Dear Dr. Najmanovich,

Thank you very much for submitting your manuscript "Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation" 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, we do think a comparison with MD-simulations is necessary (see reviewer comments)

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,

Shi-Jie Chen

Associate Editor

PLOS Computational Biology

Arne Elofsson

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 present work offers an interesting coarse grained ENM-based approach to approximate the local dynamics of RNAs. This approach is demonstrated to be sufficiently cheap for large-scale applications while able to predict sequence-specific properties. Using this approach, the authors sucessfully related the dynamics of pri-miR-125a variants to its maturation efficiency. Yet this exciting achievement appears buried in the details and not stressed enough. Moreover, to make this manuscript more convincing, extra evidences shall be provided.

Although molecular dynamics simulations could be too expensive for large-scale validations, a comparison and this approach and MD simulations is still necessary, at least for the wild-type miR-125a and one/two variant sequences. This won't be super costly, since the system is not huge and only local dynamics around the medoid structure of pri-miR-125a need to be sampled. Such comparison not only adds more credit to ENCoM but may also offer clues on future improvements of including electro-static terms.

A related interesting question could be at what temperature shall the MD be performed and what is the relation between this temperature with the parameter \\beta and the entropy of the molecule. Some discussion on this will be helpful.

"The pinpointing of important regions in the pri-miR-125a" shall be stressed more. This could be done by (1) starting a new paragraph at Line 484 from "Strikingly, ..."; (2) Modify Figure 7 so that the GHG motif can stand out from the rest.

Other than these, I find the manuscript clearly written and scientifically sound.

Reviewer #2: In their manuscript, Maillot et al introduce an RNA-version of an elastic network model (ENCoM) originally developed for proteins.

They evaluate the accuracy of the model in predicting B-factors and motions as described by NMR bundles.

Furthermore, they train a regularised linear model to predict maturation efficiency of a miRNA using as input features dynamical information predicted from the elastic network model.

While some of the aspects of this work are innovative, I have a number of concerns.

1. If I understand correctly, the ENCoM model is based on three beads: C1', C2, and P. What are the parameters of the model in eq. 1? While some of the scaling parameters may be adapted from proteins, there is no P in proteins. Also, how were the equilibrium distances/angles (e.g. r0, theta0, etc in eq 1) determined? Since C1'/C2/P are not covalently bonded there is no simple way to determine those parameters e.g. from known RNA structures. Additionally, the improvements with respect to simpler models with much fewer parameters is only marginal, it is thus not clear why one should use ENCoM rather than an ANM.

2. I like the idea of using the entropic signature as features in a LASSO model to predict miRNA maturation, but the authors should explain and motivate their choice more clearly. If I understand correctly, the underlying idea is that each miRNA sequence display a given internal dynamics which is (assumed to be) tightly linked to its maturation efficiency. Since the internal dynamics could be captured by the 'entropic signature', one should be able to predict maturation efficiency from ENCoM.

While I understand and appreciate that the authors constructed a non-trivial test dataset, the resulting R2 is very low, suggesting that the model does not generalize to a sufficient degree. As such, I am not convinced that the ENCoM model can help the prediction of maturation efficiency. In order to improve the model, the authors can consider to introduce additional features to the model (e.g. the ensemble diversity as predicted from sequence, others?)

Minor points:

- the NMR bundle does not directly represent dynamics, but rather a collection of structural models that all agree to a certain extent to the measured data.

As such, NMR bundles do not necessarily represent the internal structural dynamics.

- Check several typos throughout the manuscript (chages -> changes on page 3, line 80. Diplacement -> displacement line 189 page 6. etropic -> entropic page 13 line 450, constructed -> constructed page 9 , line 286)

Reviewer #3: In this paper, the authors used a variation of the Elastic Network Model that includes sequence/context based information using atomic contacts - the ENCoM model to study the dynamics of RNA applying it to a particular class of RNA – microRNA 125a. The authors have already shown that the model works well for proteins. The paper is well structured, the analysis is thorough and covers all important aspects of model building (dataset acquisition, benchmarking, comparison to existing methods, testing, computational cost). The performance of the model given the level of coarse-graining and computational costs, is remarkable, for the pri-Mir 125a maturation efficiency predictions. However, there are a few model/parameter choices that should be explained better and a few things that should be resolved.

Eq 2 should be explained better. What do Ni and Nj represent? What do the authors mean by interaction between atoms of types? – interaction energy? What are the values for this interaction energy for the different types?

Nucleic acid folding and dynamics is inherently driven by different forces compared to proteins. The authors should provide more justification on why they chose to use the same parameter set for their ENCoM model that worked for proteins, on RNA as well? The protein parameters performs better than cut-ANM and PD-ANM, but wouldn’t RNA optimized parameter set perform even better.

Can they demonstrate that the protein parameters when applied to RNA are for example sequence sensitive? Are G-C contacts stronger than A-U contacts. Does the model with protein parameters distinguish base-pairs, wobble pairs from mismatches?

In supplementary table 1, the authors should add structures showing atom names used to assign types. e.g. Which atom in Cytosine is referred to as N5? What is the type for N1 in Uridine?

Also, how do the atom types translate to the interaction strengths in the 3 bead RNA models? It will be nice to have an illustrative example – a figure - for RNA like the authors provide in [11] for proteins.

Several RNA structures contain modified nucleotides which are structurally and functionally important. Can the authors also add stats on the numbers of structures containing modified nucleotides and the number of these altered structures that were included in their dataset and comment on the potential impact this may have on their results?

“Only the pairwise comparison between Cut-ANM and PD-ANM 363is statistically significant at p = 0:0038 despite ENCoM having the worst average 364performance of the three ENMs. The reason for this is that the two flavors of ANM 365have a similar performance profile across the 34 sequence clusters, while ENCoM tends 366to perform well where they don't and vice versa.”

Can the authors comment on what causes this difference in performance? Are there any commonalities -structural or otherwise- between the cases that ENCoM yields better results?

RMSD of 2 Angstrom seems like a very low cut off to imply that the difference is a result of a conformation change. What is the RMSD distribution for the 240 “distinct” pairs?

A few minor edits:

The colors are hard to see in Figs 7B and 7F.

Typos in page 3, line 74, 80.

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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: No: Are the parameters/examples provided online at https://nrgten.readthedocs.io/en/latest/index.html?

Reviewer #3: No: All data set has been provided. It will be great if the authors could provide the python code that implements the ENCoM model for RNA.

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Reviewer #1: No

Reviewer #2: Yes: Sandro Bottaro

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.

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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

Dear Dr. Najmanovich,

Thank you very much for submitting your manuscript "Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation" 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,

Arne Elofsson

Section Editor

PLOS Computational Biology

Arne Elofsson

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: All previous comments have been properly addressed.

Reviewer #2: This reviewer thanks the authors for the extensive work and for providing detailed answers to all the comments.

- Would it be possible to include the link to the code in the manuscript? (I guess it is already available at https://nrgten.readthedocs.io/)

- It would be useful for potential users to include in the documentation an example relative to the application on miRNA.

- Page 6, line 199: is purine-purine stacking less stable compared to py-py stacking?

Reviewer #3: The authors have satisfactorily addressed all my comments and concerns.

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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: No: See comments to the authors

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 #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. Najmanovich,

We are pleased to inform you that your manuscript 'Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation' 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,

Shi-Jie Chen

Academic Editor

PLOS Computational Biology

Arne Elofsson

Section Editor

PLOS Computational Biology

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