Dear Dr Novacek,

Thank you very much for submitting your manuscript "Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs" 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. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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

[1] A letter containing a detailed list of your responses to the 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).

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Thank you again for your submission. 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,

Anand R. Asthagiri

Associate Editor

PLOS Computational Biology

Douglas Lauffenburger

Deputy 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 authors present a novel approach based on knowledge graphs to predict kinase-substrate interactions that benchmarks favourably against well established methods in the field. Moreover, they experimentally confirm several of the top predicted interactions. This work tackles an important challenge and utilises a methodological distinct method that expands substrate predictions to many kinases that are not covered by existing approaches.

While this approach represents a significant development the method needs to be benchmarked using independent data-sets to better understand the precision of its predictions. Moreover, in several sections conclusions are not well supported. Thus, it would be important to expand the current set of figure panels and supplementary materials to provide clear evidence supporting the conclusions made.

1. It would be important to exclude that the precisions reported are not inflated due to overfitting to the training data-set. Besides the 90-10% train-test split, can the authors use, for example, 60-40% and 80-20%?

2. Taking advantage of a recent systematic study of kinase-substrate interactions (PMID:31959955), the authors could use this as an independent test set, while making sure the version of PhosphositePlus used for training does not contain this study to avoid circular training-testing. Can the authors show how well LinkPhinder captures these interactions and how it compares to the other approaches?

3. LinkPhinder identifies 2,009,171 and 7,232,636 high and medium stringency interactions, respectively. It would be important to see overall statistics of the predicted kinase-substrate networks of each method, for example, the number of kinases covered, distribution and average number of predicted substrates per kinase, distribution and average number of kinases targeting each phosphorylation-site.

4. Some sections of the manuscript, for example section 2.3 and 2.4, have statements that are not supported with links to either figures, tables or supplementary material. I would recommend the authors to expand the number of figure panels accordingly to provide better support to the text. Also, data not shown statements are problematic to assess and reproduce. I would suggest either to provide the data as supplementary information or remove this statement.

5. Improved kinase-substrate networks are fundamental for methods that aim to estimate kinase activity profiles based on their substrates phosphorylation status. It would be interesting to see if LinkPhinder predicted network improves kinase activity predictions. An existing benchmark data-set (PMID:28200105) of quantitative phosphoproteomics measurements upon kinase inhibition could be used to test this.

Minor revisions

1. Is the predictive power of LinkPhinder networks significantly different between each run? How would LinkPhinder benchmark against other methods if instead of 100 runs of the experiment only one was performed?

2. Table 1 could be complemented with plots of the AU-PR and AU-ROC as supplementary figures.

3. Typo in “equal” in Table 1 legend.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

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Reviewer #1: No

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, PLOS recommends that you deposit 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. For instructions, please see http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methods

Dear Dr Novacek,

Thank you very much for submitting your manuscript "Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs" 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.

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,

Anand R. Asthagiri

Associate Editor

PLOS Computational Biology

Douglas Lauffenburger

Deputy 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]

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The authors have substantially addressed my comments and I believe this method is of overall interest for the community, although I still have some significant concerns. I found it hard to navigate through the changes because point-by-point replies are mostly pointers to the manuscript and the generated PDF seems to have the manuscript included three times, with revised and unrevised versions, hence the total pages of 258.

Despite the fact that LinkPhinder outperforms all other methods, the poor results with the independent data-set, with AUPR and AUROC < 0.54 for all methods (Table 3), raises some substantial concerns regarding the performance of LinkPhinder in the internal validation (Table 1). It is important to understand if this is (i) an inherent problem of the validation data-set; (ii) potential overfitting of LinkPhinder to the test data-set; or (iii) methodological problem of the validation, e.g. data leakage from train to test.

Can the authors comment on the fact that PhosphoSitePlus is both used to train LinkPhinder and to generate the true positive sets for the AUPR and AUROC curves. This could lead to data leakage between train and test, i.e. training data is also used to test. Could the authors generate random sets of positive/negative interactions that are kept from LinkPhinder training and use them only for validation and comparison with other methods? In addition, please report the total number of positive and negative statements (ideally have the list as supplementary material), add details to the methods on how these are generated, and plot the AUPR and AUROC curves with the mean curve obtained for each model.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: 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

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, PLOS recommends that you deposit 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. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

Dear Dr Novacek,

We are pleased to inform you that your manuscript 'Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs' 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,

Anand R. Asthagiri

Associate Editor

PLOS Computational Biology

Douglas Lauffenburger

Deputy Editor

PLOS Computational Biology

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