add initial curation code

.gitignore

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+*~

src/00_setup_curation.sh

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+
+
+
+
+# from a base directory
+
+
+mkdir data
+mkdir intermediate
+
+git clone https://<user_name>:<security_token>huggingface.co/RosettaCommons/MegaScale
+
+# needed to get splits
+git clone https://github.com/Kuhlman-Lab/ThermoMPNN.git
+
+
+
+
+# Run each numbered script in MegaScale/src/ in order (starting with this one)
+#
+# Tips:
+#    1) Make sure to set the working directory to the base directory (outside of the HF repo)
+#    2) While most of the scripts should work, I recommend running them interactively
+#    3) Some stages require more memory than others, all can be done with < 400GB of memory
+#       but perhaps more more could reduce memory requirements
+#    4) 
+
+
+

src/01_gather_data.R

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+
+
+# Download data from: https://zenodo.org/records/7992926
+# Mega-scale experimental analysis of protein folding stability in biology and design
+
+# 1.2 GB
+system("cd data && curl -o data.zip https://zenodo.org/api/records/7992926/files-archive")
+
+md5sum_expected <- "b76b523d10e12d34b481916b1f57f31c  data/data.zip" 
+md5sum <- system(
+    "md5sum data/data.zip",
+    intern = TRUE)
+if (md5sum != md5sum_expected) {
+    cat("Expected and obtained md5sum values don't match\n")
+}
+
+system("cd data && unzip data.zip && rm data.zip")
+
+

src/02.1_assemble_K50_dG_dataset.R

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+system("cd data; unzip Processed_K50_dG_datasets.zip")
+
+
+### Dataset1 ###
+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")
+
+
+
+dataset23 <- readr::read_csv(
+    file = "data/Processed_K50_dG_datasets/Tsuboyama2023_Dataset2_Dataset3_20230416.csv"
+
+
+####
+
+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")
+

src/02.8_gather_ThermoMPNN_splits.py

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+
+
+import pandas
+import pyarrow.parquet
+import pickle
+
+with open("ThermoMPNN/dataset_splits/mega_splits.pkl", "rb") as f:
+    mega_splits  = pickle.load(f)
+
+splits = []
+for split_name, split_ids in mega_splits.items():
+    splits.append(
+        pandas.DataFrame({
+            'split_name': split_name,
+            'id': split_ids}))
+
+splits = pandas.concat(splits)
+pyarrow.parquet.write_table(
+    pyarrow.Table.from_pandas(splits),
+    where = "intermediate/ThermoMPNN_splits.parquet")