Fix Biohub Platform SDK snippets (ESMC + ESMFold2) and use carbonic anhydrase II

README.md

@@ -110,14 +110,24 @@ Then import the necessary libraries and instantiate your desired model.
 
 ```py
 from esm.sdk import esmc_client
-
+from esm.sdk.api import ESMProtein, LogitsConfig
+
+# Human carbonic anhydrase II (PDB 2CBA)
+protein = ESMProtein(
+    sequence=(
+        "MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTHTAKYDPSLKPLSVSYDQATSLRILNNGHAFNVEFDD"
+        "SQDKAVLKGGPLDGTYRLIQFHFHWGSLDGQGSEHTVDKKKYAAELHLVHWNTKYGDFGKAVQQPDGLAVL"
+        "GIFLKVGSAKPGLQKVVDVLDSIKTKGKSADFTNFDPRGLLPESLDYWTYPGSLTTPPLLECVTWIVLKEP"
+        "ISVSSEQVLKFRKLNFNGEGEPEELMVDNWRPAQPLKNRQIKASFK"
+    )
+)
 model = esmc_client(
     model="esmc-600m-2024-12", url="https://biohub.ai", token="<your API token>"
 )
 
 protein_tensor = model.encode(protein)
 logits_output = model.logits(
-  protein_tensor, LogitsConfig(sequence=True, return_embeddings=True)
+    protein_tensor, LogitsConfig(sequence=True, return_embeddings=True)
 )
 
 print(logits_output.logits, logits_output.embeddings)
@@ -234,21 +244,28 @@ Import the necessary libraries.
 
 ```py
 from esm.sdk.forge import SequenceStructureForgeInferenceClient
-from esm.sdk.api import ESMProtein, ESMProteinError, LogitsConfig, LogitsOutput
+from esm.sdk.api import FoldingConfig
+from esm.utils.structure.input_builder import ProteinInput, StructurePredictionInput
 ```
 
 Call the inference client with the selected model of choice and replace <your API token> with your token name.
 
 ```py
 client = SequenceStructureForgeInferenceClient(model="esmfold2-fast-2026-05", url="https://biohub.ai", token="<your API token>")
 
-gfp_sequence = "MSKGEELFTGVVPILVELDGDVNGHKFSVRGEGEGDATNGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKRHDFFKSAMPEGYVQERTISFKDDGTYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNFNSHNVYITADKQKNGIKANFKIRHNVEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSVLSKDPNEKRDHMVLLEFVTAAGITHGMDELYK"
-gfp_input = input_builder.StructurePredictionInput(
-    sequences=[gfp_sequence]
+# Human carbonic anhydrase II (PDB 2CBA)
+ca2_sequence = (
+    "MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTHTAKYDPSLKPLSVSYDQATSLRILNNGHAFNVEFDD"
+    "SQDKAVLKGGPLDGTYRLIQFHFHWGSLDGQGSEHTVDKKKYAAELHLVHWNTKYGDFGKAVQQPDGLAVL"
+    "GIFLKVGSAKPGLQKVVDVLDSIKTKGKSADFTNFDPRGLLPESLDYWTYPGSLTTPPLLECVTWIVLKEP"
+    "ISVSSEQVLKFRKLNFNGEGEPEELMVDNWRPAQPLKNRQIKASFK"
+)
+ca2_input = StructurePredictionInput(
+    sequences=[ProteinInput(id="A", sequence=ca2_sequence)]
 )
 
-config = FoldingConfig(num_loops=3, num_sampling_steps=32, seed=0)
-result = client.fold_all_atom(gfp_input, config=config)
+config = FoldingConfig(num_loops=3, num_sampling_steps=32)
+result = client.fold_all_atom(ca2_input, config=config)
 
 with open("result.cif", "w") as f:
     f.write(result.complex.to_mmcif())

cookbook/tutorials/esmfold2.ipynb

@@ -267,7 +267,6 @@
     "\n",
     "* **num_sampling_steps**: Number of diffusion sampling steps. Higher values produce more accurate structures but take longer (typical range: 200-400).\n",
     "\n",
-    "* **seed**: Random seed for reproducibility. Using the same seed with identical inputs will produce the same structure.\n",
     "\n",
     "We use a moderate number of loops (3) and sampling steps (32) to balance accuracy and runtime for this example. For more accurate results, especially when modeling flexible ligands or uncertain binding modes, you can increase num_loops and num_sampling_steps at the cost of longer runtimes."
    ]
@@ -278,7 +277,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "config = FoldingConfig(num_loops=3, num_sampling_steps=32, seed=2, include_pae=True)"
+    "config = FoldingConfig(num_loops=3, num_sampling_steps=32, include_pae=True)"
    ]
   },
   {