Add Epitope data from TDC

data/iedb_jespersen_et_al/meta.yaml

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+---
+name: iedb_jespersen_et_al
+description: |-
+    Epitope prediction is to predict the active region in the antigen.
+    This dataset is from Bepipred, which curates a dataset from IEDB. It collects B-cell
+    epitopes and non-epitope amino acids determined from crystal structures.
+targets:
+    - id: active_position
+      description: amino acids sequence position that is active in binding
+      units:
+      type: categorical
+      names:
+          - noun: epitope
+          - noun: amino acids sequence active in the antigen binding
+          - noun: epitope sequence active in the antigen binding
+          - noun: epitope sequence active in the binding
+      uris:
+          - http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C13189
+identifiers:
+    - id: Antigen_sequence
+      type: Other
+      names:
+          - amino acid sequence
+          - AA sequence
+          - epitope amino acid sequence
+          - epitope AA sequence
+      description: amino acid sequence
+license: CC BY 4.0
+links:
+    - url: https://doi.org/10.1093/nar/gky1006
+      description: corresponding publication
+    - url: https://doi.org/10.1093/nar/gkx346
+      description: corresponding publication
+    - url: https://tdcommons.ai/single_pred_tasks/epitope/#iedb-jespersen-et-al
+      description: data source
+num_points: 3159
+bibtex:
+    - |-
+      @article{Vita2018,
+      doi = {10.1093/nar/gky1006},
+      url = {https://doi.org/10.1093/nar/gky1006},
+      year = {2018},
+      month = oct,
+      publisher = {Oxford University Press (OUP)},
+      volume = {47},
+      number = {D1},
+      pages = {D339--D343}},
+      author = {Randi Vita and Swapnil Mahajan and James A Overton and
+      Sandeep Kumar Dhanda and Sheridan Martini and Jason R Cantrell and
+      Daniel K Wheeler and Alessandro Sette and Bjoern Peters},
+      title = {The Immune Epitope Database (IEDB): 2018 update},
+      journal = {Nucleic Acids Research}
+    - |-
+      @article{Jespersen2017,
+      doi = {10.1093/nar/gkx346},
+      url = {https://doi.org/10.1093/nar/gkx346},
+      year = {2017},
+      month = may,
+      publisher = {Oxford University Press (OUP)},
+      volume = {45},
+      number = {W1},
+      pages = {W24--W29},
+      author = {Martin Closter Jespersen and Bjoern Peters and Morten Nielsen and Paolo Marcatili},
+      title = {BepiPred 2.0: improving sequence-based B-cell epitope prediction using
+      conformational epitopes},
+      journal = {Nucleic Acids Research}

data/iedb_jespersen_et_al/transform.py

@@ -0,0 +1,191 @@
+import pandas as pd
+import yaml
+from tdc.single_pred import Epitope
+
+
+def get_and_transform_data():
+    # get raw data
+    target_subfolder = "IEDB_Jespersen"
+    splits = Epitope(name=target_subfolder).get_split()
+    df_train = splits["train"]
+    df_valid = splits["valid"]
+    df_test = splits["test"]
+    df_train["split"] = "train"
+    df_valid["split"] = "valid"
+    df_test["split"] = "test"
+    df = pd.concat([df_train, df_valid, df_test], axis=0)
+
+    fn_data_raw = "data_raw.csv"
+    df.to_csv(fn_data_raw, index=False)
+    del df
+
+    def get_active_position(seq, active_position, sequence_only=False):
+        """
+        Input: given a sequence and list of active index
+        Output: return active sequence and other sequence convert to _
+        MASQKRPS ,[1,2,3,4,6] -> _ASQK_P_
+        """
+        if isinstance(
+            active_position, str
+        ):  # if list is casted to string after loading from raw csv data file.
+            active_position = [int(x) for x in active_position[1:-1].split(", ")]
+
+        if sequence_only:
+            _seq = "".join([seq[x] for x in active_position])
+            return _seq
+
+        _seq = ["_" for a in range(len(seq))]
+        for x in active_position:
+            _seq[x] = seq[x]
+        _seq = "".join(_seq)
+        return _seq
+
+    # proceed raw data
+    df = pd.read_csv(fn_data_raw, sep=",")
+    fields_orig = df.columns.tolist()
+    assert fields_orig == ["Antigen_ID", "Antigen", "Y", "split"]
+
+    # Rename columns of raw data
+    fields_clean = [
+        "Antigen_ID",
+        "Antigen_sequence",
+        "active_positions_indices",
+        "split",
+    ]
+    df.columns = fields_clean
+
+    # get active position
+    antigen_seq = df.Antigen_sequence.tolist()
+    a_pos_ind_list = df.active_positions_indices.tolist()
+    df["active_position"] = [
+        get_active_position(x, o, sequence_only=True)
+        for x, o in zip(antigen_seq, a_pos_ind_list)
+    ]
+
+    # save data to original
+    fn_data_original = "data_original.csv"
+    df.to_csv(fn_data_original, index=None)
+    df = pd.read_csv(fn_data_original, sep=",")
+    fields_orig = df.columns.tolist()
+    assert fields_orig == [
+        "Antigen_ID",
+        "Antigen_sequence",
+        "active_positions_indices",
+        "split",
+        "active_position",
+    ]
+
+    # get right columns
+
+    df = df[["Antigen_sequence", "active_position"]]
+    fields_clean = ["Antigen_sequence", "active_position"]
+    df.columns = fields_clean
+    assert fields_orig != fields_clean
+    assert not df.duplicated().sum()
+    # save to csv
+    fn_data_csv = "data_clean.csv"
+    df.to_csv(fn_data_csv, index=False)
+
+    meta = {
+        "name": "iedb_jespersen_et_al",
+        "description": """Epitope prediction is to predict the active region in the antigen.
+This dataset is from Bepipred, which curates a dataset from IEDB. It collects B-cell
+epitopes and non-epitope amino acids determined from crystal structures.""",
+        "targets": [
+            {
+                "id": "active_position",  # name of the column in a tabular dataset
+                "description": "amino acids sequence position that is active in binding",
+                "units": None,  # units of the values in this column (leave empty if unitless)
+                "type": "categorical",  # can be "categorical", "ordinal", "continuous"
+                "names": [  # names for the property (to sample from for building the prompts)
+                    {"noun": "epitope"},
+                    {"noun": "amino acids sequence active in the antigen binding"},
+                    {"noun": "epitope sequence active in the antigen binding"},
+                    {"noun": "epitope sequence active in the binding"},
+                ],
+                "uris": [
+                    "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C13189",
+                ],
+            }
+        ],
+        "identifiers": [
+            {
+                "id": "Antigen_sequence",  # column name
+                "type": "Other",  # can be "SMILES", "SELFIES", "IUPAC", "Other"
+                "names": [
+                    "amino acid sequence",
+                    "AA sequence",
+                    "epitope amino acid sequence",
+                    "epitope AA sequence",
+                ],
+                "description": "amino acid sequence",  # d
+            }
+        ],
+        "license": "CC BY 4.0",  # license under which the original dataset was published
+        "links": [  # list of relevant links (original dataset, other uses, etc.)
+            {
+                "url": "https://doi.org/10.1093/nar/gky1006",
+                "description": "corresponding publication",
+            },
+            {
+                "url": "https://doi.org/10.1093/nar/gkx346",
+                "description": "corresponding publication",
+            },
+            {
+                "url": "https://tdcommons.ai/single_pred_tasks/epitope/#iedb-jespersen-et-al",
+                "description": "data source",
+            },
+        ],
+        "num_points": len(df),  # number of datapoints in this dataset
+        "bibtex": [
+            """@article{Vita2018,
+doi = {10.1093/nar/gky1006},
+url = {https://doi.org/10.1093/nar/gky1006},
+year = {2018},
+month = oct,
+publisher = {Oxford University Press (OUP)},
+volume = {47},
+number = {D1},
+pages = {D339--D343}},
+author = {Randi Vita and Swapnil Mahajan and James A Overton and
+Sandeep Kumar Dhanda and Sheridan Martini and Jason R Cantrell and
+Daniel K Wheeler and Alessandro Sette and Bjoern Peters},
+title = {The Immune Epitope Database (IEDB): 2018 update},
+journal = {Nucleic Acids Research}""",
+            """@article{Jespersen2017,
+doi = {10.1093/nar/gkx346},
+url = {https://doi.org/10.1093/nar/gkx346},
+year = {2017},
+month = may,
+publisher = {Oxford University Press (OUP)},
+volume = {45},
+number = {W1},
+pages = {W24--W29},
+author = {Martin Closter Jespersen and Bjoern Peters and Morten Nielsen and Paolo Marcatili},
+title = {BepiPred 2.0: improving sequence-based B-cell epitope prediction using
+conformational epitopes},
+journal = {Nucleic Acids Research}""",
+        ],
+    }
+
+    def str_presenter(dumper, data):
+        """configures yaml for dumping multiline strings
+        Ref: https://stackoverflow.com/questions/8640959/how-can-i-control-what-scalar-form-pyyaml-uses-for-my-data
+        """
+        if data.count("\n") > 0:  # check for multiline string
+            return dumper.represent_scalar("tag:yaml.org,2002:str", data, style="|")
+        return dumper.represent_scalar("tag:yaml.org,2002:str", data)
+
+    yaml.add_representer(str, str_presenter)
+    yaml.representer.SafeRepresenter.add_representer(
+        str, str_presenter
+    )  # to use with safe_dum
+    fn_meta = "meta.yaml"
+    with open(fn_meta, "w") as f:
+        yaml.dump(meta, f, sort_keys=False)
+
+    print(f"Finished processing {meta['name']} dataset!")
+
+
+if __name__ == "__main__":
+    get_and_transform_data()

data/pdb_jespersen_et_al/meta.yaml

@@ -0,0 +1,64 @@
+---
+name: pdb_jespersen_et_al
+description: |-
+    Epitope prediction is to predict the active region in the antigen.
+    This dataset is from Bepipred, which curates a dataset from PDB.
+    It collects B-cell epitopes and non-epitope amino acids determined from crystal structures.
+targets:
+    - id: active_position
+      description: amino acids sequence position that is active in binding
+      units:
+      type: categorical
+      names:
+          - noun: epitope
+          - noun: amino acids sequence active in antigen binding
+          - noun: epitope sequence active in antigen binding
+          - noun: epitope sequence active in binding
+      uris:
+          - http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C13189
+identifiers:
+    - id: Antigen_sequence
+      type: Other
+      names:
+          - amino acid sequence
+          - AA sequence
+          - epitope amino acid sequence
+          - epitope AA sequence
+      description: amino acid sequence
+license: CC BY 4.0
+links:
+    - url: https://doi.org/10.1093/nar/gkx346
+      description: corresponding publication
+    - url: https://doi.org/10.1093/nar/28.1.235
+      description: corresponding publication
+    - url: https://tdcommons.ai/single_pred_tasks/epitope/#pdb-jespersen-et-al
+      description: data source
+num_points: 447
+bibtex:
+    - |-
+      @article{Jespersen2017,
+      doi = {10.1093/nar/gkx346},
+      url = {https://doi.org/10.1093/nar/gkx346},
+      year = {2017},
+      month = may,
+      publisher = {Oxford University Press (OUP)},
+      volume = {45},
+      number = {W1},
+      pages = {W24--W29},
+      author = {Martin Closter Jespersen and Bjoern Peters and Morten Nielsen and Paolo Marcatili},
+      title = {BepiPred 2.0: improving sequence-based B-cell epitope prediction using
+      conformational epitopes},
+      journal = {Nucleic Acids Research}
+    - |-
+      @article{Berman2000,
+      doi = {10.1093/nar/28.1.235},
+      url = {https://doi.org/10.1093/nar/28.1.235},
+      year = {2000},
+      month = jan,
+      publisher = {Oxford University Press (OUP)},
+      volume = {28},
+      number = {1},
+      pages = {235--242},
+      author = {H. M. Berman},
+      title = {The Protein Data Bank},
+      journal = {Nucleic Acids Research}