Dear Dr. Bayly-Jones,

Thank you very much for submitting your manuscript "Mining folded proteomes in the era of accurate structure 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.

Importantly, please provide a command line tool or ideally a web server to perform the structural similarity search and analysis from the paper.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[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).

Important additional instructions are given below your reviewer comments.

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,

Dina Schneidman

Software Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Specifically, please provide a command line tool or ideally a web server to perform the structural similarity search and analysis from the paper.

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The manuscript presents a case study on mining the computationally predicted structures in the recently released AlphaFold Protein Structure Database for structural similarities. The authors use established fold recognition algorithms to compare proteins from the entire database to known members of several pore-forming protein families and succeed in identifying previously unknown members of such families.

The authors' methods represent a clever and straightforward-to-apply workflow to explore computationally predicted structures. The paper explains well why the particular results from their examples are of biological interest. Beyond corroborating the approach itself, these results demonstrate that computationally predicted protein structures can be used to discover relevant connections and relations between different proteins that cannot be obtained from sequence databases.

This study is the first of its kind (as far as I am aware) which comes with a lot of merit but also necessitates some additional tests and discussion to form a solid basis for future similar ventures. In particular, the following points should be addressed:

- It is not clear to me whether using a different reference protein from one family would lead to the same result. For example, if instead of GSDM-D one had searched for similarities to a MACPF, would the results have included C11orf42, too? Similarly, could one identify all MACPF/GSDM members by using C11orf42 as a reference?

- When expanding the analysis to all proteomes (line 52ff), the computational cost increases strongly. Could one in principle alleviate this problem with a less expensive pre-screen that excludes proteins that, by some simpler criteria, clearly cannot have a similar fold?

- The authors introduce the term "foldome" for a set of structures corresponding to a proteome. According to their definition (one of many potential folds per sequence), a proteome has multiple potential foldomes. This definition leaves no room to describe a set of structural ensembles, which would intuitively be associated with the term foldome meaning "entire set of folds."

- While the "era of accurate structure prediction" is indubitably owed to the machine learning techniques, as much credit should be attributed to advances in the experimental techniques that determine the structures from which these models learn.

- The paper alludes to "more comprehensive evolutionary analyses" in line 59. It would help to elaborate on that or provide a few examples.

Reviewer #2: Summary of the paper

The authors point out a fairly obvious use-case for ML predictions of protein structure and then describe some interesting applications. Structures predicted by AlphaFold2 or similar neural networks are queried using fold recognition tools such as DALI in order to find structural homologs with very low sequence identity and impute function to uncharacterized proteins. The primary contributions are:

- The authors identify new pore-forming proteins, including a new human perforin / GSDM with only 1% sequence homology to known examples

- The authors query human proteins with confident structural predictions against the remainder of the AF2 human foldome, identifying possible functions for several uncharacterized proteins

- The authors query predicted structures for Pfam domains of unknown function against the S. aureus AF2 foldome

Main Review

Strengths

In general, the paper is concise and clear.

I appreciated the discussion of limitations and possible improvements

The example of structural homology with only 1% sequence homology is impressive!

Major Weaknesses

I think the contribution would be stronger if the authors also made it easier (with a command-line tool or web server) for scientists with less-specialized knowledge to do structural homology search against AF2 predictions.

Minor weaknesses

The paper could be stronger with justifications or reasoning for tool choices. Why use DALI vs SA Tableau for different tasks? Or why use trRosetta predictions instead of AF2 or RosettaFold predictions for some tasks?

Likewise, how were the foldomes chosen? Why limit searches to the human foldome or the S. aureaus foldome? Would it be better to search against a deduplicated, "representative" predicted structure library? If not, why not?

While acknowledging that bench verification of imputed functions is out of scope for this paper, I would like some discussion of the trustworthiness of functions imputed via structural homology, even under the assumption that the structure predictions are accurate.

Given that AF2 predicts accurate protein structures given MSAs, could we do homology search by just comparing MSAs?

Summary of the review

While the idea of applying structural homology search to ML structure predictions is fairly obvious, the authors do it carefully and find convincing, biologically-interesting results. Together, this makes a strong case for making this sort of search a part of the standard toolbox when working with proteins of unknown function, and the work is of broad interest to protein biologists and engineers. Overall, this paper is suitable publication in PLOS Comp Bio with minor revisions.

**********

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

**********

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 #2: Yes: Kevin Kaichuang Yang

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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.

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Dear Dr. Bayly-Jones,

We are pleased to inform you that your manuscript 'Mining folded proteomes in the era of accurate structure 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,

Dina Schneidman

Software Editor

PLOS Computational Biology

***********************************************************

Please address the comment of Reviewer 2 in the final version

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: My questions and concerns have been sufficiently addressed.

Congratulations on the impressive results!

Reviewer #2: The authors respond thoroughly to the concerns from the first set of reviews. My one remaining concern is about this paragraph:

"Unlike domain assignment by primary sequence analysis, fold-matching algorithms are sensitive and

robust. We anticipate that domain assignment by fold-matching will likely provide more accurate and

informative predictions over existing sequence analysis methods, especially in contexts where

sequences have poor overall homology or possess discontinuous breaks and insertions. Of course,

imputed function remains to be experimentally validated."

Intuitively, it makes sense that fold-matching would be more sensitive and robust than primary sequence analysis. However, citing examples from the literature where these are compared head-to-head would strengthen this section.

**********

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

**********

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: Yes: Martin Vögele

Reviewer #2: Yes: Kevin Kaichuang Yang