PCOMPBIOL-D-25-00157

Integrating AlphaFold2 Models and Clinical Data to Improve the Assessment of Short Linear Motifs (SLiMs) and Their Variants’ Pathogenicity

PLOS Computational Biology

Dear Dr. Brunello,

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Kind regards,

Mohammad Sadegh Taghizadeh, Ph.D.

Academic Editor

PLOS Computational Biology

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one of the reviews is uploaded as an attachment.

Reviewer #1: review attached.

Reviewer #2: SLiMs are important and understanding consequences of mutations and how they affect cellular wiring may be informative for molecular disease underpinnings and development of treatment. Molecular and especially structural studies have lagged behind as these modules can be difficult to capture, in part due to their intrinsically disordered nature. The manuscript is timely as novel methodologies like AF2 enable modelling of these interactions, and it is crucial to find out how well predictions of specificity on such models work. The manuscript is well-written, and easy to follow.

The main limitation is that the work only compares predictions on experimental structures vs. AF2 models, but not to experimental data such as sequences and affinities from phage display. The consensus patterns are by necessity limited in accuracy, and clinical data, while very interesting for applications, is not systematically available. Further, it appears that a large fraction of the contribution relates to the peptide alone, rather than the interaction (see below for details). While the peptide itself will of course have important contributions, this might be misleading and also not the intention.

Major comments:

P8 “For genes with at least 10 pathogenic variants, the allele frequency threshold was determined based on the most frequent pathogenic variant reported in ClinVar.” - given how many random events affect allele frequency, such as population bottlenecks, this seems like a risky strategy. Is there a way to test how reliable this is? Are false positives or false negatives more problematic here? Would it be possible to consider sequences from great apes as controls?

Figure 2: what are the units here?

It is interesting that even the incorrect models achieve fairly high accuracy. What can we learn from this? To be the devil’s advocate, would it be enough to study the peptide in isolation? In other words, are most of the effects you find those on the monomer conformation? Of course these also affect whether there is an interaction, but it would be very nice if your work could also assess how much the domain side actually contributes? Is this largely a limitation of the tool used (FoldX), and have you explored alternatives?

Within FoldX, why do you use PositionScan and not AnalyseComplex (https://foldxsuite.crg.eu/command/AnalyseComplex) which is for interface analysis?

In this context, it is worth noting that the label “interaction energy”is misleading.

https://doi.org/10.1002/anie.202213362 looks relevant in the context of this work and should perhaps be included.

Generally, the figure legends need more detail, including for supplementary figures. For example, for Suppl. Figure S1, what are RC, FxC, FIC? In Suppl. Figure S4, where exactly is the source for “previously classified” cases? Reference?

p9 describes how ClinVar evidence is “scored”. A common minimum standard is “at least one star”. While in this work, the stars are counted, it is unclear what would happen for a variant with 5 pubs saying patho and 5 saying benign, this might still add up to 1?

Did you consider other predictors beyond AlphaMissense, e.g. ESM1v?

Minor comments

P4, “on the contrary” - flow is strange, rephrase

P13 “kery to our approach” - typo, should be key?

P15 “classified as reproducible” what does this mean? Technically reproducible? Or finding the same conformation as in the xtal structure? Yet a third option?

P17 “Summarizying” typo

P12 + Fig. S1 - Do the clinical significance cycle and structural analysis cycle happen simultaneously (Fig. S1) or one after another (text on page 12)?

Fig. S5 - again, no details. What is this motif shown as an example? I don’t think it’s in the main text, either

P15 - If I am reading this correctly, they get the Perason’s R of 0.65 with the error of 0.22 for the FoldX predictions on crystals and on AF2. This is quite low? Is this because some AF2 predictions were way off? + As a figure for this paragraph we get Fig. S5, which is one individual case out of many, and we are supposed to inspect it visually.

Fig. 2 has a bad resolution (difficult to read)

Reviewer #3: In “Integrating AlphaFold2 Models and Clinical Data to Improve the Assessment of Short

Linear Motifs (SLiMs) and Their Variants’ Pathogenicity” the authors present an improved prediction method for SLIMs with Mendelian variants. The project holds promise but the manuscript lacks important details, especially in the method section. This makes it difficult to fully evaluate the work and review the manuscript. The authors are encouraged to consider the following examples and apply them throughout their manuscript:

1. Some things are described in methods but not presented in results. For example:

a. For the SLIM predictions described in methods, what was found? This could at least be presented in the supplementary materials.

b. For some SLIMs, experimental models were found and used while for others AlphaFold models were used, how many of each? This could at least be in the supplementary materials.

2. Methods are not sufficient. For example:

a. The AlphaFold2 (AF2) models: it is mentioned that the best complex is selected and the mean plddt for best complexes is 65, which is in the low confidence range. What are the confidence levels of the SLIMs and their models – this should be included in the manuscript.

b. Further, for the AF2 models, the method section is very brief yet the results states that “analyzed several parameters of the AF2 models to assess their ability to discriminate between correct and incorrect SLiM positions” but how these analyses were performed is not described.

3. Limited result presentation. For example:

a. Instead of showing a few select examples to claim model accuracy, what does the overall data show?

b. The results state a global accuracy metric was calculated and that the percent accuracy was 74% for correct models and 63% for incorrect models. How this was performed is not described and if there is a statistical difference between the correct and incorrect models is also not mentioned. This must be improved.

c. The figure legends need to provide more detail to describe what is shown. If an example of a SLIM is shown, include the reg ex pattern.

4. More detail on the comparison with AlphaMissense is needed. What dataset was used? How was it selected?

5. More context for the ACMG standards and aims is needed.

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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: No:  The pipeline is available through a Zenodo link, but the manuscript does not provide clear instructions on how users can access or implement MotSASi. Providing a public GitHub repository with all scripts and comprehensive documentation would significantly enhance its usability.

Reviewer #2: Yes

Reviewer #3: No:  Data and scripts are listed as a Zenodo file on the submission cover but not within the manuscript. Critical data that belong in the manuscript at least as summary tables in the supplementary material are missing.

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Reviewer #1: Yes:  Tamas Korcsmaros

Reviewer #2: No

Reviewer #3: No

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Reproducibility:

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Attachments

Attachment

Submitted filename: renamed_45276.pdf

PCOMPBIOL-D-25-00157R1

Integrating AlphaFold2 Models and Clinical Data to Improve the Assessment of Short Linear Motifs (SLiMs) and Their Variants’ Pathogenicity

PLOS Computational Biology

Dear Dr. Brunello,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript within 60 days (July 13, 2025 at 11:59 PM). If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Mohammad Sadegh Taghizadeh, Ph.D.

Academic Editor

PLOS Computational Biology

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

Additional Editor Comments :

Dear authors,

The esteemed reviewer #3 states that the manuscript is not well revised and her/his concerns have not been addressed. Also, the esteemed reviewer #2 has requested additional revisions. Therefore, I request that all the concerns be addressed point by point in the next step, and I sincerely inform you that if it is not so, I will reject this manuscript.

With the utmost respect

Journal Requirements:

1) We note that your MotSASi2.0_Revised_Manuscript files are duplicated on your submission. Please remove any unnecessary or old files from your revision, and make sure that only those relevant to the current version of the manuscript are included.

Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: The authors properly addressed all the comments and made the github repo available.

Reviewer #2: The authors have thoroughly worked on the manuscript and addressed most of my comments. One remaining issue is the statement that, in clinical assessment, false negatives would be less concerning than false positives. Missing true positives clearly is a problem, especially if diagnosis is actionable (e.g. BRCA variants). That said, given that computational results alone will currently not be used for diagnosing, perhaps this point is not as critical. I would nevertheless recommend phrasing impacts of false positive/negative carefully - for any given patient, this is a massive impact on their quality of life.

I would like to include this paper: https://doi.org/10.1038/s44320-023-00005-6 which I believe would also be relevant for the authors to consider.

Reviewer #3: I don't find the improvements of this ms sufficient. There is still a disconnect between the methods and the results. The results are incomplete. It is briefly mentioned that most SLIMs are in disordered loops and perhaps why this methods outperforms AlphaMissense. Can the authors better describe what characterizes the SLIMs in their data and what characterizes accurately and inaccurately predicted SLIMs for the two methods? How do GO terms help the prediction?

The authors state "Our pipeline significantly expanded the repertoire of known functionally" but since these are predictions only and not functional assays, this must be corrected.

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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: No:  The results are incomplete

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Reviewer #1: Yes:  Tamas Korcsmaros

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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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. Registration is free. 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 PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. If there are other versions of figure files still present in your submission file inventory at resubmission, please replace them with the PACE-processed versions.

Reproducibility:

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Dear Dr. Brunello,

We are pleased to inform you that your manuscript 'Integrating AlphaFold2 Models and Clinical Data to Improve the Assessment of Short Linear Motifs (SLiMs) and Their Variants’ Pathogenicity' 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.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Mohammad Sadegh Taghizadeh, Ph.D.

Academic Editor

PLOS Computational Biology

Ilya Ioshikhes

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 #2: The authors have addressed all my concerns.

Reviewer #4: The paper describes an application of the previously published method (MotSASi) for assessment of SLIMS using structures from AlphaFold2. The major novelty is incorporation of predicted structures instead of experimental ones.

The authors addressed the comments from previous review rounds.

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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 #2: None

Reviewer #4: Yes

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Reviewer #2: No

Reviewer #4: No