# Resource specific interaction attributes

Denes Turei1*

1Institute for Computational Biomedicine, Heidelberg University

*turei.denes@gmail.com

#### 16 July 2025

#### Abstract

OmniPath provides a broad variety of protein annotations, but for interactions, until recently, only a standard set of essential attributes (direction, effect, etc) and a handful of others (e.g. DoRothEA confidence level) were available. The newly introduced `extra_attrs` column consists of JSON encoded custom, resource specific attributes from network databases. We also revised the processing of these resources to ensure that we include as many useful attributes as possible. In the OmnipathR package we added a few new functions to support the processing of the JSON encoded column: to scan it for keys and values, and to extract specific variables of interest into new columns. We give a brief overview of these here.

#### Package

OmnipathR 3.17.4

# Contents

1 Institute for Computational Biomedicine, Heidelberg University

# 1 Loading a network

    library(OmnipathR)

First we retrieve the complete directed PPI network. Importantly, the extra attributes are only included if the `fields = "extra_attrs"` argument is provided.

    i <- post_translational(fields = 'extra_attrs')
    dplyr::select(i, source_genesymbol, target_genesymbol, extra_attrs)

    ## # A tibble: 134,282 × 3
    ##    source_genesymbol target_genesymbol extra_attrs
    ##    <chr>             <chr>             <list>
    ##  1 CALM1             TRPC1             <named list [1]>
    ##  2 CALM3             TRPC1             <named list [1]>
    ##  3 CALM2             TRPC1             <named list [1]>
    ##  4 CAV1              TRPC1             <named list [1]>
    ##  5 DRD2              TRPC1             <named list [1]>
    ##  6 MDFI              TRPC1             <named list [1]>
    ##  7 ITPR2             TRPC1             <named list [1]>
    ##  8 MARCKS            TRPC1             <named list [1]>
    ##  9 TRPC1             GRM1              <named list [0]>
    ## 10 GRM1              TRPC1             <named list [1]>
    ## # ℹ 134,272 more rows

Above we see, the `extra_attrs` column is a list type column. Each list is a nested list itself, containing the extra attributes from all resources, as it was extracted from the JSON.

# 2 Which extra attributes are available?

Which attributes present in the network depends only on the interactions: if none of the interactions is from the `SPIKE` database, obviously the `SPIKE_mechanism` won’t be present. The names of the extra attributes consist of the name of the resource and the name of the attribute, separated by an underscore. The resource name never contains underscore, while some attribute names do. To list the extra attributes available in a particular data frame use the `extra_attrs` function:

    extra_attrs(i)

    ##  [1] "TRIP_method"                "SIGNOR_mechanism"           "PhosphoSite_noref_evidence"
    ##  [4] "PhosphoPoint_category"      "PhosphoSite_evidence"       "HPRD-phos_mechanism"
    ##  [7] "Li2012_mechanism"           "Li2012_route"               "SPIKE_effect"
    ## [10] "SPIKE_mechanism"            "SPIKE_LC_effect"            "SPIKE_LC_mechanism"
    ## [13] "CA1_effect"                 "CA1_type"                   "Macrophage_type"
    ## [16] "Macrophage_location"        "ACSN_effect"                "Cellinker_type"
    ## [19] "CellChatDB_category"        "talklr_putative"            "CellPhoneDB_type"
    ## [22] "Ramilowski2015_source"      "HPMR_partner_role"          "ARN_effect"
    ## [25] "ARN_is_direct"              "ARN_is_directed"            "NRF2ome_effect"
    ## [28] "NRF2ome_is_direct"          "NRF2ome_is_directed"

The labels listed here are the top level keys in the lists in the `extra_attrs` column. Note, the coverage of these variables varies a lot, typically in agreement with the size of the resource.

# 3 Inspecting one attribute

The values of each extra attribute, in theory, can be arbitrarily complex nested lists, but in reality, these are most often simple numeric, logical or character values or vectors. To see the unique values of one attribute use the `extra_attr_values` function. Let’s see the values of the `SIGNOR_mechanism` attribute:

    extra_attr_values(i, SIGNOR_mechanism)

    ##  [1] "phosphorylation"                    "binding"
    ##  [3] "dephosphorylation"                  "Phosphorylation"
    ##  [5] "ubiquitination"                     "N/A"
    ##  [7] "Physical Interaction"               "Proteolytic Processing"
    ##  [9] "cleavage"                           "Ubiquitination"
    ## [11] "Deubiqitination"                    "deubiquitination"
    ## [13] "relocalization"                     "Dephosphorylation"
    ## [15] "Other"                              "guanine nucleotide exchange factor"
    ## [17] "Transcription Regulation"           "gtpase-activating protein"
    ## [19] "Indirect"                           ""
    ## [21] "Sumoylation"                        "sumoylation"
    ## [23] "palmitoylation"                     "Acetylation"
    ## [25] "acetylation"                        "polyubiquitination"
    ## [27] "Demethylation"                      "demethylation"
    ## [29] "mRNA stability"                     "methylation"
    ## [31] "Methylation"                        "trimethylation"
    ## [33] "hydroxylation"                      "monoubiquitination"
    ## [35] "Deacetylation"                      "deacetylation"
    ## [37] "Translational Regulation"           "Protein Degradation"
    ## [39] "Glycosylation"                      "s-nitrosylation"
    ## [41] "phosphomotif_binding"               "chemical activation"
    ## [43] "Proteolytic Cleavage"               "tyrosination"
    ## [45] "post transcriptional regulation"    "post translational modification"
    ## [47] "translation regulation"             "carboxylation"
    ## [49] "neddylation"                        "Carboxylation"
    ## [51] "desumoylation"                      "glycosylation"
    ## [53] "ADP-ribosylation"                   "stabilization"
    ## [55] "catalytic activity"                 "deglycosylation"
    ## [57] "destabilization"                    "chemical inhibition"
    ## [59] "isomerization"                      "Neddylation"
    ## [61] "lipidation"                         "chemical modification"
    ## [63] "oxidation"                          "Alkylation"

The values are provided as they are in the original resource, including potential typos and inconsistencies, e.g. see above the capitalized vs. lowercase forms of each value.

# 4 Converting extra attributes to columns

To make use of the attributes, it is convenient to extract the interesting ones into separate columns of the data frame. With the `extra_attrs_to_cols` function multiple attributes can be converted in a single call. Custom column names can be passed by argument names. As an example, let’s extract two attributes:

    i0 <- extra_attrs_to_cols(
        i,
        si_mechanism = SIGNOR_mechanism,
        ma_mechanism = Macrophage_type,
        keep_empty = FALSE
    )

    dplyr::select(
        i0,
        source_genesymbol,
        target_genesymbol,
        si_mechanism,
        ma_mechanism
    )

    ## # A tibble: 61,406 × 4
    ##    source_genesymbol target_genesymbol si_mechanism ma_mechanism
    ##    <chr>             <chr>             <list>       <list>
    ##  1 PRKG1             TRPC3             <list [1]>   <NULL>
    ##  2 PRKG1             TRPC7             <list [1]>   <NULL>
    ##  3 OS9               TRPV4             <list [1]>   <NULL>
    ##  4 PTPN1             TRPV6             <list [1]>   <NULL>
    ##  5 RACK1             TRPM6             <list [1]>   <NULL>
    ##  6 PRKACA            MCOLN1            <list [1]>   <NULL>
    ##  7 MAPK14            MAPKAPK2          <list [1]>   <list [1]>
    ##  8 MAPKAPK2          HNRNPA0           <list [2]>   <NULL>
    ##  9 MAPKAPK2          PARN              <list [2]>   <NULL>
    ## 10 JAK2              EPOR              <list [2]>   <NULL>
    ## # ℹ 61,396 more rows

Above we disabled the `keep_empty` option, otherwise the new columns would have `NULL` values for most of the records, simply because out of the 80k interactions in the data frame only a few thousands are from either SIGNOR or Macrophage. The new columns are list type, individual values are character vectors. Let’s look into one value:

    dplyr::pull(i0, si_mechanism)[[7]]

    ## [[1]]
    ## [1] "phosphorylation"

Here we have two values, but only because the inconsistent names in the resource.

Depending on downstream methods, atomic columns might be preferable instead of lists. In this case one interaction record might yield multiple rows in the resulted data frame, depending on the number of attributes it has. To have atomic columns, use the `flatten` option:

    i1 <- extra_attrs_to_cols(
        i,
        si_mechanism = SIGNOR_mechanism,
        ma_mechanism = Macrophage_type,
        keep_empty = FALSE,
        flatten = TRUE
    )

    dplyr::select(
        i1,
        source_genesymbol,
        target_genesymbol,
        si_mechanism,
        ma_mechanism
    )

    ## # A tibble: 63,434 × 4
    ##    source_genesymbol target_genesymbol si_mechanism ma_mechanism
    ##    <chr>             <chr>             <list>       <list>
    ##  1 PRKG1             TRPC3             <chr [1]>    <NULL>
    ##  2 PRKG1             TRPC7             <chr [1]>    <NULL>
    ##  3 OS9               TRPV4             <chr [1]>    <NULL>
    ##  4 PTPN1             TRPV6             <chr [1]>    <NULL>
    ##  5 RACK1             TRPM6             <chr [1]>    <NULL>
    ##  6 PRKACA            MCOLN1            <chr [1]>    <NULL>
    ##  7 MAPK14            MAPKAPK2          <chr [1]>    <chr [1]>
    ##  8 MAPKAPK2          HNRNPA0           <chr [1]>    <NULL>
    ##  9 MAPKAPK2          HNRNPA0           <chr [1]>    <NULL>
    ## 10 MAPKAPK2          PARN              <chr [1]>    <NULL>
    ## # ℹ 63,424 more rows

# 5 Filtering records based on extra attributes

Another useful application of extra attributes is filtering the records of the interactions data frame. The `with_extra_attrs` function filters to records which have certain extra attributes. For example, to have only interactions with `SIGNOR_mechanism` given:

    nrow(with_extra_attrs(i, SIGNOR_mechanism))

    ## [1] 61111

This results around 11 thousands rows. Filtering for multiple attributes the records which have at least one of them will be selected. Adding some more attributes results more interactions:

    nrow(with_extra_attrs(i, SIGNOR_mechanism, CA1_effect, Li2012_mechanism))

    ## [1] 62017

It is possible to filter the records not only by the names but the values of the extra attributes. Let’s select the interactions which are phosphorylation according to SIGNOR:

    phos <- c('phosphorylation', 'Phosphorylation')

    si_phos <- filter_extra_attrs(i, SIGNOR_mechanism = phos)

    dplyr::select(si_phos, source_genesymbol, target_genesymbol)

    ## # A tibble: 4,353 × 2
    ##    source_genesymbol                                target_genesymbol
    ##    <chr>                                            <chr>
    ##  1 PRKG1                                            TRPC3
    ##  2 PRKG1                                            TRPC7
    ##  3 PRKACA                                           MCOLN1
    ##  4 MAPK14                                           MAPKAPK2
    ##  5 MAPKAPK2                                         HNRNPA0
    ##  6 MAPKAPK2                                         PARN
    ##  7 JAK2                                             EPOR
    ##  8 MAPK14                                           ZFP36
    ##  9 MAPKAPK2                                         ZFP36
    ## 10 PRKAA1_PRKAA2_PRKAB1_PRKAB2_PRKAG1_PRKAG2_PRKAG3 CRTC2
    ## # ℹ 4,343 more rows

# 6 Example: finding ubiquitination interactions

First let’s search for the word “ubiquitination” in the attributes. Below is a slow but simple solution:

    keys <- extra_attrs(i)
    keys_ubi <- purrr::keep(
        keys,
        function(k){
            any(stringr::str_detect(extra_attr_values(i, !!k), 'biqu'))
        }
    )
    keys_ubi

    ## [1] "SIGNOR_mechanism"    "HPRD-phos_mechanism" "SPIKE_mechanism"     "SPIKE_LC_mechanism"
    ## [5] "CA1_type"            "Macrophage_type"

We found five attributes that have at least one value which matches “biqu”. Next take a look at their values:

    ubi <- rlang::set_names(
        purrr::map(
            keys_ubi,
            function(k){
                stringr::str_subset(extra_attr_values(i, !!k), 'biqu')
            }
        ),
        keys_ubi
    )
    ubi

    ## $SIGNOR_mechanism
    ## [1] "ubiquitination"     "Ubiquitination"     "deubiquitination"   "polyubiquitination" "monoubiquitination"
    ##
    ## $`HPRD-phos_mechanism`
    ## [1] "Ubiquitination"
    ##
    ## $SPIKE_mechanism
    ## [1] "Ubiquitination"     "Polyubiquitination"
    ##
    ## $SPIKE_LC_mechanism
    ## [1] "Ubiquitination"     "Polyubiquitination"
    ##
    ## $CA1_type
    ## [1] "Ubiquitination"
    ##
    ## $Macrophage_type
    ## [1] "Ubiquitination"

Actually to match all ubiquitination interactions, it’s enough to filter for “ubiquitination” in its lowercase and capitalized forms (note, we could also include deubiqutination and polyubiquitination):

    ubi_kws <- c('ubiquitination', 'Ubiquitination')

    i_ubi <-
        dplyr::distinct(
            dplyr::bind_rows(
                purrr::map(
                    keys_ubi,
                    function(k){
                        filter_extra_attrs(i, !!k := ubi_kws, na_ok = FALSE)
                    }
                )
            )
        )

    dplyr::select(i_ubi, source_genesymbol, target_genesymbol)

    ## # A tibble: 49,308 × 2
    ##    source_genesymbol target_genesymbol
    ##    <chr>             <chr>
    ##  1 NUMB              NOTCH1
    ##  2 BTRC_CUL1_SKP1    PER2
    ##  3 PRKN              RANBP2
    ##  4 PRKN              SNCA
    ##  5 FBXW7             MYC
    ##  6 UBE2T             FANCL
    ##  7 BIRC2             TRAF2
    ##  8 TRAF2             MAP3K14
    ##  9 TRAF6             MAP3K7
    ## 10 BTRC_CUL1_SKP1    WEE1
    ## # ℹ 49,298 more rows

We found 405 ubiquitination interactions. We had to use `map`, `bind_rows` and `distinct` because otherwise `filter_extra_attrs` would return the intersection of the matches, instead of their union.

In this data frame we have 150 unique ubiquitin E3 ligases:

    length(unique(i_ubi$source_genesymbol))

    ## [1] 365

UniProt annotates E3 ligases by the “Ubl conjugation” keyword. We can check how many of those 150 proteins have this annotation:

    uniprot_kws <- annotations(
        resources = 'UniProt_keyword',
        entity_type = 'protein',
        wide = TRUE
    )

    ## Warning in readLines(con = path, encoding = encoding): incomplete final line found on
    ## 'https://omnipathdb.org/resources'

    e3_ligases <- dplyr::pull(
        dplyr::filter(uniprot_kws, keyword == 'Ubl conjugation'),
        genesymbol
    )

    length(e3_ligases)

    ## [1] 2542

    length(intersect(unique(i_ubi$source_genesymbol), e3_ligases))

    ## [1] 106

    length(setdiff(unique(i_ubi$source_genesymbol), e3_ligases))

    ## [1] 259

We retrieved 2503 E3 ligases from UniProt. 83 of these has substrates in the interaction database, while 67 of the effectors of the interactions are not annotated in UniProt.

In the OmniPath enzyme-substrate database we collect ubiquitination interactions from enzyme-PTM resources. However, these contain only a small number of interactions:

    es_ubi <- enzyme_substrate(types = 'ubiquitination')
    es_ubi

    ## # A tibble: 70 × 12
    ##    enzyme substrate enzyme_genesymbol substrate_genesymbol residue_type residue_offset modification   sources
    ##    <chr>  <chr>     <chr>             <chr>                <chr>                 <dbl> <chr>          <chr>
    ##  1 Q12933 Q13546    TRAF2             RIPK1                K                       377 ubiquitination SIGNOR
    ##  2 Q8IUD6 O95786    RNF135            RIGI                 K                       907 ubiquitination SIGNOR
    ##  3 Q8IUD6 O95786    RNF135            RIGI                 K                       909 ubiquitination SIGNOR
    ##  4 P60604 Q92813    UBE2G2            DIO2                 K                       237 ubiquitination SIGNOR
    ##  5 P60604 Q92813    UBE2G2            DIO2                 K                       244 ubiquitination SIGNOR
    ##  6 Q13489 Q13546    BIRC3             RIPK1                K                       377 ubiquitination SIGNOR
    ##  7 Q96J02 Q7Z434    ITCH              MAVS                 K                       420 ubiquitination SIGNOR
    ##  8 Q96J02 Q7Z434    ITCH              MAVS                 K                       371 ubiquitination SIGNOR
    ##  9 Q66K89 P04637    E4F1              TP53                 K                       319 ubiquitination HPRD;SI…
    ## 10 Q66K89 P04637    E4F1              TP53                 K                       321 ubiquitination HPRD;SI…
    ## # ℹ 60 more rows
    ## # ℹ 4 more variables: references <chr>, curation_effort <dbl>, n_references <int>, n_resources <int>

With only two exception, all these have been recovered by using the extra attributes from the network database:

    es_i_ubi <-
        dplyr::inner_join(
            es_ubi,
            i_ubi,
            by = c(
                'enzyme_genesymbol' = 'source_genesymbol',
                'substrate_genesymbol' = 'target_genesymbol'
            )
        )

    nrow(dplyr::distinct(dplyr::select(es_i_ubi, enzyme, substrate, residue_offset)))

    ## [1] 57

# 7 Session information

    sessionInfo()

    ## R version 4.5.1 (2025-06-13 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                           LC_CTYPE=English_United States.utf8
    ## [3] LC_MONETARY=English_United States.utf8 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] magrittr_2.0.3   ggraph_2.2.1     igraph_2.1.4     ggplot2_3.5.2    dplyr_1.1.4      OmnipathR_3.17.4
    ## [7] BiocStyle_2.37.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tidyselect_1.2.1    viridisLite_0.4.2   farver_2.1.2        blob_1.2.4          viridis_0.6.5
    ##  [6] R.utils_2.13.0      fastmap_1.2.0       tweenr_2.0.3        promises_1.3.3      XML_3.99-0.18
    ## [11] digest_0.6.37       timechange_0.3.0    lifecycle_1.0.4     processx_3.8.6      RSQLite_2.4.1
    ## [16] compiler_4.5.1      rlang_1.1.6         sass_0.4.10         progress_1.2.3      tools_4.5.1
    ## [21] utf8_1.2.6          yaml_2.3.10         knitr_1.50          labeling_0.4.3      graphlayouts_1.2.2
    ## [26] prettyunits_1.2.0   bit_4.6.0           curl_6.4.0          xml2_1.3.8          RColorBrewer_1.1-3
    ## [31] websocket_1.4.4     withr_3.0.2         purrr_1.1.0         R.oo_1.27.1         polyclip_1.10-7
    ## [36] grid_4.5.1          scales_1.4.0        MASS_7.3-65         tinytex_0.57        dichromat_2.0-0.1
    ## [41] cli_3.6.5           rmarkdown_2.29      crayon_1.5.3        generics_0.1.4      httr_1.4.7
    ## [46] tzdb_0.5.0          sessioninfo_1.2.3   readxl_1.4.5        DBI_1.2.3           cachem_1.1.0
    ## [51] chromote_0.5.1      ggforce_0.5.0       stringr_1.5.1       rvest_1.0.4         parallel_4.5.1
    ## [56] BiocManager_1.30.26 selectr_0.4-2       cellranger_1.1.0    vctrs_0.6.5         jsonlite_2.0.0
    ## [61] bookdown_0.43       hms_1.1.3           ggrepel_0.9.6       bit64_4.6.0-1       archive_1.1.12
    ## [66] magick_2.8.7        jquerylib_0.1.4     tidyr_1.3.1         glue_1.8.0          ps_1.9.1
    ## [71] lubridate_1.9.4     stringi_1.8.7       gtable_0.3.6        later_1.4.2         tibble_3.3.0
    ## [76] logger_0.4.0        pillar_1.11.0       rappdirs_0.3.3      htmltools_0.5.8.1   R6_2.6.1
    ## [81] httr2_1.2.0         tcltk_4.5.1         tidygraph_1.3.1     vroom_1.6.5         evaluate_1.0.4
    ## [86] readr_2.1.5         R.methodsS3_1.8.2   backports_1.5.0     memoise_2.0.1       bslib_0.9.0
    ## [91] Rcpp_1.1.0          zip_2.3.3           gridExtra_2.3       checkmate_2.3.2     xfun_0.52
    ## [96] fs_1.6.6            pkgconfig_2.0.3