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README.md

@@ -52,105 +52,115 @@ Please install `esm` via PyPi:
 pip install esm
 ```
 
-You can fold your first protein with
+You can fold your first protein with:
 
 ```py
-from esm.models.esmfold2 import (
-    ESMFold2InputBuilder,
-    ProteinInput,
-    StructurePredictionInput,
-)
 from transformers.models.esmfold2.modeling_esmfold2 import ESMFold2Model
 
 # Ubiquitin (PDB 1UBQ)
-sequence = (
-    "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
-)
+sequence = "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
 
 model = ESMFold2Model.from_pretrained("biohub/ESMFold2").cuda().eval()
-processor = ESMFold2InputBuilder()
-spi = StructurePredictionInput(sequences=[ProteinInput(id="A", sequence=sequence)])
-
-result = processor.fold(
-    model, spi, num_loops=3, num_sampling_steps=50, num_diffusion_samples=1, seed=0
-)
+output = model.infer_protein(sequence, num_loops=3, num_sampling_steps=50)
 
-print(f"pLDDT mean: {float(result.plddt.mean()):.3f}")
-print(f"pTM: {float(result.ptm):.3f}")
+print(f"pLDDT mean: {float(output['plddt'].mean()):.3f}, pTM: {float(output['ptm'].mean()):.3f}")
 ```
 
-You may directly use the model through huggingface/transformers
+You can also fold complex biomolecules — proteins, DNA/RNA (with modified residues), and small-molecule ligands all at once. Here's an example folding the HhaI DNA methyltransferase + its cognate DNA (with a trapped 5-fluoro-2′-deoxycytidine, CCD `C36`) + the SAH cofactor (PDB [1MHT](https://www.rcsb.org/structure/1MHT)):
 
 ```py
+from esm.models.esmfold2 import (
+    DNAInput,
+    ESMFold2InputBuilder,
+    LigandInput,
+    Modification,
+    ProteinInput,
+    StructurePredictionInput,
+)
 from transformers.models.esmfold2.modeling_esmfold2 import ESMFold2Model
 
-# Ubiquitin (PDB 1UBQ)
-sequence = (
-    "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
+HHAI_SEQ = (
+    "MIEIKDKQLTGLRFIDLFAGLGGFRLALESCGAECVYSNEWDKYAQEVYEMNFGEKPEGDITQVNEKTIPDH"
+    "DILCAGFPCQAFSISGKQKGFEDSRGTLFFDIARIVREKKPKVVFMENVKNFASHDNGNTLEVVKNTMNELD"
+    "YSFHAKVLNALDYGIPQKRERIYMICFRNDLNIQNFQFPKPFELNTFVKDLLLPDSEVEHLVIDRKDLVMTN"
+    "QEIEQTTPKTVRLGIVGKGGQGERIYSTRGIAITLSAYGGGIFAKTGGYLVNGKTRKLHPRECARVMGYPDS"
+    "YKVHPSTSQAYKQFGNSVVINVLQYIAYNIGSSLNFKPY"
 )
 
 model = ESMFold2Model.from_pretrained("biohub/ESMFold2").cuda().eval()
-output = model.infer_protein(sequence, num_loops=3, num_sampling_steps=50)
-
-print(f"pLDDT mean: {float(output['plddt'].mean()):.3f}")
-print(f"pTM: {float(output['ptm'].mean()):.3f}")
-```
-
-And the Biohub API:
 
-```py
-TODO minimal biohub API code snippet
-```
-
-First install the `esm` Python package.
-
-```
-pip install esm
-```
-
-Import the necessary libraries.
+spi = StructurePredictionInput(
+    sequences=[
+        ProteinInput(id="A", sequence=HHAI_SEQ),
+        DNAInput(
+            id="B",
+            sequence="GATAGCGCTATC",
+            modifications=[Modification(position=5, ccd="C36")],
+        ),
+        DNAInput(
+            id="C",
+            sequence="TGATAGCGCTATC",
+            modifications=[Modification(position=6, ccd="C36")],
+        ),
+        LigandInput(id="L", ccd=["SAH"]),
+    ]
+)
 
-```py
-from esm.sdk.forge import SequenceStructureForgeInferenceClient
-from esm.sdk import client
-from esm.sdk.api import ESMProtein, ESMProteinError, LogitsConfig, LogitsOutput
-```
+result = ESMFold2InputBuilder().fold(
+    model, spi, num_loops=3, num_sampling_steps=50, num_diffusion_samples=1, seed=0
+)
 
-Generate an [API key](https://biohub.ai/developer-console/api-keys) and add it to your Biohub account. This API key manages your access to credits and tokens, and the term API key/token is often used interchangeably within documentation. Call the inference client with the selected model of choice and replace with your token name.
+print(f"pLDDT mean: {float(result.plddt.mean()):.3f}, pTM: {float(result.ptm):.3f}, ipTM: {float(result.iptm):.3f}")
 
-```py
-client = SequenceStructureForgeInferenceClient(model="esmfold2-fast-2026-05", url="https://biohub.ai", token="<your API token>")
+with open("1mht_pred.cif", "w") as f:
+    f.write(result.complex.to_mmcif())
 ```
 
-####
-
-The Hugging Face implementation directly supports proteins only. For complex biomolecules, we recommend using the internal API. Here's an example of folding a Ubiquitin with ESMFold2:
+For the Biohub API, first generate an [API key](https://biohub.ai/developer-console/api-keys) and add it to your Biohub account. The code below assumes the environment variable `ESM_API_KEY=$YOUR_API_KEY`.
 
 ```py
 import os
 
-from esm.models.esmfold2 import LigandInput, ProteinInput, StructurePredictionInput
+from esm.models.esmfold2 import (
+    DNAInput,
+    LigandInput,
+    Modification,
+    ProteinInput,
+    StructurePredictionInput,
+)
 from esm.sdk import esmfold2_client
 from esm.sdk.api import FoldingConfig
 
-# Ubiquitin (PDB 1UBQ) + ATP cofactor (illustrative pairing).
-protein = ProteinInput(
-    id="A",
-    sequence=(
-        "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
-    ),
+HHAI_SEQ = (
+    "MIEIKDKQLTGLRFIDLFAGLGGFRLALESCGAECVYSNEWDKYAQEVYEMNFGEKPEGDITQVNEKTIPDH"
+    "DILCAGFPCQAFSISGKQKGFEDSRGTLFFDIARIVREKKPKVVFMENVKNFASHDNGNTLEVVKNTMNELD"
+    "YSFHAKVLNALDYGIPQKRERIYMICFRNDLNIQNFQFPKPFELNTFVKDLLLPDSEVEHLVIDRKDLVMTN"
+    "QEIEQTTPKTVRLGIVGKGGQGERIYSTRGIAITLSAYGGGIFAKTGGYLVNGKTRKLHPRECARVMGYPDS"
+    "YKVHPSTSQAYKQFGNSVVINVLQYIAYNIGSSLNFKPY"
 )
-ligand = LigandInput(id="L", ccd=["ATP"])
 
 client = esmfold2_client(model="esmfold2-fast-2026-05", token=os.environ["ESM_API_KEY"])
 
-spi = StructurePredictionInput(sequences=[protein, ligand])
-result = client.fold_all_atom(
-    spi, config=FoldingConfig(num_loops=3, num_sampling_steps=50)
+spi = StructurePredictionInput(
+    sequences=[
+        ProteinInput(id="A", sequence=HHAI_SEQ),
+        DNAInput(
+            id="B",
+            sequence="GATAGCGCTATC",
+            modifications=[Modification(position=5, ccd="C36")],
+        ),
+        DNAInput(
+            id="C",
+            sequence="TGATAGCGCTATC",
+            modifications=[Modification(position=6, ccd="C36")],
+        ),
+        LigandInput(id="L", ccd=["SAH"]),
+    ]
 )
 
-print(f"pLDDT mean: {float(result.plddt.mean()):.3f}")
-print(f"pTM: {float(result.ptm):.3f}")
+result = client.fold_all_atom(spi, config=FoldingConfig(num_loops=3, num_sampling_steps=50))
+
+print(f"pLDDT mean: {float(result.plddt.mean()):.3f}, pTM: {float(result.ptm):.3f}, ipTM: {float(result.iptm):.3f}")
 ```
 
 ## Training Data