1 Introduction

The JohnsonKinaseData package provides substrate affinities in the form of position-specific weight matrices (PWMs) for 396 human kinases originally published in Johnson et al. (Johnson et al. 2023) and Yaron-Barir et al. (Yaron-Barir et al. 2024). It includes basic functionality to pre-process user-provided phosphopetides and match them against all kinase PWMs. The aim is to give the user a simple way of predicting kinase-substrate relationships based on PWM-phosphosite matching. These predictions can serve to infer kinase activity from differential phospho-proteomic data.

2 Installation

The JohnsonKinaseData package can be installed using the following code:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ExperimentHub")
BiocManager::install("JohnsonKinaseData")

3 Load PWM annotation

Annotation data for all provides kinase PWMs can be accessed with:

library(JohnsonKinaseData)
anno <- getKinaseAnnotation()
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache

head(anno)
#>   MatrixName GeneName UniprotID EntrezID                   Description
#> 1       AAK1     AAK1    Q2M2I8    22848       AP2 associated kinase 1
#> 2       ALK2    ACVR1    Q04771       90     activin A receptor type 1
#> 3       ALK4   ACVR1B    P36896       91    activin A receptor type 1B
#> 4     ACVR2A   ACVR2A    P27037       92    activin A receptor type 2A
#> 5     ACVR2B   ACVR2B    Q13705       93    activin A receptor type 2B
#> 6       AKT1     AKT1    P31749      207 AKT serine/threonine kinase 1
#>   AcceptorSpecificity KinaseSubType KinaseFamily
#> 1             Ser/Thr          <NA>        Other
#> 2             Ser/Thr          <NA>          TKL
#> 3             Ser/Thr          <NA>          TKL
#> 4             Ser/Thr          <NA>          TKL
#> 5             Ser/Thr          <NA>          TKL
#> 6             Ser/Thr          <NA>          AGC

Its includes PWM names and associated gene information, such as gene symbol, description, Entrez and Uniprot IDs. PWMs are classified by their specificity:

xtabs(~AcceptorSpecificity, anno)
#> AcceptorSpecificity
#> Ser/Thr     Tyr 
#>     303      93

Tyrosine specific kinase PWMs are additionally classified by sub-type: receptor (RTK), non-receptor (nRTK) and non-canonical tyrosine kinases (ncTK).

xtabs(~AcceptorSpecificity + KinaseSubType, anno)
#>                    KinaseSubType
#> AcceptorSpecificity RTK nRTK ncTK
#>             Ser/Thr   0    0    0
#>             Tyr      46   32   15

PWMs for non-canonical tyrosine kinases, i.e. kinases which also phosphorylate serine/threonine residues, are indicated by the _TYR suffix in the matrix name.

All PWMs are grouped into kinase families:

xtabs(~AcceptorSpecificity + KinaseFamily, anno)
#>                    KinaseFamily
#> AcceptorSpecificity ABL ACK AGC Alpha CAMK CK1 CMGC CSK DDR EPHR ErbB FAK FAM20
#>             Ser/Thr   0   0  52     4   56  11   54   0   0    0    0   0     1
#>             Tyr       2   2   0     0    0   0    0   2   2   12    3   2     0
#>                    KinaseFamily
#> AcceptorSpecificity FES FGFR FRK HGFR INSR JAK LTKR MUSK NGFR OTHER Other PDGFR
#>             Ser/Thr   0    0   0    0    0   0    0    0    0     0    52     0
#>             Tyr       2    4   3    2    3   4    2    1    3     2     0     5
#>                    KinaseFamily
#> AcceptorSpecificity PDHK PIKK RETR ROSR SRC STE SYK TAMR TEC TIER TKL VEGFR WEE
#>             Ser/Thr    3    5    0    0   0  41   0    0   0    0  24     0   0
#>             Tyr        3    0    1    1   8   3   2    3   5    1   5     3   2

4 Loading kinase PWMs

Kinase PWMs can be loaded with the getKinasePWM() function which returns the full list of 396 kinase PWMs.

library(JohnsonKinaseData)
pwms <- getKinasePWM()
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache

head(names(pwms))
#> [1] "AAK1"   "ACVR2A" "ACVR2B" "AKT1"   "AKT2"   "AKT3"

Each PWM is a numeric matrix with amino acids as rows and positions as columns. Matrix elements are log2-odd scores measuring differential affinity relative to a random frequency of amino acids (Johnson et al. 2023).

pwms[["PLK2"]]
#>             -5           -4          -3         -2           -1           0
#> A -0.036821844 -0.277009455 -0.83856373 -0.4463446 -0.186229068          NA
#> C  0.009633819 -0.034899138 -0.24690897  0.4799548 -0.467333943          NA
#> D  0.549718451  0.795766948  0.82130204  1.6459783  1.329410671          NA
#> E  0.614756952  1.127897364  2.86862751  1.2354207  0.689388627          NA
#> F  0.449006639  0.078199920 -0.41273103 -0.9773836 -0.602963759          NA
#> G  0.326652391 -0.151522275 -0.77793738 -0.6106535 -0.767584829          NA
#> H  0.148478616 -0.172018427 -0.67807191 -0.3219281  0.214995135          NA
#> I -0.311864412 -0.172018427 -1.65154094 -0.8406292 -0.519941731          NA
#> K -0.469329925 -0.647467443 -1.77349147 -1.7345631 -0.656307931          NA
#> L -0.245197993  0.144568518 -0.71785677  0.3032255 -0.511690664          NA
#> M -0.248793390 -0.206894852 -0.38948891  0.3123167 -0.194955239          NA
#> N -0.065823218  0.002018361 -0.54077824  0.9076598  0.307545102          NA
#> P -0.066578437 -0.108114249 -1.05139915 -0.4418303  0.542703792          NA
#> Q -0.530739153 -0.241782116 -0.48096139 -0.1800049 -0.264477823          NA
#> R -0.528032212 -0.715485867 -1.58640592 -1.1059389 -0.339345148          NA
#> S -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239  0.00000000
#> T -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 -0.09585422
#> V -0.401253684 -0.367545642 -1.89324968 -1.3562361 -0.152804813          NA
#> W -0.034160317 -0.140189435 -1.05799229 -1.1256358 -1.093879047          NA
#> Y  0.083383588 -0.242293983 -1.12217724 -0.5640514 -0.004045212          NA
#> s  0.059632160  0.750692249  0.06873959  0.1075540  0.101650076          NA
#> t  0.059632160  0.750692249  0.06873959  0.1075540  0.101650076          NA
#> y  0.707878133  0.679784089  0.26351522 -0.1321035  2.184534212          NA
#>              1            2           3           4
#> A -0.812485602 -0.109981413 -0.53574997 -0.33515312
#> C -0.310253562  0.145612247  0.00000000  0.04362448
#> D -0.942307133  1.124791311  1.17957474  0.98389654
#> E -0.201410261  1.154194325  1.37389873  1.13638828
#> F  1.906390375 -0.122334266 -0.21541226 -0.12610808
#> G -0.918660373 -0.888701547 -0.30329392 -0.24827921
#> H -0.671163536 -0.002165667 -0.13020754 -0.01785518
#> I  0.374065718 -0.042308229 -0.25963366 -0.03785821
#> K -1.145924538 -2.141143704 -1.48196851 -1.17755536
#> L  0.032665112 -0.500013836 -0.19379970 -0.02664588
#> M  0.833902077  0.008200014 -0.23463499 -0.20273795
#> N -0.818579360 -0.015082595  0.07710624 -0.20706138
#> P -2.650181828 -0.911044318 -0.71667083  0.10218779
#> Q  0.266756562 -0.411003598 -0.01873185 -0.18852897
#> R -0.532824877 -1.190338611 -1.33715648 -1.18082233
#> S -0.532824877 -0.109981413 -0.21541226 -0.12610808
#> T -0.532824877 -0.109981413 -0.21541226 -0.12610808
#> V -0.008682243 -0.249993850 -0.38571419 -0.85152138
#> W -0.550465037  0.385154897  0.11769504  0.30836088
#> Y  0.360757558  0.526569660  0.07546417 -0.04751733
#> s  0.412402175  1.196984664  1.25574242  1.70655265
#> t  0.412402175  1.196984664  1.25574242  1.70655265
#> y  0.490467444  3.461305904  1.53012070  1.85199884

Beside the 20 standard amino acids, also phosphorylated serine, threonine and tyrosine residues are included. These phosphorylated residues are distinct from the central phospho-acceptor (serine, threonine or tyrosine at position 0) and can have a strong impact on the affinity of a given kinase-substrate pair (phospho-priming).

For serine/threonine specific kinase PWMs, the central phospho-acceptor measures the favorability of serine over threonine. The user can exclude this favorability measure by setting the parameter includeSTfavorability to FALSE, in which case the central position doesn’t contribute to the PWM score.

getKinasePWM(includeSTfavorability=FALSE)[["PLK2"]]
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#>             -5           -4          -3         -2           -1  0            1
#> A -0.036821844 -0.277009455 -0.83856373 -0.4463446 -0.186229068 NA -0.812485602
#> C  0.009633819 -0.034899138 -0.24690897  0.4799548 -0.467333943 NA -0.310253562
#> D  0.549718451  0.795766948  0.82130204  1.6459783  1.329410671 NA -0.942307133
#> E  0.614756952  1.127897364  2.86862751  1.2354207  0.689388627 NA -0.201410261
#> F  0.449006639  0.078199920 -0.41273103 -0.9773836 -0.602963759 NA  1.906390375
#> G  0.326652391 -0.151522275 -0.77793738 -0.6106535 -0.767584829 NA -0.918660373
#> H  0.148478616 -0.172018427 -0.67807191 -0.3219281  0.214995135 NA -0.671163536
#> I -0.311864412 -0.172018427 -1.65154094 -0.8406292 -0.519941731 NA  0.374065718
#> K -0.469329925 -0.647467443 -1.77349147 -1.7345631 -0.656307931 NA -1.145924538
#> L -0.245197993  0.144568518 -0.71785677  0.3032255 -0.511690664 NA  0.032665112
#> M -0.248793390 -0.206894852 -0.38948891  0.3123167 -0.194955239 NA  0.833902077
#> N -0.065823218  0.002018361 -0.54077824  0.9076598  0.307545102 NA -0.818579360
#> P -0.066578437 -0.108114249 -1.05139915 -0.4418303  0.542703792 NA -2.650181828
#> Q -0.530739153 -0.241782116 -0.48096139 -0.1800049 -0.264477823 NA  0.266756562
#> R -0.528032212 -0.715485867 -1.58640592 -1.1059389 -0.339345148 NA -0.532824877
#> S -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 NA -0.532824877
#> T -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 NA -0.532824877
#> V -0.401253684 -0.367545642 -1.89324968 -1.3562361 -0.152804813 NA -0.008682243
#> W -0.034160317 -0.140189435 -1.05799229 -1.1256358 -1.093879047 NA -0.550465037
#> Y  0.083383588 -0.242293983 -1.12217724 -0.5640514 -0.004045212 NA  0.360757558
#> s  0.059632160  0.750692249  0.06873959  0.1075540  0.101650076 NA  0.412402175
#> t  0.059632160  0.750692249  0.06873959  0.1075540  0.101650076 NA  0.412402175
#> y  0.707878133  0.679784089  0.26351522 -0.1321035  2.184534212 NA  0.490467444
#>              2           3           4
#> A -0.109981413 -0.53574997 -0.33515312
#> C  0.145612247  0.00000000  0.04362448
#> D  1.124791311  1.17957474  0.98389654
#> E  1.154194325  1.37389873  1.13638828
#> F -0.122334266 -0.21541226 -0.12610808
#> G -0.888701547 -0.30329392 -0.24827921
#> H -0.002165667 -0.13020754 -0.01785518
#> I -0.042308229 -0.25963366 -0.03785821
#> K -2.141143704 -1.48196851 -1.17755536
#> L -0.500013836 -0.19379970 -0.02664588
#> M  0.008200014 -0.23463499 -0.20273795
#> N -0.015082595  0.07710624 -0.20706138
#> P -0.911044318 -0.71667083  0.10218779
#> Q -0.411003598 -0.01873185 -0.18852897
#> R -1.190338611 -1.33715648 -1.18082233
#> S -0.109981413 -0.21541226 -0.12610808
#> T -0.109981413 -0.21541226 -0.12610808
#> V -0.249993850 -0.38571419 -0.85152138
#> W  0.385154897  0.11769504  0.30836088
#> Y  0.526569660  0.07546417 -0.04751733
#> s  1.196984664  1.25574242  1.70655265
#> t  1.196984664  1.25574242  1.70655265
#> y  3.461305904  1.53012070  1.85199884

In order to disable scoring of phosphosites that do no contain a matching phospho-acceptor, i.e. S/T in case of serine/threonine PWMs or K in case of tyrosine PWMs, parameter matchAcceptorSpecificity can be set to TRUE. In this case the log2-odd score of non matching residues is set to -Inf:

getKinasePWM(matchAcceptorSpecificity=TRUE)[["PLK2"]]
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#>             -5           -4          -3         -2           -1           0
#> A -0.036821844 -0.277009455 -0.83856373 -0.4463446 -0.186229068        -Inf
#> C  0.009633819 -0.034899138 -0.24690897  0.4799548 -0.467333943        -Inf
#> D  0.549718451  0.795766948  0.82130204  1.6459783  1.329410671        -Inf
#> E  0.614756952  1.127897364  2.86862751  1.2354207  0.689388627        -Inf
#> F  0.449006639  0.078199920 -0.41273103 -0.9773836 -0.602963759        -Inf
#> G  0.326652391 -0.151522275 -0.77793738 -0.6106535 -0.767584829        -Inf
#> H  0.148478616 -0.172018427 -0.67807191 -0.3219281  0.214995135        -Inf
#> I -0.311864412 -0.172018427 -1.65154094 -0.8406292 -0.519941731        -Inf
#> K -0.469329925 -0.647467443 -1.77349147 -1.7345631 -0.656307931        -Inf
#> L -0.245197993  0.144568518 -0.71785677  0.3032255 -0.511690664        -Inf
#> M -0.248793390 -0.206894852 -0.38948891  0.3123167 -0.194955239        -Inf
#> N -0.065823218  0.002018361 -0.54077824  0.9076598  0.307545102        -Inf
#> P -0.066578437 -0.108114249 -1.05139915 -0.4418303  0.542703792        -Inf
#> Q -0.530739153 -0.241782116 -0.48096139 -0.1800049 -0.264477823        -Inf
#> R -0.528032212 -0.715485867 -1.58640592 -1.1059389 -0.339345148        -Inf
#> S -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239  0.00000000
#> T -0.065823218 -0.172018427 -0.77793738 -0.4463446 -0.194955239 -0.09585422
#> V -0.401253684 -0.367545642 -1.89324968 -1.3562361 -0.152804813        -Inf
#> W -0.034160317 -0.140189435 -1.05799229 -1.1256358 -1.093879047        -Inf
#> Y  0.083383588 -0.242293983 -1.12217724 -0.5640514 -0.004045212        -Inf
#> s  0.059632160  0.750692249  0.06873959  0.1075540  0.101650076          NA
#> t  0.059632160  0.750692249  0.06873959  0.1075540  0.101650076          NA
#> y  0.707878133  0.679784089  0.26351522 -0.1321035  2.184534212        -Inf
#>              1            2           3           4
#> A -0.812485602 -0.109981413 -0.53574997 -0.33515312
#> C -0.310253562  0.145612247  0.00000000  0.04362448
#> D -0.942307133  1.124791311  1.17957474  0.98389654
#> E -0.201410261  1.154194325  1.37389873  1.13638828
#> F  1.906390375 -0.122334266 -0.21541226 -0.12610808
#> G -0.918660373 -0.888701547 -0.30329392 -0.24827921
#> H -0.671163536 -0.002165667 -0.13020754 -0.01785518
#> I  0.374065718 -0.042308229 -0.25963366 -0.03785821
#> K -1.145924538 -2.141143704 -1.48196851 -1.17755536
#> L  0.032665112 -0.500013836 -0.19379970 -0.02664588
#> M  0.833902077  0.008200014 -0.23463499 -0.20273795
#> N -0.818579360 -0.015082595  0.07710624 -0.20706138
#> P -2.650181828 -0.911044318 -0.71667083  0.10218779
#> Q  0.266756562 -0.411003598 -0.01873185 -0.18852897
#> R -0.532824877 -1.190338611 -1.33715648 -1.18082233
#> S -0.532824877 -0.109981413 -0.21541226 -0.12610808
#> T -0.532824877 -0.109981413 -0.21541226 -0.12610808
#> V -0.008682243 -0.249993850 -0.38571419 -0.85152138
#> W -0.550465037  0.385154897  0.11769504  0.30836088
#> Y  0.360757558  0.526569660  0.07546417 -0.04751733
#> s  0.412402175  1.196984664  1.25574242  1.70655265
#> t  0.412402175  1.196984664  1.25574242  1.70655265
#> y  0.490467444  3.461305904  1.53012070  1.85199884

5 Processing user-provided phosphosites

Phosphorylated peptides are often represented in two different formats: (1) the phosphorylated residues are indicated by an asterix as in SAGLLS*DEDC, (2) phosphorylated residues are given by lower case letters as in SAGLLsDEDC. In order to unify the phosophosite representation for PWM matching, JohnsonKinaseData provides the function processPhosphopeptides(). It takes a character vector with phospho-peptides, aligns them to the central phospho-acceptor position and pads and/or truncates the surrounding residues. By default this means, 5 upstream residues, a central acceptor and 5 downstream residues. The central phospho-acceptor position is defined as the left closest phosphorylated residue to the midpoint of the peptide given by floor(nchar(sites)/2)+1. This midpoint definition is also the default alignment position if no phosphorylated residue was recognized.

ppeps <- c("SAGLLS*DEDC", "GDtND", "EKGDSN__", "HKRNyGsDER", "PEKS*GyNV")

sites <- processPhosphopeptides(ppeps)

sites
#> # A tibble: 5 × 3
#>   sites       processed   acceptor
#>   <chr>       <chr>       <chr>   
#> 1 SAGLLS*DEDC SAGLLSDEDC_ S       
#> 2 GDtND       ___GDTND___ T       
#> 3 EKGDSN__    _EKGDSN____ S       
#> 4 HKRNyGsDER  _HKRNYGsDER Y       
#> 5 PEKS*GyNV   __PEKSGyNV_ S

If a peptide contains several phosphorylated residues, option onlyCentralAcceptor controls how to select the acceptor position. Setting onlyCentralAcceptor=FALSE will return all possible aligned phosphosites for a given input peptide. Note that in this case the output is not parallel to the input.

sites <- processPhosphopeptides(ppeps, onlyCentralAcceptor=FALSE)

sites
#> # A tibble: 7 × 3
#>   sites       processed   acceptor
#>   <chr>       <chr>       <chr>   
#> 1 SAGLLS*DEDC SAGLLSDEDC_ S       
#> 2 GDtND       ___GDTND___ T       
#> 3 EKGDSN__    _EKGDSN____ S       
#> 4 HKRNyGsDER  _HKRNYGsDER Y       
#> 5 HKRNyGsDER  KRNyGSDER__ S       
#> 6 PEKS*GyNV   __PEKSGyNV_ S       
#> 7 PEKS*GyNV   PEKsGYNV___ Y

A warning is raised if the central acceptor is not serine, threonine or tyrosine.

6 Scoring of user-provided phosphosites

Once peptides are processed to sites, the function scorePhosphosites() can be used to create a matrix of kinase-substrate match scores.

selected <- sites |> 
  dplyr::pull(processed)

scores <- scorePhosphosites(pwms, selected)

dim(scores)
#> [1]   7 396

scores[,1:5]
#>                  AAK1     ACVR2A      ACVR2B       AKT1       AKT2
#> SAGLLSDEDC_ -6.794078 -0.1666423  0.30390179 -5.8821117 -4.7783302
#> ___GDTND___ -4.803921 -1.0410203 -0.56120674 -2.8360934 -2.5125933
#> _EKGDSN____ -8.274386 -1.5402977 -0.92960511 -0.6188352 -0.8554523
#> _HKRNYGsDER  1.286478 -2.5560251 -1.74870769  1.0901866  2.7031941
#> KRNyGSDER__ -6.290564 -1.9202469 -1.38766899 -3.0601553 -1.7486155
#> __PEKSGyNV_  1.695554 -0.1171313  0.06161951 -4.7296786 -3.6486856
#> PEKsGYNV___ -3.099221 -2.1144168 -0.86427402 -0.3383336 -0.4906393

The PWM scoring can be parallelized by supplying a BiocParallelParam object to BPPARAM=.

scores <- scorePhosphosites(pwms, selected, BPPARAM=BiocParallel::SerialParam())

By default, the resulting score is the log2-odds score of the PWM. Alternatively, by setting scoreType="percentile", a percentile rank of the log2-odds score is calculated, using for each PWM a background score distribution.

scores <- scorePhosphosites(pwms, selected, scoreType="percentile")
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache

scores[,1:5]
#>                  AAK1   ACVR2A   ACVR2B     AKT1     AKT2
#> SAGLLSDEDC_ 22.375586 79.73910 83.79933 14.73447 14.59609
#> ___GDTND___ 53.371824 67.48779 74.89617 56.34769 53.31220
#> _EKGDSN____  7.927565 57.36739 69.80942 79.14942 74.56646
#> _HKRNYGsDER 98.050345 32.25318 54.60051 88.90565 93.83754
#> KRNyGSDER__ 29.304770 48.35330 61.93582 53.01150 64.98986
#> __PEKSGyNV_ 98.620247 80.26811 81.54857 28.17005 32.26440
#> PEKsGYNV___ 75.754887 43.40279 70.76463 81.09288 77.47741

Quantifying PWM matches by percentile rank was first described in Yaffe et al. 2001 (Yaffe et al. 2001). The background score distributions used here are derived from matching each PWM to either the 85’603 unique phosphosites published in Johnson et al. 2023 (serine/threonine PWMs) or the 6659 unique phosphosites published in Yaron-Barir et al. 2024 (tyrosine PWMs). They can be accessed with:

bg <- getBackgroundScores(phosphoAcceptor='Tyr')
#> see ?JohnsonKinaseData and browseVignettes('JohnsonKinaseData') for documentation
#> loading from cache

where phosphoAcceptor can be either Ser/Thr or Tyr. The corresponding mappings of log2-odd scores to percentile ranks can be accessed with function getScoreMaps() which returns a list of mapping functions, one for each kinase PWM.

Note that these percentile ranks do not account for phospho-priming, as non-central phosphorylated residues were missing in the background sites. I.e. the percentile ranks cannot reflect the impact of phospho-priming.

7 Session info

sessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] JohnsonKinaseData_1.3.0 BiocStyle_2.35.0       
#> 
#> loaded via a namespace (and not attached):
#>  [1] KEGGREST_1.47.0         xfun_0.49               bslib_0.8.0            
#>  [4] Biobase_2.67.0          vctrs_0.6.5             tools_4.5.0            
#>  [7] generics_0.1.3          stats4_4.5.0            curl_5.2.3             
#> [10] parallel_4.5.0          tibble_3.2.1            fansi_1.0.6            
#> [13] AnnotationDbi_1.69.0    RSQLite_2.3.7           blob_1.2.4             
#> [16] pkgconfig_2.0.3         checkmate_2.3.2         dbplyr_2.5.0           
#> [19] S4Vectors_0.45.0        lifecycle_1.0.4         GenomeInfoDbData_1.2.13
#> [22] stringr_1.5.1           compiler_4.5.0          Biostrings_2.75.0      
#> [25] codetools_0.2-20        GenomeInfoDb_1.43.0     htmltools_0.5.8.1      
#> [28] sass_0.4.9              yaml_2.3.10             tidyr_1.3.1            
#> [31] pillar_1.9.0            crayon_1.5.3            jquerylib_0.1.4        
#> [34] BiocParallel_1.41.0     cachem_1.1.0            mime_0.12              
#> [37] ExperimentHub_2.15.0    AnnotationHub_3.15.0    tidyselect_1.2.1       
#> [40] digest_0.6.37           stringi_1.8.4           purrr_1.0.2            
#> [43] dplyr_1.1.4             bookdown_0.41           BiocVersion_3.21.1     
#> [46] fastmap_1.2.0           cli_3.6.3               magrittr_2.0.3         
#> [49] utf8_1.2.4              withr_3.0.2             backports_1.5.0        
#> [52] filelock_1.0.3          UCSC.utils_1.3.0        rappdirs_0.3.3         
#> [55] bit64_4.5.2             rmarkdown_2.29          XVector_0.47.0         
#> [58] httr_1.4.7              bit_4.5.0               png_0.1-8              
#> [61] memoise_2.0.1           evaluate_1.0.1          knitr_1.48             
#> [64] IRanges_2.41.0          BiocFileCache_2.15.0    rlang_1.1.4            
#> [67] glue_1.8.0              DBI_1.2.3               BiocManager_1.30.25    
#> [70] BiocGenerics_0.53.1     jsonlite_1.8.9          R6_2.5.1               
#> [73] zlibbioc_1.53.0

References

Johnson, Jared L., Tomer M. Yaron, Emily M. Huntsman, Alexander Kerelsky, Junho Song, Amit Regev, Ting-Yu Lin, et al. 2023. “An Atlas of Substrate Specificities for the Human Serine/Threonine Kinome.” Journal Article. Nature 613 (7945): 759–66. https://doi.org/10.1038/s41586-022-05575-3.
Yaffe, Michael B., German G. Leparc, Jack Lai, Toshiyuki Obata, Stefano Volinia, and Lewis C. Cantley. 2001. “A Motif-Based Profile Scanning Approach for Genome-Wide Prediction of Signaling Pathways.” Journal Article. Nature Biotechnology 19 (4): 348–53. https://doi.org/10.1038/86737.
Yaron-Barir, Tomer M., Brian A. Joughin, Emily M. Huntsman, Alexander Kerelsky, Daniel M. Cizin, Benjamin M. Cohen, Amit Regev, et al. 2024. “The Intrinsic Substrate Specificity of the Human Tyrosine Kinome.” Journal Article. Nature 629 (8014): 1174–81. https://doi.org/10.1038/s41586-024-07407-y.