PONE-D-21-33646Functional classification and validation of yeast prenylation motifs using machine learning and genetic reportersPLOS ONE

Dear Dr. Schmidt,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Summary:

C-terminal CaaX motifs, such as those found in the Ras GTPase and the yeast a-factor mating pheromone, are canonically thought to direct cellular machinery to prenylate the cysteine side chain, cleave the aaX residues, and methylate the newly exposed cysteine carboxy terminus. This paper by Brittany M Berger and colleagues in Walter Schmidt’s lab follows up on the 2016 eLife study from the Schmidt lab, which revealed that not all CaaX proteins are modified in this canonical manner: the yeast J-domain protein/Hsp40 chaperone Ydj1 is prenylated but avoids classical downstream processing steps (carboxymethylation and cleavage), and this “shunted” modification is critical for Ydj1 to allow cells to grow at elevated temperature. Here, the authors test various machine learning algorithms in combination with various feature tabulation methods to attempt to classify which CaaX motifs direct proteins to the canonical pathway vs. the “shunted” pathway utilized by Ydj1. By using the non-canonical prenylation reporter Ydj1, they are able to expand the predicted set of prenylation sites beyond those whose final fate is carboxy methylation. The authors found multiple sequences (often, without the canonical aliphatic amino acids) that were prenylated but not cleaved. These sites would not be found by previous prenylation detection assays, which rely on cleavage as a readout of prenylation. With reporters of cleaved and non-cleaved prenylation targets in hand, the authors were also able to develop a cleavage prediction algorithm that performs well with their small training set. Unfortunately, the machine learning algorithm in this paper is limited in its predictive power because the training sets were small. As the authors acknowledge, this prevents a conclusive analysis and comparison of their machine learning algorithms and feature tabulation methods. However, the algorithm has improved predictive power, and coupled with the establishment of reporter assays for both the canonical and shunted pathways, this paper advances the field.

Specific comments:

1) The figures begin abruptly and it’s difficult to understand the first result. It would be helpful to include a cartoon figure at the beginning showing the canonical and shunted pathways and schematizing the workflow of the machine learning process.

2) The rationale for use of the 19-sequence validation set was unclear. The authors should justify more clearly why only these 19 sequences were used.

3) For the uninitiated reader, it would be helpful to explain why the prenylated proteins migrate faster in the gel than the unmodified proteins, and what to expect in terms of mobility for cleaved vs. shunted proteins.

4) Describe which features of Cxxx sequences are leading to the differences in the ESM-1b vs. Aaindex feature representation method results.

5) Figures are in reverse order in the manuscript and some figures are mislabeled.

6) Figure 1a: Label how many data points are graphed. n=489+508?

7) Fig 1c: Label how many data points are graphed. n=136+140

8) Page 5, Line 115 should also cite prenylation effect on Ydj1-Hsp90 interaction: Flom GA, Lemieszek M, Fortunato EA, Johnson JL. Farnesylation of Ydj1 is required for in vivo interaction with Hsp90 client proteins. Mol Biol Cell. 2008 Dec;19(12):5249-58. doi: 10.1091/mbc.e08-04-0435. Epub 2008 Oct 1. PMID: 18829866; PMCID: PMC2592663.

9) Figure legend 2B: cleaved vs shunted figure legend and bar colors are reversed. Shunted should have probability = 0 of being cleaved.

10) Figure legend and bar colors in Fig 2A are also reversed.

11) Figure 3: Include legends and labels on the figure

12) Pg 18, Line 424: Add figure reference (Fig 3B, C)

13) Table 3: What does NA mean in this context?

14) Typo: Fig 4 Legend: Delete “A, B)” before “Prenylation of the indicated naturally occurring”

15) Page 21, Line 495: Incorrect Figure reference. Should reference Fig 4C.

16) Line 504: Incorrect Figure reference. Should reference Fig 4C.

17) Line 411 pg 17: Cite previous studies

18) Table 4 and 5: The numbers are not displaying correctly.

Reviewer #2: The article titled "Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters" describes a new computational method to identify amino acid motifs in proteins that are prenylated. This method can also differentiate between prenylation followed by the canonical proteolysis and carboxy-methylation and the shunted, which only entails prenylation.

The manuscript if overall well-written and follows a logical structure.

The combination of the in-silico data and validation using yeast experiments is commendable.

It is clear that the new analysis tools are a substantial improvement of previously published methods.

Limitations are discussed.

Minor issues:

line 113 - thermotolerance does not describe the phenotype used here. "Growth defect at higher temperatures" would be more accurate.

line 307 - why do the different algorithms give such different results? Maybe it would be interesting to explain this.

line 446 - "some degree" can you be more specific?

In the Discussion the biological reasons why a protein might not be prenylated could be expanded (steric hindrance, protein-protein interactions etc.)

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Reviewer #1: No

Reviewer #2: No

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Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters

PONE-D-21-33646R1

Dear Dr. Schmidt,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Patrick Lajoie, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed all the comments, and the manuscript is now suitable for publication

Reviewer #2: The revised version of the manuscript implemented all major chnages and additions suggested by the reviewers.

I think the manusciprt is no ready for publication.

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7. 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

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