# D. Benchmarking with ProteinGym

Original version: 8 October, 2024

    library(AlphaMissenseR)
    library(ExperimentHub)
    library(dplyr)
    library(tidyr)
    library(ggdist)
    library(gghalves)
    library(ggplot2)

# Introduction

Benchmarking is essential for assessing different variant effect prediction approaches, potentially aiding in decisions about which models are most suitable for specific research questions or clinical applications.

To evaluate the performance of AlphaMissense and its predictions, we integrate with the ProteinGym database, a comprehensive collection of benchmarks aimed at comparing the ability of models that predict the effects of protein mutations. It consists of an extensive dataset of deep mutational scanning (DMS) assays covering approximately 2.7 million missense variants across 217 experiments, as well as annotated human clinical variants for 2,525 proteins. DMS assays are highly valuable as they systematically test all possible single mutations in a protein, recording their fitness effects. They can reveal the impacts of both deleterious and beneficial mutations on fitness, offering insights into protein structure and activity.

Furthermore, ProteinGym provides several standardized model evaluation metric scores (“AUC”, “MCC”, “NDCG”, “Spearman”, “Top_recall”) for 62 models calculated across the 217 DMS assays, offering a consistent and extensive benchmarking framework across approaches.

This vignette demonstrates how to 1) compare AlphaMissense predictions to DMS scores on a per-protein basis, and 2) how to benchmark AlphaMissense against other models aimed at predicting protein mutation effects.

# Access ProteinGym datasets through `ExperimentHub`.

The ProteinGym DMS assays can be accessed by querying `ExperimentHub`.

    eh <- ExperimentHub::ExperimentHub()
    dms_data <- eh[['EH9555']]
    #> ProteinGymR not installed.
    #>   Full functionality, documentation, and loading of data might not be possible without installing
    #> loading from cache

    head(names(dms_data))
    #> [1] "A0A140D2T1_ZIKV_Sourisseau_2019"  "A0A192B1T2_9HIV1_Haddox_2018"
    #> [3] "A0A1I9GEU1_NEIME_Kennouche_2019"  "A0A247D711_LISMN_Stadelmann_2021"
    #> [5] "A0A2Z5U3Z0_9INFA_Doud_2016"       "A0A2Z5U3Z0_9INFA_Wu_2014"

Each element of the list is an individual DMS assay with the following information for each column: the UniProt protein identifier, the DMS experiment assay identifier, the mutant at a given protein position, the mutated protein sequence, the recorded DMS score, and a binary DMS score bin categorizing whether the mutation has high (1) or low fitness (0). For more details, reference the publication from Notin et al. 2023.

A supplementary table of AlphaMissense pathogencity scores for ~1.6 M substitutions matching those in the ProteinGym DMS assays is provided by Cheng et al. 2023, and can also be accessed through `ExperimentHub`.

    am_scores <- eh[['EH9554']]
    #> ProteinGymR not installed.
    #>   Full functionality, documentation, and loading of data might not be possible without installing
    #> loading from cache
    am_scores |> head()
    #>                                   DMS_id      Uniprot_ID variant_id
    #> 1 A0A140D2T1_ZIKV_Sourisseau_growth_2019 A0A140D2T1_ZIKV      I291A
    #> 2 A0A140D2T1_ZIKV_Sourisseau_growth_2019 A0A140D2T1_ZIKV      I291Y
    #> 3 A0A140D2T1_ZIKV_Sourisseau_growth_2019 A0A140D2T1_ZIKV      I291W
    #> 4 A0A140D2T1_ZIKV_Sourisseau_growth_2019 A0A140D2T1_ZIKV      I291V
    #> 5 A0A140D2T1_ZIKV_Sourisseau_growth_2019 A0A140D2T1_ZIKV      I291T
    #> 6 A0A140D2T1_ZIKV_Sourisseau_growth_2019 A0A140D2T1_ZIKV      I291S
    #>   AlphaMissense
    #> 1     0.9124037
    #> 2     0.8899629
    #> 3     0.9694002
    #> 4     0.2470229
    #> 5     0.8939576
    #> 6     0.8617630

The `data.frame` shows the `DMS_id` matching a ProteinGym assay, the UniProt entry name of the protein evaluated, the mutation and position, and the aggregated AlphaMissense score for that mutation. For more details about the table, reference the AlphaMissense paper.

# Correlate DMS scores and AlphaMissense predictions

The DMS scores serve as an experimental measure to compare the accuracy of AlphaMissense mutation effect predictions. For a given protein, we can plot the relationship between the two measures and report their Spearman correlation.

Here, we demonstrate using the “NUD15_HUMAN” assay. First, we filter both datasets to the chosen assay.

    NUD15_dms <- dms_data[["NUD15_HUMAN_Suiter_2020"]]

    NUD15_am <- am_scores |>
        filter(DMS_id == "NUD15_HUMAN_Suiter_2020")

Wrangle and merge the DMS and AlphaMissense tables together by the UniProt and mutant identifers.

    NUD15_am <- NUD15_am |>
        mutate(Uniprot_ID = "Q9NV35")

    merged_table <-
        left_join(
            NUD15_am, NUD15_dms,
            by = c("Uniprot_ID" = "UniProt_id", "variant_id" = "mutant"),
            relationship = "many-to-many"
        ) |>
        select(Uniprot_ID, variant_id, AlphaMissense, DMS_score) |>
        na.omit()

Now, we plot the correlation.

    correlation_plot <-
        merged_table |>
        ggplot(
            aes(y = .data$AlphaMissense, x = .data$DMS_score)
        ) +
        geom_bin2d(bins = 60) +
        scale_fill_continuous(type = "viridis") +
        xlab("DMS score") +
        ylab("AlphaMissense score") +
        theme_classic() +
        theme(
            axis.text.x = element_text(size = 16),
            axis.text.y = element_text(size = 16),
            axis.title.y = element_text(size = 16, vjust = 2),
            axis.title.x = element_text(size = 16, vjust = 0),
            legend.title = element_text(size = 16),
            legend.text = element_text(size = 16)
        )

    correlation_plot

If mutating a residue resulted in reduced fitness in the DMS assay (low DMS score), we would expect it to be predicted as pathogenic (higher AlphaMissense score). Therefore, a stronger negative correlation represents a tighter relationship between the two measures. We can see this with the NUD15 protein.

We can also print the Spearman correlation.

    cor.test(
        merged_table$AlphaMissense, merged_table$DMS_score,
        method="spearman",
        exact = FALSE
    )
    #>
    #>  Spearman's rank correlation rho
    #>
    #> data:  merged_table$AlphaMissense and merged_table$DMS_score
    #> S = 6440239302, p-value < 2.2e-16
    #> alternative hypothesis: true rho is not equal to 0
    #> sample estimates:
    #>        rho
    #> -0.6798269

The correlation is r = -0.67 and is statistically significant.

# Benchmark AlphaMissense with other variant effect prediction models

We can calculate the Spearman correlation across multiple DMS assays in which we have corresponding AlphaMissense scores, and use this metric to benchmark across multiple models aimed at predicting mutation effects. For this analysis, we will load in the metric scores for 62 models calculated across the 217 DMS assays from the ProteinGym database available through `ExperimentHub`.

    metrics_scores <- eh[['EH9593']]
    #> ProteinGymR not installed.
    #>   Full functionality, documentation, and loading of data might not be possible without installing
    #> loading from cache

`metrics_scores` is a `list` object where each element is a `data.frame` corresponding to one of five model evaluation metrics (“AUC”, “MCC”, “NDCG”, “Spearman”, “Top_recall”) available from ProteinGym. Briefly, these metrics were calculated on the DMS assays for 62 models in a zero-shot setting. The Protein Gym paper provides more information about these metrics.

To demonstrate, we will benchmarking using the Spearman correlation calculated on 10 DMS assays for 5 models. The following code subsets and merges the relevant datasets.

    chosen_assays <- c(
        "A0A1I9GEU1_NEIME_Kennouche_2019",
        "A0A192B1T2_9HIV1_Haddox_2018",
        "ADRB2_HUMAN_Jones_2020",
        "BRCA1_HUMAN_Findlay_2018",
        "CALM1_HUMAN_Weile_2017",
        "GAL4_YEAST_Kitzman_2015",
        "Q59976_STRSQ_Romero_2015",
        "UBC9_HUMAN_Weile_2017",
        "TPK1_HUMAN_Weile_2017",
        "YAP1_HUMAN_Araya_2012")

    am_subset <-
        am_scores |>
        filter(DMS_id %in% chosen_assays)

    dms_subset <-
        dms_data[names(dms_data) %in% chosen_assays]

    dms_subset <-
        bind_rows(dms_subset) |>
        as.data.frame()

    metric_subset <-
        metrics_scores[["Spearman"]] |>
        filter(DMS_ID %in% chosen_assays) |>
        select(-c(Number_of_Mutants, Selection_Type, UniProt_ID,
            MSA_Neff_L_category, Taxon))

    merge_am_dms <-
        left_join(
            am_subset, dms_subset,
            by = c("DMS_id" = "DMS_id", "variant_id" = "mutant"),
            relationship = "many-to-many"
        ) |>
        na.omit()

The Spearman scores are already provided for the 62 models in the ProteinGym metrics table, but we will need to calculate this for AlphaMissense.

    spearman_res <-
        merge_am_dms |>
        group_by(DMS_id) |>
        summarize(
            AlphaMissense = cor(AlphaMissense, DMS_score, method = "spearman")
        ) |>
        mutate(
            AlphaMissense = abs(AlphaMissense)
        )

    DMS_IDs <- metric_subset |> pull(DMS_ID)

    metric_subset <-
        metric_subset |>
        select(-DMS_ID) |>
        abs() |>
        mutate(DMS_id = DMS_IDs)

    all_sp <-
        left_join(
            metric_subset, spearman_res,
            by = "DMS_id"
        )

`all_sp` is a table of Spearman correlation values (absolute values to handle difference in directionality of certain DMS assays) across the ProteinGym and AlphaMissense models for the 10 assays.

Prepare the data for plotting.

    res_long <-
        all_sp |>
        select(-DMS_id) |>
        tidyr::pivot_longer(
            cols = everything(),
            names_to = "model",
            values_to = "score"
        ) |>
        group_by(model) |>
        mutate(
            model_mean = mean(score)
        ) |>
        mutate(
            model = as.factor(model)
        ) |>
        ungroup()

    unique_ordered_models <- unique(res_long$model[order(res_long$model_mean,
                                                            decreasing = TRUE)])

    res_long$model <- factor(res_long$model,
                       levels = unique_ordered_models)

    topmodels <- levels(res_long$model)[1:5]

    top_spearmans <-
        res_long |>
        filter(model %in% topmodels) |>
        droplevels()

Visualize with a raincloud plot.

    top_spearmans |>
        ggplot(
            aes(x = model, y = score, fill = model, group = model)
        ) +
        ggdist::stat_halfeye(
            adjust = .5,
            width = .6,
            .width = 0,
            justification = -.2,
            point_colour = NA
        ) +
        geom_boxplot(
            width = .15,
            outlier.shape = NA
        ) +
        gghalves::geom_half_point(
            side = "l",
            range_scale = .4,
            alpha = .2
        ) +
        coord_cartesian(clip = "off") +
        scale_fill_discrete(name = "Models") +
        theme_classic() +
        ylab("Spearman") +
        theme(
            axis.text.x = element_text(size = 11, angle = -12),
            axis.text.y = element_text(size = 16),
            axis.title.y = element_text(size = 16),
            axis.title.x = element_blank(),
            legend.title = element_text(size = 16),
            legend.text = element_text(size = 11)
        )

Based on the Spearman correlation for the 10 assays we chose, AlphaMissense performed the best. For a realistic and comprehensive benchmark, one would want to apply this framework across all 217 DMS assays available in ProteinGym.

# Session information

    sessionInfo()
    #> R version 4.5.0 RC (2025-04-03 r88103 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows Server 2022 x64 (build 20348)
    #>
    #> Matrix products: default
    #>   LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] LC_COLLATE=C
    #> [2] LC_CTYPE=English_United States.utf8
    #> [3] LC_MONETARY=English_United States.utf8
    #> [4] LC_NUMERIC=C
    #> [5] LC_TIME=English_United States.utf8
    #>
    #> time zone: America/New_York
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats     graphics  grDevices utils     datasets  methods   base
    #>
    #> other attached packages:
    #>  [1] gghalves_0.1.4       ggplot2_3.5.2        ggdist_3.3.2
    #>  [4] tidyr_1.3.1          ExperimentHub_2.17.0 AnnotationHub_3.17.0
    #>  [7] BiocFileCache_2.17.0 dbplyr_2.5.0         BiocGenerics_0.55.0
    #> [10] generics_0.1.3       AlphaMissenseR_1.5.0 dplyr_1.1.4
    #>
    #> loaded via a namespace (and not attached):
    #>  [1] tidyselect_1.2.1        viridisLite_0.4.2       farver_2.1.2
    #>  [4] blob_1.2.4              filelock_1.0.3          Biostrings_2.77.0
    #>  [7] fastmap_1.2.0           duckdb_1.2.1            promises_1.3.2
    #> [10] digest_0.6.37           mime_0.13               lifecycle_1.0.4
    #> [13] r3dmol_0.1.2            KEGGREST_1.49.0         RSQLite_2.3.9
    #> [16] magrittr_2.0.3          compiler_4.5.0          rlang_1.1.6
    #> [19] sass_0.4.10             tools_4.5.0             utf8_1.2.4
    #> [22] yaml_2.3.10             knitr_1.50              labeling_0.4.3
    #> [25] htmlwidgets_1.6.4       bit_4.6.0               spdl_0.0.5
    #> [28] curl_6.2.2              withr_3.0.2             purrr_1.0.4
    #> [31] grid_4.5.0              stats4_4.5.0            xtable_1.8-4
    #> [34] colorspace_2.1-1        scales_1.3.0            cli_3.6.4
    #> [37] rmarkdown_2.29          crayon_1.5.3            httr_1.4.7
    #> [40] BiocBaseUtils_1.11.0    DBI_1.2.3               cachem_1.1.0
    #> [43] parallel_4.5.0          AnnotationDbi_1.71.0    BiocManager_1.30.25
    #> [46] XVector_0.49.0          vctrs_0.6.5             jsonlite_2.0.0
    #> [49] IRanges_2.43.0          S4Vectors_0.47.0        bit64_4.6.0-1
    #> [52] jquerylib_0.1.4         bio3d_2.4-5             glue_1.8.0
    #> [55] distributional_0.5.0    gtable_0.3.6            BiocVersion_3.22.0
    #> [58] later_1.4.2             GenomeInfoDb_1.45.0     GenomicRanges_1.61.0
    #> [61] UCSC.utils_1.5.0        munsell_0.5.1           tibble_3.2.1
    #> [64] pillar_1.10.2           rappdirs_0.3.3          htmltools_0.5.8.1
    #> [67] GenomeInfoDbData_1.2.14 R6_2.6.1                shiny.gosling_1.5.0
    #> [70] evaluate_1.0.3          shiny_1.10.0            Biobase_2.69.0
    #> [73] png_0.1-8               memoise_2.0.1           httpuv_1.6.15
    #> [76] bslib_0.9.0             RcppSpdlog_0.0.21       rjsoncons_1.3.2
    #> [79] Rcpp_1.0.14             whisker_0.4.1           xfun_0.52
    #> [82] pkgconfig_2.0.3