Author response:
The following is the authors’ response to the original reviews.
Reviewer #1 (Public review):
Summary:
In the manuscript "Conformational Variability of HIV-1 Env Trimer and Viral Vulnerability", the authors study the fully glycosylated HIV-1 Env protein using an all-atom forcefield. It combines long all-atom simulations of Env in a realistic asymmetric bilayer with careful data analysis. This work clarifies how the CT domain modulates the overall conformation of the Env ectodomain and characterizes different MPER-TMD conformations. The authors also carefully analyze the accessibility of different antibodies to the Env protein.
Strengths:
This paper is state-of-the-art, given the scale of the system and the sophistication of the methods. The biological question is important, the methodology is rigorous, and the results will interest a broad audience.
Weaknesses:
The manuscript lacks a discussion of previous studies. The authors should consider addressing or comparing their work with the following points:
(1) Tilting of the Env ectodomain has also been reported in previous experimental and theoretical work: https://doi.org/10.1101/2025.03.26.645577
(2) A previous all-atom simulation study has characterized the conformational heterogeneity of the MPER-TMD domain: https://doi.org/10.1021/jacs.5c15421
(3) Experimental studies have shown that MPER-directed antibodies recognize the prehairpin intermediate rather than the prefusion state: https://doi.org/10.1073/pnas.1807259115
(4) How does the CT domain modulate the accessibility of these antibodies studied? The authors are in a strong position to compare their results with the following experimental study: https://doi.org/10.1126/science.aaa9804
Based on the Reviewer’s comments and suggestions, we have added a discussion related to each previous study mentioned above.
(1) Tilting of the Env ectodomain has also been reported in previous experimental and theoretical work: https://doi.org/10.1101/2025.03.26.645577
At the end of the third paragraph (originally the second paragraph) in the Discussion section we added:
“Shehata et al. also built a model of full-length gp120–gp41 trimer embedded in a lipid bilayer and performed all-atom simulations, in which a tilting motion of the ectodomain was observed. Based on the analysis of accessible surface area using different probe radii, they reported that antibody epitopes on the ectodomain are largely shielded by glycans, while the MPER epitope is mainly occluded by the membrane with tilt angles above 30° required to achieve greater MPER exposure (Shehata et al., 2025).”
(2) A previous all-atom simulation study has characterized the conformational heterogeneity of the MPER-TMD domain: https://doi.org/10.1021/jacs.5c15421
In the middle of the first paragraph in the Discussion section we added:
“This is consistent with the all-atom simulations of MPER–TMD–CT and MPER–TMD in an asymmetric membrane conducted by Majumder et al., which likewise show multiple different conformational states of MPER and TMD (Majumder et al., 2025).”
(3) Experimental studies have shown that MPER-directed antibodies recognize the prehairpin intermediate rather than the prefusion state: https://doi.org/10.1073/pnas.1807259115
The paper mentioned by the Reviewer mainly reports the NMR structure of the MPER and TMD. In this study, the authors experimentally examined a series of MPER mutations to assess whether alterations in the MPER affect epitope accessibility in other regions of the Env ectodomain. This study did not investigate whether MPER-directed antibodies recognize the prehairpin intermediate. Instead, it cited prior studies (Frey et al.; 2008, Alam et al., 2009; and Chen et al., 2014) reporting that MPER-directed antibodies target the prehairpin intermediate conformation. We have already cited two of them (Alam et al., 2009 and Chen et al., 2014) in the original preprint, and we have now added the third one (Frey et al., 2008) in the revised manuscript.
In the middle of the third paragraph (originally the second paragraph) in the Discussion section we added:
“This is consistent with experiment studies indicating that MPER-targeting antibodies bind effectively only after the gp120–gp41 trimer undergoes major conformational rearrangements toward a fusion-intermediate or post-fusion state (Frey et al., 2008; Alam et al., 2009; Chen et al., 2014; Lee et al., 2016).”
(4) How does the CT domain modulate the accessibility of these antibodies studied? The authors are in a strong position to compare their results with the following experimental study: https://doi.org/10.1126/science.aaa9804
At the beginning of the second paragraph in the Discussion section we added:
“Comparison of the full-length and CT-truncated systems shows that the primary difference arises from changes in the lipid bilayer, particularly in the exoplasmic leaflet, whereas differences in protein conformation and dynamics are less evident. Previous experimental studies have reported that mutations of the TMD residue and CT truncation can substantially affect antigenicity of ectodomain (Edwards et al., 2002; Chen et al., 2015; Dev et al., 2016). However, the ectodomain remains relatively rigid in our simulations for both full-length and CT-truncated systems. It is unclear whether this behavior reflects insufficient conformational sampling or artifacts associated with the model structures. Structural information for the CT is very limited, and the NMR structure (PDB ID: 7LOI) was the only available CT structure at the time the simulation systems were constructed. As a result, the extent to which this structure represents the native CT conformation remains uncertain. Additional experimental structural characterization of the CT will be important for achieving a more complete understanding of its functional role.”
Reviewer #1 (Recommendations for the authors):
A minor point: The RMSD values in Figure 3-figure supplement 1, seem a little too small. Please check the units.
Figure 3-figure supplement 1 shows the RMSD of the ectodomain. Prior to RMSD calculation, the snapshots extracted from each trajectory were aligned to the initial structure using the ectodomain as the reference to avoid falsely high RMSD values arising from different orientations of the ectodomain. The relatively small RMSD values therefore reflect the intrinsic structural stability of the ectodomain, indicating that its internal conformation remains stable even though it undergoes substantial tilting motions.
Reviewer #2 (Public review):
Summary:
In this work, the authors aim to elucidate how a viral surface protein behaves in a membrane environment and how its large-scale motions influence the exposure of antibody-binding sites. Using long-timescale, all-atom molecular dynamics simulations of a fully glycosylated, full-length protein embedded in a virus-like membrane, the study systematically examines the coupling between ectodomain motion, transmembrane orientation, membrane interactions, and epitope accessibility. By comparing multiple model variants that differ in cleavage state, initial transmembrane configuration, and presence of the cytoplasmic tail, the authors aim to identify general features of protein-membrane dynamics relevant to antibody recognition.
Strengths:
A major strength of this study is the scope and ambition of the simulations. The authors perform multiple microsecond-scale simulations of a highly complex, biologically realistic system that includes the full ectodomain, transmembrane region, cytoplasmic tail, glycans, and a heterogeneous membrane. Such simulations remain technically challenging, and the work represents a substantial computational and methodological effort.
The analysis provides a clear and intuitive description of large-scale protein motions relative to the membrane, including ectodomain tilting and transmembrane orientation. The finding that the ectodomain explores a wide range of tilt angles while the transmembrane region remains more constrained, with limited correlation between the two, offers useful conceptual insight into how global motions may be accommodated without large rearrangements at the membrane anchor.
Another strength is the explicit consideration of membrane and glycan steric effects on antibody accessibility. By evaluating multiple classes of antibodies targeting distinct regions of the protein, the study highlights how membrane proximity and glycan dynamics can differentially influence access to different epitopes. This comparative approach helps place the results in a broader immunological context and may be useful for readers interested in antibody recognition or vaccine design.
Overall, the results are internally consistent across multiple simulations and model variants, and the conclusions are generally well aligned with the data presented.
Weaknesses:
The main limitations of the study relate to sampling and model dependence, which are inherent challenges for simulations of this size and complexity. Although the simulations are long by current standards, individual trajectories explore only portions of the available conformational space, and several conclusions rely on pooling data across a limited number of replicas. This makes it difficult to fully assess the robustness of some quantitative trends, particularly for rare events such as specific epitope accessibility states.
In addition, several aspects of the model construction, including the treatment of missing regions, loop rebuilding, and initial configuration choices, are necessarily approximate. While these approaches are reasonable and well motivated, the extent to which some conclusions depend on these modeling choices is not always fully clear from the current presentation.
Finally, the analysis of antibody accessibility is based on geometric and steric criteria, which provide a useful first-order approximation but do not capture potential conformational adaptations of antibodies or membrane remodeling during binding. As a result, the accessibility results should be interpreted primarily as model-based predictions rather than definitive statements about binding competence.
Despite these limitations, the study provides a valuable and carefully executed contribution, and its datasets and analytical framework are likely to be useful to others interested in protein-membrane interactions and antibody recognition.
Based on the Reviewer’s comments, we have revised the Discussion section to emphasize the limitation related to model construction and analysis of antibody accessibility.
In the middle of the second paragraph in the Discussion section we added:
“Similar limitations apply to other modeled regions where structural information is incomplete, including missing loops in the ectodomain, the cleavage site and heptad repeat 2 where two PDB structures (IDs: 6B0N and 7LOI) were merged. These regions introduce additional uncertainty, and the extent to which they influence the interpretation of our results remains an open question.”
In the middle of the third paragraph (originally the second paragraph) in the Discussion section we added:
“In addition, this analysis is based on geometric and steric criteria without accounting for potential conformational adaptations of gp120–gp41, antibodies, or the membrane; therefore, the calculated frequency of antibody accessibility should be interpreted as an approximation rather than a definitive indicator of binding competence.”
Reviewer #2 (Recommendations for the authors):
(1) Lines 45-47: The phrase "A major breakthrough was the design of ..." may be confusing. The gp140 trimer refers to a naturally occurring form of the HIV envelope protein rather than a structure designed de novo. If this statement refers to the development of a specific experimental construct or model system, this should be clarified to avoid misunderstanding.
We have revised the sentence to clarify that the statement refers to soluble gp140 trimer constructs developed to stabilize the prefusion Env ectodomain for structural and immunological studies.
At the beginning of the second paragraph in the Introduction section, we have modified the following:
“A major advance was the development of soluble gp140 trimers, composing gp120 and the ectodomain portion of gp41, designed to stabilize the prefusion Env trimer for structural and immunological characterization.”
(2) Figure 1A: The figure displays a model structure lacking the cytoplasmic tail. Given that the full-length model is central to the study, the authors may wish to explain why the truncated structure is shown here or consider displaying the full-length model to better reflect the complete system analyzed.
We have combined Figure 1 and Figure 1—figure supplements 1 to show both full-length and CT-truncated models in one figure. We have also added an explanation of why the CT-truncated model was used as the primary system for analysis.
In the middle of the third paragraph in the Introduction section we added:
“However, structural information for the CT remains limited, leading to uncertainty in its conformational organization. To reduce potential bias arising from this uncertainty, we also generated a CT-truncated model and used it as the primary system for analysis (Figure 1, Figure 1—figure supplements 1).”
We have modified Figure 1
We removed Figure 1—figure supplements 1
(3) Line 106: The probability distributions of θEC and θTM are cited in support of the statement that the angles "typically range from ... with occasional tilting." Providing explicit quantitative measures (for example, means, percentiles, or fractions of time spent in different angular regimes) would strengthen this claim.
We have revised the text to explicitly indicate that only 0.7‰ of the sampled θ<sub>EC</sub> values are greater than 40°.
In the middle of the first paragraph in the subsection “The ectodomain maintains a rigid internal structure and tilts independently of the TMD” we have modified the following:
“Across trajectories, θ<sub>EC</sub> typically ranges from 0° to 40°, with only 0.7‰ exceeding 40°”.
(4) Figure 2: The meaning of the contour lines is not clearly explained. If these represent probability density estimates of angular values over the trajectory, this should be stated explicitly. In addition, because the angles may evolve over time, it would be helpful to clarify how temporal drift is accounted for in the contour representation.
We have clarified in both the main text and the figure caption that the contour lines in Figure 2B represent the joint probability density of the ectodomain and TMD tilt angles. We have also added Figure 2—figure supplements 5–8 showing the temporal evolution of the ectodomain and TMD tilt angles.
In the middle of the first paragraph in the subsection “The ectodomain maintains a rigid internal structure and tilts independently of the TMD” we have modified the following:
“The temporal evolution of θ<sub>EC</sub> and θ<sub>TM</sub> is additionally shown in Figure 2—figure supplements 5–8. For the CT-truncated systems, the joint probability densities of θ<sub>EC</sub> and θ<sub>TM</sub> calculated from the final 0.5 µs of each trajectory are shown in Figure 2B, while those for the full-length systems are shown in Figure 2—figure supplement 9.”
In the caption of Figure 2 we have modified the following:
“(B) Probability densities of ectodomain and TMD tilt angles, calculated from CT-truncated systems with various initial configurations.”
We have added Figure 2—figure supplements 5–8.
We have modified the following:
“The original Figure 2—figure supplements 5 has been renumbered as Figure 2—figure supplements 9.”
(5) Figure 2 (supplements): Some datasets are shown using scatter plots, while others are presented as contour plots. Using a consistent visualization style across panels or clearly explaining the rationale for the different representations would improve clarity.
The contour plots in Figure 2B and Figure 2—figure supplements 9 show the joint distribution of the ectodomain and TMD tilt angles during the final 0.5 µs of each trajectory, whereas the scatter plots in Figure 2—figure supplements 1–4 illustrate the variations of the tilt angles across different time intervals. Each 1-µs trajectory was divided into four 0.25-µs intervals, indicated by light gray, dark gray, black, and red respectively, as shown in the legends of Figure 2—figure supplements 1–4. We have clarified in the main text that the multi-colored scatter plots are intended to demonstrate that large conformational changes predominantly occurred during the first 0.5 µs of each trajectory.
In the middle of the first paragraph in the subsection “The ectodomain maintains a rigid internal structure and tilts independently of the TMD” we have modified the following:
“Each 1-µs trajectory is divided into four consecutive 0.25-µs intervals, and data points from each interval are distinguished by four different colors (Figure 2—figure supplements 1–4). The variations of θ<sub>EC</sub> and θ<sub>TM</sub> over time show that large conformational changes predominantly occurred during the first 0.5 µs, followed by convergence of the θ<sub>EC</sub> and θ<sub>TM</sub> distributions during the second 0.5 µs in most trajectories.”
(6) As noted in Line 97, θEC and θTM tilt independently. In this context, presenting time series plots of θEC and θTM separately would be highly informative. Such plots would help readers distinguish between equilibration behavior, drift from initial conditions, and equilibrium fluctuations.
We have added Figure 2—figure supplements 5–8 showing the temporal evolution of the ectodomain and TMD tilt angles, as noted in our response to comment (4).
(7) Figure 3A: It is not immediately clear which panels correspond to top views and which correspond to side views. Explicitly labeling these views in the figure or caption would reduce ambiguity.
We have added labels in Figure 3A to clearly denote the top-view and side-view panels.
(8) Figure 3B: The description "...by solid and transparent colors..." is ambiguous, as it is unclear whether this refers to color intensity or transparency. The caption would benefit from explicitly stating the visual encoding used (for example, darker/lighter colors or left/right bars).
We have revised the figure caption to clarify which boxes correspond to cleaved systems and which correspond to uncleaved systems.
In the caption of Figure 3 we have modified the following:
“For each residue, the distribution from cleaved systems is shown in dark color (left), and that from uncleaved systems is shown in light color (right).”
(9) Figure 4H: The definition of "frequency" expressed as a percentage is unclear. If this represents the fraction of snapshots in which two atoms fall within a specified distance range, this should be stated explicitly. The authors should also clarify whether the reported quantity is a probability or a rate, and ensure that the units and terminology are consistent.
We have revised the figure caption to clarify that the frequency represents the fraction of snapshots in which the heavy atoms of a TMD residue and the interacting component are within 5 Å.
In the caption of Figure 4 we have modified the following:
“For each TMD residue–interacting component pair, the frequency represents the fraction of snapshots in which the heavy atoms of the TMD residue and the corresponding component are within 5 Å. Bar shading reflects this fraction, with fully filled bars indicating 100% and empty bars indicating 0%.”
(10) Line 170: The manuscript describes a "rapid rearrangement" of the transmembrane domain at early simulation times. It would be helpful to clarify whether this regime is considered equilibration and whether it is excluded from subsequent analyses. Plotting time series of the relevant tilting angles and transmembrane rearrangement metrics could help address this point.
We have clarified that the TMD underwent conformational changes early in the equilibration stage to enable R696 to interact with lipid headgroups, ions, or CT residues, and these interactions were largely maintained throughout the production stage. The time series of TMD tilting angles are now shown in Figure 2—figure supplements 5–8. Notably, the TMD exhibits heterogeneous conformational changes, including tilting, bending, and partial loss of helical structure. Therefore, no single metric or limited set of metrics can comprehensively capture the full extent of TMD conformational variability.
In the middle of the first paragraph in the subsection “The energetically unfavorable R696 in the hydrophobic core results in asymmetric, kinked TMD conformations and disrupts membrane integrity” we have modified the following:
“Early in the equilibration stage, the TMD rapidly rearranged to allow R696 residues to interact with more favorable partners, including negatively charged lipid headgroups from either leaflet, ions and water molecules diffusing into the bilayer center, as well as polar and positively charged groups in the CT when present. Once the interactions between R696 residues and their binding partners (lipid headgroup, ions or CT residues) were established, they remained stable with minimal changes throughout the production stage.”
(11) Line 213: As with earlier sections, time series plots of θEC and θTM, similar to those shown in Figure 3-figure supplement 1, would greatly aid interpretation by showing whether these angles drift or fluctuate around stable values.
The time series of θ<sub>EC</sub> and θ<sub>TM</sub> are now shown in Figure 2—figure supplements 5–8. Line 213 refers to the conformational variability of the MPER. For the same reason discussed in our response to comment (10), the MPER exhibits even greater conformational heterogeneity than the TMD, and therefore cannot be adequately described by a single or small set of geometric metrics such as tilt or bending angles.
(12) Lines 216-222: The term "trajectories" may be misleading in this context. It is unclear whether the differences discussed arise from different trajectories of the same system or from different systems altogether. Clarifying this distinction would improve interoperability.
In this paragraph, we describe MPER conformational variations observed across all trajectories from all systems. A preceding sentence has been modified to emphasize that all trajectories from all systems are included. In addition, we have clarified which specific trajectory is referred to when discussing each example.
At the beginning of the first paragraph in the subsection “MPER adopts diverse conformations, and its exposure depends on both MPER and TMD conformations” we have modified the following:
“…, and a wide variety of conformations were sampled across all trajectories from all systems.”
“Such conformation and orientation were maintained in some trajectories such as CL<sup>ΔCT</sup>3 (the third trajectory of the cleaved, CT-truncated system with the low TMD position, Figure 4—figure supplement 2C). In other trajectories, such as CL<sup>CT</sup>1, the helix-turn-helix MPER in one protomer shifted into a horizontal orientation parallel to the membrane surface (Figure 4—figure supplement 6A). In UL<sup>ΔCT</sup>1, the entire MPER adopted a more vertical arrangement, with both MPER-N and MPER-C tilted outward (Figure 4E, Figure 4—figure supplement 4A). We also observed in UH<sup>ΔCT</sup>3 and UL<sup>ΔCT</sup>3 that the HR2 helix in the ectodomain, MPER, and TMD merged into a continuous long helix (Figure 4C, F, Figure 4—figure supplement 3C, 4C). In addition, loss of helical structure within the MPER was common, particularly in the MPER-C region, which often transitioned to a random coil.”
(13) Lines 280 and 287: Similar concerns apply to the use of the term "trajectories." If observations differ primarily between systems rather than between trajectories within a system, revising the wording accordingly would avoid confusion.
We have revised the text to clarify that all trajectories from all systems are considered collectively.
In the middle of the second paragraph in the subsection “Ectodomain epitopes are conditionally accessible, whereas MPER epitopes are virtually inaccessible in the closed prefusion state” we have modified the following:
“When considering all trajectories from all systems collectively, approximately half of them exhibited at least one protomer with >35% accessibility (Supplementary file 1–Supplementary Table 2).”
(14) Figure 5B: Providing a time series of the distance dF673, at least in the Supporting Information, would help assess sampling and equilibration. Such plots would complement the probability distributions and increase confidence in the reported trends.
We have added Figure 5—figure supplement 1 showing the time series of the distance d<sub>F673</sub> to complement the probability distribution in Figure 5B.
In the middle of the second paragraph in the subsection “MPER adopts diverse conformations, and its exposure depends on both MPER and TMD conformations”, we have modified the following:
“In the initial ‘low’ and ‘high’ TMD configurations, dF673 was 6.1 Å and 9.1 Å, respectively, but across simulations it spanned a wide range from -15 Å to 20 Å (Figure 5A, B, Figure 5—figure supplement 1).”
We have added Figure 5—figure supplement 1.
Reviewer #3 (Public review):
Summary:
This study uses large-scale all-atom molecular dynamics simulations to examine the conformational plasticity of the HIV-1 envelope glycoprotein (Env) in a membrane context, with particular emphasis on how the transmembrane domain (TMD), cytoplasmic tail (CT), and membrane environment influence ectodomain orientation and antibody epitope exposure. By comparing Env constructs with and without the CT, explicitly modeling glycosylation, and embedding Env in an asymmetric lipid bilayer, the authors aim to provide an integrated view of how membrane-proximal regions and lipid interactions shape Env antigenicity, including epitopes targeted by MPER-directed antibodies.
Strengths:
A key strength of this work is the scope and realism of the simulation systems. The authors construct a very large, nearly complete Env-scale model that includes a glycosylated Env trimer embedded in an asymmetric bilayer, enabling analysis of membrane-protein interactions that are difficult to capture experimentally. The inclusion of specific glycans at reported sites, and the focus on constructs with and without the CT, are well motivated by existing biological and structural data.
The simulations reveal substantial tilting motions of the ectodomain relative to the membrane, with angles spanning roughly 0-30° (and up to ~50° in some analyses), while the ectodomain itself remains relatively rigid. This framing, that much of Env's conformational variability arises from rigid-body tilting rather than large internal rearrangements, is an important conceptual contribution. The authors also provide interesting observations regarding asymmetric bilayer deformations, including localized thinning and altered lipid headgroup interactions near the TMD and CT, which suggest a reciprocal coupling between Env and the surrounding membrane.
The analysis of antibody-relevant epitopes across the prefusion state, including the V1/V2 and V3 loops, the CD4 binding site, and the MPER, is another strength. The study makes effective use of existing experimental knowledge in this context, for example, by focusing on specific glycans known to occlude antibody binding, to motivate and interpret the simulations.
Weaknesses:
While the simulations are technically impressive, the manuscript would benefit from more explicit cross-validation against prior experimental and computational work throughout the Results and Discussion, and better framing in the introduction. Many of the reported behaviors, such as ectodomain tilting, TMD kinking, lipid interactions at helix boundaries, and aspects of membrane deformation, have been described previously in a range of MD studies of HIV Env and related constructs (e.g., PMC2730987, PMC2980712, PMC4254001, PMC4040535, PMC6035291, PMC12665260, PMID: 33882664, PMC11975376). Clearly situating the present results relative to these studies would strengthen the paper by clarifying where the simulations reproduce established behavior and where they extend it to more complete or realistic systems.
A related limitation is that the work remains largely descriptive with respect to conformational coupling. Numerous experimental studies have demonstrated functional and conformational coupling between the TMD, CT, and the antigenic surface, with effects on Env stability, infectivity, and antibody binding (e.g., PMC4701381, PMC4304640, PMC5085267). In this context, the statement that ectodomain and TMD tilting motions are independent is a strong conclusion that is not fully supported by the analyses presented, particularly given the authors' acknowledgment that multiple independent simulations are required to adequately sample conformational space. More direct analyses of coupling, rather than correlations inferred from individual trajectories, would help align the simulations with the existing experimental literature. Given the scale of these simulations, a more thorough analysis of coupling could be this paper's most seminal contribution to the field.
The choice of membrane composition also warrants deeper discussion. The manuscript states that it relies on a plasma membrane model derived from a prior simulation-based study, which itself is based on host plasma membrane (PMID: 35167752), but experimental analyses have shown that HIV virions differ substantially from host plasma membranes (e.g., PMC46679, PMC1413831, PMC10663554, PMC5039752, PMC6881329). In particular, virions are depleted in PC, PE, and PI, and enriched in phosphatidylserine, sphingomyelins, and cholesterol. These differences are likely to influence bilayer thickness, rigidity, and lipid-protein interactions and, therefore, may affect the generality of the conclusions regarding Env dynamics and antigenicity. Notably, the citation provided for membrane composition is a laboratory self-citation, a secondary source, rather than a primary experimental study on plasma membrane composition.
Finally, there are pervasive issues with citation and methodological clarity. Several structural models are referred to only by PDB ID without citation, and in at least one case, a structure described as cryo-EM is in fact an NMR-derived model. Statements regarding residue flexibility, missing regions in structures, and comparisons to prior dynamics studies are often presented without appropriate references. The Methods section also lacks sufficient detail for a system of this size and complexity, limiting readers' ability to assess robustness or reproducibility.
With stronger integration of prior experimental and computational literature, this work has the potential to serve as a valuable reference for how Env behaves in a realistic, glycosylated, membrane-embedded context. The simulation framework itself is well-suited for future studies incorporating mutations, strain variation, antibodies, inhibitors, or receptor and co-receptor engagement. In its current form, the primary contribution of the study is to consolidate and extend existing observations within a single, large-scale model, providing a useful platform for future mechanistic investigations.
Following the Reviewer’s comments and suggestions, we have revised the manuscript accordingly.
While the simulations are technically impressive, the manuscript would benefit from more explicit cross-validation against prior experimental and computational work throughout the Results and Discussion, and better framing in the introduction. Many of the reported behaviors, such as ectodomain tilting, TMD kinking, lipid interactions at helix boundaries, and aspects of membrane deformation, have been described previously in a range of MD studies of HIV Env and related constructs (e.g., PMC2730987, PMC2980712, PMC4254001, PMC4040535, PMC6035291, PMC12665260, PMID: 33882664, PMC11975376). Clearly situating the present results relative to these studies would strengthen the paper by clarifying where the simulations reproduce established behavior and where they extend it to more complete or realistic systems.
We have added a summary of the prior computational studies in the Introduction section.
At the beginning of the third paragraph in the Introduction section we added:
“Molecular dynamics (MD) simulations have been employed to investigate the stability and conformational properties of monomeric and trimeric helical TMD in both aqueous and lipid bilayer environments since late 2000s (Kim et al., 2009; Gangupomu et al., 2010; Baker et al., 2014; Baker et al., 2014; Hollingsworth et al., 2018). Early studies were constrained by limited computational resources and therefore the simulation times are relatively short. Subsequent work employed metadynamics to probe rare events (Gangupomu et al., 2010; Baker et al., 2014), and simulations performed on Anton supercomputers extended sampling to multi-microsecond time scale (Baker et al., 2014). Piai and coworkers determined the NMR structure of a construct comprising the MPER, TMD, and CT, and carried out MD simulations to access the structural stability of the trimeric MPER–TMD–CT complex (Piai et al., 2021). Majumder et al. subsequently simulated the same MPER–TMD–CT complex and applied a machine learning-based approach to classify its conformational ensemble (Majumder et al., 2025). Maillie et al. combined conventional MD, steered MD, and coarse-grained simulations to examine interactions between MPER-targeting antibodies and membrane lipids (Maillie et al., 2025). In addition, MD simulations have been extensively applied to the well-studied ectodomain. Despite these advances, it remains challenging to investigate the gp120–gp41 trimer as an intact entity considering its structural complexity.”
We have also added a discussion of previous MD simulation studies to the Result section regarding interactions of the TMD residue R696 with ions and lipid headgroups.
At the end of the first paragraph in the subsection “The energetically unfavorable R696 in the hydrophobic core results in asymmetric, kinked TMD conformations and disrupts membrane integrity”
“Previously, Kim et al. reported that the inter-chain interactions between protonated R696 gradually diminished over a short simulation time (23 ns), leading to increased crossing angles and reduced bundle length (Kim et al., 2009). Gangupomu et. al and Baker et. al observed that R696 snorkeled toward either exoplasmic or endoplasmic headgroups in simulations of the TMD monomer, resulting in TMD tilting and membrane thinning due to water penetration and lipid headgroups interacting with R696 (Gangupomu et al., 2010; Baker et al., 2014; Baker et al., 2014). These observations are consistent with our finding. Hollingsworth et. al also reported membrane thinning; however, they attributed this effect to interfacial interactions of R683 and R707 with both leaflets and proposed that R696 only interacted with water and ions permeating into the center of the TMD timer (Hollingsworth et al., 2018).”
A related limitation is that the work remains largely descriptive with respect to conformational coupling. Numerous experimental studies have demonstrated functional and conformational coupling between the TMD, CT, and the antigenic surface, with effects on Env stability, infectivity, and antibody binding (e.g., PMC4701381, PMC4304640, PMC5085267). In this context, the statement that ectodomain and TMD tilting motions are independent is a strong conclusion that is not fully supported by the analyses presented, particularly given the authors' acknowledgment that multiple independent simulations are required to adequately sample conformational space. More direct analyses of coupling, rather than correlations inferred from individual trajectories, would help align the simulations with the existing experimental literature. Given the scale of these simulations, a more thorough analysis of coupling could be this paper's most seminal contribution to the field.
We have added a discussion of the coupling between TMD, CT and Env antigenicity, and the independent motion of ectodomain and TMD in our simulation.
In the middle of the second paragraph in the Discussion section
“Our analysis of the ectodomain and TMD coupling indicates that the motions of these two domains are largely independent. This observation does not contradict experimental studies demonstrating functional coupling between the TMD, CT, and the antigenic profiles of Env (Chen et al., 2015; Dev et al., 2016). Munro et al. proposed that unliganded Env is intrinsically dynamic, transitioning among three distinct prefusion conformations: a closed ground state (predominant), a transient state, and a CD4-/co-receptor-stabilized state. Both laboratory-adapted and clinically isolated strains can spontaneously transition among these three states, although their relative occupancies differ (Munro et al., 2014). It is therefore possible that TMD mutations or CT truncation also alter the equilibrium distribution among three states, thereby affecting the epitope exposure, particularly for epitopes that are occluded in the closed ground state while exposed in the CD4-/co-receptor-stabilized state. However, transition among three states occur on millisecond-to-second timescales. Our simulations on microsecond timescales primarily capture conformational variations within the closed ground state and suggest that the MPER acts as a hinge, providing substantial flexibility that enables the ectodomain and TMD to move independently while Env remains in the closed ground state.”
We have also calculated the dynamical cross-correlation maps showing very weak correlations between the ectodomain and the TMD.
At the end of the first paragraph in the subsection “The ectodomain maintains a rigid internal structure and tilts independently of the TMD”
“We also calculated the dynamical cross-correlation maps (Ichiye et al., 1991) of Cα atoms for all systems using CPPTRAJ (Roe et al., 2013). The results indicate only very weak correlations between the ectodomain and the TMD (Figure 2—figure supplements 10–13).”
We have added Figure 2—figure supplements 10–13.
The choice of membrane composition also warrants deeper discussion. The manuscript states that it relies on a plasma membrane model derived from a prior simulation-based study, which itself is based on host plasma membrane (PMID: 35167752), but experimental analyses have shown that HIV virions differ substantially from host plasma membranes (e.g., PMC46679, PMC1413831, PMC10663554, PMC5039752, PMC6881329). In particular, virions are depleted in PC, PE, and PI, and enriched in phosphatidylserine, sphingomyelins, and cholesterol. These differences are likely to influence bilayer thickness, rigidity, and lipid-protein interactions and, therefore, may affect the generality of the conclusions regarding Env dynamics and antigenicity. Notably, the citation provided for membrane composition is a laboratory self-citation, a secondary source, rather than a primary experimental study on plasma membrane composition.
We have added references to primary experimental studies on plasma membrane composition (van Meer et al., 2008; Sampaio et al., 2011), as well as the prior simulation study proposing the lipid and cholesterol distributions (Ingolfsson et al., 2014).
At the beginning of the Membrane subsection in the Materials and methods section
We have modified the following:
The full-length and CT-truncated gp120–gp41 models were embedded into an asymmetric lipid bilayer with the lipid composition corresponding to a mammalian plasma membrane (van Meer et al., 2008; Sampaio et al., 2011; Ingolfsson et al., 2014; Pogozheva et al., 2022),
We have also clarified the limitations associated with the choice of lipid composition and emphasized the need to investigate its influence in future studies.
At the end of the second paragraph in the Discussion section we added:
“In addition to the limitations inherent to protein structure modeling, the choice of lipid composition remains an open question. In this work, we selected an asymmetric mammalian plasma membrane because it is one of the 18 complex biomembrane systems we previously studied (Pogozheva et al., 2022), and among them, it provides the closest available approximation to the HIV membrane. Nevertheless, experimental studies have reported differences in lipid composition between HIV virions and the host plasma membrane (Aloia et al., 1993; Brugger et al., 2006; Huarte et al., 2016; Mucksch et al., 2019; Tomishige et al., 2023). Although we do not anticipate that our main conclusions regarding Env domain motions and MPER flexibility would change substantially, evaluating the influence of lipid composition represents an important direction for future work.”
Finally, there are pervasive issues with citation and methodological clarity. Several structural models are referred to only by PDB ID without citation, and in at least one case, a structure described as cryo-EM is in fact an NMR-derived model. Statements regarding residue flexibility, missing regions in structures, and comparisons to prior dynamics studies are often presented without appropriate references. The Methods section also lacks sufficient detail for a system of this size and complexity, limiting readers' ability to assess robustness or reproducibility.
We have corrected the error in which PDB structure 7LOI was described as a cryo-EM structure; it is in fact an NMR structure. We have also verified that all PDB structures are properly cited at their first occurrence in the manuscript.
We have clarified that the modeling of palmitoylation sites, glycans and lipid bilayers are done in an automated fashion by different modules in CHARMM-GUI, and added Supplementary file 1–Supplementary Table 8 showing the simulation settings for equilibration and production stages.
At the end of the subsection “Modeling of full-length gp120–gp41 trimer” we have modified the following:
“Two mutations (S764C and S837C) were introduced in the CT to restore the palmitoylation sites, and lipid tails oriented towards the hydrophobic core of the bilayer were then attached to the palmitoylation sites using the PDB Manipulation module in CHARMM-GUI (Jo et al., 2008; Jo et al., 2014; Park et al., 2023) (Figure 1D).”
At the end of the subsection “Glycosylation” we added:
“The select glycan sequences were represented in the Glycan Reader Sequence format (Jo et al., 2011; Park et al., 2017) and added to the corresponding glycosylation sites using the Glycan Reader & Modeler graphical interface.”
In the middle of the subsection “Membrane” we added:
“Membrane systems were constructed using CHARMM-GUI Membrane Builder, which provides a user-friendly graphical interface for selecting lipid types and defining their numbers in each leaflet (Jo et al., 2007; Jo et al., 2009; Wu et al., 2014; Lee et al., 2016; Lee et al., 2019).”
In the middle of the subsection “Simulation details” we added:
We have modified the following:
“Positional and dihedral restraints were applied to proteins, glycans, and lipids, with force constants progressively reduced over successive intervals (Supplementary file 1–Supplementary Table 8).”
We added Supplementary file 1–Supplementary Table 8.
Reviewer #3 (Recommendations for the authors):
Major concerns:
(1) Strengthen analysis of conformational coupling: Consider analyses that more directly assess coupling between the TMD/CT and ectodomain, such as residue-residue correlation networks, comparisons to smFRET-defined conformational states, or data-driven (e.g., machine learning-based) trajectory analyses. Machine-learning analysis would be particularly helpful in understanding otherwise elusive allosteric networks that could govern large-scale behavior. Discuss how, due to the apparent local minima that occur after ~0.5 us, enhanced sampling methods might be employed to better cover the Env conformational landscape.
We have calculated the dynamical cross-correlation maps showing very weak correlations between the ectodomain and the TMD.
At the end of the first paragraph in the subsection “The ectodomain maintains a rigid internal structure and tilts independently of the TMD”
“We also calculated the dynamical cross-correlation maps (Ichiye et al., 1991) of Cα atoms for all systems using CPPTRAJ (Roe et al., 2013). The results indicate only very weak correlations between the ectodomain and the TMD (Figure 2—figure supplements 10–13).”
We added Figure 2—figure supplements 10–13.
We have also noted in the Discussion section that enhanced sampling methods could be employed to better explore the conformational landscape of Env trimer, including fluctuations within the closed state as well as transitions among the closed ground, transient and CD4/co-receptor-stabilized states proposed in the previous experimental study (Munro et al., 2014).
In the middle of the second paragraph in the Discussion section we added:
“Enhanced sampling methods could be applied to more thoroughly explore the conformational landscape, including not only variations within the closed ground state but also transitions among the closed ground, transient and CD4-/co-receptor-stabilized states.”
(2) Qualify strong independence claims: Rephrase or further support statements asserting independence of ectodomain and TMD motions, particularly in light of known experimental evidence for coupling (PMC4701381, PMC4304640, PMC5085267).
In addition to adding the dynamical cross-correlation maps showing very weak correlations between the ectodomain and the TMD, we have added a discussion of the coupling between TMD, CT, and Env antigenicity, and the independent motion of ectodomain and TMD in our simulation.
In the middle of the second paragraph in the Discussion section we added:
“Our analysis of the ectodomain and TMD coupling indicates that the motions of these two domains are largely independent. This observation does not contradict experimental studies demonstrating functional coupling between the TMD, CT, and the antigenic profiles of Env (Chen et al., 2015; Dev et al., 2016). Munro et al. proposed that unliganded Env is intrinsically dynamic, transitioning among three distinct prefusion conformations: a closed ground state (predominant), a transient state, and a CD4-/co-receptor-stabilized state. Both laboratory-adapted and clinically isolated strains can spontaneously transition among these three states, although their relative occupancies differ (Munro et al., 2014). It is therefore possible that TMD mutations or CT truncation also alter the equilibrium distribution among three states, thereby affecting the epitope exposure, particularly for epitopes that are occluded in the closed ground state while exposed in the CD4-/co-receptor-stabilized state. However, transition among three states occur on millisecond-to-second timescales. Our simulations on microsecond timescales primarily capture conformational variations within the closed ground state and suggest that the MPER acts as a hinge, providing substantial flexibility that enables the ectodomain and TMD to move independently while Env remains in the closed ground state.”
(3) Clarify membrane composition assumptions: Provide a clearer rationale for the chosen lipid composition, and explicitly discuss how differences between host plasma membranes and HIV virions (e.g., PS, sphingomyelin, and cholesterol enrichment) may affect the conclusions.
We have clarified the limitations associated with the choice of lipid composition and emphasized the need to investigate its influence in future studies.
At the end of the second paragraph in the Discussion section we added:
“In addition to the limitations inherent to protein structure modeling, the choice of lipid composition remains an open question. In this work, we selected an asymmetric mammalian plasma membrane because it is one of the 18 complex biomembrane systems we previously studied (Pogozheva et al., 2022), and among them, it provides the closest available approximation to the HIV membrane. Nevertheless, experimental studies have reported differences in lipid composition between HIV virions and the host plasma membrane (Aloia et al., 1993; Brugger et al., 2006; Huarte et al., 2016; Mucksch et al., 2019; Tomishige et al., 2023). Although we do not anticipate that our main conclusions regarding Env domain motions and MPER flexibility would change substantially, evaluating the influence of lipid composition represents an important direction for future work.”
(4) Address citation and reference issues: Replace PDB-only references with proper citations, correct mischaracterizations of structure determination methods, and ensure all supplementary citations are fully referenced.
We have corrected the error in which PDB structure 7LOI was described as a cryo-EM structure; it is in fact an NMR structure. We have also verified that all PDB structures are properly cited at their first occurrence in the manuscript.
(5) Expand the Methods section: Provide additional detail on system construction, glycan modeling, lipid asymmetry, equilibration, sampling, and limitations, including a discussion of potential benefits of enhanced-sampling approaches.
We have clarified that the modeling of palmitoylation sites, glycans and lipid bilayers are done in an automated fashion by different modules in CHARMM-GUI, and added Supplementary file 1–Supplementary Table 8 showing the simulation settings for equilibration and production stages.
At the end of the subsection “Modeling of full-length gp120–gp41 trimer” we have modified the following:
“Two mutations (S764C and S837C) were introduced in the CT to restore the palmitoylation sites, and lipid tails oriented towards the hydrophobic core of the bilayer were then attached to the palmitoylation sites using the PDB Manipulation module in CHARMM-GUI (Jo et al., 2008; Jo et al., 2014; Park et al., 2023) (Figure 1D).”
At the end of the subsection “Glycosylation” we added:
“The select glycan sequences were represented in the Glycan Reader Sequence format (Jo et al., 2011; Park et al., 2017) and added to the corresponding glycosylation sites using the Glycan Reader & Modeler graphical interface.”
In the middle of the subsection “Membrane” we added:
“Membrane systems were constructed using CHARMM-GUI Membrane Builder, which provides a user-friendly graphical interface for selecting lipid types and defining their numbers in each leaflet (Jo et al., 2007; Jo et al., 2009; Wu et al., 2014; Lee et al., 2016; Lee et al., 2019).”
In the middle of the subsection “Simulation details” we have modified the following:
“Positional and dihedral restraints were applied to proteins, glycans, and lipids, with force constants progressively reduced over successive intervals (Supplementary file 1–Supplementary Table 8).”
We added Supplementary file 1–Supplementary Table 8.
The discussion of potential benefits of enhanced-sampling approaches is included in our response to major concern (1).
(6) Data availability: In addition to code, deposit all MD trajectories for re-analysis. The scale of this simulation was likely costly (GPU time), and so data availability is imperative.
We have deposit MD simulation trajectories to Zenodo.
At the end of the section “Data availability” we added:
“The simulation trajectories can be found at https://doi.org/10.5281/zenodo.18853902, https://doi.org/10.5281/zenodo.18854615, and https://doi.org/10.5281/zenodo.18854639.”
Minor:
(1) Stylistic: Suggested to revise Figure 1 to provide a clearer overview of all constructs with consistent nomenclature (e.g., "full-length" versus "ΔCT") and explicit domain boundaries. With a better overview figure, the current figures could comprise the Figure 1 associated with Figures 1 and 2.
We have combined Figure 1 and Figure 1—figure supplement 1 to show both full-length and CT-truncated models in one figure.
We have modified Figure 1.
We have removed Figure 1—figure supplements 1.
(2) Explicitly cross-validate against prior studies: Integrate comparisons to existing MD simulations and experimental studies (e.g., PMC2730987, PMC2980712, PMC4254001, PMC4040535, PMC6035291, PMC4701381, PMC5085267) directly into the Results and Discussion.
We have added discussion of previous MD simulation studies to the Result section regarding interactions of the TMD residue R696 with ions and lipid headgroups.
At the end of the first paragraph in the subsection “The energetically unfavorable R696 in the hydrophobic core results in asymmetric, kinked TMD conformations and disrupts membrane integrity” we have modified the following:
“Previously, Kim et al. reported that the inter-chain interactions between protonated R696 gradually diminished over a short simulation time (23 ns), leading to increased crossing angles and reduced bundle length (Kim et al., 2009). Gangupomu et. al and Baker et. al observed that R696 snorkeled toward either exoplasmic or endoplasmic headgroups in simulations of the TMD monomer, resulting in TMD tilting and membrane thinning due to water penetration and lipid headgroups interacting with R696 (Gangupomu et al., 2010; Baker et al., 2014; Baker et al., 2014). These observations are consistent with our finding. Hollingsworth et. al also reported membrane thinning; however, they attributed this effect to interfacial interactions of R683 and R707 with both leaflets and proposed that R696 only interacted with water and ions permeating into the center of the TMD timer (Hollingsworth et al., 2018).”
The discussion of PMC4701381 and PMC5085267 is included in our response to major concern (2).
(3) "In the cryo-EM structure (PDB ID: 7LOI)": This is an NMR model and lacks citation.
We have corrected this error and added the citation at the first occurrence of PDB ID: 7LOI in the Result section.
In the middle of the first paragraph in the subsection “The energetically unfavorable R696 in the hydrophobic core results in asymmetric, kinked TMD conformations and disrupts membrane integrity” we have modified the following:
“In the NMR structure (PDB ID: 7LOI) (Piai et al., 2021),”
(4) "Higher RMSF values were observed in the residues missing from the cryo-EM structure": This is lacking citation, as there are multiple cryo-EM structures and several dynamics studies using NMR.
The missing residues here specifically refer to those absent in the cryo-EM structure (PDB ID: 6B0N) used for model building, rather than all cryo-EM structures in the PDB. We have revised the text to clarify this distinction.
In the middle of the second paragraph in the subsection “The ectodomain maintains a rigid internal structure and tilts independently of the TMD” we have modified th following:
“Higher RMSF values were observed in the residues missing from the cryo-EM structure (PDB ID: 6B0N) (Sarkar et al., 2018), which was used for the ectodomain in model building (these missing residues are highlighted in red in Figure 1A, B),”
