon grid (Quantifoil) inside the chamber equilibrated to 4°C and 100% humidity, and immediately blotted for 3.5 s, 4 s and 4.5 s with a blotting force set to 3. To minimize unwanted photoreaction, the chamber light was kept off and the UV was turned on manually when the blotting process had finished, and the blotting arms had retracted back to the rest position. Thus, the grid along with the sample travels through the collimated UV beam for approximately 17 ms before being frozen in liquid ethane cooled by liquid nitrogen. The travel time of grids between the end of UV exposure and the contact with ethane is approximately 55 ms.

Cryo-EM data collection and processing of apo-GtrB
Micrographs were collected with a Technai T30 Polara (FEI), at the Columbia University Cryo-Electron Microscopy Center, equipped with an energy filter and a K3 direct electron detection filter camera (Gatan K3-BioQuantum) with a 0.95 Å nominal pixel size. An energy filter slit width of 20 eV was used during the collection and was aligned automatically every hour using Leginon45. Data collection was performed using a dose of ~70.91 e-/Å2 across 40 frames (100 ms per frame) at a dose rate of approximately 16 e–/pix/s, using a set defocus range of −2.5 μm to −1 μm. A 100 μm objective aperture was used. 3757 micrographs were recorded over a one-day collection (Supplementary Table 2).

Movie frames were imported and aligned using Patch Motion Correction implemented in cryoSPARC v.4.6.2. Contrast transfer function (CTF) estimation was performed using Patch CTF. Initial template picking was used, resulting in 946,644 particles, which were then extracted with a 300-pixel box size binned 3 times (Supplementary Fig. 1f). The particles were classified using 2D classification using a batch size per class of 200 with 40 online-EM iterations and one full iteration. 2D classes with well-defined high-resolution features were selected, resulting in a particle stack of 308,978 particles. The particles were then re-extracted using a 360-pixel box size without binning. One round of ab initio reconstruction was performed using three classes, with a maximum resolution set at 7 Å and an initial resolution at 9 Å. This resulted in one class that appeared to be an octamerized GtrB and two classes that were likely junk. We then went back to the binned particle stack of 308,978 particles and ran heterogeneous refinement using the one octamer class from the ab initio reconstruction, two decoy classes, and a “Batch size per class” of 1000. This heterogeneous refinement was run three times using the particles from the three ab initio classes from the previous heterogeneous refinement as input for the next heterogeneous refinement. The octamer class (227,610 particles) was re-extracted without binning and used for non-uniform refinement to yield a resolution of 2.83 Å with D4 symmetry imposed. A mask covering the octamer was used to run a Local Refinement with D4 symmetry and reference-based motion correction to a resolution of 2.79 Å with D4 symmetry imposed.

Cryo-TREM data collection and processing
Images were collected with a Titan Krios electron microscope (FEI), at the Columbia University Cryo-Electron Microscopy Center, equipped with an energy filter and a K3 direct electron detection filter camera (Gatan K3-BioQuantum) with a 0.83 Å nominal pixel size. An energy filter slit width of 20 eV was used during the collection and was aligned automatically every hour using Leginon45. Data collection was performed using a dose of ~58.5 e-/Å2 across 50 frames (50 ms per frame) at a dose rate of approximately 16 e–/pix/s, using a set defocus range of −2.5 μm to −1 μm. A 100 μm objective aperture was used. 9219 micrographs were recorded over a two-day collection (Supplementary Table 2).

Movie frames were imported and aligned using Patch Motion Correction implemented in cryoSPARC v.4.6.2. Contrast transfer function (CTF) estimation was performed using Patch CTF. Initial particle picks and micrographs were used to train the Topaz neural network particle picking46 implemented in cryoSPARC resulting in 2,693,490 particles which were then extracted with a 360 pixel box size binned 3 times (Supplementary Fig. 4a). The particles were classified using 2D classification using a batch size per class of 200 with 40 online-EM iterations and one full iteration. 2D classes with well-defined secondary features were selected resulting in a particle stack of 1,457,435 particles. The particles were then re-extracted using a 360-pixel box size without binning. One round of ab initio reconstruction was performed using three classes, with a maximum resolution set at 7 Å and an initial resolution at 9 Å. This resulted in one class that appeared to be an octamerized GtrB and two classes that were GtrB tetramers. We then went back to the binned particle stack of 2,693,490 particles and ran heterogeneous refinement using the three classes from the ab initio reconstruction, two decoy classes, and a “Batch size per class” of 1000. This heterogeneous refinement was run three times using the particles from the three ab initio classes from the previous heterogeneous refinement as input for the next heterogeneous refinement. From this, the class that pertained to the tetramer volume was separated (443,237 particles), further classified with 2D classification to yield 33,816 particles, and extracted with a box size of 300 pixels. These particles were used to refine by Local Refinement using C4 symmetry with a mask to 3.19 Å (Supplementary Fig. 4a). The octamer class (748,694 particles) was subjected to a final heterogeneous refinement using the map from the previous heterogeneous refinement and a 10 Å low pass filtered map as input. The particles were re-extracted without binning and used for non-uniform refinement to yield a resolution of 2.59 Å. To exploit the inherent symmetry of octamerized GtrB, particles were symmetry expanded to D1, and the 3D center coordinates were adjusted to a single tetramer. Particles were reextracted using a box size of 256 pixels to yield 1,016,679 particles. A mask covering the centered tetramer was used to run a Local Refinement with C4 symmetry to a resolution of 2.42 Å. To classify the dynamics within the dataset, particles were fed into 3D variability analysis with a binary mask (soft padding width: 0) used in the previous refinement with a filter resolution of 4 Å and a high pass resolution of 15 Å with 10 components. Component 0 was displayed by intermediates with a filter resolution of 4 Å and the intermediate window set to −1. Particles from component 0 were further classified by 3D classification with a 4 Å filter and 15 Å high-pass resolution with 10 classes. Classes with the best density for UndP (314,552 particles) were refined with Local Refinement with “Use pose/shift Gaussian prior during alignment” set to true, Standard deviation (deg) of prior over rotations set to 6, Standard deviation (A) of prior over shifts set to 2, and symmetry to C4 to yield a resolution of 2.54 Å and the map we refer to as the substrate-bound state. Classes with lower JM helix density to the previous map were further separated by a 5-class 3D classification with filter resolution set to 3 Å and high pass at 15 Å. A single class (46,382 particles) was refined using the same parameters as before with Local Refinement to a resolution of 2.85 Å containing the pre-catalysis and product-bound states.

Structure model building and refinement
An initial model of GtrB was built using the published GtrB X-ray crystal structure20. The model was fitted to the map as a rigid body in ChimeraX47, the model was subsequently adjusted to the current density and further refined using Coot48 and PHENIX49–52, iteratively.

Molecular dynamics system preparation
The starting configurations for GtrB bound to UDP-Glc, Mg2+ and UndP in their substrate-bound state were taken directly from the cryo-EM structure of the complex. Intermediates and product configurations were observed in one chain of the complex in different structures. For this reason, models containing the same conformation of the substrates in different configurations were created, duplicating the chain of interest four times, obtaining two different models: pre-catalysis state and product-bound state. In this way, each model contains the same configuration in the four chains of the complex. A key difference between the cryo-EM and simulation models is the length of the UndP substrate. While the experimental density only resolved five isoprenyl units, it was crucial to model the full-length, 55-carbon lipid for the MD simulations to accurately represent the complete substrate and its interactions within a continuous lipid bilayer. To achieve this, the unresolved portion of the tail was computationally built onto the experimentally derived headgroup using Maestro. Crucially, during the subsequent extensive equilibration phase, this modeled tail was left completely unrestrained, allowing it to freely relax and equilibrate its conformation within the lipid bilayer. This ensures that the final dynamics are representative of a physically plausible state and not an artifact of the initial modeled placement. The system was then pre-processed for simulation with the Schrodinger’s Protein Preparation Wizard53 tool, and residue protonation states were determined by PROPKA at pH = 7.0. The system was embedded in a 141 × 141 Å PPPE—PPPC (75:25 molar ratio) bilayer and solvated using the TIP3P water model with the aid of the membrane-builder tool of CHARMM-GUI54, and neutralized using K+ and Cl- ions, reaching the 0.1 M salt concentration.

The ff19SB55 and lipid2156 Amber force fields were used to parametrize the protein and the lipids, respectively. For the substrate molecules, GAFF2 parameters were adopted. Their atomic partial charges were computed using the restrained electrostatic potential (RESP57) fitting procedure. The electrostatic potentials (ESPs) were calculated through Gaussian at the HF/6-31G* level after optimization of the molecules at the same level. The catalytic Mg²⁺ ion was treated as a non-bonded particle using standard Amber parameters, supplemented with weak harmonic distance restraints to the coordinating oxygen atoms to maintain the experimental geometry, a strategy that shown to help preventing artifacts in several metalloenzymes58. The choice of TIP3P water model represents a balance between optimal choice for different components of the system. Previous studies have indeed shown the possibility of using TIP3P in combination with ff19sb protein force field, while lipid21 and GAFF2 are extensively validated for use with TIP3P. To rigorously ensure that our conclusions were not dependent on the water model, we performed a full set of cross-validation simulations using the recommended OPC water model (3 independent replicates of 1 μs for each system). As shown in Supplementary Figs. 7 and 8, this validation confirmed the congruence between results obtained with both water models. A summary of the simulation system parameters is provided in Supplementary Table 4.

A multistage equilibration protocol was applied to remove unfavorable contacts and provide a reliable starting point for the subsequent simulations. The system was first subjected to 2000 steps of steepest descent energy minimization, with positional restraints (4000 kJ mol–1 nm–2 on protein backbone, and substrate heavy atoms; 2000 kJ mol–1 nm–2 on protein side chains; 1000 kJ mol–1 nm–2 on lipid heads) and dihedral restraints on lipids (1000 kJ mol–1 nm–2); Then the system was heated from 0 to 100 K in 1 ns in NVT, followed by a heating from 100 to 300 K in NPT with the same restraint as the precedent step; Finally, six steps of equilibration steps were performed, using the protocol suggested by CHARMM-GUI, but with increased simulation time. The total duration of the equilibration protocol was indeed increased to 100 ns. During the equilibration stages restraints were progressively lowered.

Unbiased MD simulations
With structures properly equilibrated, unbiased MD simulations was performed for each system in NPT ensemble, using the Nose-Hoover thermostat59 to set the temperature to 303.15 K and setting the pressure to 1 bar with the Parrinello–Rahman barostat60. A time step of 2.0 fs was used, together with the LINCS61 algorithm, to constrain all bonds involving hydrogens. Each system was simulated for a total of 2.0 μs.

Steered MD (SMD) simulations
Two sets of constant velocity SMD simulations were performed: 1) starting from the substrate-bound state and pulling the UndP towards the deep pre-catalysis state; 2) starting from the product state and pulling the UndP-Glc out of the binding site. For 1), the collective variable chosen for the description of the desired motion was the distance between the UndP headgroup and the Magnesium ion inside the closest catalytic site (d1). For 2), the CV is the distance between the center of mass of the glycosyl phosphate group of UndP-Glc and K180 α-carbon, roughly corresponding to the inner wall of the catalytic site. In both sets of simulations, positional restraints were applied to the Mg²⁺ ions to prevent artificial destabilization of the catalytic site and to cancel out simulation-induced torque. SMD were performed by harmonically restraining the CV and moving the center of the bias (using a force constant of 2500 kJ mol–1 nm–2) at a constant velocity. In 1), distance d1 was decreased from the initial value (average 1.45 +/− 0.05 nm) to 0.52 nm in 200 ns, corresponding to a pulling velocity of ~0.005 nm/ns. In 2), distance d2 was increased from the initial value (0.68 +/− 0.04 nm) to 3.2 nm in 300 ns, corresponding to a pulling velocity of ~0.008 nm/ns. Then the center of the bias was kept in its final position for 50 additional ns. Each SMD simulation was performed independently on each chain of the tetramer, over three replicates, resulting in a total of 12 replicates. The force applied to the harmonic spring was monitored during each replicate, and the work computed by the bias was calculated. The work profiles for all 12 replicates of each process were collected and are reported in Supplementary Fig. 11. The free energy difference (ΔF) was estimated using the Jarzynski equality by calculating the exponential average over all trajectories. In our analysis, we focus on the trajectory with the lowest total work as a practical representative of the most probable transition path, since these low-work realizations are the dominant contributors to the Jarzynski average. From the SMD work, we estimated the magnitude of the first passage time for the two processes through the Eyring–Polanyi equation: τ = h/kT eΔFn dagger, with ΔFn dagger being the estimated free energy barrier of each process. The free energy barrier is computed as half of the work computed during the simulations, assuming that the simulated process is 2 to 5 orders of magnitude faster than the predicted characteristic time62. Estimated free energies from faster pulling (1 nm/ns, 0.1 nm/ns) are also reported in Supplementary Fig. 13 for multiple replicas and two different water models. However, none yielded physically accessible energies, justifying the use of the slower pulling velocities. All MD simulations were performed using Gromacs 2023.3, on the MareNostrum 5 pre-exascale EuroHPC supercomputer hosted at BSC-CNS and Leonardo hosted at CINECA. SMDs were performed using the MOVINGRESTRAINT function of PLUMED 2.9.063.

GtrB-mediated UDP-Glc binding
Binding of UDP-Glc to GtrB incorporated in nanodiscs was assessed with the scintillation proximity assay (SPA) method34. Equilibrium binding of 0.25 μM UDP-[14C]-Glc (300 Ci/mmol, American Radiolabeled Chemicals, Inc.) to 1.5 μM GtrB immobilized to 250 μg of Cu2+-coated Yttrium silicate (YSi) SPA beads (Revvity, Inc.) in 100 μL assay buffer containing 20 mM HEPES 7.5 pH, 150 mM NaCl, and 1 mM MgCl2 was performed in the presence or absence of the following non-radioactive compounds at varying concentrations: UDP, UDP-Glc, caged-UDP-Glc, UDP-Gal, Glc, UDP-Glc plus UndP (note that the UndP concentration was kept constant at 50 μM for this assay). The reactions were performed in individual wells of clear-bottom/white-wall 96-well plates in the dark at 23°C with vigorous shaking on a vibrating platform for 1 h. The counts per minute (cpm) were determined in the SPA mode of a Wallac 1450 MicroBeta plate counter. The non-proximity background signal was determined for each sample in the presence of 800 mM imidazole, which prevents the interaction of the His-tagged GtrB protein with the Cu2+-coated SPA beads, and then the non-proximity cpm was subtracted from the cpm determined in the absence of imidazole to obtain the specific binding activity. The activity of GtrB in the absence of non-labelled compounds was set as 100%, and data were normalized accordingly. Data (n = 9 for all compounds, except for caged-UDP-Glc where n = 3) are shown as mean ± s.e.m., and fits of binding isotherms were obtained using nonlinear regression algorithms in Prism 10 (GraphPad).

Proteoliposomes (PL) preparation
Detergent-purified GtrB was reconstituted into preformed liposomes composed of E. coli total lipids (Avanti Polar Lipids, Inc.) at a protein-to-lipid ratio of 1:80 (w/w) following a previously described method64. The lumen of the proteoliposomes was composed of 20 mM HEPES 7.5 pH, 150 mM NaCl, and 1 mM MgCl2. The proteoliposomes were aliquoted, rapidly frozen in liquid nitrogen, and stored at −80°C to maintain their stability. All experimental setups used liposomes with the same composition but lacking the GtrB protein as a control. The amount of GtrB in proteoliposomes was assayed with the amido black assay65.

Determining the GtrB-mediated incorporation of UDP-Glc into the lipid bilayer
Before the assay, 200 μM UndP was added to the proteoliposome membrane by sonication. Incorporation of 0.25 μM UDP-[14C]-Glc (300 Ci/mmol, American Radiolabeled Chemicals, Inc.) was performed in proteoliposomes (~5 μg per assay) in 250 μL assay buffer composed of 20 mM HEPES 7.5 pH, 150 mM NaCl, and 1 mM MgCl2. After the indicated time periods, the reactions were quenched by the addition of ice-cold 100 mM KP, pH 6.6, 100 mM LiCl and filtered through 0.45 μm nitrocellulose filters (Millipore). Dried filters were incubated in scintillation cocktail, and the retained radioactivity on the filters was determined by scintillation counting (Hidex, SL300). Specific radioactivity of UDP-Glc was confirmed by counting known amounts of UDP-[14C]-Glc and was used to transform decays per minute (dpm) into pmol of UDP-Glc. The GtrB-specific activity was determined by subtracting the background signal (dpm) determined in control liposomes (lacking GtrB) from the dpm measured in GtrB-containing proteoliposomes. Non-linear regression analysis of the data was performed by fitting the data to the Michaelis–Menten equation in GraphPad Prism 10 to obtain the Michaelis-Menten constant (Km) and the maximum velocity of the reaction (Vmax). The catalytic turnover number (kcat) was determined from the Vmax by transforming known amounts of GtrB in the assay to pmol.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Morgan et al. describes the use of time-resolved cryo-EM (cryo-TREM) on the bacterial transmembrane\nglycosyltransferase GtrB. The objective is to attempt to identify conformational states during the catalysis of undecaprenol\nphosphate (UndP) glucosylation to obtain UndP-Glc. The study is very well performed and the manuscript well written.\nThere are however major issues in the interpretation of the experimental cryo-EM maps that seriously challenge the\nproposed mechanism.\n1) Choice of methodology\nThe cryo-TREM method is not new but still under development. The strength of the technique is that it can capture\nconformational changes of dynamic proteins that may be dificult to obtain with conventional cryo-EM or X-ray\ncrystallography. However, cryo-TREM works best for proteins that undergo large conformational changes and is of more\nlimited use for proteins like GtrB where the catalytic cycle is driven by a series of subtle structural changes within a confined\nregion of the protein.\nThe drawback of using the method on GtrB is apparent from inspecting the cryo-EM density for the proposed states which all\nshow similar overall structural arrangement in the donor, metal and acceptor binding sites, but with varying degree of\ndisorder (convolution of states and flexibility) in the fine details that are of actual biological interest such as side chain and\nloop conformations, metal coordination etc. The structural states presented for GtrB can be captured with conventional high-\nresolution techniques (X-ray and cryo-EM) and give experimental maps with better definition and less disorder, as has\nalready been done previously for several GT2 glycosyltransferases (soluble and membrane integrated).\nWhile the authors have done an excellent effort trying to apply cryo-TREM on GtrB, the current study does not provide\nsubstantial new understanding of this class of glycosyltransferases beyond previously reported structures of GtrB and other\nGT2 glycosyltransferases. One new result of the present study is the re-tracing of the density of the loop connecting the two\ntransmembrane helices TM1 and TM2. TM1 and TM2 were incorrectly connected in the previous study from 2016. While this\ncorrection is important, it does not provide the level of scientific novelty expected for this journal and would likely be more\nappropriate for a journal with a different focus.\n2) Analysis of the catalytically relevant states 9NYC, 9NYD, 9NYK\n9NYC: The map is stated by the authors to correspond to the substrate-bound state with bound donor substrate UDP-Glc\nand acceptor substrate UndP. The density for the Glc moiety in UDP-Glc is very weak in all four subunits. The density of\nUndP is also ambiguous, especially with respect to the connection to the phosphate group. Since the map is obviously\nconvoluted by several conformers, the state cannot be unambiguously assigned as the substrate-bound state of GtrB, and\ninterpretation of details belonging to that state (metal binding, side chain conformations, ligand binding etc) is therefore not\npossible.\n9NYD: The map is stated by the authors to correspond to a mixture of pre-catalysis and product-bound states. Specifically,\nsubunits A/B/D are supposed to contain the substrates UDP-Glc and UndP, and subunit C would contain the products UDP\nand UndP-Glc. The region containing the donor and acceptor-binding sites (including the loop delineating his region) is\nheavily disordered and convoluted. The binding of substrates and/or products cannot be confirmed, and modeling of the loop\nis also not trustworthy due to extremely low definition of the backbone (and even worse for the side chains).\n9NYK: The map is stated by the authors to correspond to a pre-intermediate state. Specifically, UDP-Glc and UndP has\nbeen modeled in all four subunits. The map has serious disorder. If contouring the map at a very low level (close to noise\nlevel) there is some possible residual density for a Glc moiety bound to UDP but no density for UndP in any of the subunits.\nThis state is probably a disordered donor-bound state.\n3) Correction of GtrB topology\np.6. The improved density for the TM1-TM2 loop allowed correction of the topology of TMD. The disordered density for this\nloop in the previous GtrB structures led to the incorrect tracing of TM1-TM2 connection such that TM1 in one subunit was\nconnected to TM2 in a nearby subunit. Since the reassignment of the TMD is an important result of the present study, this\nshould be more clearly described in the text and in Ext.Data 2D. As it is presented now, it is difficult for even an experienced\nstructural biologist to get a clear understanding of the original error in models 5EKE/5EKP.", "reviewer_2": "This manuscript by Morgan and co-workers delivers several snapshots of GtrB during the glycosyltransferase reaction\ncatalysed by this enzyme. The aim is rather challenging, since GtrB is a membrane protein, and the authors design a time\nresolved cryoEM experiment using a caged photolabile UDP-Glc derivative. This substrate is activated by UV light just prior\nto grid freezing so the reaction is observed within 50-70 ms. Buy analysing one cryoEM sample of working GtrB, the authors\nseparate several states by 3D classification and postulate a mechanism of action. Their model is supported by MD\nsimulations.\nThe topic is sound, since lipid glycosylation is a universal process required to construct glycosylated cellular membranes.\nThe manuscript is clear and well written, and the figures are really good.\nI just have one overall concern. Although the figures of rendered maps seem to support the separation of some catalytic\nstates, I have some doubts about the classification strategy.\nIt seems that the catalytic events can be understood in the context of one subunit (as depicted in Figure 5b) with no need for\nlarge or concerted conformational changes between subunits. The refinement and the classification (Extended Data Figure\n4) have been performed at the level of the tetramer, even after symmetry expansion. With no information about the relative\ncatalytic states of monomers within a tetramer, it is reasonable to expect mixtures of subunits within the tetramers after\nclassification. Actually, two of the conformations (product-bound and pre-catalytic states) are seen in different subunits within\nthe same tetramer).\nThe only way to circumvent this potentially large heterogeneity would be to work subunit-wise at the level of refinement and\nclassification. Thus, a D4 symmetry expansion (instead of D1) and a classification within a mask covering one subunit\n(instead of a full tetramer) could deliver more clear separation of uniform states. Maybe the authors tried this, or the authors\ncan give a good reason not to use this strategy.\nAdditionally, GtrB tetramers associate in double tetramers. The authors do not discuss at any point whether this association\ncan have any sort of impact in the functioning of the enzyme. One possibility could be to perform some of the MD simulations\nusing the double tetramer instead of the tetramer.", "reviewer_3": "In this study, the authors successfully resolved multiple conformational states of GtrB (substrate-bound, pre-catalysis,\nproduct-bound and pre-intermediate states), using cryogenic time-resolved electron microscopy (cryo-TREM) combined with\nUV photolysis of a caged substrate. It shows insights into the catalytic mechanism of GtrB. The integration of structural data,\nMD simulation data and biochemical assays provides a framework for understanding glycosyltransferase mechanisms.\nHowever, there are still some key issues that need to be addressed in this study, a major revision is necessary.\n1. In the authors’ previous study (reference 4), the authors proposed the catalytic mechanism of GtrB, no catalytic base is\nneeded, D157 was suggested as a catalytic residue. However, in this study, the authors suggested that D94 or D157 could\nbe the catalytic base, so further experimental validation (mutagenesis with kinetic assays) is still needed to resolve this\nambiguity.\n2. In most glycosyltransferases, there is a conserved DXD motif, of which the two ASP residues are coordinated with Mn2+\nor Mg2+. And there is also a third residue (most likely HIS residue) coordinated with Mn2+ or Mg2+. Even though the authors\nprovided suggestions of the DXD (D94, D96), possibly Q98 could be the third coordinated residue, no structure obtained in\nthis study showed a complete coordination around Mg2+. Why is that? This is a critical question. Previous mutagenesis\nstudies suggest that mutations of the two ASP or the third residues could greatly reduce the enzymatic activities. If the DXD\nmotif and the third residues could not coordinate with Mg2+ stably, how could the catalysis happen? At least, the authors\nneed to address this question.\n3. The authors stated that multiple conformational states of GtrB were captured experimentally, substrate-bound, pre-\ncatalysis, product-bound and pre-intermediate states. The authors used the distance between the phosphate group of UndP\nand C1 of the glucose of the donor as a criterion to describe the substrate-bound, pre-catalysis and pre-intermediate states.\n2500-ns MD simulations were performed for the three systems, respectively. The authors should also include the distance\nplots showing the dynamics of the three conformational states. Are all three conformational states stable?\n4. In this study, the authors claimed that the catalytic mechanism of GtrB is elucidated. In their previous study (reference 4),\nthe authors proposed one. However, further investigation is still needed to fully elucidate the catalytic mechanism. A QM/MM\nstudy is highly recommended in the near future.\nSome other comments:\n1. References are not recommended to be cited in the Abstract.\n2. A table listing the conformational states for each chain of the structures is recommended\n3. For the MD simulations, the authors used Amber ff19SB force field for protein and TIP3P water model.\nIn the FF19SB paper (J. Chem. Theory Comput. 2020, 16, 528−552), the authors clearly recommend against using FF19SB\nwith TIP3P water model, but with OPC water model. How do the authors comment on this issue?\n4. In the MD simulations, how did the authors choose the force field parameters of Mg2+? It was not mentioned in the\nMethods sections. Furthermore, the authors also assigned two bonds between the Mg2+ and two phosphate oxygen atoms,\nrespectively, while still keeping the net charge of -2e for Mg2+. Have the authors verified the parameters? Is it reasonable?\n5. In the steered MD simulations, the authors applied positional restraints on the Mg ions, what is the purpose of this?\n6. In the manuscript, the authors mentioned that UndP is a 55-carbon isopreoid, however, in the cryo-EM structure, it is not.\nIn the MD simulations, it is 55-carbon again. The authors should comment on it.\nIt is also recommended to show the interactions between UndP and the lipids during the unbiased MD and steered MD\nsimulations.\n7. In Figure 5a, the phosphate group of UndP is protonated, but in the MD simulations, it is not protonated. Why is that?\n8. The recommended abbreviation of steered molecular dynamics should be SMD, not sMD.\n9. For the SMD, page 43 lines 952-953:\n“In accordance with Jarzynski equality, we reported only the minimum path work computed over the replicates for the two\nprocesses.”\nJarzynski equality is an ensemble average, how did the authors determine the minimum path work?\n10. page 43 lines 955-958:\n“The free energy barrier is computed as half of the work computed during the simulations, assuming that the simulated\nprocess is 2 to 5 orders of magnitude faster than the predicted characteristic time (reference 62)”\nFor the SMD simulations, this only applies to fast pulling (10 m/s = 10 nm/ns or faster, check the reference 62). In this study,\nthe pulling is 0.005 nm/ns, far slower than the slow pulling (1 nm/1ns = 1m/s) in the reference. So it is incorrect to apply this\nrule here.\nFurthermore, the pulling speed is too slow, less than the dynamics in unbiased MD simulations. Possible noise could be\nintroduced in such a slow and long SMD simulations. Pulling a distance of 5 angstrom, 10 ns SMD is already enough, the\nnumber of pulling can be much bigger, which could greatly increase the accuracy of the free energy barrier. Of course,\nmetadynamics is also recommended.\n11. The authors shared the MD data, however, for each of the systems, only a number of snapshots were selected to share.\nDifferent systems have different number of snapshots (hundreds to thousands), which make the simulation data less\nauthentic."}, "author_response": "Response to Reviewers\nMorgan et al. “Mechanistic snapshots of lipid-linked sugar transfer\nWe are thankful for the overall positive and constructive feedback from the Reviewers. Here we\nprovide a point-by-point response to their comments:\nReviewer #1:\nThe manuscript by Morgan et al. describes the use of time-resolved cryo-EM (cryo-TREM) on\nthe bacterial transmembrane glycosyltransferase GtrB. The objective is to attempt to identify\nconformational states during the catalysis of undecaprenol phosphate (UndP) glucosylation to\nobtain UndP-Glc. The study is very well performed and the manuscript well written. There are\nhowever major issues in the interpretation of the experimental cryo-EM maps that seriously\nchallenge the proposed mechanism.\nWe thank the Reviewer for their assessment of our study. While we respectfully disagree with\nthe Reviewer’s conclusions regarding our cryo-EM maps, we have taken their comments as\nconstructive criticism, and have addressed each point in detail below. We have also included\nadditional validations to support our data, and also note that our MD simulations and kinetic\nassays provide further independent support for the experimental results.\n1) Choice of methodology\nThe cryo-TREM method is not new but still under development. The strength of the technique is\nthat it can capture conformational changes of dynamic proteins that may be dificult to obtain\nwith conventional cryo-EM or X-ray crystallography. However, cryo-TREM works best for\nproteins that undergo large conformational changes and is of more limited use for proteins like\nGtrB where the catalytic cycle is driven by a series of subtle structural changes within a confined\nregion of the protein.\nWe respectfully disagree with that cryo-TREM is not broadly applicable to enzymatic reactions.\nWhile to date cryo-TREM has indeed primarily been demonstrated on proteins undergoing large\nconformational changes (ref. 10, ref. 11, and ref. 12), our UV-based approach presented here is\ndesigned to address this limitation. Our method enables the capture of faster millisecond\ndynamics, particularly of enzymes, aiming to observe tightly controlled mechanisms, a gap that\nhas remained unresolved in the field. We describe more below on how this was key to\nunderstanding the mechanism of GtrB catalysis.\nThe drawback of using the method on GtrB is apparent from inspecting the cryo-EM density for\nthe proposed states which all show similar overall structural arrangement in the donor, metal\nand acceptor binding sites, but with varying degree of disorder (convolution of states and\nflexibility) in the fine details that are of actual biological interest such as side chain and loop\nconformations, metal coordination etc. The structural states presented for GtrB can be captured\nwith conventional high-resolution techniques (X-ray and cryo-EM) and give experimental maps\nwith better definition and less disorder, as has already been done previously for several GT2\nglycosyltransferases (soluble and membrane integrated).\nWhile traditional cryo-EM and X-ray crystallography may achieve higher resolutions, we believe\nit is not accurate to characterize our approach as unsuccessful. Our reconstructions reached 2.6–\n2.8 Å for a membrane protein with inherent flexibility. Although the conformational changes in\nGtrB were less extensive than initially anticipated, we resolved a highly coordinated mechanism\nsupported by both MD and SMD simulations. Moreover, the generation of the product-bound\nstate demonstrates that a complete catalytic cycle was captured, rather than an artifact of\n“varying degrees of disorder.” We argue that the structural states captured in this study,\nespecially those like the pre-catalysis state, are not easily capturable by conventional techniques.\nCapturing the donor-acceptor complex as we did for the substrate-bound state represents in our\nopinion a rarity. This is underrepresented in the literature (see ref. 18) as the sugar-donor is\nusually present as an empty nucleotide, thus requiring the use of our caged-substate approach.\nWhile the authors have done an excellent effort trying to apply cryo-TREM on GtrB, the current\nstudy does not provide substantial new understanding of this class of glycosyltransferases\nbeyond previously reported structures of GtrB and other GT2 glycosyltransferases. One new\nresult of the present study is the re-tracing of the density of the loop connecting the two\ntransmembrane helices TM1 and TM2. TM1 and TM2 were incorrectly connected in the previous\nstudy from 2016. While this correction is important, it does not provide the level of scientific\nnovelty expected for this journal and would likely be more appropriate for a journal with a\ndifferent focus.\nWe disagree with the assessment that the information gained from our investigation is not\nsubstantial. Our work introduces a time-resolved cryo-EM approach that, to our knowledge, has\nnot previously been applied to membrane-bound glycosyltransferases. Using a caged substrate,\nwe were able to capture GtrB in substrate-bound, pre-catalysis, and product-bound states within\nmilliseconds of reaction initiation, providing direct visualization of catalytic events that were\npreviously inaccessible.\nThese structures uncover mechanistic details not previously described, including the\ncoordination of UndP by key arginine and lysine residues, the role of a swinging tryptophan\nresidue in product release, and the dynamic reorganization of the lipid substrate during catalysis.\nOur combined use of structural variability analysis, MD simulations, and kinetic assays further\nvalidates the proposed reaction mechanism and the order of catalytic events. Importantly, our\nobservations suggest a reassignment of potential catalytic residues (D94/Q98), providing\nmechanistic insight that challenges prior assumptions about D157 as the catalytic acid.\nFinally, our results have implications beyond bacterial GtrB, offering insight into the dynamic\nmechanisms of related eukaryotic enzymes such as Dolichol Phosphate Mannose Synthase\n(DPMS) and advancing the broader field of lipid-linked glycosyltransferases. Taken together, our\nstudy provides both methodological innovation and substantive mechanistic understanding of\nglycosyltransferase catalysis.\n2) Analysis of the catalytically relevant states 9NYC, 9NYD, 9NYK\n9NYC: The map is stated by the authors to correspond to the substrate-bound state with bound\ndonor substrate UDP-Glc and acceptor substrate UndP. The density for the Glc moiety in UDP-\nGlc is very weak in all four subunits. The density of UndP is also ambiguous, especially with\nrespect to the connection to the phosphate group. Since the map is obviously convoluted by\nseveral conformers, the state cannot be unambiguously assigned as the substrate-bound state of\nGtrB, and interpretation of details belonging to that state (metal binding, side chain\nconformations, ligand binding etc) is therefore not possible.\nIn our model 9NYC, the map was refined with C4 symmetry, and each chain contains the same\nsubstrate-bound state. The density for UndP is robust to model up to the third isoprene unit and\nthe position of the phosphate head is consistent with our previous tungstate-replacement X-ray\ncrystallography results (ref. 20). The head position is also in agreement with the DolP-bound\nmodel of PfDPMS (ref. 21). The phosphate head is positioned in the conserved R122/R125\npocket with a local resolution from 2.5-3 Å, similar to the surrounding area. Included are three\nvideos showing the density of the map for both UndP and UDP-Glc (Response to Reviewers\nVideos 1-3). Additionally, our unbiased MD simulations further validate the stability of the UndP\nin this state across all four chains (Extended Data Figure 9e; formally 7e), and of the protein\nbackbone (shown below). A more detailed explanation of the stability of each state validated by\nour MD is included in our response to Reviewer #3.\nThe Glc moiety density is indeed weaker than that of UndP, but it is sufficient to model and\nposition relative to UndP, having a local resolution closer to 3 Å. We note that Glc is not strongly\ncoordinated within the active site, leading to considerable rotational freedom, which could result\nin poorer density. The inherent rotational freedom is supported by our unbiased MD data as well\nas our kinetic assays, which suggested promiscuity in the active site with similar binding strength\nof UDP-Gal, UDP, Glc, and caged-UDP-Glc when compared to UDP-Glc (see Figure 4d). We\nhave added a description of these details in the text (lines 174-179). This is also consistent with\nthe Glc moiety needing to undergo a conformational change during the reaction (i.e. transfer onto\nUndP) meaning its position is transient and dynamic.\nThe substrate-bound state was separated independently from the product-bound and pre-catalysis\nstates and does not share particle sets with those reconstructions. In addition, the particles\nunderwent three rounds of 3D classification, yielding a largely uniform set. While it is possible\nthat several thousand particles represent alternative states, the final reconstruction is based on\n>500,000 particles, strongly enriching for the most dominant conformer, as we consider typical\nin cryo-EM studies. However, we do not claim that this state corresponds to time = 0 or\nimmediate post-substrate-binding, and we are in total agreement with the Reviewer that such an\nassignment would be premature. However, guided by MD and steered MD simulations, as well\nas comparisons with PfDPMS (ref. 21) and ArnC (ref. 22 and ref. 23) structures, we place this\nconformation earlier than the pre-catalysis and product-bound states. Thus, while we remain\ncautious about its exact temporal placement, the dominance of this conformer in the dataset\nsuggests it represents a stable and functionally relevant state. Given this, we believe that based\non the quality of the density, interpreting features such as metal binding, side-chain\nconformations, and ligand interactions are both justified and informative. We added this\nexplanation to lines 188-191 of the manuscript.\n9NYD: The map is stated by the authors to correspond to a mixture of pre-catalysis and product-\nbound states. Specifically, subunits A/B/D are supposed to contain the substrates UDP-Glc and\nUndP, and subunit C would contain the products UDP and UndP-Glc. The region containing the\ndonor and acceptor-binding sites (including the loop delineating his region) is heavily\ndisordered and convoluted. The binding of substrates and/or products cannot be confirmed, and\nmodeling of the loop is also not trustworthy due to extremely low definition of the backbone (and\neven worse for the side chains).\nWe thank the Reviewer for this comment and agree that the density in the membrane-facing loop\n(specifically β7-JM2) in subunits B/C/D of model 9NYD is difficult to interpret for both the\nbackbone and sidechains. Subunit A contains sufficient density to completely model the β7-JM2\nloop consistent with the substrate-bound state. Thus, we have decided not to include residues\n201-206 in the product-bound state or subunit C (residues 200-206 were removed in the pre-\ncatalysis state or subunit B, and in subunit D) because the modeling was based on the substrate-\nbound state and not on the density. This change does not alter the conclusions of our study,\nhowever we agreed with the Reviewer that the modeling of the loop was not justifiable, and have\ncorrected this accordingly (lines 206-208 and lines 215-217).\nWe believe that in subunit B (the pre-catalysis state) there is clear and interpretable density for\nUndP and the Glc moiety at our recommended contour level (0.142). Included are videos\nshowing the density of the map for both UndP and UDP-Glc (Response to Reviewers Videos 4-\n6). Both steered and unbiased MD simulations confirmed the stability of this state; in unbiased\nMD, the structure remained stable without reverting to the substrate-bound state, while in steered\nMD, we were able to sample the pre-catalysis state with minimal applied force, yielding a near-\nidentical conformation to the experimental cryo-EM map. Furthermore, recent preprint data on\nthe related ArnC enzyme demonstrated a so-called ‘State 3’ in MD simulations (ref. 23, see\nFigure 6A) that closely parallels our experimentally observed pre-catalysis state.\nIn subunit C (product-bound state), we believe the density for the product UDP and UndP-Glc is\nof sufficient quality to model, with a local resolution between 2.5 and 3Å. Included are videos\nshowing the density of the map for both UndP-Glc and UDP (Response to Reviewers Videos 7-\n9). Our product-bound state closely resembles the product-bound conformation of the related PP-\nGT PfDPMS bound to DolP-Man (ref. 21, see Figure 3b). Finally, MD simulations confirmed the\nstability of this product-bound state across four replicates, which we have included in an updated\nExtended Data Figure 10h (formally Extended Data Figure 8) and an acknowledgement in the\ntext (lines 267-268). Our steered MD simulations recapitulated the product release pathway,\nincluding the conformational change of W207 (demonstrated in Supplemental Video 5),\nconsistent with our cryo-EM observations.\n9NYK: The map is stated by the authors to correspond to a pre-intermediate state. Specifically,\nUDP-Glc and UndP has been modeled in all four subunits. The map has serious disorder. If\ncontouring the map at a very low level (close to noise level) there is some possible residual\ndensity for a Glc moiety bound to UDP but no density for UndP in any of the subunits. This state\nis probably a disordered donor-bound state.\nWe acknowledge the Reviewer’s comment, but our results suggest that at our recommended\ncontour level (0.149), density for the Glc moiety is present in all four subunits, and density is\nobserved for at least the partial isoprenyl tail of UndP in each case. That said, this model is not\ncentral to our main conclusions and was included only to illustrate the lowering of the β7–JM2\nloop observed in other models. The positioning of UndP above UDP-Glc does not appear to play\na significant role in the catalytic mechanism and in our opinion may instead reflect diffusion of\nthe substrate within the active site. To avoid overinterpretation, we have elected to remove this\nmodel from the study rather than assign it as a donor-bound state, which we do not believe would\nnecessarily be completely accurate. Thus, we have revised Extended Data Figure 2 and 6 without\nthe pre-intermediate state and removed its mention in the text. The PDB model (9NYK) and map\n(EMD-49935) will still be available for those that are interested.\n3) Correction of GtrB topology\np.6. The improved density for the TM1-TM2 loop allowed correction of the topology of TMD.\nThe disordered density for this loop in the previous GtrB structures led to the incorrect tracing of\nTM1-TM2 connection such that TM1 in one subunit was connected to TM2 in a nearby subunit.\nSince the reassignment of the TMD is an important result of the present study, this should be\nmore clearly described in the text and in Ext.Data 2D. As it is presented now, it is difficult for\neven an experienced structural biologist to get a clear understanding of the original error in\nmodels 5EKE/5EKP.\nWe agree with the Reviewer that this particular section could be confusing for those not familiar\nwith the original study. A revised Extended Data Figure 2d has been amended with better clarity\nfor the updated TM assignment and additional explanation has been added to the text (lines 120-\n124).\nReviewer #2:\nThis manuscript by Morgan and co-workers delivers several snapshots of GtrB during the\nglycosyltransferase reaction catalysed by this enzyme. The aim is rather challenging, since GtrB\nis a membrane protein, and the authors design a time resolved cryoEM experiment using a caged\nphotolabile UDP-Glc derivative. This substrate is activated by UV light just prior to grid\nfreezing so the reaction is observed within 50-70 ms. Buy analysing one cryoEM sample of\nworking GtrB, the authors separate several states by 3D classification and postulate a\nmechanism of action. Their model is supported by MD simulations.\nThe topic is sound, since lipid glycosylation is a universal process required to construct\nglycosylated cellular membranes. The manuscript is clear and well written, and the figures are\nreally good.\nWe thank the Reviewer for their kind comments and assessment of our study.\nI just have one overall concern. Although the figures of rendered maps seem to support the\nseparation of some catalytic states, I have some doubts about the classification strategy.\nIt seems that the catalytic events can be understood in the context of one subunit (as depicted in\nFigure 5b) with no need for large or concerted conformational changes between subunits. The\nrefinement and the classification (Extended Data Figure 4) have been performed at the level of\nthe tetramer, even after symmetry expansion. With no information about the relative catalytic\nstates of monomers within a tetramer, it is reasonable to expect mixtures of subunits within the\ntetramers after classification. Actually, two of the conformations (product-bound and pre-\ncatalytic states) are seen in different subunits within the same tetramer).\nThe only way to circumvent this potentially large heterogeneity would be to work subunit-wise at\nthe level of refinement and classification. Thus, a D4 symmetry expansion (instead of D1) and a\nclassification within a mask covering one subunit (instead of a full tetramer) could deliver more\nclear separation of uniform states. Maybe the authors tried this, or the authors can give a good\nreason not to use this strategy.\nThe Reviewer raises an important point regarding the use of D1 versus D4 symmetry when\nanalyzing variability among GtrB particles and are grateful for this. We agree that D4 symmetry\nexpansion can, in principle, provide insight into monomer-specific movements rather than\ntetramer-wide variations. We tested this approach by taking 3DVA-selected particles, expanding\nto D4, and performing 3D classification with a tight mask around a singular monomer. While the\napparent resolution was artificially improved, likely due to oversampling, the resulting maps\nwere substantially noisier, less ordered, and lacked distinct variability. This likely reflects the\nvery large number of D4 symmetry-expanded particles (>6 million) used in classification.\nFor this reason, we chose to use D1 symmetry expansion, which reduced the dataset size\n(~700,000 particles) and produced maps with greater clarity, while still capturing the tetramer-\nwide variations of interest. Moreover, although GtrB functions catalytically as a monomer, it is\nphysiologically a tetramer, and we felt it was important to preserve this context in the maps. We\nhave added additional explanation to this reasoning in the text (lines 164-166).\nAdditionally, GtrB tetramers associate in double tetramers. The authors do not discuss at any point\nwhether this association can have any sort of impact in the functioning of the enzyme. One\npossibility could be to perform some of the MD simulations using the double tetramer instead of\nthe tetramer.\nWe agree with the Reviewer that this requires an explanation. In our previous study (ref. 20),\nGtrB also formed a head-to-head dimer of tetramers in the crystal lattice, suggesting this is a\nstable state adopted by the purified enzyme in both detergent and lipid nanodiscs. This\narrangement reflects an in vitro interaction between two residues on stacking tetramers (Q58 and\nR61) and cannot form under physiological membrane conditions, where GtrB functions as a\nsingle membrane-embedded tetramer. Nevertheless, the enzyme remains catalytically competent\nin the octameric state. This is evidenced in our previous study (ref. 20) in which kinetic assays on\ncrude, solubilized membranes and assays in proteoliposomes both yielded consistent results. We\nalso add that the formation of the product, as evidenced by our product-bound state in the cryo-\nEM data, further leads us to believe that the enzyme remains functional in the octamer state. In\nour view, this indicates that each monomer can catalyze the reaction independently (as opposed\nto an allosteric modulation model). We have added an explanation of this in the text (lines 306-\n310).\nBecause the octameric assembly may not be stable within a single bilayer, our MD simulations\nfocused on the physiologically relevant tetrameric form, thereby linking the cryo-EM structure to\nthe native state of the enzyme. Performing molecular dynamics simulations of the octamer would\nrequire a double-membrane system and non-physiological conditions. Such a setup would\ninvolve non-standard practices (e.g., atomic fluctuations are drastically reduced), making any\napparent “stability” in MD an unreliable indicator of true thermodynamic stability. We have\ndecided not to pursue this avenue.\nReviewer #3:\nIn this study, the authors successfully resolved multiple conformational states of GtrB (substrate-\nbound, pre-catalysis, product-bound and pre-intermediate states), using cryogenic time-resolved\nelectron microscopy (cryo-TREM) combined with UV photolysis of a caged substrate. It shows\ninsights into the catalytic mechanism of GtrB. The integration of structural data, MD simulation\ndata and biochemical assays provides a framework for understanding glycosyltransferase\nmechanisms.\nWe thank the Reviewer for their comments and overall assessment of our study.\nHowever, there are still some key issues that need to be addressed in this study, a major revision\nis necessary.\n1. In the authors’ previous study (reference 4), the authors proposed the catalytic mechanism of\nGtrB, no catalytic base is needed, D157 was suggested as a catalytic residue. However, in this\nstudy, the authors suggested that D94 or D157 could be the catalytic base, so further\nexperimental validation (mutagenesis with kinetic assays) is still needed to resolve this\nambiguity.\nWe agree with the Reviewer that this remains an ambiguous point. To address it, we originally\nperformed mutagenesis of D94 and D157 and attempted to incorporate these variants into\nliposomes, as we did with other mutants in this study. D157N successfully incorporated into\nliposomes, but as with all other tested mutants exhibited a diminished ability to efficiently bind\nUDP-Glc when compared to wild-type. We contend that mutation at these sites effectively\nrenders the enzyme catalytically “dead.” Although the D94 and D99 mutants were successfully\nexpressed, repeated attempts to reconstitute them into liposomes for uptake assays were\nunsuccessful (Extended Data Figure 12a, formally Extended Data Figure 9a). In our original\nstudy (ref. 20), a D157N mutant abolished activity, consistent with the interpretation that D157\nmay function as the catalytic base. Nevertheless, this assignment remains debated in the field,\nand mutagenesis alone may not provide a definitive answer, as such substitutions can disrupt\nfolding or metal coordination, or simply the electrostatics of the active site in a way that\njeopardizes function. In our discussion, we suggest that D94 could be the more likely candidate,\nas related enzymes such as ArnC (ref. 23) point towards a mechanism by which the DxD motif\nengages in catalysis rather than strictly metal coordination. Our cryo-EM data, however, show an\nequidistant UndP position to both D94 and D157. The evidence from our previous study, which\nshows that mutation at D157 abolishes activity in crude membranes and incorporated liposomes,\nis further supported by the kinetic assays in the present study.\n2. In most glycosyltransferases, there is a conserved DXD motif, of which the two ASP residues\nare coordinated with Mn2+ or Mg2+. And there is also a third residue (most likely HIS residue)\ncoordinated with Mn2+ or Mg2+. Even though the authors provided suggestions of the DXD\n(D94, D96), possibly Q98 could be the third coordinated residue, no structure obtained in this\nstudy showed a complete coordination around Mg2+. Why is that? This is a critical question.\nPrevious mutagenesis studies suggest that mutations of the two ASP or the third residues could\ngreatly reduce the enzymatic activities. If the DXD motif and the third residues could not\ncoordinate with Mg2+ stably, how could the catalysis happen? At least, the authors need to\naddress this question.\nWhile the deposited PDB files may appear to lack complete Mg²⁺ coordination, this largely\nreflects the limitations of PDB deposition rules, which impose strict criteria on assigning metal\ncoordination bonds. To avoid over-interpretation, we took a conservative approach and did not\nassign bonds that were not automatically assigned during deposition. So, while we originally had\nmodelled complete coordination of the Mg2+ in nearly all of the presented states, which is\nsupported by the density, we defer to the PDB deposition to stay consistent with other entries. We\nnote that in the deposited pre-catalysis state there remains complete coordination of the Mg2+.\nImportantly, in membrane-bound glycosyltransferases with a DxD motif, only one aspartate is\nobserved to coordinate the Mg²⁺ ion at a time. Across all known acceptor PP-GT enzymes, the\nfirst aspartate of the motif (D89 in PfDPMS (ref. 21), D94 in GtrB, and D100 in ArnC (ref. 22\nand ref. 23) does not participate in Mg²⁺ coordination, making it a plausible candidate for\ncatalytic function.\nBased on our observations, we propose that Q98 may instead contribute to the motif, effectively\nreplacing D94’s classical role. We consistently observe Q98 coordinating the metal in several\nstates, and in 9NYD chain B (the pre-catalysis state) we observe complete Mg²⁺ coordination\n(between the two phosphates groups of UDP, Q98, and D96). While we remain cautious in\nassigning a definitive catalytic residue, these structural and comparative data support Q98 as a\nstrong candidate for metal coordination.\n3. The authors stated that multiple conformational states of GtrB were captured experimentally,\nsubstrate-bound, pre-catalysis, product-bound and pre-intermediate states. The authors used the\ndistance between the phosphate group of UndP and C1 of the glucose of the donor as a criterion\nto describe the substrate-bound, pre-catalysis and pre-intermediate states. 2500-ns MD\nsimulations were performed for the three systems, respectively. The authors should also include\nthe distance plots showing the dynamics of the three conformational states. Are all three\nconformational states stable?\nTo assess the stability of the experimentally captured states, we analyzed both the key\nintermolecular distances and the Root Mean Square Deviation (RMSD) of the backbone of the\nprotein and of the ligands throughout the MD simulations. Our analysis confirms that all three\nstates—substrate-bound, pre-catalysis, and product-bound—are dynamically stable. In the\nfollowing figures, we report both the graph obtained with simulations performed with TIP3P\nand with OPC solvent models, as suggested in a following comment by the Reviewer.\nIn the substrate-bound state, our simulations revealed significant flexibility of the glucose moiety\non UDP-Glc. This is expected, as this part of the substrate is solvent-exposed and lacks strong,\nstabilizing interactions with the protein. This flexibility naturally leads to fluctuations in the\ndistance between the UndP phosphate headgroup and the C1 atom of glucose in the substrate-\nbound state.\nHowever, the UndP itself remains stably anchored in its binding pocket. The RMSD plot for the\nUndP phosphate and its first two isoprene units shows low deviation, confirming that this part of\nthe substrate maintains the conformation observed in the cryo-EM structure, considering the\nfluctuations at room temperature.\nFor the pre-catalysis state, the distance plot between the UndP headgroup and the glucose C1\natom is particularly informative. The distance is consistently maintained between 4 and 6 Å,\nwhich is close to the distance found in our cryo-EM structure. The observed fluctuations around\nthis value are expected, as our MD simulations at ambient temperature capture the system's\nthermal energy, unlike the static, low-temperature cryo-EM experiment.\nFinally, in the product-bound state, the UndP-Glc product also remains globally stable. The\nRMSD plots show slightly higher values compared to the substrate-bound UndP, which can again\nbe attributed to the flexibility of the newly transferred glucose moiety.\nIn addition to these analyses, the distance plots in the original Extended Data Figures 7-8 (now\nExtended Data Figures 9-10) and in the new Extended Data Figures 7 and 8 show the stability of\nthe interactions of UndP with the surrounding residues in the different states.\nIn summary, despite the expected thermal fluctuations and the flexibility of the sugar groups, all\nthree states remain in their distinct conformations throughout the simulations and do not\nspontaneously transition into one another, thus confirming the stability of the states captured\nexperimentally.\n4. In this study, the authors claimed that the catalytic mechanism of GtrB is elucidated. In their\nprevious study (reference 4), the authors proposed one. However, further investigation is still\nneeded to fully elucidate the catalytic mechanism. A QM/MM study is highly recommended in the\nnear future.\nWe thank the Reviewer for their suggestion, which we will keep in mind for future studies.\nSome other comments:\n1. References are not recommended to be cited in the Abstract.\nWe thank for Reviewer for catching this.\n2. A table listing the conformational states for each chain of the structures is recommended\nAn updated Extended Data Table 2 has been amended with relevant chain numbers.\n3. For the MD simulations, the authors used Amber ff19SB force field for protein and TIP3P\nwater model. In the FF19SB paper (J. Chem. Theory Comput. 2020, 16, 528−552), the authors\nclearly recommend against using FF19SB with TIP3P water model, but with OPC water model.\nHow do the authors comment on this issue?\nWe thank the Reviewer for raising this important point regarding the choice of the water model.\nThe selection of a water model in a multi-component system, such as our membrane-protein-\nligand complex, often involves balancing the optimal parameter sets for each component. While\nwe are aware that the OPC water model is recommended for Amber ff19SB, the combination\nwith TIP3P remains a robust and published approach. For instance, a recent, extensive\nbenchmark study (J. Chem. Inf. Model. 2021, 61, 284–297) on protein-ligand binding\nthermodynamics explicitly used the Amber ff19SB force field in combination with TIP3P water\nfor all their simulations, demonstrating the validity of this setup in the recent peer-reviewed\nliterature. Furthermore, it is important to note that the ff19SB parameter set is specific to the\nprotein, whereas our ligands and membrane lipids were modeled with GAFF2 and Lipid21,\nrespectively—force fields that are well-established for use with TIP3P. Our choice, therefore,\nrepresented a balanced approach for this complex system.\nNevertheless, to rigorously ensure that our conclusions are robust and not dependent on this\nchoice, we have taken the reviewer's suggestion as an opportunity to perform a cross-validation.\nWe conducted a new set of simulations for all systems using the recommended OPC water model\n(3 replicates of 1μs for each system). We now include a two new Extended Figures (Extended\nData Figure 7 and 8) that provides a direct comparison of key geometric parameters (including\nligand RMSD and the specific distances discussed in the main text) between the original\nsimulations with TIP3P and the new ones with OPC. The plots demonstrate near congruence\nbetween the results obtained with both water models, confirming the stability of the observed\nconformational states in both setups.\nThis agreement validates the robustness of our original findings and strongly indicates that our\nconclusions are independent of the specific water model used. We thank the Reviewer again, as\ntheir insightful comment has allowed us to add a further layer of validation to our study.\n4. In the MD simulations, how did the authors choose the force field parameters of Mg2+? It was\nnot mentioned in the Methods sections. Furthermore, the authors also assigned two bonds\nbetween the Mg2+ and two phosphate oxygen atoms, respectively, while still keeping the net\ncharge of -2e for Mg2+. Have the authors verified the parameters? Is it reasonable?\nThe accurate parameterization of divalent cations like Mg²⁺, particularly their coordination\ngeometry, is a well-known challenge for classical force fields. As no predefined parameters exist\nfor the specific coordination environment in our system, we initially employed a standard non-\nbonded model (the amber Mg2+ ion parameter), relying on the default electrostatic and van der\nWaals interactions. While this model was stable in most simulations, we observed in a subset of\nour long-timescale simulations (>500 ns) a gradual loss of the native coordination geometry. We\nbelieve this instability is a known artifact arising from small inaccuracies in the non-bonded\nforce field, which can sometimes fail to fully capture the quantum mechanical nature of metal\ncoordination over extended timescales, rather than a physically meaningful event.\nTo prevent this artifact and ensure the structural integrity of the catalytic site throughout the\nsimulation, we introduced weak harmonic distance restraints between the Mg²⁺ ion and the\ncoordinating oxygen atoms. The equilibrium distance for these restraints was set to the precise\nvalue observed in our experimental cryo-EM structure. This strategy offers two key advantages:\n- It anchors the simulation to high-quality experimental data, ensuring the catalytic site\nmaintains a physically realistic geometry.\n- The restraints are ‘soft’, meaning they allow for natural thermal fluctuations around the\nequilibrium distance but prevent the larger, unphysical dissociation events seen with the\npure non-bonded model.\nRegarding the ion's parameters, the Mg²⁺ was always treated as a non-bonded particle with a\nformal charge of +2e. The restraints are an additional potential energy term and do not represent\ncovalent bonds; they do not alter the ion's charge or its electrostatic interactions. A hybrid\napproach similar to this was already proposed for simulating other metalloenzymes, as it corrects\nfor known force field limitations while preserving the system's dynamics. Please, see for\nexample the following works:\n1. Hu, L., & Ryde, U. J. Chem. Theory Comput. 2011, 7, 2452–2463.\n2. Shen, R., et al. PLOS Comput. Biol. 2015, 11, e1004368.\n3. Foster, C. A., & West, A. H. (2017). Proteins: Structure, Function, and Bioinformatics,\n85, 155-176.\nWe have now clarified this procedure in the SMD Methods section in the revised manuscript.\n5. In the steered MD simulations, the authors applied positional restraints on the Mg ions, what\nis the purpose of this?\nThe purpose of applying positional restraints to the Mg²⁺ ion was to ensure that the steering force\nacted specifically on the translocating lipid, preventing an artificial destabilization of the\ncatalytic site. For the UndP entrance simulations, our collective variable (CV) was defined as the\ndistance between the UndP headgroup and the Mg²⁺ ion itself. Without restraints, the external\nforce applied along this CV would be directly exerted on the ion. As we observed in preliminary\ntests, this caused the Mg²⁺ to be pulled from its coordinating residues, leading to an unphysical\ncollapse of the catalytic site's geometry. The restraints were therefore essential to study the lipid's\nmovement while keeping the enzyme's core machinery intact.\nFor the product exit simulations, a different CV was used where the Mg²⁺ ion was not the direct\nreference. However, the tangential nature of the pulling force required to extract the product from\nthe pocket generated a rotational torque on the protein. In this case, we maintained the same\npositional restraints on the Mg²⁺ ions for two reasons: first, to act as a stable anchor to cancel this\nsimulation-induced torque, and second, to maintain methodological consistency across both sets\nof SMD simulations.\nThis approach ensures that in both processes, the work performed and the conformational\nchanges observed are representative of the lipid's translocation, providing a more physically\nrealistic depiction of the events.\n6. In the manuscript, the authors mentioned that UndP is a 55-carbon isopreoid, however, in the\ncryo-EM structure, it is not. In the MD simulations, it is 55-carbon again. The authors should\ncomment on it.\nWe have expanded our explanation in the manuscript to clarify our methodology. As correctly\nnoted, our cryo-EM model includes only the first five isoprenyl units of UndP. This is because\nthe experimental density for the remainder of the 55-carbon tail was not resolved, likely due to\nits high flexibility within the membrane environment of the nanodisc. Therefore, we built into\nthe map only the portion of the substrate for which we had clear structural evidence. For the MD\nsimulations, however, it was crucial to include the full-length, 55-carbon lipid to accurately\nrepresent the complete substrate and its interactions within a continuous lipid bilayer. To achieve\nthis, the unresolved portion of the tail was computationally modeled onto the experimentally\nderived headgroup using Maestro (Schrödinger Suite). The initial conformation was built to\nadopt a low-energy state, occupying the available space within the membrane along the z-axis.\nCrucially, during the subsequent extensive equilibration phase, this modeled portion of the UndP\ntail was left completely unrestrained. This allowed it to freely relax and equilibrate its\nconformation within the lipid bilayer, driven by natural physical interactions with the\nsurrounding lipids. This ensures that the final, equilibrated structure and its dynamics are not an\nartifact of the initial modeled placement but are instead representative of a physically plausible\nstate. This approach allows us to bridge the gap between the static, density-limited experimental\nstructure and a more complete, dynamic model of the enzyme–substrate complex in a realistic\nmembrane environment.\nIt is also recommended to show the interactions between UndP and the lipids during the\nunbiased MD and steered MD simulations.\nThe simulated lipids are present in both the unbiased and steered MD simulations, but they are\nhidden for clarity in the Supplementary Videos otherwise it may be difficult to observe the\nmovement of UndP or UndP-Glc. We have included a short video of the steered MD simulation\nof the UndP-Glc exit from the active site with the lipids shown (Response to Reviewers Video\n10). We also added the lipids to the attached trajectories; all skipped every 100 frames (10 ns).\n7. In Figure 5a, the phosphate group of UndP is protonated, but in the MD simulations, it is not\nprotonated. Why is that?\nIn Figure 5a, the protonation of the UndP phosphate reflects our assignment of D157 as a\ncatalytic base, in contrast to its assignment as a catalytic acid in our previous study (ref. 4). UndP\nis often depicted as doubly deprotonated in physiological pH, but this is likely an\noversimplification. At physiological pH, UndP is most likely a mixture of mono- and double-\ndeprotonated molecules, possibly also depending on local membrane environment and salt\nconcentration. If the UndP that goes into the active site is double-deprotonated, then catalysis can\nhappen as soon as UndP is coordinated in a catalytically permissive position. No deprotonation is\nnecessary. But, if a mono-deprotonated UndP enters the active site, the second proton will need\nto be abstracted for catalysis to happen. Thus, a catalytic base in the vicinity will be required.\nThe protonation state cannot be directly determined from the cryo-EM data, and whether GtrB\nuses a catalytic acid or base remains unresolved. Based on precedent from related enzymes ArnC\n(ref. 22 and ref. 23) and PfDPMS (ref. 21), we chose to assign D157 as a catalytic base. MD\nsimulations were conducted using the double-deprotonated UndP molecules due to the ambiguity\nof the possible catalytic base in GtrB, whereas the figure recognizes that for a certain portion of\nUndP molecules the deprotonation will be necessary, thus the possible catalytic bases\naccomplishing the deprotonation are depicted. Our MD simulations are also not intended to\nsimulate the complete chemical reaction as this occurs on a larger timescale; rather they aim to\nvalidate the stability of the experimental cryo-EM models. Thus, protonation states at the time of\nreaction are not as relevant as the physiological conditions that GtrB may experience during\nsubstrate binding, UndP translocation, or product exit.\n8. The recommended abbreviation of steered molecular dynamics should be SMD, not sMD.\nWe have made this correction in the text.\n9. For the SMD, page 43 lines 952-953:\n“In accordance with Jarzynski equality, we reported only the minimum path work computed over\nthe replicates for the two processes.”\nJarzynski equality is an ensemble average, how did the authors determine the minimum path\nwork?\nWe thank the Reviewer for this important clarification. In our study, we performed a total of 12\nindependent SMD simulations. We achieved this by pulling over three system replicas and four\nindependent steering events (one per each monomer chain). In the revised version, we report all\ncorresponding work profiles in a new Extended Data Figure 11. We focused our analysis just on\nthe trajectory with the lowest total work as a practical representative of the Jarzynski-weighted\nensemble average. Indeed, because the exponential average is dominated by the lowest-work\nrealizations, the trajectories with minimal total work contribute the most to the free-energy\nestimate, see Response to Reviewers Figure 1 and 2, top panels.\n10. page 43 lines 955-958:\n“The free energy barrier is computed as half of the work computed during the simulations,\nassuming that the simulated process is 2 to 5 orders of magnitude faster than the predicted\ncharacteristic time (reference 62)”\nFor the SMD simulations, this only applies to fast pulling (10 m/s = 10 nm/ns or faster, check the\nreference 62). In this study, the pulling is 0.005 nm/ns, far slower than the slow pulling (1 nm/1ns\n= 1m/s) in the reference. So it is incorrect to apply this rule here.\nThe empirical “½ × Work” relation cited from ref. 62 was originally proposed for cases where\nthe driven process occurs “at least three orders of magnitude faster” than the corresponding\nspontaneous timescale, rather than for a specific absolute pulling velocity. Indeed, the absolute\nvelocity should consider the intrinsic diffusivity of the system along the reaction coordinate, that\nwould be different for different systems. In our simulations, the a-posteriori estimation of the\ntransition rate, using the ½ correction, indicates that the steered process is indeed 3–5 orders of\nmagnitude faster than the expected spontaneous event, which in our view justifies the use of this\napproximation. Moreover, the corrected values are consistent with realistic kinetic scales,\nwhereas omitting the correction would imply first-passage times on the order of days or months.\nNotably, removing the correction only increases the implied timescale, reinforcing our main\nconclusion that the event is far slower than the times accessible to unbiased MD.\nFurthermore, the pulling speed is too slow, less than the dynamics in unbiased MD simulations.\nPossible noise could be introduced in such a slow and long SMD simulations. Pulling a distance\nof 5 angstrom, 10 ns SMD is already enough, the number of pulling can be much bigger, which\ncould greatly increase the accuracy of the free energy barrier. Of course, metadynamics is also\nrecommended.\nRegarding the pulling speed, in this circumstance, we respectfully disagree with the Reviewer.\nOur estimations indicate that the simulated process is at least three orders of magnitude faster\nthan the spontaneous one. It is important to recall that equality between the external work\nperformed during a perturbation and the corresponding free-energy difference is achieved only in\nthe reversible limit, i.e., for an infinitely slow process, as stated by the Second Law of\nThermodynamics. Other free-energy methods, such as Umbrella Sampling or Restrained MD,\nreconstruct the free-energy difference from a series of quasi-equilibrium states through\nthermodynamic integration.\nIn contrast, for far-from-equilibrium perturbations such as SMD, Jarzynski’s theory establishes\nthat the average work equals the true free-energy difference for any perturbation, but only if a\nsufficiently large number of trajectories are available. Ref. 62 notes that when the dissipative\nbias exceeds ~15 kBT, “the number of required samples becomes prohibitively large for both\nsimulations and experiments.” Based on this consideration, we performed SMD at the slowest\nfeasible pulling speed while using multiple replicas, within our available computational\nresources. In our case, reducing the simulation time, as suggested by the Reviewer, would\nincrease dissipation rather than improve accuracy.\nTo directly verify this, we also tested the Reviewer’s suggestion by performing additional\nsimulations at 0.1 and 1 nm/ns for the substrate-bound state. None of these faster pulls yielded a\nlower total work than the slower protocol (see Response to Reviewers Fig. 1), even if five times\nmore replicas are conducted (five independent replicas from three independent equilibrated\nsystem).\nMoreover, the mean work, the exponential average, and the standard deviation were all smaller\nfor the slower pulling speed, confirming that slower SMD reduces dissipation and hysteresis, as\nexpected. We also performed analogous fast-pulling tests for the product-bound state, at 1 nm/ns,\nwhich also in this case resulted in substantially higher barriers (Response to Reviewers Fig. 2).\nIn this case, the fast pulling prevents proper molecular rearrangement of the loop and the\nsurrounding cavity, leading only to high-energy paths. The new fast-pulling SMD simulations\nwere conducted using another water model, OPC, as a comparison to show that the water model\ndoes not affect significantly the processes; see also the comment above.\nOverall, thanks to the Reviewer’s suggestion, we could confirm that among all simulated\ntrajectories, the one reported in the original version of the main manuscript most likely\nrepresents the physically correct and energetically consistent path.\nWe also emphasize that our goal was not to compute a complete free-energy profile, but rather to\nidentify and characterize plausible transition pathways connecting the relevant states.\n11. The authors shared the MD data, however, for each of the systems, only a number of\nsnapshots were selected to share. Different systems have different number of snapshots (hundreds\nto thousands), which make the simulation data less authentic.\nWe thank the Reviewer for noticing this. We adjusted all the files extracting the reduced\ntrajectories using the same timestep, 10 ns.\nResponse to Reviewers Figure 1. Substrate-bound Steered MD.\nTop panel shows the total pulling work for multiple replicas (3 SMD simulations for three\nindependent equilibrated systems), using a pulling speed 0.005 nm/ns, the dataset originally\ncomputed for the manuscript from which the minimum path was extracted. In each SMD\nsimulation, we sampled four independent pulling variable, one for each monomer chain, for a\ntotal of 12 pulling work measurements. Middle panels show 15 SMD simulations (total 60\npulling works), starting from 3 independent equilibrated systems (3x5 SMD simulations), at 0.1\nnm/ns speed, performed using either TIP3P or OPC water model. Bottom panel shows the\nresults for pulling speed of 1 nm/ns.\nResponse to Reviewers Figure 2. Product-bound Steered MD.\nTop panel shows the total pulling work for multiple replicas (3 SMD simulations for three\nindependent equilibrated systems), using a pulling speed 0.005 nm/ns, the dataset originally\ncomputed for the manuscript from which the minimum path was extracted. In each SMD\nsimulation, we sampled four independent pulling variable, one for each monomer chain, for a\ntotal of 12 pulling work measurements. Bottom panels show 15 SMD simulations (total 60\npulling works), starting from 3 independent equilibrated systems (3x5 SMD simulations), at 1\nnm/ns speed, performed using either TIP3P or OPC water model."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The manuscript can be accepted for publication.", "reviewer_2": "The authors have addressed my previous concerns regarding cryoEM strategy during classification. In my opinion the\nmanuscript is now ready for publication.", "reviewer_3": "All my questions have been properly addressed, no further issues now."}, "author_response": ""}]

Decision letter