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Datasets:
zonca
/
openfold3-mcl1-expanse

Languages:
English
Tags:
biology
structure-prediction
protein-ligand
alphafold3
openfold3
MCL1
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1391, in _parse
                  self.obj = DataFrame(
                             ~~~~~~~~~^
                      ujson_loads(json, precise_float=self.precise_float), dtype=None
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 782, in __init__
                  mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
                  return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
                  index = _extract_index(arrays)
                File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 677, in _extract_index
                  raise ValueError("All arrays must be of the same length")
              ValueError: All arrays must be of the same length
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 506, in __iter__
                  yield from self.ex_iterable
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 398, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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OpenFold3 MCL1 protein-ligand ensemble (20 predictions) on SDSC Expanse V100

Ensemble of 20 AlphaFold3-equivalent structure predictions for the MCL1 protein–ligand complex (official OpenFold3 example, PDB 5FDR context), generated with OpenFold3 0.4.5 on a single NVIDIA V100 32 GB GPU on SDSC Expanse (gpu-shared partition).

Data

  • 20 predicted structures (PDB): predictions/seed_{42,1337,2024,2026}/mcl1_*_model.pdb
    • 4 seeds × 5 diffusion samples = 20 independent predictions
    • protein chains A–D (MCL1), ATP ligands (chains F/G/H), small-molecule ligand (chain Z)
  • Per-atom confidence JSON: *_confidences.json (plddt, pae, pde)
  • Aggregated confidence JSON: *_confidences_aggregated.json (avg_plddt, ptm, iptm, bespoke_iptm, sample_ranking_score, has_clash, ...)
  • Timing: per seed timing.json
  • Analysis: mcl1_ensemble_metrics.csv (all 20 predictions with confidence + ligand RMSD), mcl1_ensemble_metrics.json, analyze_ensemble.py

Run details

  • Model: OpenFold3 0.4.5 (open weights, Apache-2.0), native PyTorch kernels (V100 sm_70; cuEquivariance/deepspeed not supported on V100)
  • MSAs: real, via ColabFold MSA server (outbound access worked from compute node)
  • GPU: 1× NVIDIA V100 32 GB, SDSC Expanse gpu-shared, account QoS gpu-shared-normal
  • Wall time: 24 min 13 s (job 53383743), exit 0

Key result

Confidence ranking vs ligand-pose consistency: corr(sample_ranking_score, ligand RMSD) = −0.58 — higher-ranked predictions place the ligand more consistently (moderate effect), supporting the hypothesis that confidence can help rank ligand poses in an ensemble. Per-chain pTM ~0.87 on the top-ranked structure (chains A–D are 4 identical MCL1 copies that permute across samples, inflating global protein RMSD; use a single-chain query for cleaner 5FDR ligand-placement benchmarks).

Links

Related

Sister dataset (AlphaFold3 via nf-core, TetR dimer+DNA): github.com/zonca/proteinfold-on-expanse

Citation

If you use this data, please cite OpenFold3 and AlphaFold3 (see repo README), and link this dataset (DOI 10.5281/zenodo.21926059).

License

Data CC-BY-4.0. OpenFold3 model Apache-2.0; AlphaFold3 cited per its paper.
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