PCOMPBIOL-D-26-00680

A zero-parameter first-principles gate framework for full-length TP53 missense variant interpretation

PLOS Computational Biology

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Academic Editor

PLOS Computational Biology

Nir Ben-Tal

Section Editor

PLOS Computational Biology

Additional Editor Comments:

This article was carefully reviewed by one invited reviewer as well as myself, the editor. Both myself and the reviewer are of the opinion that this article do not suffer of any methodological pitfalls, nor does it reach beyond what it claims to achieve. Namely, to create a framework with which to understand based on physical factors, as opposed to a machine learning framework, the effect of mutations on P53. The reviewers concerns as well as my own are the same: Whereas the proposed framework is intuitive and exhaustive, in the manner in which it is presented, it is uniquely applicable to this one specific protein. Considering the extensive existing data on the effect of mutations on P53, it is unclear how useful is this methodology, even to understand P53 alone, given the amount of existing data on it. Yet, as I believe it to be important to foster diversity of approaches in an AI dominated scientific landscape, particularly approaches that strive to provide clear physics/chemistry based explanations, I believe that this is a contribution worth of publication at Plos Computational Biology. However, it will be important to expand the work to include a thorough discussion of what channels/rules are applicable to other proteins. Of course it would a tremendous task to create a framework applicable to all proteins and this is not what is being asked of the author - but a discussion of what subset of channels, and perhaps even the creation of code applicable to other soluble proteins at least, will create the foundation for the future expansion of this framework. Essentially, what I ask is the author to add a section and ideally but not necessarily code, that goes in great detail about what channels can be used for other proteins and in what manner, to make this article relevant for those studying other proteins or wishing to work on the this framework as a foundation.

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Reviewer #1: I have read the manuscript with a lot of interest and captivation. My review focuses on the structural biology aspect of the work.

Generally, this work is timely and invites the structural and cancer biology fields as well as clinicians to reflect on the scientific value and accuracy of the prediction of oncogenic (pathogenic) variants of proto-oncogenes and tumor suppressors (alike TP53) existing deep leaning approaches prediction of the pathogenicity of missense variants. These are based on large databases and optimization of weight that factor recurrent patterns to achieve prediction. Whereas these are performing well, the optimized weight do not inform us on the exact molecular etiology or physicochemical origin of the disruption of the normal or WT molecular function. In this context and to complement (or rationalize) machine leaning approaches XX et al. propose a simple computational framework that is called Gate and Channel. Each channel represent a molecular or physical mechanism of disruption of function caused by the variant residues: cavity formation or clash in folded domains, mutation at the interface of the tetramerization domain and MDM2

The failure to close a gate or to identify pathogenic variant is interpreted as a lack of “physics” or a rule in a channel. The list of rules in the number of channels in the V17 framework is quite exhaustive. Do the authors have possibily in mind? Failure to phase separate in proper condensates? Or to be excluded? Incomplete list of interactors? In this regard, do the structure of the complex of the p53 TAD with the TAZ2 domain of CBP (a coactivator) included in a channel like the interaction with MDM2?

Although I completely concur with the necessity of understanding the etiology of pathogenic mutations as well as complementing the existing machine learning approaches, I am not sure how this framework can be deployed at large. P53 is one of the most structurally and mechanistically studied tumor suppressor. This is certainly contributing to the actual success of the approach. However, not all proto-oncogenes and tumor suppressors have attracted the same scrutiny and annotations. Can some of the channels be based on predictions such as structural models from AlphaFold and AlphaFold Multimer when experimental structural knowledge is lacking? In addition, users may not be as expertly versed and aware of all the physicochemical and structural determinants of protein stability, molecular recognition, PTM and polymer physics as the authors. It is not clear to me how both approaches can or could be used in an integrated fashion. Maybe the authors can expand in this perspective.

Notwithstanding these comments, I find this contribution is of very high quality and absolutely found in all aspects of the fundamentals missense variants can affect the structure and the function of proto-oncogenes and tumor suppressors.

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Reviewer #1: Yes

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Reviewer #1: No

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Dear Mr. iizumi,

We are pleased to inform you that your manuscript 'A zero-parameter first-principles gate framework for full-length TP53 missense variant interpretation' has been provisionally accepted for publication in PLOS Computational Biology.

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Best regards,

Rafael J. Najmanovich

Academic Editor

PLOS Computational Biology

Nir Ben-Tal

Section Editor

PLOS Computational Biology

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The authors satisfactorily addressed all concerns from the reviewer and editor. The paper is acceptable for publication.