Dear Prof Quezada,

Thank you very much for submitting your manuscript "Genesis: A modular protein language modelling approach to immunogenicity prediction" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Yang Lu, Ph.D.

Academic Editor

PLOS Computational Biology

Amber Smith

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Summary

Genesis is a modular protein language model designed to enhance immunogenicity prediction for CD8+ reactive epitopes. It comprises a peptide-MHC (pMHC) encoding module trained on three key tasks—binding affinity, eluted ligand prediction, and stability—alongside optional T-cell receptor (TCR) encoding modules and context-specific immunogenicity prediction heads. The model's interpretability analysis identifies weaknesses and biases in existing datasets, offering a better understanding of immunogenicity features.

Minor Comments

1. For all figures, please increase the font size of x and y axis labels. They are currently too small and difficult to read.

2. What masking rate was used during the pretraining phase?

3. For Figure 5 feature maps, consider including a PCA-based visualization as a supplementary figure, which may provide easier interpretation. In Figure 6 Panel G, the x-axis label is missing for half of the plot.

4. Regarding data leakage control, why did the authors choose to use 8-mer matching? Is there a specific rationale for this choice? Typically, we see controls based on 3, 6, or 9 (3-based) mers. Additionally, have authors tried identity clustering for example?

5. A more comprehensive analysis of position-specific SHAP values would be beneficial. How do these masking changes relate to known TCR contacts or mutations/variants across alleles? Instead of masking, could direct mutation effects be examined?

Suggestions

Note: Suggestions are not formal comments. Authors are not required to address items in the suggestions section, which do not affect the reviewer's decision.

1. Have you considered using a pretrained model such as ESM2 as your backbone? This approach might yield better performance or faster convergence given its inherent knowledge.

2. It is increasingly common to see performance improvements by augmenting protein language models with some degree of structural information, especially for antigens, given the importance of structural complementarity.

3. How would an end-to-end trained model that jointly learns pMHC and TCR features compare to the current approach of separate training?

Reviewer #2: Comment: In 4.2 Peptide MHC Encoding Module, there are 4 encoder layers in the encoder block. Generally protein LMs like ESM [1], ProtTrans [2] and ProteinLM [3] have >= 12 transformer layers to ensure the model can capture enough information.

Increasing the number of layers and hidden size of your model can help with the performance even more.

Question: I wonder have you performed any ablation study on the optimal number of layers in encoder? As well as using general purpose protein language model as the protein sequence encoder, then use the extracted embedding to do the immunogenicity prediction?

[1] Rives, Alexander, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences.

[2] Elnaggar, Ahmed, et al. ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning.

[3] Xiao, Yijia, et al, Modeling Protein Using Large-scale Pretrain Language Model.

Reviewer #3: The review is uploaded as an attachment.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Ruidong Wu

Figure Files:

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References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

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Attachments

Attachment

Submitted filename: Genesis.docx

Dear Prof Quezada,

We are pleased to inform you that your manuscript 'A modular protein language modelling approach to immunogenicity prediction' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Yang Lu, Ph.D.

Academic Editor

PLOS Computational Biology

Amber Smith

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Thanks for the authors' reply and all revisions. The authors' revisions have fully addressed my comments.

Reviewer #3: Thanks for the response, my questions are well addressed.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: None

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: Yes: Ruidong Wu