Design of a First-in-Class homoPROTAC to Induce ICP0 Degradation in Human Herpes Simplex Virus 1

Response to Reviewer 1 Comments

1. Summary

We sincerely thank the reviewer for their thorough and highly constructive feedback, which has led to a significant improvement of the manuscript. In response to your valuable suggestions, we have performed a comprehensive revision of the text to improve clarity, tone, and conciseness. We have restructured the introduction and added a new summary figure (Figure 1) to better explain our novel concept. Furthermore, we have enriched the manuscript with key quantitative data for our structural model, added specific details on the linker design, expanded the discussion on the linker's importance, and made the abstract more informative. All suggested grammatical and formatting corrections have also been implemented. We believe these extensive revisions have significantly strengthened the paper.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Must be improved

We thank the reviewer for this critical feedback. We have substantially revised the introduction to provide a more comprehensive background and better contextualize our novel approach, as detailed in our responses to Comments 1 and 5.

Is the research design appropriate?

Yes

We thank the reviewer for their positive assessment of our research design.

Are the methods adequately described?

Can be improved

We agree that certain aspects of the methodology could be clarified. We have added specific details regarding our AI model selection and the linker design rationale, as detailed in our response to Comment 8.

Are the results clearly presented?

Can be improved

We thank the reviewer for this feedback. The Results section has been revised for greater clarity by adding quantitative data for our model's confidence and refining key terminology, as detailed in our responses to Comments 6 and 7.

Are the conclusions supported by the results?

Must be improved

We thank the reviewer for this important critique. We agree the original conclusions were too assertive and have revised the text to be more conditional, better reflecting the computational nature of our findings, as detailed in our response to Comment 12.

Are all figures and tables clear and well-presented?

Yes

We thank the reviewer for their positive assessment of our figures and tables.

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Throughout the manuscript, the writing style is sometimes far too pompous, indicating a possible use of AI, and can sometimes detract from the scientific clarity of the message in favor of unnecessary verbiage. The authors have therefore been asked to revise all the sections concerned.

Response 1: We thank the reviewer for their valuable feedback on the manuscript's writing style. We acknowledge the comment regarding the need for greater clarity and have revised the entire manuscript to be more direct and concise. The language has been simplified throughout to ensure the scientific message is clear and straightforward.

Comments 2: Abstract: the abstract of this work is rather uninformative and would benefit from tangible, quantified information, rather than very general data. Specifically, the Methods section is rather unclear (lines 27-30) and would benefit from figures in “Objectives”. In the second part of “Methods”, more information on the conduct of the study is expected. Similarly, in the Results section, objective data on the scaffold identified and the putative properties of the selected molecule are expected.

Response 2: We sincerely thank the reviewer for this constructive feedback. We agree that the original abstract lacked specific details and have revised it extensively to be more informative and quantitative. The Methods section of the abstract has been updated to clearly name the specific AI platforms and computational tools used in our workflow. The Results section now provides objective data by identifying the lead scaffold and naming the final candidate degrader, ICP0-deg-01. We believe these changes make the abstract a much stronger and more comprehensive summary of our work.

Now it reads like below (lines 26-36):
“Methods: A structural model of ICP0, generated via the Chai-1 AI platform, was analyzed with fpocket, P2Rank, and KVFinder to identify a superior allosteric target site. An iterative de novo design workflow with CReM-dock then yielded a lead scaffold based on its predicted affinity and drug-like properties. This selected "warhead" was used to rationally design the final bivalent degrader, ICP0-deg-01, for the ICP0 dimer model. Results: The generative process yielded a lead chemical scaffold that was selected based on its predicted binding affinity and favorable drug-like properties. This scaffold was used to rationally design a single candidate bivalent degrader, ICP0-deg-01. Our structural model predicts that ICP0-deg-01 can successfully bridge two ICP0 protomers, forming an energetically favorable ternary complex.”

Comments 3: line 54: please write “the virus ability”.

Response 3: We thank the reviewer for pointing out this grammatical error. This has been corrected to "the virus's ability". The revised sentence now reads as follows:

“At the core of this challenge is the virus ability to establish a state of latency, hiding as a silent episome within the nuclei of sensory neurons, completely shielded from both the host immune system and current antiviral therapies [6,7].”

Comments 4: lines 70-71, 73-75, 292-293: please write “targeted protein degradation”, proteolysis-targeted chimera”, “ubiquitin-proteasome system” and so on without capital letters, as these words are neither proper nouns, nor the first word of a sentence.

Response 4: We thank the reviewer for this helpful style suggestion. All instances of these terms have been corrected to lowercase throughout the manuscript, unless they appear at the beginning of a sentence. The corrected text from the introduction (lines 67-74) is shown below:

“In recent years, the field of drug discovery has been transformed by the rise of targeted protein degradation (TPD) [17]. This innovative modality seeks not just to inhibit a target protein, but to eliminate it from the cell entirely [17,18]. The flagship technology of TPD is the proteolysis-targeting chimera (PROTAC), a bifunctional molecule designed to hijack the cell's own quality control machinery, the ubiquitin-proteasome system (UPS) [17,19]. By simultaneously binding a protein of interest (POI) and a cellular E3 ubiquitin ligase—one of over 600 enzymes responsible for substrate recognition within the UPS—a PROTAC induces the formation of a temporary ternary complex [19,20].”

Comment 5: lines 83-107: the last two paragraphs of the introduction lack clarity, whereas their purpose should be to convey a clear message about the objective of the work. A more straightforward rewording would be appreciated, ideally supported by a summary figure.

In particular, it is difficult to understand why POI and ubiquitin ligase are, a priori, the same protein in the approach adopted.

Response 5: We sincerely thank the reviewer for highlighting this lack of clarity and for the excellent suggestion to add a summary figure. This was a critical insight that has significantly improved the manuscript.

We have added new paragraph to the end of the introduction to more clearly explain our novel "viral self-destruction" strategy and to explicitly address why the Protein of Interest (POI) and the E3 ligase are the same molecule in our approach. We clarify that unlike conventional PROTACs that recruit a host E3 ligase to degrade a target, our homoPROTAC is designed to force the viral E3 ligase, ICP0, to tag another ICP0 molecule for degradation.

Now it reads as below (91-100):
“The viral protein ICP0 acts as a central hub that disrupts host cell defenses by targeting key proteins across multiple immunity pathways. To counteract intrinsic immunity, ICP0 targets components of the DNA damage response, including RNF168, RNF8, and DNA-PKcs. It also dismantles innate immunity by interfering with the interferon response and NF-κB signaling. Specifically, it targets IκBα, as well as the TLR signaling adaptors MyD88 and Mal, which ultimately suppresses inflammatory mechanisms. A primary function of ICP0 is the disruption of ND10 nuclear bodies by targeting their core components PML, Sp100, and IFI16. This action overcomes viral genome silencing and promotes viral transcription. Additionally, ICP0 interacts with USP7, which leads to its own destabilization and further suppresses TLR and NF-κB signaling [29]. (Figure A1).”

As suggested, we have also added a new summary diagram (now Figure 1) to the introduction . This figure visually illustrates the entire proposed catalytic cycle, from the induced dimerization of two ICP0 molecules by our homoPROTAC to the final degradation of the viral protein. We believe these revisions now provide the clear, straightforward explanation the reviewer requested.

Comment 6: line 120: can this local confidence be encrypted, at least with a range of values?

Response 6: We thank the reviewer for this excellent suggestion. You are correct that this claim should be supported by quantitative data. We have revised the manuscript to include the specific local confidence scores for the region of interest (residues 110-200). The text now reads (lines 142-144):

" Specifically, this region yielded a mean predicted Local Distance Difference Test (pLDDT) score of 66.6, substantially higher than the 46.9 average for the full-length protein (Figure A2)."

Furthermore, to provide a more detailed view, we have added a new figure (Figure A2) in the Appendix. This figure plots the per-residue confidence score (pLDDT) across the entire protein and highlights the high-confidence region used in our study.

Comment 7: line 148 and in the rest of the manuscript: the term “fragment” can be misleading as it suggests a fragment-based drug design approach. It is expected that several “fragments” will be identified and then linked to form a candidate POI ligand interacting with multiple sites. However, this is not the case here. Please replace the term “fragments” by, for example, “small molecules” for greater clarity.

Response 7: We thank the reviewer for raising this very important point of clarification. We completely agree that the term "fragment" was misleading, as our approach does not follow a traditional fragment-based drug design workflow. Using the term "small molecules" is indeed much more accurate and appropriate for our study. Following this excellent suggestion, we have replaced all instances of "fragment" and "fragment library" with "small molecule" and "small molecule library," respectively, throughout the entire manuscript also in Figure 3B. This change has been implemented in the Results section, the Materials and Methods, and all relevant figure captions to ensure clarity.

Comment 8: line 181: on Hit 7, which anchoring point was chosen/determined for the linker, and how?

Response 8: We thank the reviewer for this excellent question and for pointing out that this important detail was missing from our manuscript. We have updated the "Materials and Methods" section to include the specific details of the linker attachment and the rationale for its selection. The text now explains that the linker was attached at the C11 carbon of the Hit 7 scaffold. Now it reads as below (359-367):

“To construct the final bivalent degrader, the 16-carbon alkyl linker was computationally attached at the C11 carbon of the Hit 7 warhead scaffold. As shown in the 2D interaction diagram, this site was determined to be the optimal anchor point. This linker attachment site allows the linker to extend towards the second protomer without interfering with the critical hydrogen bond between the warhead and the side chain of Glu112, thus pre-serving the key interaction that anchors the molecule in the binding pocket. This pro-cedure yielded a single, rationally designed bivalent degrader, and the resulting model of its ternary complex (ICP0–Degrader–ICP0) was advanced for further evaluation.”

The choice of this specific site was based on a careful analysis of the docked pose. Our model shows that the warhead is anchored by two main forces: a critical hydrogen bond with the Glu112 residue and extensive hydrophobic interactions with residues such as Ile140, Val165, and Val167. The goal was to find an attachment point that would not disrupt the key molecular interactions that anchor the warhead in its binding pocket. The C11 position was determined to be optimal because it points away from the protein interface, allowing the linker to extend towards the second protomer without interfering with either the hydrogen bond or these essential hydrophobic contacts. To further clarify this, we have also updated the 2D interaction diagram (Figure 4a) to visually indicate the linker's attachment site and the key molecular interactions.

Comment 9: lines 237-239: although this aspect has not been investigated in the present work, the discussion of linker selection and optimization, and the central importance of this stage, should be developed a little further here, with supporting bibliography.

Response 9: We thank the reviewer for this excellent suggestion. We agree that the linker's critical role deserved a more thorough discussion. Following your advice, we've expanded the Discussion section to detail the importance of the linker, explaining how its properties affect not only degradation efficiency but also the molecule's overall drug-like characteristics . A supporting reference on modern linker strategies has also been added as requested . Now it reads as follows (lines 252-264):
“The model predicts that the Hit 7 warheads are anchored by a network of specific hy-drogen bonds and hydrophobic interactions, while the 16-carbon linker is of sufficient length to bridge the two protomers in an energetically favorable conformation.

The use of a single, non-optimized alkyl linker represents a significant simplification. The linker is now understood to be a critical component that governs a degrader's success, not merely a passive spacer. Its specific length, chemical makeup, and rigidity directly dictate the geometry and stability of the induced ternary complex, which in turn de-termines the efficiency of protein degradation [32]. Furthermore, the linker's properties have a profound impact on the molecule's overall drug-like characteristics, including its solubility, cell permeability, and metabolic stability. A comprehensive follow-up study would therefore require the systematic exploration of a linker library to optimize these parameters for the highest possible therapeutic effect.”

Comment 10: line 256: please write “Zn²⁺” with the number of charges in superscript.

Response 10: We thank the reviewer for spotting this formatting error. This has been corrected to "Zn²⁺" throughout the manuscript.

Comment 11: line 279: please write “sp³” with the 3 in superscript.

Response 11: We thank the reviewer for catching this formatting detail. This has been corrected to "sp³" throughout the manuscript.

Comment 12: line 353: the conclusion should be more conditional on the future of the drug candidates presented. For example, it would be more appropriate to speak of a “potential new class” of antivirals.

Response 12: We thank the reviewer for this excellent advice on the manuscript's tone. We completely agree that the original conclusion was overly assertive. We have revised it to be more conditional and prospective, emphasizing that this work provides a computational foundation for future experimental validation. The revised conclusion now reads as follows (line 387-398):
“This in silico investigation has described a scalable computational strategy for the rational design of the first-in-class homoPROTAC targeting the essential HSV-1 E3 ligase ICP0. Our workflow, which combines AI-driven structure prediction with an iterative generative design process, successfully identified a superior druggable pocket on ICP0 and yielded a lead candidate, ICP0-deg-01, with structural features predicted to cause the viral ICP0 to self-destruct. This work establishes computational proof-of-concept for a potential new class of antiviral therapeutics designed to address the dual challenge of drug resistance and viral latency. In conclusion, this study represents an example of the convergence of molecular virology and modern computational chemistry. It proposes and provides a model for a paradigm of “viral self-destruction” that could potentially be transferable to other persistent pathogens and suggests a promising new direction for rational antiviral design.”

Response to Reviewer 2 Comments

1. Summary

We are grateful to the reviewer for their expert feedback and important suggestions for future validation. We agree that molecular dynamics simulations and binding free energy calculations are critical next steps for this project, and we have noted these as a high priority for our follow-up studies. For the current revision, we have clarified our rationale for selecting the Chai-1 AI model in the Methods section and have re-rendered the figures at a higher resolution to improve their quality, as requested.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

We thank the reviewer for their positive assessment of the introduction.

Is the research design appropriate?

Can be improved

We appreciate the reviewer's feedback and are always open to suggestions for refining our research design in future studies.

Are the methods adequately described?

Can be improved

We thank the reviewer for their suggestion. We have revised the Methods section to include more specific details regarding the rationale for our AI model selection and linker design to improve clarity.

Are the results clearly presented?

Yes

We are pleased that the reviewer found the results to be clearly presented.

Are the conclusions supported by the results?

Can be improved

We thank the reviewer for this feedback. We have revised the Conclusion to ensure the tone is appropriately conditional and that our claims are well-supported by the computational results presented.

Are all figures and tables clear and well-presented?

Yes

We thank the reviewer for their positive assessment.

3. Point-by-point response to Comments and Suggestions for Authors

We thank the reviewer for their positive feedback on the clarity of our figures and tables.

Comments 1: Incorporate Molecular Dynamics (MD) simulations to assess protein flexibility, solvent effects, and the dynamic stability of the proposed complexes over time.

Response 1: We thank the reviewer for this excellent and important suggestion. We completely agree that Molecular Dynamics (MD) simulations are a critical next step to assess the dynamic stability of the ternary complex and validate the interactions predicted by our docking studies.

While these computationally intensive simulations are beyond the scope of the current manuscript, which aims to provide the initial computational proof-of-concept, they are a high priority for our follow-up work. We will certainly incorporate MD simulations in future studies to build upon the findings presented here.

Comments 2: Include Binding Free Energy Calculations, such as MM/PBSA or MM/GBSA, to provide a more quantitative estimation of the binding affinity for both the binary and ternary complexes.

Response 2: We thank the reviewer for this very helpful suggestion. We agree that Binding Free Energy Calculations, such as MM/PBSA or MM/GBSA, are an excellent method for obtaining a more quantitative and accurate estimation of binding affinity for our proposed complexes. These calculations are a planned next step in our research pipeline and will be performed on the stable trajectories obtained from the future Molecular Dynamics simulations we intend to run. As they represent a significant computational effort beyond the scope of this initial proof-of-concept study, we will include them in our follow-up work.

Comments 3: Clarity on AI Model Selection: In Section 2.1, the authors state that they selected the Chai-1 model. While Figure 1a effectively shows the structural consensus in the domain of interest, the text could be enhanced by more explicitly stating why the Chai-1 model was considered superior to the other models.

Response 3: We thank the reviewer for pointing out that our rationale for model selection was not explicit enough. We have revised the manuscript to provide a clear justification.

The Chai-1 model was selected for two primary reasons. First, as shown in the RMSD matrix in Figure 2b, its predicted structure for the key domain showed a high degree of consensus with other top-performing models like AlphaFold 3. More importantly, the deciding factor was its ability to produce a chemically correct and functionally competent C3HC4 RING finger domain. This was validated by meticulously checking that the coordination geometry of the two critical zinc ions (Zn²⁺) was correctly modeled, which is essential for the protein's biological function. This detailed rationale has now been added to the "Materials and Methods" section as reads below (lines 288-297):

“From this pool, a final consensus model was selected by prioritizing models with both a high mean predicted Local Distance Difference Test (pLDDT) score and, critically, a chemically correct and functionally competent C3HC4 RING finger domain [24]. This involved meticulous validation of the coordination geometry of the two explicitly modeled zinc (Zn²⁺) ions with the surrounding Cys/His residues. While the catalytic C3HC4 RING finger domain is defined as residues 116-156, we selected a larger fragment (residues 110-200) for all subsequent analyses. This expanded boundary was chosen to fully incorporate the functional domain and to account for the potential influence of adjacent, flexible regions on ligand accessibility and binding.”

Comments 4: Increase the resolution of figures, such as Figures 1B and 2B.

Response 4: We thank the reviewer for pointing out the issue with figure quality. We have revised these figures by re-rendering them at a higher resolution to improve their clarity. The revised figures include the RMSD matrix (now Figure 2b) and the CReM-dock workflow diagram (now Figure 3b).