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system("cd data; unzip Processed_K50_dG_datasets.zip")
ThermoMPNN_splits <- arrow::read_parquet("intermediate/ThermoMPNN_splits.parquet")
### Dataset1 ###
# Dataset1 consists of all cDNA proteolysis measurements of stability
dataset1 <- readr::read_csv(
file = "data/Processed_K50_dG_datasets/Tsuboyama2023_Dataset1_20230416.csv",
col_types = readr::cols(
name = readr::col_character(),
dna_seq = readr::col_character(),
log10_K50_t = readr::col_double(),
log10_K50_t_95CI_high = readr::col_double(),
log10_K50_t_95CI_low = readr::col_double(),
log10_K50_t_95CI = readr::col_double(),
fitting_error_t = readr::col_double(),
log10_K50unfolded_t = readr::col_double(),
deltaG_t = readr::col_double(),
deltaG_t_95CI_high = readr::col_double(),
deltaG_t_95CI_low = readr::col_double(),
deltaG_t_95CI = readr::col_double(),
log10_K50_c = readr::col_double(),
log10_K50_c_95CI_high = readr::col_double(),
log10_K50_c_95CI_low = readr::col_double(),
log10_K50_c_95CI = readr::col_double(),
fitting_error_c = readr::col_double(),
log10_K50unfolded_c = readr::col_double(),
deltaG_c = readr::col_double(),
deltaG_c_95CI_high = readr::col_double(),
deltaG_c_95CI_low = readr::col_double(),
deltaG_c_95CI = readr::col_double(),
deltaG = readr::col_double(),
deltaG_95CI_high = readr::col_double(),
deltaG_95CI_low = readr::col_double(),
deltaG_95CI = readr::col_double(),
log10_K50_trypsin_ML = readr::col_double(),
log10_K50_chymotrypsin_ML = readr::col_double()))
# note that some of the log10_K50_trypsin_ML and log10_K50_chmotrypsin_ML values are "-" and ">2.5".
# These are parsed as NA values"
dataset1 |>
arrow::write_parquet(
"intermediate/dataset1.parquet")
### Dataset2 and Dataset3 ###
# Dataset2 (for dG ML) consists of cDNA proteolysis measurements of stability that are of class G0 + G1
# Datase3 (for ddG ML) consists of cDNA proteolysis measurements of stability that are of class G0
# G0: Good (wild-type ΔG values below 4.75 kcal mol^−1), 325,132 ΔG measurements at 17,093 sites in 365 domains
# G1: Good but WT outside dynamic range
dataset2 <- readr::read_csv(
file = "data/Processed_K50_dG_datasets/Tsuboyama2023_Dataset2_Dataset3_20230416.csv",
show_col_types = FALSE)
# 776,298 rows
dataset2 |>
arrow::write_parquet(
"intermediate/dataset2.parquet")
dataset3 <- dataset2 |>
dplyr::filter(ddG_ML != "-")
dataset3 |>
arrow::write_parquet(
"intermediate/dataset3.parquet")
dataset3_single <- dataset3 |>
dplyr::filter(!(mut_type |> stringr::str_detect("(ins|del|[:])")))
ThermoMPNN_splits |> dplyr::group_by(split_name) |>
dplyr::do({
split <- .
split_name <- split$split_name[1]
mutant_set <- dataset3_single |>
dplyr::filter(mut_type != "wt") |>
dplyr::semi_join(split, by = c("WT_name" = "id"))
cat("Writing out split ", split_name, ", nrow: ", nrow(mutant_set), "\n", sep = "")
arrow::write_parquet(
x = mutant_set,
sink = paste0("intermediate/dataset3_single_", split_name, ".parquet"))
data.frame()
})
####
system("cd data && unzip AlphaFold_model_PDBs.zip")
assemble_models <- function(
data_path,
dataset_tag,
pattern,
output_path) {
cat(
"data path: ", data_path, "\n",
"dataset_tag: ", dataset_tag, "\n",
"pattern: ", pattern, "\n",
"output path: ", output_path, "\n",
sep = "")
file_index <- 1
models <- list.files(
path = data_path,
full.names = TRUE,
pattern = pattern,
recursive = TRUE) |>
purrr::map_dfr(.f = function(path) {
file_handle <- path |>
file(open = "rb") |>
gzcon()
if( file_index %% 10 == 0) {
cat("Reading '", path, "' ", file_index, "\n", sep = "")
}
file_index <<- file_index + 1
lines <- file_handle |> readLines()
file_handle |> close()
data.frame(
dataset_tag = dataset_tag,
id = path |> basename() |> stringr::str_replace(".pdb", ""),
pdb = lines |> paste0(collapse = "\n"))
})
models |> arrow::write_parquet(output_path)
}
assemble_models(
data_path = "data/AlphaFold_model_PDBs",
dataset_tag = "all",
pattern = "*.pdb",
output_path = "intermediate/all_pdbs.parquet")
#
# assemble_models(
# data_path = "data/AlphaFold_model_PDBs",
# dataset_tag = "EA",
# pattern = "EA[:]run.*pdb",
# output_path = "intermediate/EA_pdbs.parquet")
#
#
# assemble_models(
# data_path = "data/AlphaFold_model_PDBs",
# dataset_tag = "EEHEE",
# pattern = "EEHEE.*pdb",
# output_path = "intermediate/EEHEE_pdbs.parquet")
#
#
# assemble_models(
# data_path = "data/AlphaFold_model_PDBs",
# dataset_tag = "EHEE",
# pattern = "EHEE.*pdb",
# output_path = "intermediate/EHEE_pdbs.parquet")
#
#
# assemble_models(
# data_path = "data/AlphaFold_model_PDBs",
# dataset_tag = "GG",
# pattern = "GG[:]run.*pdb",
# output_path = "intermediate/GG_pdbs.parquet")
#
#
# assemble_models(
# data_path = "data/AlphaFold_model_PDBs",
# dataset_tag = "HEEH_KT",
# pattern = "HEEH_KT_rd.*pdb",
# output_path = "intermediate/HEEH_KT_pdbs.parquet")
#
# assemble_models(
# data_path = "data/AlphaFold_model_PDBs",
# dataset_tag = "HEEH",
# pattern = "HEEH_rd.*pdb",
# output_path = "intermediate/HEEH_pdbs.parquet")
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