From the journal Digital Discovery Peer review history

14-May-2022

Dear Dr Pozzo:

Manuscript ID: DD-ART-02-2022-000017
TITLE: Multivariate Analysis of Peptide-Driven Nucleation and Growth of Au Nanoparticles

Firstly, thank you for your patience as we completed the review process. Due to several factors this process took far longer than anticipated and I extend my apologies for the delay. I sent your manuscript to reviewers and I have now received their reports which are copied below.

After careful evaluation of your manuscript and the reviewers’ reports, I will be pleased to accept your manuscript for publication after revisions.

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Professor Jason Hein
Associate Editor, Digital Discovery

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Reviewer 1

Dear authors,
The peptide driven and particle growth dynamics of Au particles via chemical reduction method. The data analysis pipeline for studying large synthesis spaces using high-throughput experimentation is another interesting aspect. The study emphasizes the utility of peptides for the growth of Au might expand the scope of the work in the synthesis of plasmonic materials. The overall organization of the article is clear without any ambiguity. The listed comments may be considered for better understanding about the work.

Minor comments

Abstract
(1) Specify the concentration range of peptides within the parenthesis for clarity.
(2) Expand ‘HEPES’ before abbreviating it.
(3) The size of Au nanoparticles obtained may be briefed.

Section 2.2
(1) It should be ‘h’ instead of ‘H’!

Results and discussion
(1) The Au possesses a threshold SPR band at ~540 nm! Therefore, measuring the concentration of Au(0) at 400 nm may be justified.
(2) The figure numbers are abruptly placed and should be ordered. For instance, Figure 7D appears immediately after Figure 2.
(3) Section 3.1: The particle growth dynamics of Au should also be discussed.
(4) The morphological evolution of Au nanoparticles considering the nature of the peptides should be discussed.
(5) How about varying the concentration of HEPES on particle growth dynamics of Au with reference to a given peptide.

Reviewer 2

I enjoyed reading and reviewing this manuscript. The authors propose an innovative platform for peptide-driven nucleation and growth of Au nanoparticles. The platform makes creative use of functional data representations and analysis techniques (FPCA and graph Fourier transforms) and the authors show that these tools provide an effective way to navigate the design space. I am not an expert in the specific application space, so I will mostly comment on the methodological component of the paper associated with data analysis.

Overall, the data analysis techniques presented are interesting and clearly effective at helping navigate the synthesis space (they particularly reveal clear domains in the synthesis space). I have no problem understanding the functional PCA component of the paper, but I struggled following the subsequent discussion on graph representations and application of graph Fourier transforms. Specifically, it is not clear to me how the vertices and edges of the graph G(V,E) are constructed; the paper uses quite technical terminology that is not always easy to follow. For instance, the paper states that “Suppose G(V,E) is a graph constructed from Euclidean embedding of component concentrations.” I must admit that I cannot follow this statement, I think that the paper would benefit from being a bit more tutorial. What is a Euclidean embedding of component concentrations? Maybe a figure that illustrates how the graph is constructed would be great (clearly specify what is a node and what is an edge). I had no trouble following the subsequent graph Fourier analysis, but the construction of the graph representation is a key element of the paper that needs to be explained in more detail.

Would be good for the authors to provide a bit more perspective on why they selected the proposed data analysis approaches (compared to others that exist). The approach presented is creative and clearly effective but I think it would be good for the readers with limited experience in functional data analysis to provide a bit more perspective on the reasoning behind selecting such approaches (e.g., Why not autoencoders? Why using graph representations?). It would also be good for the authors to provide a bit more perspective on previous uses of functional PCA (e.g., has this been used before for spectral data analysis)?

Would be good for the authors to provide more perspective on how one could “close-the-loop” with an automated experimental platform to drive synthesis of nanoparticles. I understand that the current work is mostly exploratory, but would be good to comment on the potential and challenges behind having a fully exploratory framework.

Reviewer 3

It was difficult for me to determine with high confidence the satisfaction of criteria in the data reviewer checklist by this manuscript. I give you the benefit of my doubt and lack of time to adequately evaluate the criteria. There is certainly a great deal of content and form to suggest that thought was given here wrt folder structure, code, comment blocks in code, copious metadata present for the Zenodo repos, etc. I highly recommend including a "FAQ-style" "executive summary" that briefly addresses the reviewer checklist criteria, if only to point to relevant sections within the folder structure and to particular files that highlight adherence. The bottom line is that, while I feel that a motivated reader could perhaps reproduce what you've done, your work is more likely to be effectively cited and reused if you provide a more pointed summary of the checklist concerns.

Dear editors,
We would like to thank you and the reviewers for their valuable feedback, comments and suggestions.
Based on the suggestions, we have significantly improved the manuscript in terms of text, figures
and other various aspects. We addressed all the questions from the reviewers in the ‘response to
referees’ document attached along with this submission. As requested in the email correspondence,
we have included the following items along with our updated submission:
1. The revised manuscript with all changes highlighted in blue.
2. A .zip file of the overleaf TeX folder and a compiled version of the manuscript without
highlighted edits.
3. A .zip file containing .pdf versions of all the figures (with a resolution of 600 dpi) numbered
based on their reference number in the manuscript is attached.
4. A TOC image is attached as .docx (with editable text).
5. A separate .pdf file of the Electronic Supplementary Information.
6. A .docx containing a point by point response to reviewer 3 (data reviewer).

We look forward to a positive response to this paper and are happy to provide further information
if needed.

Sincerely,
Kacper J. Lachowski, Kiran Vaddi, Nada Y. Naser, François Baneyx, Lilo D. Pozzo
University of Washinton, Seattle, WA, 98195
Molecular Engineering and Sciences Institute
Department of Chemical Engineering
Department of Material Science and Engineering
{lachok, kiranvad, nynaser, banyex, dpozzo}@uw.edu

This text has been copied from the PDF response to reviewers and does not include any figures, images or special characters:

We thank the reviewers for their valuable comments and suggestions. We address them below point-by-point. Changes made to the text in the main manuscript are also pasted (highlighted in a blue color) here for convenience. Reviewer comments are typeset in italic while our answers follow in normal font.
Reviewer 1
Abstract
1. Specify the concentration range of peptides within the parenthesis for clarity We have now added all the concentrations as suggested.
2. Expand ‘HEPES’ before abbreviating it. We have made this change. Thank you
3. The size of Au nanoparticles obtained may be briefed.
Thanks for the suggestion. We have now added the nanoparticle ’size’ range of 10-50 nm. However, we note that different peptides and reaction conditions result in variations in size and shape.
Section 2.2
1. It should be ‘h’ instead of ‘H’!
This has been resolved, thank you.
Results and discussion
1. The Au possesses a threshold SPR band at 540 nm! Therefore, measuring the concentration of Au(0) at 400 nm may be justified.
We agree that the absorption at 400 nm is useful for comparing the relative concentrations of Au(0) between samples containing an SPR band at 540 nm. We simply wished to caution the reader that the molar extinction coefficient at 400 nm can also depend other factors (e.g. presence of very small clusters, turbidity) and the concentration estimate could be affected by this. We added a clarification statement to the revised manuscript.
"Additionally, absorption at 400 nm can be used to determine the concentration of Au(0) in-situ[1]. However, as the cited work points out, the extinction coefficient of Au(0) at 400 nm depends on particle shape, size distribution, and the particle’s surface chemistry (e.g. peptide or HEPES). Other contributions to optical extinction at this wavelength may include Au clusters and turbidity. Nevertheless, optical extinction at 400 nm is useful for identifying major differences in Au reduction when analyzing UV-Vis spectra."
2. The figure numbers are abruptly placed and should be ordered. For instance, Figure 7D appears immediately after Figure 2.
We have now ensured that all figures appear as close as possible to when they are first mentioned and that they are in order.
3. Section 3.1: The particle growth dynamics of Au should also be discussed. We agree that the dynamics of particle growth in the presence of peptides is an interesting area to explore. In fact, we have begun studying growth dynamics by using in-situ UVVis spectroscopy (rev:kinetic) and SAXS. We see that at this low peptide concentration the apparent rate of reduction of Z2M246I and Z2 are similar, but the sample with MZ2 lags significantly. This is interesting because the spectra formed with MZ2 and Z2 at this condition are similar to one another after 24H (Figure 2B in the manuscript). We believe that the importance and complexity of studying dynamics of growth warrants much more detailed exploration and discussion in a future manuscript and that a detailed discussion, given the large number of conditions, is outside of the scope of what we can adequately present here. Nevertheless, we have added several new statements to section 3.1 mentioning that there are indeed notable differences in the kinetics of growth as a function of sequence and conditions, and that these are the subject of ongoing investigations.
The following will be added to the end of section 3.1:
"The present work is focused on measurement of spectra at just one time point (24H) in order to connect peptide properties and reaction conditions with nanoparticle structure. Nevertheless, we also include measurements of the optical extinction at 400 nm over time in nanoparticle syntheses carried out in the presence of Z2, Z2M246I, or MZ2. These measurements demonstrate how peptide modifications affected the Au reduction rate (see supporting information). The slower reduction rate in the presence of MZ2 and the decrease in LSPR features in spectra prepared with high peptide concentrations after 24 hours (e.g. (fig:spectra D) both demonstrate that peptide lipidation has a significant impact on growth dynamics. These differences in reduction rates are interesting given the similarity of spectra obtained with these reagent concentrations after 24 hours in the case of Z2 and MZ2 (fig:spectra B). The similarity in reduction rates between Z2 and Z2M246I on the other hand show that, at least in these conditions, the presence of methionine in the peptide does not appear to have an affect on the Au reduction rate. However, the connection between peptide properties, reaction conditions, and nanoparticle growth dynamics will require extensive in-situ UV-Vis spectroscopy and SAXS studies in the future."
4. The morphological evolution of Au nanoparticles considering the nature of the peptides should be discussed.
We agree with this suggestion, and have added to section 3.5 in which we focus on using direct methods of characterizing particle structure.
5. How about varying the concentration of HEPES on particle growth dynamics of Au with reference to a given peptide.
We agree that the concentration of HEPES is likely to have an impact on particle growth dynamics. However, we reiterate that the kinetics of particle growth were outside of the scope of this study. Our future work will include variations in HEPES concentrations to identify the importance of this experimental factor.
Reviewer 2
I enjoyed reading and reviewing this manuscript. The authors propose an innovative platform for peptide-driven nucleation and growth of Au nanoparticles. The platform makes creative use of functional

Figure 1: Absorbance at 400 nm as a function of time (minutes) for Z2, Z2M246I, and MZ2. Samples were prepared by adding all reagents except HAuCl4. As soon as the latter was added, the solution was pipetted up and down and the samples were moved into the plate reader for measurement.
data representations and analysis techniques (FPCA and graph Fourier transforms) and the authors show that these tools provide an effective way to navigate the design space. I am not an expert in the specific application space, so I will mostly comment on the methodological component of the paper associated with data analysis.
Overall, the data analysis techniques presented are interesting and clearly effective at helping navigate the synthesis space (they particularly reveal clear domains in the synthesis space). I have no problem understanding the functional PCA component of the paper, but I struggled following the subsequent discussion on graph representations and application of graph Fourier transforms.
We thank the reviewer for the valuable feedback on the data analysis method proposed and used in this manuscript. We address the reviewer 2 comments below point-by-point.
1. Specifically, it is not clear to me how the vertices and edges of graph G(V, E) are constructed; the paper uses quite technical terminology that is not always easy to follow. For instance, the paper states that “Suppose G(V, E) is a graph constructed from Euclidean embedding of component concentrations.” I must admit that I cannot follow this statement, I think that the paper would benefit from being a bit more tutorial. What is a Euclidean embedding of component concentrations? Maybe a figure that illustrates how the graph is constructed would be great (clearly specify what is a node and what is an edge). I had no trouble following the subsequent graph Fourier analysis, but the construction of the graph representation is a key element of the paper that needs to be explained in more detail.
The Euclidean embedding refers to using concentrations of a given sample as coordinates in Euclidean space (i.e. an embedding) to construct the graph using a k-nearest neighbors approach for example. This was mentioned earlier in section 3.2 in the following sentence: “To correlate different synthesis spaces of peptide sequences, we first parameterize the sampled synthesis space by a graph (i.e. a network of first-order connections) using the different component concentrations as nodes and edges obtained using a k-nearest neighbors approach” We agree with the reviewer that this particular sentence about the graph construction may be too abstract. We have thus replaced the sentence with the following: Suppose G = (V, E) is a graph constructed from k-NN approach described earlier.
2. Would be good for the authors to provide a bit more perspective on why they selected the proposed data analysis approaches (compared to others that exist). The approach presented is creative and clearly effective but I think it would be good for the readers with limited experience in functional data analysis to provide a bit more perspective on the reasoning behind selecting such approaches (e.g., Why not autoencoders? Why using graph representations?).
We address this comment in two parts:
(a) Functional Data Analysis (FDA) is selected primarily to account for the known mathematical structure of our data points. For any spectrum, considering each evaluation as a dimension assumes that response (feature value) at any given wavelength (dimension) is completely independent of the response values before and after– which is not true. FDA allows us to encode this dependency via an inner product defined for functions using an integral over the domain for example instead of only wavelength-to-wavelength dot product used for vectors. The particular modeling choices can be different but in our case a simple PCA model with a functional data representation was sufficient. Extending this approach to auto-encoders etc. is a part of the future work.
(b) Graph representations are used mainly to take advantage of the well-developed methodologies around Fourier transforms to compress signals. Moreover, graphs provide a natural way to encode continuity and connectivity in the composition space of the synthesis space wherein samples closer in compositions are likely to have similar synthesis outcomes.
To further aid readers, we have added the following sentences to the manuscript:
To expand on usage of graph Fourier transform :
The graph signal approach also allows us to further compress the signals and delineate regions of synthesis spaces for comparison using a Fourier transform [2, 3] that has been well developed for graphs with readily available open-source code [4].
To expand on usage of function representation of spectra :
This choice of representation allows us to: a) encode the dependency between responses at different wavelengths via an inner product using integrals; b) learn underlying basis functions to represent the functional variations such as peak position and broadening.
3. It would also be good for the authors to provide a bit more perspective on previous uses of functional PCA (e.g., has this been used before for spectral data analysis)?
We thank the reviewer for this suggestion and added the following sentence to our manuscript:
FDA and related models therein (such as FPCA) are widely used techniques in the statistics community [5, 6] for analyzing time series data, shape analysis of curves, and data of input/output pair type.
To our knowledge, FDA techniques have not been applied in the context of spectral data analysis. We also mention this in the revised manuscript and thank the reviewer for this suggestion.
4. Would be good for the authors to provide more perspective on how one could “close the loop” with an automated experimental platform to drive the synthesis of nanoparticles. I understand that the current work is mostly exploratory, but would be good to comment on the potential and challenges behind having a fully exploratory framework.
This is a really good suggestion but we think the closed-loop experimentation would be quite tangential to the current work as the reviewer also pointed out.
However, the pipeline presented has been internally used to perform closed-loop highthroughput experimentation as shown [7] which we also cite in the conclusion section of this paper to address this suggestion:
The hardware and software introduced in this work can also be used to perform highthroughput closed-loop experimentation for material retrosynthesis [7], to deduce a phase diagram in an accelerated fashion, etc.
Reviewer 3
It was difficult for me to determine with high confidence the satisfaction of criteria in the data reviewer checklist by this manuscript. I give you the benefit of my doubt and lack of time to adequately evaluate the criteria. There is certainly a great deal of content and form to suggest that thought was given here wrt folder structure, code, comment blocks in code, copious metadata present for the Zenodo repos, etc. I highly recommend including a "FAQ-style" "executive summary" that briefly addresses the reviewer checklist criteria, if only to point to relevant sections within the folder structure and to particular files that highlight adherence. The bottom line is that, while I feel that a motivated reader could perhaps reproduce what you’ve done, your work is more likely to be effectively cited and reused if you provide a more pointed summary of the checklist concerns. We are including the summary of how we meet the criteria.
Changes Proposed by the Authors
• In Figure 1 and its caption, "reaction space(s)" will be changed to synthesis space to match wording in the rest of the document.
References
[1] Thomas Hendel, Maria Wuithschick, Frieder Kettemann, Alexander Birnbaum, Klaus Rademann, and Jörg Polte. In Situ Determination of Colloidal Gold Concentrations with UV–Vis Spectroscopy: Limitations and Perspectives. Analytical Chemistry, 86(22):11115–11124, nov 2014.
[2] Benjamin Ricaud, Pierre Borgnat, Nicolas Tremblay, Paulo Gonçalves, and Pierre Vandergheynst. Fourier could be a data scientist: From graph fourier transform to signal processing on graphs. Comptes Rendus Physique, 20(5):474–488, 2019.
[3] Aliaksei Sandryhaila and José MF Moura. Discrete signal processing on graphs: Graph fourier transform. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 6167–6170. IEEE, 2013.
[4] Michaël Defferrard, Lionel Martin, Rodrigo Pena, and Nathanaël Perraudin. Pygsp: Graph signal processing in python.
[5] JO Ramsay and BW Silvermann. Functional data analysis. springer series in statistics, 1998.
[6] Anuj Srivastava and Eric P Klassen. Functional and shape data analysis, volume 1. Springer, 2016.
[7] Kiran Vaddi, Huat Thart Chiang, and Lilo D Pozzo. Autonomous retrosynthesis of nanoscale structures via spectral shape matching. 2022.

Summary:
• Once the Zenodo repository is downloaded, the user should install a Conda environment using /Notebooks/environment.yml. The user can then execute the notebooks included in /Notebooks/ to reproduce all of the figures which will then be saved to /Figures.
• The following changes were made to the Zenodo repository:
o The Zenodo repository was updated to include data and a notebook for plotting the UV-Vis kinetics experiments shared in the supplemental information.
o The README.docx was updated to reflect this addition and a clarification was added for using sasmodels.
o The Notebooks/environment.yml was updated to make it more compatible with different platforms. Build specifications were removed. Dependencies which were specific to a particular platform were removed.
• The gist was also updated to pull data from the above Zenodo repository instead of a different source. Helper code was included in the gist instead of being downloaded from an online repository.
1. Data sources
a. Are all data sources listed and publicly available?
i. Yes they are in the Zenodo repository.
b. If using an external database, is an access date or version number provided?
i. The Zenodo repository cited in the data availability section has been assigned a DOI which satisfies these criteria.
c. Are any potential biases in the source dataset reported and/or mitigated?
i. No biases exist to the best of our knowledge.
2. Data cleaning
a. Are the data cleaning steps clearly and fully described, either in text or as a code pipeline?
i. UV-Vis data reduction is described in the Zenodo repository README.docx. Reduction of USAXS data was done using the macros in IGOR. UV-Vis measurements include wavelengths between 300 and 400 nm. These data were excluded from visualizations and functional data analysis due to non-negligible contributions in thickness variation between microplates. Beyond that, all of the data which was included in this dataset was used to generate the figures and perform data analysis.
b. Is an evaluation of the amount of removed source data presented?
i. No source data was excluded beyond what is mentioned in the previous point.
c. Are instances of combining data from multiple sources clearly identified, and potential issues mitigated?
i. This is not an issue in this work.
3. Data representations
a. Are methods for representing data as features or descriptors clearly articulated, ideally with software implementations?
i. Data are not treated as features or descriptors in this text.
b. Are comparisons against standard feature sets provided?
i. Not applicable.
4. Model choice
a. Is a software implementation of the model provided such that it can be trained and tested with new data?
i. Yes. The code in the Github gist (included in data availability statement) may be used to repeat the analysis on new data obtained from experiments done with different peptides. We even included code within the Zenodo repository (/Synthesis_Protocol/Example.ipynb) for controlling the OT2 liquid handling robot in order to generate this data.
b. Are baseline comparisons to simple/trivial models (for example, 1-nearest neighbour, random forest, most frequent class) provided?
i. Examples of functional principal component analysis and graph Fourier transform with trivial datasets are already provided in the supplementary information.
c. Are baseline comparisons to current state-of-the-art provided?
i. Not applicable. Current state-of-the-art of evaluating the effect of peptides on nanoparticle synthesis avoids repeating synthesis in multiple reaction conditions.
5. Model training and validation
a. Does the model clearly split data into different sets for training (model selection), validation (hyperparameter optimization), and testing (final evaluation)?
i. Not applicable.
b. Is the method of data splitting (for example, random, cluster- or time-based splitting, forward cross-validation) clearly stated? Does it mimic anticipated real-world application?
i. Not applicable.
c. Does the data splitting procedure avoid data leakage (for example, is the same composition present in the training and test sets)?
i. Not applicable.
6. Code and Reproducibility
a. Is the code or workflow available in a public repository?
i. Yes, please see the data availability statement for links to the Zenodo repository and the Github gist.
b. Are scripts to reproduce the findings in the paper provided?
i. Yes – the Github gist contains the scripts for performing all steps of functional data analysis, the repository includes all data used to generate figures and perform the analysis, the repository also includes all of the code needed to repeat the synthesis on the OT2 liquid handling robot.

31-May-2022

Dear Dr Pozzo:

Manuscript ID: DD-ART-02-2022-000017.R1
TITLE: Multivariate Analysis of Peptide-Driven Nucleation and Growth of Au Nanoparticles

Thank you for submitting your revised manuscript to Digital Discovery. I am pleased to accept your manuscript for publication in its current form.

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Associate Editor, Digital Discovery


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