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ESM2_script.py

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+import torch
+import esm
+import numpy as np
+import pandas as pd
+from Bio import SeqIO, SwissProt
+from tqdm import tqdm
+import pickle
+from io import StringIO
+import requests
+import time
+
+#model, alphabet = esm.pretrained.esm2_t48_15B_UR50D()
+
+# Load from disk?
+#checkpoint_path = "esm2_t48_15B_UR50D.pt"
+#checkpoint_path = "esm2_t6_8M_UR50D.pt"
+#model_data = torch.load(checkpoint_path, weights_only = False)
+#model, alphabet = esm.pretrained.load_model_and_alphabet_core(checkpoint_path, model_data = model_data)
+#batch_converter = alphabet.get_batch_converter()
+
+model.eval()
+
+device = torch.device("cpu")
+model = model.to(device)
+
+try:
+    df = pd.read_csv(
+        'parsed.tsv.gz',
+        compression = 'gzip',
+        sep = '\t'
+    )
+
+    print('Read data from disk.')
+
+except:
+    print('Generating data...')
+    records = []
+    for record in tqdm(SwissProt.parse(open("uniprot_sprot.dat"))):
+
+        go_terms = [xref[1] for xref in record.cross_references if xref[0] == "GO"]
+
+        # Ignorar proteínas sin ninguna función anotada
+        if len(go_terms) < 1:
+            go_terms = ['No_annotation']
+
+        function_comments = [
+            c for c in record.comments if c.startswith("-!- FUNCTION:")
+        ]
+
+        records.append({
+            "Entry": record.entry_name,
+            "Accession": record.accessions[0],
+            "Protein Name": record.description,
+            "Gene Name": record.gene_name,
+            "Organism": record.organism,
+            "Sequence": record.sequence,
+            "Length": len(record.sequence),
+            "GO": "; ".join(go_terms),
+            "Function": "\n".join(c for c in record.comments if c.startswith("-!- FUNCTION:")),
+        })
+
+    df = pd.DataFrame(records)
+
+    nones = df["GO"].str.contains("No_annotation").sum()
+    print('Sequences with no GO annotation: %d' % nones)
+
+    df.to_csv(
+        "parsed.tsv.gz",
+        compression = 'gzip',
+        sep = '\t',
+        index = False
+    )
+
+# Don't keep any non-annotated proteins
+df = df[df['GO'].notna()]
+
+kept = [
+    "Homo sapiens (Human).",
+    #"Escherichia coli.",
+    #"Rattus norvegicus (Rat).",
+    "Mus musculus (Mouse).",
+    #"Severe acute respiratory syndrome coronavirus (SARS-CoV).",
+    #"Severe acute respiratory syndrome coronavirus 2 (2019-nCoV) (SARS-CoV-2).",
+    #"Saccharomyces cerevisiae (Baker's yeast).",
+    #"Arabidopsis thaliana (Mouse-ear cress).",
+    #"Mycobacterium tuberculosis."
+]
+
+# Keep sequences belonging to our species of interest
+df = df[df['Organism'].isin(kept)]
+df.to_csv('Kept.csv.gz', compression = 'gzip', index = False)
+
+ids = df["Entry"].tolist()
+sequences = df["Sequence"].tolist()
+
+with open("metadata.pkl", "wb") as f:
+    pickle.dump({"ids": ids, "sequences": sequences}, f)
+
+data = list(zip(ids, sequences))
+
+if model.num_layers > 6:
+    layer = 33 # Embeddings obtained from the 33rd layer
+else:
+    layer = 6
+
+try:
+    embeddings = []
+
+    # ROW NUMBER: SPECIFY THIS NUMBER TO START FROM THAT POSITION
+    # (if you for some reason stopped the generation and want to continue)
+    history = 0
+
+    start = history
+    data = data[history:]
+    print('REMAINING ENTRIES:', len(data))
+
+    print("Generating embeddings...")
+
+    print("Starting on row %d" % history)
+
+    print("Using layer %d of %d" % (layer, model.num_layers))
+    for i in tqdm(range(len(data))):
+        batch_labels, batch_strs, batch_tokens = batch_converter([data[i]])
+        batch_tokens = batch_tokens.to(device)
+
+        with torch.no_grad():
+            results = model(batch_tokens, repr_layers=[layer])
+        
+        token_representations = results["representations"][layer]
+
+        tokens = batch_tokens[0]
+
+        sequence_length = (tokens != alphabet.padding_idx).nonzero().size(0)
+        residue_embeddings = token_representations[0, 1:sequence_length-1]
+        
+        # Mean pooling
+        emb = residue_embeddings.mean(dim=0).cpu().numpy()
+
+        embeddings.append(emb)
+
+        history += 1
+
+except:
+    # This will catch the event of stopping before the end. A file will
+    # be saved (NPY format). Look at its name to specify
+    # where you should be continuing from next time.
+    X = np.stack(embeddings)
+    np.save("X_embeddings_incomplete_%d_to_%d.npy" % (start, history-1), X)
+    with open('Data_incomplete_%d_to_%d.pkl' % (start, history-1), 'wb') as f:
+        pickle.dump(data, f)
+    exit()
+
+X = np.stack(embeddings)
+np.save("X_embeddings_incomplete_%d_to_%d.npy" % (start, history), X)
+
+# Use np.concatenate([part1, part2, ...]) to obtain the whole thing.
+# Kept.csv.gz should have the corresponding metadata to all of those embeddings.
+# Check the row numbers saved in the file names to parse the metadata in case of doubt.

Final_embeddings.npy

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+oid sha256:beeb378529839823e338973da7a173529050f9e224a0a1875e0a1dfb9757574f
+size 739983488

Final_metadata.csv.gz

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+oid sha256:d6a0c81a2f0992bc4e8ee598bc4c1827232eb020b2d95d87fe9ad934f0f7bac8
+size 11914082

parsed.tsv.gz

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+oid sha256:ce19f0ecdcc9b8a72eac714810a83f38f7051c29cb56b0185722e8cc2a5048ba
+size 101357737