Dear Dr. Nikoloski,

Thank you very much for submitting your manuscript "PARROT: Prediction of enzyme abundances using protein constrained metabolic models" 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.

In particular, the reviewers suggest edits to better clarify the results and substantiate your claims.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

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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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,

Christos A. Ouzounis

Academic Editor

PLOS Computational Biology

Stacey Finley

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 authors present their method, PARROT, which employs constraint-based approaches for minimizing the enzyme allocations to assess the protein allocations in microorganisms growing under different suboptimal conditions. These constraint-based approaches attempt at optimizing the enzyme allocations rather than other contending approaches which rely on flux redistributions. The authors benchmarked the performance of their approach/es on two model microorganisms---E. coli and S. cerevisiae---and propose the use of protein-constrained genome-scale metabolic models (pcGEMs) for studying microbes in different conditions.

Major Issues:

1. Given that the growth of microorganisms in different conditions, particularly suboptimal conditions, is central to the positioning of this method and the manuscript, it is important that the authors provide some more motivation and context for the need to study the microbes is such varied conditions.

The authors can easily find suitable literature from antimicrobial resistance where microbes, like the superbug ESKAPE pathogens, evolve and continue to grow even under the effect of antibiotics---the suboptimal growth conditions. There are studies that have reported evidences of a microbial strain becoming resistant to an antibiotic under prolonged exposures.

- doi.org/10.3389/fmicb.2021.714284

- doi.org/10.1093/femsre/fux013

Another avenue for reference literature could be the clinical growth of microbial communities using selective enrichment media.

2. The Results section should be rewritten to document the specifics and performance details of all approaches being compared/benchmarked. There are subsections titling PARROT to have outperformed other contending methods, but this is an inference to be made from the specific results. The exact specifics about the differences and similarities in results from different approaches are either not well documented/highlighted or are mentioned in the supplementary material. Therefore, from a reader's point of view, the actual results and performance of PARROT are not easy to follow and interpret.

Reviewer #2: PARROT: Prediction of enzyme abundances using protein constrained metabolic models

Summary:

This manuscript introduces a method for calculating Protein allocation Adjustment foR suboptimal enviROnmenTs (PARROT). With this method, the authors aim to study the metabolic phenotype of new, suboptimal conditions. The method is based on the principle of minimal adjustment of the proteome starting from a reference state. This is represented in the objective function which minimizes the distance between the reference condition and the suboptimal conditions. The authors compared different distance metrics (Manhattan and Euclidean distance) and the effect of adding the minimization of the flux distance to the objective functions. They found that for models of Saccharomyces cerevisiae and Escherichia coli, their method outperformed normal pFBA (minimizing the sum of all fluxes). Furthermore, they observed that the adding minimization of difference in flux distributions does not improve the results, even worsens it.

Major comments

General:

• Variants are used with different intentions (different conditions or different distance functions). This is confusing for the reader. Please be concise in how you refer to the different models or distance functions

Abstract:

• L. 21-22: In this sentence, you try to explain the main function of PARROT. I think this sentence is confusing for someone who hasn’t read the paper yet, since the meaning of ‘enzyme allocation’ and ‘enzyme allocation adjustment’ is unclear from the first part of the abstract. I recommend clarifying these terms or rephrasing to something a lay reader can understand.

• L 25: From the abstract it is unclear between what the distance is calculated. This could be easily added by replacing ‘of enzyme allocations’ with ‘between the allocation of enzymes of a reference and suboptimal condition’

Materials and methods:

• In the code 5% flexibility is allowed on the growth rate, which is not described in the Materials and methods.

• L. 357: It is not clear if 'values' refer to the original protein measurements or to the relaxed protein values.

• L. 382: What does ‘The second norm in constructing a baseline’ mean in mathematical terms? This is not clear from the text and is important in interpreting this analysis.

Results:

• L. 132: ‘18-336’ and L. 156: ‘19-141’. Why is the range of number of enzymes in the predicted suboptimal conditions so wide? How does this influence the interpretation of the results? I can imagine that the number of predictions influence the value of the accuracy metrics.

• L. 140-141: Here you mention that the kcat values are used as direct measure of the enzyme usage. What does this mean? Do you mean that you use the values of the enzyme variables as enzyme concentrations? I miss a description of this validation method in the ‘Methods’ section.

• l. 153: Why did you choose the smallest dilution rate as the reference condition? What is the motivation for this?

• Figure 2, 3, S2 and S3: These type of plots do not give a FAIR visualization of the results. Due to the scale of the plot, the distribution of the data in each box is impaired. I suggest adjusting the scale and trying out different ways of data visualization, such as a violin plot with individual data-points or even a raincloud plot.

• L. 189: Consistent with what? You can clarify this by adding ‘with this observation’ after ‘consistent’.

Discussion:

• L. 199-202 and 241-255: In the Results you describe the effect of alternating lambda on the occurrence of higher/lower Pearson correlation values. Later, in the Discussion you explain the meaning of a lambda value of 1 or 0. This part contains sufficient technical explanations, but I miss the biological explanation of this factor. What mechanism does this represent? What did you learn about Biology from this result?

• L. 280: ‘underestimating capacity’ I think capacity is not the correct word here. Capacity is a feature, not a result. I would use ‘the underestimation of protein concentrations’ instead.

• L. 281: Another approach is to specifically consider un/under-used enzymes as a separate protein sector as done in Alter et al. (2021) (https://doi.org/10.1128/msystems.00625-20). Consider discussing/citing this source.

Additional minor comments:

General: the use of ‘suboptimal‘ as term for the condition which is not the reference condition is questionable. For example, in the chemostat cultures, you choose the lower growth rate as reference condition, making the other conditions thus ‘suboptimal‘. In terms of biology this is incorrect: the cells are growing even faster in these conditions. I would choose a word as ‘alternative‘ condition instead.

l. 21, l. 294: I think the term ‘enzyme allocations’ is confusing, as allocation is not commonly used in plural. Perhaps 'the allocation of enzymes' suits better here?

l. 178: ‘than’ should be ‘compared to’

l.180: The following sentence: ‘As observed for comparisons using the first norm baseline’ could be replaced by: ‘As observed in the comparisons with the first norm baseline’. In my opinion, this would clarify the message of what is compared with what.

l. 233: ‘using’ should be ‘with’

l. 266-267: ‘is that cell overexpress’ could be replaced by ‘cells overexpress’

l. 293: ‘associated flux state’ A flux state cannot be performed, as it is a state. You can replace part of this sentence with: ‘to perform growth and to maintain the associated flux state’ or ‘for growth and the associated flux state’

l. 355, 357, 574: 'to flexibilise’ is not an English verb. You can replace this by ‘to allow flexibility in the enzyme usage constraints' or 'relaxing the enzyme usage constraints'.

Code on GitHub:

`ptotREF = 0.61;

f_REF = 0.4;

sigma_REF = 0.4;

f_STR = 0.4;

sigma_STR = 0.4;`

Where do these values originate from and what do they represent? Please include comments or documentation to interpret these values. What is the source of these numbers (or the assumptions made to define them)?

Using a dummy example on for example a core model to get to know the programming interface would be appreciated.

Reviewer #3: See attachment. I've left the original .odt file there in case you want to put your replies directly there.

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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: Yes

Reviewer #3: Yes

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Reviewer #1: Yes: Kiran Gajanan Javkar

Reviewer #2: No

Reviewer #3: 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, 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

Attachments

Attachment

Submitted filename: parrot_comments.odt

Attachment

Submitted filename: parrot_comments.pdf

Dear Dr. Nikoloski,

Thank you very much for submitting your manuscript "PARROT: Prediction of enzyme abundances using protein-constrained metabolic models" 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.

The reviewers have recognized the effort put into the revised submission, but raised some minor comments. We believe these points can be addressed in an updated submission.

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,

Christos A. Ouzounis

Associate Editor

PLOS Computational Biology

Stacey Finley

Section 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:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I appreciate the authors for their efforts in updating the manuscript to incorporate the reviewers' feedback. I believe it has improved the manuscript substantially.

Having said that, there are still a few concerns that need to be addressed before this manuscript is accepted for publication:

1. Following the mentions from the title and the abstract section, the initial paragraphs of the introduction section should describe the need and utility of enzyme abundances and protein allocation, followed by subsequent literature on the challenges and advances in doing so. The current introduction starts with GEMs and protein-constraint GEMs and does not mention much about the need for enzyme abundances/protein allocations, with an underlying assumption that the reader already knows about the latter. It is recommended that the authors rethink and rewrite the initial paragraphs of the Introduction such that the context and scope of the manuscript are set for the readers without such major assumptions on the reader's part.

Additionally, some sentences have been worded in a bit convoluted manner while some other sentence combinations do not have a grammatical concordance that is easier to follow. For instance, the second sentence of the Abstract reads "Therefore, different experimental and machine learning approaches have been developed to quantify and predict protein abundances, respectively". The sentence structure suggests that it is contextual reasoning provided for what it described in the previous sentence. However, the previous sentence describes 'protein allocation', which is not mentioned in the following sentence at all. Such paraphrasing makes it quite difficult for a reader to follow the text. It is recommended that the authors proofread their manuscript with an end-reader in mind, preferably reviewing it with an English language expert, so that the presented content is easier to follow.

2. Line 101-102: "...the optimal growth state is disturbed". The authors need to define/describe what they refer to as the optimal growth state and the associated disturbance to it.

3. In the results section, the authors provide a count range of the enzymes by each model and indicate that since this count is similar to the experimental count, their model is performing better than the counterpart (pFBA). However, it is unclear how are count ranges alone sufficient to ascertain a better performance. Shouldn't the actual enzymes, themselves, be matching or similar? Alongside, the associated protein allocation counts also need to be accounted for, isn't it?

4. In lines 177--180, the authors mention their reasoning about their choice of the dilution rate while accounting for aerobic growth and metabolic shift prevention. This also raises a concern about the operational ranges for each of the 4 prediction models. The authors should describe the conditions that need to be satisfied for their proposed model/s to be suitable for these predictions.

Reviewer #2: Thank you for properly revising your manuscript. The adaptions and changes you made are adequate and do sufficiently consider the former concerns.

Reviewer #3: I thank the authors for considering my comments, as well as the comments from other reviewers. I am satisfied with the answers, and recommend to accept the manuscript.

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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: Yes

Reviewer #3: 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: No

Reviewer #3: 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, 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. Nikoloski,

We are pleased to inform you that your manuscript 'PARROT: Prediction of enzyme abundances using protein-constrained metabolic models' 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,

Christos A. Ouzounis

Academic Editor

PLOS Computational Biology

Stacey Finley

Section Editor

PLOS Computational Biology

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

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I thanks the authors for incorporating the feedback provided with my earlier review. I understand the authors' constraints or limitations with respect to the results benchmarking, but would sincerely like to have a better results benchmarking beyond just showing a similarity in the counts from two approaches.

Having said that, I am largely satisfied with the current manuscript, which I believe to be much improved than the earlier submission/s.

**********

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

**********

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: Kiran Gajanan Javkar