PCOMPBIOL-D-25-01510

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

PLOS Computational Biology

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Alexey Onufriev

Academic Editor

PLOS Computational Biology

James Faeder

Section Editor

PLOS Computational Biology

Additional Editor Comments:

One of the critical comments to address is novelty of the proposed model; including a detailed comparison with ARMADiLLO, as proposed by one of the reviewers, makes sense.

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At this stage, the following Authors/Authors require contributions: Mackenzie M. Johnson, Kevin Sung, Hugh K. Haddox, Yun S. Song, Julia Fukuyama, Ashni A. Vora, Tatsuya Araki, Gabriel D. Victora, and Frederick A. Matsen IV. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one review is uploaded as an attachment.

Reviewer #1: In there manuscript ”Nucleotide context models outperform protein language models for predicting antibody affinity maturation” Johnson et al. develop and benchmark a model taking nucleotide sequence context into processes for modelling of affinity maturation. This approach has scientific relevance as hypermutation is strongly influenced by the nucleotide sequence context both in terms of promoting or preventing substitution. The authors conclude that such an approach performs better that other models. The authors might however also consider the following aspects:

In the past, the ARMADiLLO tool (e.g. 10.1093/nar/gkad398) uses a similar concept to assess evolution of antibodies. How do these methodologies compare in terms of accuracy of evolution prediction?

Evolution does, as the authors predict, not only depend on the linear protein sequence but also on the nucleotide sequence that encodes it. However, evolution also depends on the structural context that neither methodology really approach. Some residues (e.g. but not limited to C41, W41, W52, and C104) are so important for the structure that they never evolve. Others may evolve but are restricted to evolution to conserved substitutions (e.g. L, I, M; nucleotide sequence prediction softwares (e.g. ARMADiLLO) would typically overestimate substitutions at such sites) while others are able to accommodate essentially any residue. Other parts, not specifically discussed, of the domains, such as the upper and lower cores, may have similar restrictions. In some cases, germline genes encode unusual resides (not encoded by other genes, such as residues 46 and 81 of human IGHV1-8-derived antibodies) that are highly mutated (see for instance DOI: 10.3389/fimmu.2017.01433), sites that may be missed by a nucleotide-focused prediction tools. The authors ought to discuss the structural context and its role in prediction. Are combinations of models as applied here better able to incorporate the structural perspective than without the need for explicitly incorporating a structural component into the computational process?

The authors removed sequences that had >10 mutations in any window of 20 consecutive sites (lines 442-445). I understand the rational but how does this impact in particular analysis of diversification of CDRs where higher levels of diverse substitutions may be observed. Please discuss.

Minor comments

Lines 338-346 appears to be Discussion and should be moved to that section.

The authors should indicate the reference sets (incl version number) that was used to annotate germline gene origin.

Have models been trained solely on sequences derived from the same germline gene or also on sequences derived from other genes? Residues far apart in the linear sequence may come into close proximity in the folded protein and this may impact mutability in particular of residues that are structurally important. Please comment.

Reviewer #2: Timely benchmark: EPAM (code released) shows nucleotide-context SHM models outperform protein LMs across human repertoires and a Replay system. With the revisions, this would be an important community reference.

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Reviewer #1: No: Raw data has been obtained from external sources. They, not the present authors, might not be able to release the data.

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Attachments

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Submitted filename: Final_version_comments_20Aug2025.docx

Dear Dr. Matsen IV,

We are pleased to inform you that your manuscript 'Nucleotide context models outperform protein language models for predicting antibody affinity maturation' has been provisionally accepted for publication in PLOS Computational Biology.

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Best regards,

Alexey Onufriev

Academic Editor

PLOS Computational Biology

James Faeder

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: The comments made by the reviewers have been appropriately addressed.

Reviewer #2: All my comments have been addressed. I have no additional comments.

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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: None

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Reviewer #1: No

Reviewer #2: No