brendaDb aims to make importing and analyzing data from the BRENDA database easier. The main functions include:
tibbleFor bug reports or feature requests, please go to the GitHub repository.
brendaDb is a Bioconductor package and can be installed through BiocManager::install().
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("brendaDb", dependencies=TRUE)Alternatively, install the development version from GitHub.
if(!requireNamespace("brendaDb")) {
devtools::install_github("y1zhou/brendaDb")
}After the package is installed, it can be loaded into the R workspace by
library(brendaDb)Download the BRENDA database as a text file here. Alternatively, download the file in R (file updated 2019-04-24):
brenda.filepath <- DownloadBrenda()
#> Please read the license agreement in the link below.
#>
#> https://www.brenda-enzymes.org/download_brenda_without_registration.php
#>
#> Found zip file in cache.
#> Extracting zip file...The function downloads the file to a local cache directory. Now the text file can be loaded into R as a tibble:
df <- ReadBrenda(brenda.filepath)
#> Reading BRENDA text file...
#> Converting text into a list. This might take a while...
#> Converting list to tibble and removing duplicated entries...
#> If you're going to use this data again, consider saving this table using data.table::fwrite().As suggested in the function output, you may save the df object to a text file using data.table::fwrite() or to an R object using save(df), and load the table using data.table::fread() or load()1 This requires the R package data.table to be installed.. Both methods should be much faster than reading the raw text file again using ReadBrenda().
Since BRENDA is a database for enzymes, all final queries are based on EC numbers.
If you already have a list of EC numbers in mind, you may call QueryBrenda directly:
brenda_txt <- system.file("extdata", "brenda_download_test.txt",
package = "brendaDb")
df <- ReadBrenda(brenda_txt)
#> Reading BRENDA text file...
#> Converting text into a list. This might take a while...
#> Converting list to tibble and removing duplicated entries...
#> If you're going to use this data again, consider saving this table using data.table::fwrite().
res <- QueryBrenda(df, EC = c("1.1.1.1", "6.3.5.8"), n.core = 2)
res
#> A list of 2 brenda.entry object(s) with:
#> - 1 regular brenda.entry object(s)
#> 1.1.1.1
#> - 1 transferred or deleted object(s)
#> 6.3.5.8
res[["1.1.1.1"]]
#> Entry 1.1.1.1
#> ├── nomenclature
#> | ├── ec: 1.1.1.1
#> | ├── systematic.name: alcohol:NAD+ oxidoreductase
#> | ├── recommended.name: alcohol dehydrogenase
#> | ├── synonyms: A tibble with 128 rows
#> | ├── reaction: A tibble with 2 rows
#> | └── reaction.type: A tibble with 3 rows
#> ├── interactions
#> | ├── substrate.product: A tibble with 772 rows
#> | ├── natural.substrate.product: A tibble with 20 rows
#> | ├── cofactor: A tibble with 7 rows
#> | ├── metals.ions: A tibble with 20 rows
#> | ├── inhibitors: A tibble with 207 rows
#> | └── activating.compound: A tibble with 22 rows
#> ├── parameters
#> | ├── km.value: A tibble with 878 rows
#> | ├── turnover.number: A tibble with 495 rows
#> | ├── ki.value: A tibble with 34 rows
#> | ├── pi.value: A tibble with 11 rows
#> | ├── ph.optimum: A tibble with 55 rows
#> | ├── ph.range: A tibble with 28 rows
#> | ├── temperature.optimum: A tibble with 29 rows
#> | ├── temperature.range: A tibble with 20 rows
#> | ├── specific.activity: A tibble with 88 rows
#> | └── ic50: A tibble with 2 rows
#> ├── organism
#> | ├── organism: A tibble with 159 rows
#> | ├── source.tissue: A tibble with 63 rows
#> | └── localization: A tibble with 9 rows
#> ├── molecular
#> | ├── stability
#> | | ├── general.stability: A tibble with 15 rows
#> | | ├── storage.stability: A tibble with 15 rows
#> | | ├── ph.stability: A tibble with 20 rows
#> | | ├── organic.solvent.stability: A tibble with 25 rows
#> | | ├── oxidation.stability: A tibble with 3 rows
#> | | └── temperature.stability: A tibble with 36 rows
#> | ├── purification: A tibble with 48 rows
#> | ├── cloned: A tibble with 46 rows
#> | ├── engineering: A tibble with 60 rows
#> | ├── renatured: A tibble with 1 rows
#> | └── application: A tibble with 5 rows
#> ├── structure
#> | ├── molecular.weight: A tibble with 119 rows
#> | ├── subunits: A tibble with 11 rows
#> | ├── posttranslational.modification: A tibble with 2 rows
#> | └── crystallization: A tibble with 22 rows
#> └── bibliography
#> | └── reference: A tibble with 285 rowsYou can also query for certain fields to reduce the size of the returned object.
ShowFields(df)
#> # A tibble: 40 x 2
#> field acronym
#> <chr> <chr>
#> 1 PROTEIN PR
#> 2 RECOMMENDED_NAME RN
#> 3 SYSTEMATIC_NAME SN
#> 4 SYNONYMS SY
#> 5 REACTION RE
#> 6 REACTION_TYPE RT
#> 7 SOURCE_TISSUE ST
#> 8 LOCALIZATION LO
#> 9 NATURAL_SUBSTRATE_PRODUCT NSP
#> 10 SUBSTRATE_PRODUCT SP
#> # … with 30 more rows
res <- QueryBrenda(df, EC = "1.1.1.1", fields = c("PROTEIN", "SUBSTRATE_PRODUCT"))
res[["1.1.1.1"]][["interactions"]][["substrate.product"]]
#> # A tibble: 772 x 7
#> proteinID substrate product commentarySubst… commentaryProdu… reversibility
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 10 n-propan… n-prop… <NA> <NA> r
#> 2 10 2-propan… aceton… <NA> <NA> <NA>
#> 3 10 n-hexano… n-hexa… <NA> <NA> r
#> 4 10 (S)-2-bu… 2-buta… <NA> <NA> r
#> 5 10 ethyleng… ? + NA… <NA> <NA> r
#> 6 10 n-butano… butyra… <NA> <NA> <NA>
#> 7 10 n-decano… n-deca… <NA> <NA> r
#> 8 10 Tris + N… ? + NA… <NA> <NA> r
#> 9 10 isopropa… aceton… <NA> <NA> <NA>
#> 10 10 5-hydrox… (furan… #10# mutant enz… <NA> <NA>
#> # … with 762 more rows, and 1 more variable: refID <chr>It should be noted that most fields contain a fieldInfo column and a commentary column. The fieldInfo column is what’s extracted by BRENDA from the literature, and the commentary column is usually some context from the original paper. # symbols in the commentary correspond to the proteinIDs, and <> enclose the corresponding refIDs. For further information, please see the README file from BRENDA.
Note the difference in row numbers in the following example and in the one where we queried for all organisms.
res <- QueryBrenda(df, EC = "1.1.1.1", organisms = "Homo sapiens")
res$`1.1.1.1`
#> Entry 1.1.1.1
#> ├── nomenclature
#> | ├── ec: 1.1.1.1
#> | ├── systematic.name: alcohol:NAD+ oxidoreductase
#> | ├── recommended.name: alcohol dehydrogenase
#> | ├── synonyms: A tibble with 41 rows
#> | ├── reaction: A tibble with 2 rows
#> | └── reaction.type: A tibble with 3 rows
#> ├── interactions
#> | ├── substrate.product: A tibble with 102 rows
#> | ├── natural.substrate.product: A tibble with 9 rows
#> | ├── cofactor: A tibble with 2 rows
#> | ├── metals.ions: A tibble with 2 rows
#> | └── inhibitors: A tibble with 36 rows
#> ├── parameters
#> | ├── km.value: A tibble with 163 rows
#> | ├── turnover.number: A tibble with 64 rows
#> | ├── ki.value: A tibble with 8 rows
#> | ├── ph.optimum: A tibble with 15 rows
#> | ├── ph.range: A tibble with 2 rows
#> | ├── temperature.optimum: A tibble with 2 rows
#> | └── specific.activity: A tibble with 5 rows
#> ├── organism
#> | ├── organism: A tibble with 3 rows
#> | ├── source.tissue: A tibble with 21 rows
#> | └── localization: A tibble with 1 rows
#> ├── molecular
#> | ├── stability
#> | | ├── general.stability: A tibble with 1 rows
#> | | ├── storage.stability: A tibble with 4 rows
#> | | ├── ph.stability: A tibble with 1 rows
#> | | ├── organic.solvent.stability: A tibble with 1 rows
#> | | └── temperature.stability: A tibble with 2 rows
#> | ├── purification: A tibble with 7 rows
#> | ├── cloned: A tibble with 5 rows
#> | ├── engineering: A tibble with 3 rows
#> | └── application: A tibble with 1 rows
#> ├── structure
#> | ├── molecular.weight: A tibble with 12 rows
#> | ├── subunits: A tibble with 3 rows
#> | └── crystallization: A tibble with 2 rows
#> └── bibliography
#> | └── reference: A tibble with 285 rowsTo transform the brenda.entries structure into a table, use the helper function ExtractField().
res <- QueryBrenda(df, EC = c("1.1.1.1", "6.3.5.8"), n.core = 2)
ExtractField(res, field = "parameters$ph.optimum")
#> Deprecated entries in the res object will be removed.
#> # A tibble: 158 x 9
#> ec organism proteinID uniprot org.commentary description fieldInfo
#> <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
#> 1 1.1.… Acetoba… 60 <NA> <NA> 5.5 NA
#> 2 1.1.… Acetoba… 60 <NA> <NA> 6 NA
#> 3 1.1.… Acetoba… 60 <NA> <NA> 8.5 NA
#> 4 1.1.… Acineto… 28 <NA> <NA> 5.9 NA
#> 5 1.1.… Aeropyr… 131 Q9Y9P9 <NA> 10.5 NA
#> 6 1.1.… Aeropyr… 131 Q9Y9P9 <NA> 8 NA
#> 7 1.1.… Arabido… 20 <NA> <NA> 10.5 NA
#> 8 1.1.… Aspergi… 14 <NA> <NA> 8.1 NA
#> 9 1.1.… Breviba… 46 <NA> <NA> 10.4 NA
#> 10 1.1.… Breviba… 46 <NA> <NA> 6 NA
#> # … with 148 more rows, and 2 more variables: commentary <chr>, refID <chr>As shown above, the returned table consists of three parts: the EC number, organism-related information (organism, protein ID, uniprot ID, and commentary on the organism), and extracted field information (description, commentary, etc.).
A lot of the times we have a list of gene symbols or enzyme names instead of EC numbers. In this case, a helper function can be used to find the corresponding EC numbers:
ID2Enzyme(brenda = df, ids = c("ADH4", "CD38", "pyruvate dehydrogenase"))
#> # A tibble: 4 x 5
#> ID EC RECOMMENDED_NAME SYNONYMS SYSTEMATIC_NAME
#> <chr> <chr> <chr> <chr> <chr>
#> 1 ADH4 1.1.1… <NA> "aldehyde reduct… <NA>
#> 2 CD38 2.4.9… <NA> "#1,3,4,6# CD38 … <NA>
#> 3 pyruvat… 1.2.1… pyruvate dehydrogena… "#1,2# pyruvate:… <NA>
#> 4 pyruvat… 2.7.1… [pyruvate dehydrogen… "kinase (phospho… ATP:[pyruvate dehydro…The EC column can be then handpicked and used in QueryBrenda().
Often we are interested in the enzymes involved in a specific BioCyc pathway. Functions BioCycPathwayEnzymes() and BiocycPathwayGenes() can be used in this case:
BiocycPathwayEnzymes(org.id = "HUMAN", pathway = "PWY66-400")
#> Found 10 reactions for HUMAN pathway PWY66-400.
#> # A tibble: 11 x 5
#> RxnID EC ReactionDirection LHS RHS
#> <chr> <chr> <chr> <chr> <chr>
#> 1 PGLUCISOM-RXN 5.3.1.9 REVERSIBLE D-glucopyrano… FRUCTOSE-6P
#> 2 GLUCOKIN-RXN 2.7.1.1 LEFT-TO-RIGHT Glucopyranose… D-glucopyranose-6-p…
#> 3 GLUCOKIN-RXN 2.7.1.2 LEFT-TO-RIGHT Glucopyranose… D-glucopyranose-6-p…
#> 4 PEPDEPHOS-RXN 2.7.1.… PHYSIOL-RIGHT-TO-… PYRUVATE + ATP PROTON + PHOSPHO-EN…
#> 5 2PGADEHYDRAT-… 4.2.1.… REVERSIBLE 2-PG PHOSPHO-ENOL-PYRUVA…
#> 6 RXN-15513 5.4.2.… REVERSIBLE 2-PG G3P
#> 7 PHOSGLYPHOS-R… 2.7.2.3 REVERSIBLE G3P + ATP DPG + ADP
#> 8 GAPOXNPHOSPHN… 1.2.1.… REVERSIBLE GAP + Pi + NAD PROTON + DPG + NADH
#> 9 TRIOSEPISOMER… 5.3.1.1 REVERSIBLE GAP DIHYDROXY-ACETONE-P…
#> 10 F16ALDOLASE-R… 4.1.2.… REVERSIBLE FRUCTOSE-16-D… DIHYDROXY-ACETONE-P…
#> 11 6PFRUCTPHOS-R… 2.7.1.… LEFT-TO-RIGHT ATP + FRUCTOS… PROTON + ADP + FRUC…BiocycPathwayGenes(org.id = "HUMAN", pathway = "TRYPTOPHAN-DEGRADATION-1")
#> Found 16 genes in HUMAN pathway TRYPTOPHAN-DEGRADATION-1.
#> # A tibble: 16 x 4
#> BiocycGene BiocycProtein Symbol Ensembl
#> <chr> <chr> <chr> <chr>
#> 1 HS14455 HS14455-MONOMER ACMSD ENSG00000153086
#> 2 HS04229 ENSG00000118514-MONO… ALDH8A1 ENSG00000118514
#> 3 HS11585 HS11585-MONOMER DHTKD1 ENSG00000181192
#> 4 G66-33844 G66-33844-MONOMER AFMID ENSG00000183077,ENST00000409257,ENS…
#> 5 HS04082 HS04082-MONOMER KMO ENSG00000117009
#> 6 HS03952 HS03952-MONOMER KYNU ENSG00000115919
#> 7 HS08749 HS08749-MONOMER HAAO ENSG00000162882
#> 8 HS05502 HS05502-MONOMER IDO1 ENSG00000131203
#> 9 G66-37884 MONOMER66-34407 IDO2 ENSG00000188676
#> 10 HS07771 HS07771-MONOMER TDO2 ENSG00000151790
#> 11 HS02769 HS02769-MONOMER GCDH ENSG00000105607
#> 12 HS01167 HS01167-MONOMER ACAT1 ENSG00000075239
#> 13 HS04399 ENSG00000120437-MONO… ACAT2 ENSG00000120437
#> 14 HS01071 HS01071-MONOMER HSD17B… ENSG00000072506
#> 15 HS06563 HS06563-MONOMER HADH ENSG00000138796
#> 16 HS05132 HS05132-MONOMER ECHS1 ENSG00000127884Similarly, the EC numbers returned from BiocycPathwayEnzymes can be used in the function QueryBrenda, and the gene IDs2 Note that sometimes there are multiple Ensembl IDs in one entry. can be used to find corresponding EC numbers with other packages such as biomaRt and clusterProfiler.
By default QueryBrenda uses all available cores, but often limiting n.core could give better performance as it reduces the overhead. The following are results produced on a machine with 40 cores (2 Intel Xeon CPU E5-2640 v4 @ 3.4GHz), and 256G of RAM:
EC.numbers <- head(unique(df$ID), 100)
system.time(QueryBrenda(df, EC = EC.numbers, n.core = 0)) # default
# user system elapsed
# 4.528 7.856 34.567
system.time(QueryBrenda(df, EC = EC.numbers, n.core = 1))
# user system elapsed
# 22.080 0.360 22.438
system.time(QueryBrenda(df, EC = EC.numbers, n.core = 2))
# user system elapsed
# 0.552 0.400 13.597
system.time(QueryBrenda(df, EC = EC.numbers, n.core = 4))
# user system elapsed
# 0.688 0.832 9.517
system.time(QueryBrenda(df, EC = EC.numbers, n.core = 8))
# user system elapsed
# 1.112 1.476 10.000sessionInfo()
#> R version 4.0.0 (2020-04-24)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 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
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] brendaDb_1.2.0 BiocStyle_2.16.0
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.4.6 compiler_4.0.0 pillar_1.4.3
#> [4] BiocManager_1.30.10 dbplyr_1.4.3 tools_4.0.0
#> [7] digest_0.6.25 bit_1.1-15.2 BiocFileCache_1.12.0
#> [10] RSQLite_2.2.0 evaluate_0.14 memoise_1.1.0
#> [13] lifecycle_0.2.0 tibble_3.0.1 pkgconfig_2.0.3
#> [16] rlang_0.4.5 cli_2.0.2 DBI_1.1.0
#> [19] parallel_4.0.0 curl_4.3 yaml_2.2.1
#> [22] xfun_0.13 xml2_1.3.2 stringr_1.4.0
#> [25] dplyr_0.8.5 httr_1.4.1 knitr_1.28
#> [28] vctrs_0.2.4 rappdirs_0.3.1 bit64_0.9-7
#> [31] tidyselect_1.0.0 glue_1.4.0 R6_2.4.1
#> [34] fansi_0.4.1 BiocParallel_1.22.0 rmarkdown_2.1
#> [37] bookdown_0.18 tidyr_1.0.2 purrr_0.3.4
#> [40] blob_1.2.1 magrittr_1.5 ellipsis_0.3.0
#> [43] htmltools_0.4.0 assertthat_0.2.1 utf8_1.1.4
#> [46] stringi_1.4.6 crayon_1.3.4