Dear Dr Doğan,

Thank you very much for submitting your manuscript "Protein Domain-Based Prediction of Compound–Target Interactions and Experimental Validation on LIM Kinases" 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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[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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Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

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,

Feixiong Cheng, Ph.D.

Guest Editor

PLOS Computational Biology

Nir Ben-Tal

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: Please address the followed questions:

1. How can you guarantee the accuracy of the predicted complex structures of ligand-protein? In other word, how do you choose the predicted complex structures among the various candidate poses given by docking simulations.

2. For some compound-target bioactivity data, different labs may give different values, how did you treat such kind of data? and why?

3. It is interesting to see how does your trained model give the evaluation of the compound-target protein relations with bioactivity levels of xC50 values between 10uM~20uM?

4. The resolution of the figures is too low to read.

Reviewer #2: In this paper, the authors use the the idea of protein domains and compound similarity to predict drug-target interactions. In particular, they use non-structural associations between proteins and drugs to infer domain-drug correlations and then validate the inferred relationships on a structurally derived dataset of domain-small molecule interactions, Interacdome. Next they assume that similar drugs would bind the same domains, and ultimately transfer information about drug-domain interactions to protein sequences. They experimentally test some predictions as well.

Overall, this work presents interesting ideas that I haven't quite seen in the literature, and thus is of value. However, the paper is pretty verbose in parts and it's hard to figure out what exactly was done. A self contained, crisp methods section would help a lot. I think a specific method for associating drugs and domains -- ie independent of validation -- would be beneficial. The authors may find some of the statistical ideas in methods to infer domain-domain interactions from protein-protein interactions useful. Using such an approach, they could then more rigorously test their predictions on interacdome (with the caveat that this is likely to be an incomplete "gold standard")

Reviewer #3: 1. The assumption behind the mapping between domains and compounds is that either the binding region of the ligand is on the mapped structural domain(s), or there is a functional relationship between the two so that the mapped domain is required for the corresponding bioactivity to occur. (page 7) --> It is not clearly explained how functional relationships can make mapping between domains and compounds. What's the meaning in terms of the biochemistry view?

2. It is not clear on the explanations on coverage extensions on page 11.

3. The Authors should provide statistics on the number of proteins/findings of all pathways not only the PI3K pathway for information to the Readers.

4. It needs more explanation why the Author focused on LIMK among four PI3K pathway proteins.

5. From the cofilin and wound assays, LIMK1 and LIMK2 seem to have different aspects in liver cancer. It is better to show the secondary protein structure with domains. It also will be necessary to check the genomic aspect or prognostic effects of these two genes from human patient data such as TCGA.

6. Also, it is better to add an explanation on potential effects or known facts of LIMKs in liver cancer in terms of working mechanisms or any relevant information for liver cancer-specific context.

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: PCOMPBIOL-D-21-01053_reviewer_7_20_reviewed.docx

Dear Dr. Doğan,

We are pleased to inform you that your manuscript 'Protein Domain-Based Prediction of Drug/Compound–Target Interactions and Experimental Validation on LIM Kinases' 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,

Feixiong Cheng, Ph.D.

Guest Editor

PLOS Computational Biology

Nir Ben-Tal

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: I suggest to accept this version

Reviewer #3: The Authors addressed the issues that I made.

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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: None

Reviewer #3: Yes

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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: Pora Kim