From 815de11defef81790d3ede784913ceec10a273f9 Mon Sep 17 00:00:00 2001 From: Will Hannon Date: Thu, 26 Oct 2023 15:43:37 -0700 Subject: [PATCH] Add crossrefs to figures --- paper/paper.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 60bd615..512cf52 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -28,7 +28,7 @@ bibliography: paper.bib ## Summary and Purpose -Understanding how mutations impact a protein's functions is valuable for many types of biological questions. High-throughput techniques such as deep-mutational scanning (DMS) have greatly expanded the number of mutation-function datasets. For instance, DMS has been used to determine how mutations to viral proteins affect antibody escape [@dadonaitePseudovirusSystemEnables2023], receptor affinity [@starrDeepMutationalScanning2020], and essential functions such as viral genome transcription and replication [@liDeepMutationalScanning2023]. With the growth of sequence databases, in some cases the effects of mutations can also be inferred from phylogenies of natural sequences [@bloomFitnessEffectsMutations2023] (**Figure 1**). +Understanding how mutations impact a protein's functions is valuable for many types of biological questions. High-throughput techniques such as deep-mutational scanning (DMS) have greatly expanded the number of mutation-function datasets. For instance, DMS has been used to determine how mutations to viral proteins affect antibody escape [@dadonaitePseudovirusSystemEnables2023], receptor affinity [@starrDeepMutationalScanning2020], and essential functions such as viral genome transcription and replication [@liDeepMutationalScanning2023]. With the growth of sequence databases, in some cases the effects of mutations can also be inferred from phylogenies of natural sequences [@bloomFitnessEffectsMutations2023] (\autoref{fig:figure1}). The mutation-based data generated by these approaches is often best understood in the context of a protein’s 3D structure; for instance, to assess questions like how mutations that affect antibody escape relate to the physical antibody binding epitope on the protein. However, current approaches for visualizing mutation data in the context of a protein’s structure are often cumbersome and require multiple steps and softwares. To streamline the visualization of mutation-associated data in the context of a protein structure, we developed a web-based tool, `dms-viz`. With `dms-viz`, users can straightforwardly visualize mutation-based data such as those from DMS experiments in the context of a 3D protein model in an interactive format. See to use`dms-viz`. @@ -54,9 +54,9 @@ Our group previously created a tool called `dms-view` [@hiltonDmsviewInteractive ## Design and Usage -Using `dms-viz` involves three components. First, using the command line tool `configure-dms-viz`, available as a Python package on PyPI (), the user formats their data into a `JSON` specification file. Then, the user uploads this specification file to `dms-viz.github.io`, a web-based interface written in Javascript ,`D3.js`, and `NGL.js` [@roseNGLViewerWebbased2018]. Finally, the specification file can either be shared directly or hosted remotely to generate a shareable URL link. (**Figure 2**). +Using `dms-viz` involves three components. First, using the command line tool `configure-dms-viz`, available as a Python package on PyPI (), the user formats their data into a `JSON` specification file. Then, the user uploads this specification file to `dms-viz.github.io`, a web-based interface written in Javascript ,`D3.js`, and `NGL.js` [@roseNGLViewerWebbased2018]. Finally, the specification file can either be shared directly or hosted remotely to generate a shareable URL link. (\autoref{fig:figure2}). -Upon uploading the specification file to `dms-viz`, users will see a visualization composed of four components, as illustrated in **Figure 3**. +Upon uploading the specification file to `dms-viz`, users will see a visualization composed of four components, as illustrated in \autoref{fig:figure3}. 1. **Context plot**: Located at the top of the visualization, this component allows users to zoom into specific sites on the Focus plot while maintaining an overview of the entire dataset. @@ -115,18 +115,18 @@ JDB is on the scientific advisory boards of Apriori Bio, Aerium Therapeutics, In ## Figures -![Figure 3.1](figures/figure-1.png) -**Figure 1**: *Large mutation-associated datasets are used in a variety of experimental contexts*. They can be used to map antibody footprints on viral glycoproteins, assess the impact of mutations on protein function in a laboratory setting, and identify patterns of selection from natural mutation frequencies. +![Figure 1](figures/figure-1.png) +**Figure 1**: *Large mutation-associated datasets are used in a variety of experimental contexts*. They can be used to map antibody footprints on viral glycoproteins, assess the impact of mutations on protein function in a laboratory setting, and identify patterns of selection from natural mutation frequencies.\label{fig:figure1} \newpage -![Figure 3.2](figures/figure-2.png) -**Figure 2**: *Using `dms-viz` involves three components*. (1) The user formats their data using the command line tool `configure-dms-viz`. (2) The user takes the resulting `JSON` specification file and uploads it to `dms-viz.github.io`. (3) The user can choose to either share the `JSON` file, host the `JSON` file and generate a shareable URL link, or export static images. +![Figure 2](figures/figure-2.png) +**Figure 2**: *Using `dms-viz` involves three components*. (1) The user formats their data using the command line tool `configure-dms-viz`. (2) The user takes the resulting `JSON` specification file and uploads it to `dms-viz.github.io`. (3) The user can choose to either share the `JSON` file, host the `JSON` file and generate a shareable URL link, or export static images.\label{fig:figure2} \newpage -![Figure 3.3](figures/figure-3.png) -**Figure 3**: *`dms-viz` provides a compact interface for exploring mutation-associated data*. The visual component of `dms-viz` contains a line/point plot that shows a summary of the mutation-metric at all sites, in this case, mutation-escape from the constituents of a therapeutic antibody cocktail measured by DMS of the SARS-CoV-2 receptor binding domain (RBD) [@starrProspectiveMappingViral2021]. The user can zoom into specific regions of interest while maintaining context of the whole dataset using the context plot. Additionally, users can click on points in the focus plot to get details on every mutation for each site in the detail plot. Finally, sites that are selected on the focus plot by dragging are shown on the interactive protein structure colored by the summary statistic. In this example, the structure shown is the SARS-CoV-2 RBD bound to both antibodies in the therapeutic cocktail (PDB: 6XDG). A collapsible sidebar is used to configure the visualization and select the condition on the interactive protein structure. By collapsing out of view, the sidebar makes the visualization an optimal size for integrating into online platforms like websites and HTML presentation slides. +![Figure 3](figures/figure-3.png) +**Figure 3**: *`dms-viz` provides a compact interface for exploring mutation-associated data*. The visual component of `dms-viz` contains a line/point plot that shows a summary of the mutation-metric at all sites, in this case, mutation-escape from the constituents of a therapeutic antibody cocktail measured by DMS of the SARS-CoV-2 receptor binding domain (RBD) [@starrProspectiveMappingViral2021]. The user can zoom into specific regions of interest while maintaining context of the whole dataset using the context plot. Additionally, users can click on points in the focus plot to get details on every mutation for each site in the detail plot. Finally, sites that are selected on the focus plot by dragging are shown on the interactive protein structure colored by the summary statistic. In this example, the structure shown is the SARS-CoV-2 RBD bound to both antibodies in the therapeutic cocktail (PDB: 6XDG). A collapsible sidebar is used to configure the visualization and select the condition on the interactive protein structure. By collapsing out of view, the sidebar makes the visualization an optimal size for integrating into online platforms like websites and HTML presentation slides.\label{fig:figure3} \newpage