---
title: "Visualization and exploration"
author:
- name: Tram Nguyen
affiliation: Department of Biomedical Informatics, Harvard Medical School
email: Tram_Nguyen@hms.harvard.edu
- name: Pascal Notin
affiliation: Department of Systems Biology, Harvard Medical School
- name: Aaron W Kollasch
affiliation: Department of Systems Biology, Harvard Medical School
- name: Debora Marks
affiliation: Department of Systems Biology, Harvard Medical School
- name: Ludwig Geistlinger
affiliation: Department of Biomedical Informatics, Harvard Medical School
package: ProteinGymR
output:
BiocStyle::html_document:
self_contained: yes
toc: true
toc_float: true
toc_depth: 2
code_folding: show
date: "June 18, 2025"
bibliography: references.bib
vignette: >
%\VignetteIndexEntry{Visualization and exploration"}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
markdown:
wrap: 80
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE,
message = FALSE)
```
[CH]: https://bioconductor.org/packages/release/bioc/html/ComplexHeatmap.html
[r3d]: https://cran.r-project.org/web/packages/r3dmol/vignettes/r3dmol.html
[pg_publication]: https://proceedings.neurips.cc/paper_files/paper/2023/hash/cac723e5ff29f65e3fcbb0739ae91bee-Abstract-Datasets_and_Benchmarks.html
[physiochem]: https://biology.stackexchange.com/questions/105321/arrangement-of-amino-acids-in-the-protein-alphabet
# Setup
```{r, message=FALSE, warning=FALSE}
library(ProteinGymR)
library(ComplexHeatmap)
library(stringr)
library(dplyr)
library(ggplot2)
library(ggExtra)
```
# Introduction
This vignette demonstrates how to explore and visualize experimental deep
mutational scanning (DMS) fitness scores and variant effect prediction model
scores from the ProteinGym database [@Notin2023]. Specifically, it walks through
the functionality to generate heatmaps of DMS scores for all possible amino acid
substitutions and projects these scores onto 3D protein structures.
`ProteinGymR` uses functionality from the Bioconductor package
[ComplexHeatmap][CH] and [r3dmol][r3d] from CRAN under the hood to generate the
heatmaps and 3D protein structures, respectively. Finally, this vignette
demonstrates how to contrast variant effects predictions with experimental
measurements.
# Visualize DMS scores along a protein sequence
Here, we explore the "ACE2_HUMAN" DMS assay from @Chan2020 and create a heatmap
of the DMS scores with `plot_dms_heatmap()`. If the argument `dms_data` is not
specified, the default will load the most recent DMS substitution data from
ProteinGym with `dms_substitutions()`. This function only requires
a specific assay name. To obtain all assay names, run:
`names(dms_substitutions())`. By default, the function also plots the full range
of positions where DMS data is available for this assay. To plot a specific
region of interest, use the arguments `start_pos` and `end_pos` which takes
in an integer for the first and last residue position to plot in the protein.
```{r ACE default heatmap, fig.wide = TRUE}
ace2_dms <- plot_dms_heatmap(
assay_name = "ACE2_HUMAN_Chan_2020",
start_pos = 1,
end_pos = 100)
ace2_dms
```
The heatmap shows the DMS score at each position along the given protein
sequence (x-axis) where a residue was mutated (y-axis: substituted amino acid,
top; x-axis: reference amino acid). For demonstration, we subset to the first
1-100 positions and grouped the amino acids by their physiochemical properties
(DE,KRH,NQ,ST,PGAVIL,MC,FYW). See [here][physiochem] for more information. Note
that not all positions along the protein sequence may be subjected to mutation
for every DMS assay. This results from the specific research objectives,
prioritization choices of the investigators, or technical constraints inherent
to the experimental design.
A low DMS score indicates low fitness, while a higher DMS score indicates high
fitness. We can think of higher DMS scores as being more benign, while lower DMS
score indicates more pathogenic regions.
Based on the "ACE2_HUMAN_Chan_2020" assay, virtually all possible amino acid
changes at positions 90 and 92 lead to higher fitness; possibly
suggestive of a benign region of the protein. However, several mutations at
position 48 resulted in low fitness. This could represent an important region
for protein function where any perturbation would likely be deleterious.
Let's plot another assay, specifying a region and invoking the `ComplexHeatmap`
row clustering under the hood. For more details about this clustering method or
to view more function parameters, refer to the documentation of the
`plot_dms_heatmap` function.
```{r SHOC2 heatmap, fig.wide = TRUE}
shoc2_dms <- plot_dms_heatmap(assay_name = "SHOC2_HUMAN_Kwon_2022",
start_pos = 10,
end_pos = 60,
cluster_rows = TRUE)
shoc2_dms
```
For example, in this region of the SHOC2_HUMAN protein, mutating to a Lysine (K, y-axis) resulted more frequently in higher fitness.
# Visualize model scores along a protein
ProteinGymR provides functionality to generate heatmaps of zero-shot
mode scores for 79 variant effect prediction models and 11 semi-supervised
models with the function `plot_zeroshot_heatmap()`. The required arguments
for this function are the assay name to plot (same as for the DMS heatmap),
and a model to plot. For a complete list of models, run `available_models()` for
zero-shot models, and `supervised_available_models()` for the 11 semi-supervised
models. If `model_data` is not provided, the default model scores from
ProteinGym will be loaded in from default model scores from
`zeroshot_substitutions()`.
```{r, fig.width = 9, fig.height = 7}
ace2_model <- plot_zeroshot_heatmap(
assay_name = "ACE2_HUMAN_Chan_2020",
model = "GEMME",
start_pos = 1,
end_pos = 100)
ComplexHeatmap::draw(ace2_dms %v% ace2_model)
```
As for DMS scores, we are plotting the GEMME zero-shot scores for
positions 1 to 100 in the assay "ACE2_HUMAN_Chan_2020". At first glance, both
the DMS data and GEMME model reveal position 48 to be quite pathogenic across
amino acid substitutions. Note that the model scores here are mostly negative;
however because these are model prediction scores, negative values do not
necessarily indicate lower fitness after mutation as with DMS scores.
Thus, model scores are always represented with another color palette to
distinguish from experimental scores. Note further that model scores are not
rescaled or normalized across the 79 models, and comparison of the predicted
scores between models is thus not straightforward. See [@Notin2023] for more
information on model scores and how to interpret them.
It can be useful to visualize the DMS and model scores side by side for a given
assay to compare the experimental DMS scores and predicted zero-shot scores
outputted from the model. This is easily done with `%v%` which stacks the
heatmaps in one column, while `+` will display them in two columns, side by
side. This functionality is available for all Heatmap objects generated with
the ComplexHeatmap package.
# 3D protein structure
This section demonstrates how to explore and visualize DMS or model scores on
a 3D protein structure using the package r3dmol under the hood. The function
requires DMS or model assay to aggregate scores that will be projected onto the
3D structure.
By default, if no `data_scores` argument is provided, the DMS substitutions from
`dms_substitutions()` are loaded in, or if viewing model scores,
set this argument to any model available in ProteinGym v1.2. Get a list of
zero-shot and semi-supervised models with `available_models()` and
`supervised_available_model()`.
If a model is chosen, a helper function is invoked which normalizes the
model prediction scores using a rank-based normal quantile transformation.
The result is a set of normalized scores that preserve the rank order of the
models scores, while standardizing the distribution. Transformed values
typically fall between -3 and 3. This normalization ensures the scores are
approximately standard normally distributed (mean = 0, SD = 1), allowing
comparisons across models.
The user may also specify what aggregation method to use for
calculating the summary statistic at each residue position. By default,
the mean DMS score/model prediction score is calculated for each position.
See the function documentation for details: `?plot_structure()`
First, let's use all the default settings. The only required arguments are
the `assay_name`.
Importantly, because the plot shows one protein structure, all DMS fitness
scores across amino acids are aggregated within a position. By default this
aggregation function is just the average of all the DMS scores at that position.
However, it is possible to set any user-defined aggregation function with the
`aggregation_func` argument.
For DMS assays, a score of zero will always be represented as white,
corresponding to the biological interpretation of neutral fitness effect.
```{r, fig.wide = TRUE}
plot_structure(assay_name = "ACE2_HUMAN_Chan_2020")
```
In this example, we are plotting the 3D structure of the ACE2_HUMAN protein and
overlaying the mean DMS score across all mutants in a given position. @Chan2020
who generated the DMS assay data only experimentally assessed a subset
of the entire ACE2_HUMAN protein. By default, the function only colors the
regions where there is information available in the assay. Red colors represent
more pathogenic (lower DMS scores) and blue colors show more benign positions
(higher DMS scores). Regions that appear white indicate closer to no change
before and after the DMS perturbation. Grey regions represent the range of the
protein assessed in the assay; however, only the colored regions include DMS
data. Finally, by default, regions of the protein itself outside the range of
the experimental assay have the "ball and stick" representation.
We can also overlap model scores from the any of our zero-shot or
semi-supervised models. Do this by setting `data_scores` argument to any model
string matching `available_models()` or `supervised_available_models()`.
Here, let's demonstrate plotting the "Kermut" model and also allowing the
full visualization of the complete protein structure, rather than just the
"ball and stick" representation. This can be done by setting the argument `full_structure` to `TRUE`.
```{r, fig.wide = TRUE}
plot_structure(assay_name = "ACE2_HUMAN_Chan_2020",
data_scores = "Kermut",
full_structure = TRUE)
```
Now we can more clearly see the entire protein structure for ACE2_HUMAN in the
ribbon representation, and we have overlaid the model prediction scores from the
Kermut model.
Some assays extensively assessed nearly every position of the complete protein,
for example: the C6KNH7_9INFA protein from [@Lee2018]. Let's visualize
this protein and set the aggregation method to view the minimum DMS score across
all mutants at each position by setting `aggregation_fun = min`. To view a
specific region in detail: use `start_pos` and `end_pos`.
```{r, fig.wide = TRUE}
plot_structure(assay_name = "C6KNH7_9INFA_Lee_2018",
aggregate_fun = min)
```
As we might expect, the minimum DMS value (more pathogenic) is almost always a
negative number across all positions of this protein. Therefore, there seems to
be at least one amino acid mutation that could severely disrupt the fitness at
any position of this protein.
Finally, it is possible to use the same color scheme as the [popEVE
mutation portal](https://pop.evemodel.org/). We can do this for any of
the heatmaps or protein structure plots. This can be done by setting the argument `color_scheme` to "EVE".
# Correlate DMS scores with model scores
The `dms_corr_plot()` function allows the user to evaluate the correlation
between experimental and model prediction scores. By default, it takes in a
protein UniProt ID and runs a Spearman correlation between the ProteinGym DMS
assay scores and AlphaMissense predicted pathogenicity scores. However, as with `plot_structure()`, other models benchmarked in ProteinGym can also be specified via the `model` argument.
```{r, warning = FALSE, fig.wide = TRUE}
dms_corr_plot(uniprotId = "Q9NV35")
```
By default, the `dms_corr_plot()` function gathers any of the 217 DMS assays of
the chosen UniProt ID and correlates the average DMS score across relevant
assays and the AlphaMissense model predictions.
Although the default uses the AlphaMissense scores, it is simple to correlate
DMS experimental scores with predictions from any of the 79 zero-shot
or 11 supervised models. Below is an example of the workflow to accomplish this for the UniProt ID "Q9NV35" as before.
# Correlate prediction scores between two models.
Similar to the above, we can also explore the correlation between two different
models for a given protein instead of looking at the DMS experimental data.
We can do this for the protein "P04637" and the `model_corr_plot()` function.
By default, the function only requires a UniProt ID, and uses "AlphaMissense"
and "EVE_single" models as defaults. Let's change that to
"Kermut" and "ProteinNPT" for our demonstration.
```{r, fig.wide = TRUE}
model_corr_plot(
uniprotId = "P04637",
model1 = "Kermut",
model2 = "ProteinNPT"
)
```
There seems to be good correlation between
the model predictions for all variants in assays assessing the "P04637" protein.
# Session Info
```{r}
sessionInfo()
```
# References