PONE-D-23-08204

SUNi mutagenesis: scalable and uniform nicking for efficient generation of variant libraries

PLOS ONE

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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

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

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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: No

Reviewer #2: Yes

Reviewer #3: 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

Reviewer #3: 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: Taylor Mighell et al, is a manuscript focused on a method, SUNi, which improves the efficiency of missense mutational library generation. SUNi is a nicking mutation library generation pipeline which includes python code for generating primers and optimized molecular biology protocols yielding libraries with less positional bias compared to other nicking approaches. As nicking and other inverse PCR approaches are amongst the most commonly used library generation pipelines, SUNi will be quite useful to the mutational scanning community. Furthermore, this manuscript, to my knowledge, is the most extensive benchmarking and exploration of nicking mutagenesis which has been technically challenging for some groups to implement efficiently. Overall, the manuscript provides extensive details on why and how improvements were made for nicking mutagenesis. I support the publication of this manuscript with some minor suggestions on how the approach is framed and a few additional technical details.

Major Concerns:

None – overall this study and SUNi is a rigorously done and nicking is convincingly improved compared to the previous state-of-the-art for nicking mutagenesis.

Minor suggestions:

1) In the introduction and throughout the manuscript the authors compare and contrast ‘cassette’ based (synthesized oligo libraries stitched into a gene) with inverse PCR based approaches such as nicking mutagenesis. In this study the authors generated a library for a different gene, B2AR, than the target for SUNi. As such it is hard to directly compare library efficiency as nicking mutagenesis is (as the authors describe and demonstrate well) sensitive to sequence. Furthermore, there have been improvements to the cassette-based approaches which were not implemented in when the B2AR library but have by happenstance been implemented for the entirety of MOR by our group. If the authors wanted they could directly compare these libraries however I would encourage the authors to refocus the narrative and manuscript even more on the (rightful) massive benefits to folks who use nicking mutagenesis and a bit less on the comparison with cassette based approaches as they did not go nearly as deep on cassette-based for the comparison to be satisfying.

2) The authors state throughout the manuscript that in theory nicking mutagenesis provides a benefit for being scalable due to not needing to be assembled in sublibraries. In the manuscript the authors generate libraries using SUNi for two different regions (MOR2 42-81, MOR6 202-241). In reading the text, it was not clear to me (perhaps I misread it) whether these two regions were made in the same pool or in parallel. If they were made in the same pool this should be explicitly stated both in the main text and the methods – that’s awesome. If they were made in parallel than the approach is not so different compared to oligo-based libraries. Overall, it still remains to be seen whether nicking mutagenesis approaches can efficiently make libraries for an entire gene such as MOR in the same pool.

3) Deep mutational scanning ideally allows one to measure how all amino acids impact a genes function at each position. This means for a library generation pipeline such as SUNi one should explore positional bias (called uniformity) as the authors do well but also amino acid bias. Within the manuscript the authors compare the fraction programmed overall. I suggest also comparing how different amino acids are incorporated overall and also per position.

4) Currently the methods section has very little details on the specific molecular biology pipelines used and amounts of reagents which will make implementing SUNi in other labs. I encourage the authors to write a more detailed protocols for the manuscript but even better would be writing out an extremely detailed (perhaps even excruciatingly so) for an open source platform such as Bioprotocols.io to aid others in implementing their approach as some labs have faced considerable challenges in implementing the nicking mutagenesis approaches.

Technical suggestions:

Line 51: The authors state SUNi provides a ‘2-fold improvement’. However it’s unclear as written what is improved 2-fold.

Line 132: Authors state that the percent of programmed mutants is low in cassette mutagenesis – what is the percent? In Fig 2c it looks to be around 0.56. As discussed above it is difficult to compare the two approaches as the target is different.

Line 179: B2AR2 mutagenesis section has very little detail in terms of the number of cycles of PCR and what reagents were used.

My relevant background: My research lab applies genetic screening approaches to study membrane biology and disease. To enable this work we developed two library generation pipelines, SPINE and DIMPLE which use oligo pools or are ‘cassette-based approaches’. As such I am familiar with the molecular biology pipelines but have inherent bias towards ‘cassette-based’ approaches as is represented in my suggestions.

Willow Coyote-Maestas

Reviewer #2: Summary

Taylor Mighell et al, is a manuscript focused on a method, SUNi, which improves the efficiency of missense mutational library generation. SUNi is a nicking mutation library generation pipeline which includes python code for generating primers and optimized molecular biology protocols yielding libraries with less positional bias compared to other nicking approaches. As nicking and other inverse PCR approaches are amongst the most commonly used library generation pipelines, SUNi will be quite useful to the mutational scanning community. Furthermore, this manuscript, to my knowledge, is the most extensive benchmarking and exploration of nicking mutagenesis which has been technically challenging for some groups to implement efficiently. Overall, the manuscript provides extensive details on why and how improvements were made for nicking mutagenesis. I support the publication of this manuscript with some minor suggestions on how the approach is framed and a few additional technical details.

Major Concerns:

None – overall this study and SUNi is a rigorously done and nicking is convincingly improved compared to the previous state-of-the-art for nicking mutagenesis.

Minor suggestions:

1) In the introduction and throughout the manuscript the authors compare and contrast ‘cassette’ based (synthesized oligo libraries stitched into a gene) with inverse PCR based approaches such as nicking mutagenesis. In this study the authors generated a library for a different gene, B2AR, than the target for SUNi. As such it is hard to directly compare library efficiency as nicking mutagenesis is (as the authors describe and demonstrate well) sensitive to sequence. Furthermore, there have been improvements to the cassette-based approaches which were not implemented in when the B2AR library but have by happenstance been implemented for the entirety of MOR by our group. If the authors wanted they could directly compare these libraries however I would encourage the authors to refocus the narrative and manuscript even more on the (rightful) massive benefits to folks who use nicking mutagenesis and a bit less on the comparison with cassette based approaches as they did not go nearly as deep on cassette-based for the comparison to be satisfying.

2) The authors state throughout the manuscript that in theory nicking mutagenesis provides a benefit for being scalable due to not needing to be assembled in sublibraries. In the manuscript the authors generate libraries using SUNi for two different regions (MOR2 42-81, MOR6 202-241). In reading the text, it was not clear to me (perhaps I misread it) whether these two regions were made in the same pool or in parallel. If they were made in the same pool this should be explicitly stated both in the main text and the methods – that’s awesome. If they were made in parallel than the approach is not so different compared to oligo-based libraries. Overall, it still remains to be seen whether nicking mutagenesis approaches can efficiently make libraries for an entire gene such as MOR in the same pool.

3) Deep mutational scanning ideally allows one to measure how all amino acids impact a genes function at each position. This means for a library generation pipeline such as SUNi one should explore positional bias (called uniformity) as the authors do well but also amino acid bias. Within the manuscript the authors compare the fraction programmed overall. I suggest also comparing how different amino acids are incorporated overall and also per position.

4) Currently the methods section has very little details on the specific molecular biology pipelines used and amounts of reagents which will make implementing SUNi in other labs. I encourage the authors to write a more detailed protocols for the manuscript but even better would be writing out an extremely detailed (perhaps even excruciatingly so) for an open source platform such as Bioprotocols.io to aid others in implementing their approach as some labs have faced considerable challenges in implementing the nicking mutagenesis approaches.

Technical suggestions:

Line 51: The authors state SUNi provides a ‘2-fold improvement’. However it’s unclear as written what is improved 2-fold.

Line 132: Authors state that the percent of programmed mutants is low in cassette mutagenesis – what is the percent? In Fig 2c it looks to be around 0.56. As discussed above it is difficult to compare the two approaches as the target is different.

Line 179: B2AR2 mutagenesis section has very little detail in terms of the number of cycles of PCR and what reagents were used.

My relevant background: My research lab applies genetic screening approaches to study membrane biology and disease. To enable this work we developed two library generation pipelines, SPINE and DIMPLE which use oligo pools or are ‘cassette-based approaches’. As such I am familiar with the molecular biology pipelines but have inherent bias towards ‘cassette-based’ approaches as is represented in my suggestions.

Willow Coyote-Maestas

Reviewer #3: Lehner and colleagues present a modification of nicking mutagenesis for the efficient generation of variant libraries. Nicking and PFunkel mutagenesis are templated-based mutagenesis methods for constructions of highly defined mutagenesis libraries; the major issues with nicking mutagenesis are (i.) high wild-type (original plasmid sequence) carryover; and (ii.) lack of library uniformity. The major innovations in this paper are identifying the source of these inefficiencies - the 5' GC content is critically important, and improving the primer sets for mutagenesis decreases the amount of wild-type carryover and improves library uniformity. The paper is well written and figures excellent. I have two minor technical comments.

1. For Lines 117-119, metrics are reported but no statistical significance is attached to the findings. I recommend that the authors compare the distributions by an appropriate statistical test to report the significance of the lower wild-type carryover and library uniformity using their metric defined in the paper.

2. For the GC clamp statement, some reference(s) could be supplied. "A GC-rich 3’ terminus of a primer (also known as “GC clamp”) is widely thought to improve priming efficiency (CITE)".

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Reviewer #1: Yes: Willow Coyote-Maestas

Reviewer #2: Yes: Willow Coyote-Maestas

Reviewer #3: No

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SUNi mutagenesis: scalable and uniform nicking for efficient generation of variant libraries

PONE-D-23-08204R1

Dear Dr. Lehner,

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.

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Kind regards,

Paulo Lee Ho, Ph.D.

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 #3: 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 #3: Yes

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

Reviewer #1: Yes

Reviewer #3: 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 #3: Yes

**********

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 #3: 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: I am happy with the responses to comments and changes made. My bad missing the detailed methods in the supplement.

Reviewer #3: All technical concerns have been addressed - great work! ********************************************

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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: Yes: Willow Coyote-Maestas

Reviewer #3: No

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