Remove prokaryotic pairing from AFOS.

PiperOrigin-RevId: 429521382
Change-Id: Ib9bfce50834bb129af0dbd3508cd0b92856a9bb1

README.md

@@ -201,6 +201,11 @@ change the following:
     `alphafold/model/config.py`.
 *   Setting the `data_dir` flag is now needed when using `run_docker.py`.
 
+### API changes between v2.1.0 and v2.2.0
+
+The `--is_prokaryote_list` flag has been removed along with the `is_prokaryote`
+argument in `run_alphafold.predict_structure()`, eukaryotes and prokaryotes are
+now paired in the same way.
 
 ## Running AlphaFold
 
@@ -303,16 +308,12 @@ All steps are the same as when running the monomer system, but you will have to
 
 *   provide an input fasta with multiple sequences,
 *   set `--model_preset=multimer`,
-*   optionally set the `--is_prokaryote_list` flag with booleans that determine
-    whether all input sequences in the given fasta file are prokaryotic. If that
-    is not the case or the origin is unknown, set to `false` for that fasta.
 
-An example that folds a protein complex `multimer.fasta` that is prokaryotic:
+An example that folds a protein complex `multimer.fasta`:
 
 ```bash
 python3 docker/run_docker.py \
   --fasta_paths=multimer.fasta \
-  --is_prokaryote_list=true \
   --max_template_date=2020-05-14 \
   --model_preset=multimer \
   --data_dir=$DOWNLOAD_DIR
@@ -348,7 +349,7 @@ python3 docker/run_docker.py \
 
 #### Folding a homomer
 
-Say we have a homomer from a prokaryote with 3 copies of the same sequence
+Say we have a homomer with 3 copies of the same sequence
 `<SEQUENCE>`. The input fasta should be:
 
 ```fasta
@@ -365,7 +366,6 @@ Then run the following command:
 ```bash
 python3 docker/run_docker.py \
   --fasta_paths=homomer.fasta \
-  --is_prokaryote_list=true \
   --max_template_date=2021-11-01 \
   --model_preset=multimer \
   --data_dir=$DOWNLOAD_DIR
@@ -373,7 +373,7 @@ python3 docker/run_docker.py \
 
 #### Folding a heteromer
 
-Say we have a heteromer A2B3 of unknown origin, i.e. with 2 copies of
+Say we have an A2B3 heteromer, i.e. with 2 copies of
 `<SEQUENCE A>` and 3 copies of `<SEQUENCE B>`. The input fasta should be:
 
 ```fasta
@@ -394,7 +394,6 @@ Then run the following command:
 ```bash
 python3 docker/run_docker.py \
   --fasta_paths=heteromer.fasta \
-  --is_prokaryote_list=false \
   --max_template_date=2021-11-01 \
   --model_preset=multimer \
   --data_dir=$DOWNLOAD_DIR
@@ -416,15 +415,13 @@ python3 docker/run_docker.py \
 
 #### Folding multiple multimers one after another
 
-Say we have a two multimers, `multimer1.fasta` and `multimer2.fasta`. Both are
-from a prokaryotic organism.
+Say we have a two multimers, `multimer1.fasta` and `multimer2.fasta`.
 
 We can fold both sequentially by using the following command:
 
 ```bash
 python3 docker/run_docker.py \
   --fasta_paths=multimer1.fasta,multimer2.fasta \
-  --is_prokaryote_list=true,true \
   --max_template_date=2021-11-01 \
   --model_preset=multimer \
   --data_dir=$DOWNLOAD_DIR

alphafold/data/feature_processing.py

@@ -46,14 +46,12 @@ def _is_homomer_or_monomer(chains: Iterable[pipeline.FeatureDict]) -> bool:
 
 
 def pair_and_merge(
-    all_chain_features: MutableMapping[str, pipeline.FeatureDict],
-    is_prokaryote: bool) -> pipeline.FeatureDict:
+    all_chain_features: MutableMapping[str, pipeline.FeatureDict]
+    ) -> pipeline.FeatureDict:
   """Runs processing on features to augment, pair and merge.
 
   Args:
     all_chain_features: A MutableMap of dictionaries of features for each chain.
-    is_prokaryote: Whether the target complex is from a prokaryotic or
-    eukaryotic organism.
 
   Returns:
     A dictionary of features.
@@ -67,7 +65,7 @@ def pair_and_merge(
 
   if pair_msa_sequences:
     np_chains_list = msa_pairing.create_paired_features(
-        chains=np_chains_list, prokaryotic=is_prokaryote)
+        chains=np_chains_list)
     np_chains_list = msa_pairing.deduplicate_unpaired_sequences(np_chains_list)
   np_chains_list = crop_chains(
       np_chains_list,

alphafold/data/msa_identifiers.py

@@ -48,12 +48,11 @@ _UNIPROT_PATTERN = re.compile(
 
 @dataclasses.dataclass(frozen=True)
 class Identifiers:
-  uniprot_accession_id: str = ''
   species_id: str = ''
 
 
 def _parse_sequence_identifier(msa_sequence_identifier: str) -> Identifiers:
-  """Gets accession id and species from an msa sequence identifier.
+  """Gets species from an msa sequence identifier.
 
   The sequence identifier has the format specified by
   _UNIPROT_TREMBL_ENTRY_NAME_PATTERN or _UNIPROT_SWISSPROT_ENTRY_NAME_PATTERN.
@@ -63,13 +62,12 @@ def _parse_sequence_identifier(msa_sequence_identifier: str) -> Identifiers:
     msa_sequence_identifier: a sequence identifier.
 
   Returns:
-    An `Identifiers` instance with a uniprot_accession_id and species_id. These
+    An `Identifiers` instance with species_id. These
     can be empty in the case where no identifier was found.
   """
   matches = re.search(_UNIPROT_PATTERN, msa_sequence_identifier.strip())
   if matches:
     return Identifiers(
-        uniprot_accession_id=matches.group('AccessionIdentifier'),
         species_id=matches.group('SpeciesIdentifier'))
   return Identifiers()
 

alphafold/data/msa_pairing.py

@@ -25,12 +25,6 @@ import numpy as np
 import pandas as pd
 import scipy.linalg
 
-ALPHA_ACCESSION_ID_MAP = {x: y for y, x in enumerate(string.ascii_uppercase)}
-ALPHANUM_ACCESSION_ID_MAP = {
-    chr: num for num, chr in enumerate(string.ascii_uppercase + string.digits)
-}  # A-Z,0-9
-NUM_ACCESSION_ID_MAP = {str(x): x for x in range(10)}                # 0-9
-
 MSA_GAP_IDX = residue_constants.restypes_with_x_and_gap.index('-')
 SEQUENCE_GAP_CUTOFF = 0.5
 SEQUENCE_SIMILARITY_CUTOFF = 0.9
@@ -58,15 +52,11 @@ CHAIN_FEATURES = ('num_alignments', 'seq_length')
 
 
 def create_paired_features(
-    chains: Iterable[pipeline.FeatureDict],
-    prokaryotic: bool,
-    ) ->  List[pipeline.FeatureDict]:
+    chains: Iterable[pipeline.FeatureDict]) ->  List[pipeline.FeatureDict]:
   """Returns the original chains with paired NUM_SEQ features.
 
   Args:
     chains:  A list of feature dictionaries for each chain.
-    prokaryotic: Whether the target complex is from a prokaryotic organism.
-      Used to determine the distance metric for pairing.
 
   Returns:
     A list of feature dictionaries with sequence features including only
@@ -79,8 +69,7 @@ def create_paired_features(
     return chains
   else:
     updated_chains = []
-    paired_chains_to_paired_row_indices = pair_sequences(
-        chains, prokaryotic)
+    paired_chains_to_paired_row_indices = pair_sequences(chains)
     paired_rows = reorder_paired_rows(
         paired_chains_to_paired_row_indices)
 
@@ -115,8 +104,7 @@ def pad_features(feature: np.ndarray, feature_name: str) -> np.ndarray:
     num_res = feature.shape[1]
     padding = MSA_PAD_VALUES[feature_name] * np.ones([1, num_res],
                                                      feature.dtype)
-  elif feature_name in ('msa_uniprot_accession_identifiers_all_seq',
-                        'msa_species_identifiers_all_seq'):
+  elif feature_name == 'msa_species_identifiers_all_seq':
     padding = [b'']
   else:
     return feature
@@ -134,11 +122,9 @@ def _make_msa_df(chain_features: pipeline.FeatureDict) -> pd.DataFrame:
   msa_df = pd.DataFrame({
       'msa_species_identifiers':
           chain_features['msa_species_identifiers_all_seq'],
-      'msa_uniprot_accession_identifiers':
-          chain_features['msa_uniprot_accession_identifiers_all_seq'],
       'msa_row':
           np.arange(len(
-              chain_features['msa_uniprot_accession_identifiers_all_seq'])),
+              chain_features['msa_species_identifiers_all_seq'])),
       'msa_similarity': per_seq_similarity,
       'gap': per_seq_gap
   })
@@ -153,139 +139,6 @@ def _create_species_dict(msa_df: pd.DataFrame) -> Dict[bytes, pd.DataFrame]:
   return species_lookup
 
 
-@functools.lru_cache(maxsize=65536)
-def encode_accession(accession_id: str) -> int:
-  """Map accession codes to the serial order in which they were assigned."""
-  alpha = ALPHA_ACCESSION_ID_MAP        # A-Z
-  alphanum = ALPHANUM_ACCESSION_ID_MAP  # A-Z,0-9
-  num = NUM_ACCESSION_ID_MAP            # 0-9
-
-  coding = 0
-
-  # This is based on the uniprot accession id format
-  # https://www.uniprot.org/help/accession_numbers
-  if accession_id[0] in {'O', 'P', 'Q'}:
-    bases = (alpha, num, alphanum, alphanum, alphanum, num)
-  elif len(accession_id) == 6:
-    bases = (alpha, num, alpha, alphanum, alphanum, num)
-  elif len(accession_id) == 10:
-    bases = (alpha, num, alpha, alphanum, alphanum, num, alpha, alphanum,
-             alphanum, num)
-
-  product = 1
-  for place, base in zip(reversed(accession_id), reversed(bases)):
-    coding += base[place] * product
-    product *= len(base)
-
-  return coding
-
-
-def _calc_id_diff(id_a: bytes, id_b: bytes) -> int:
-  return abs(encode_accession(id_a.decode()) - encode_accession(id_b.decode()))
-
-
-def _find_all_accession_matches(accession_id_lists: List[List[bytes]],
-                                diff_cutoff: int = 20
-                                ) -> List[List[Any]]:
-  """Finds accession id matches across the chains based on their difference."""
-  all_accession_tuples = []
-  current_tuple = []
-  tokens_used_in_answer = set()
-
-  def _matches_all_in_current_tuple(inp: bytes, diff_cutoff: int) -> bool:
-    return all((_calc_id_diff(s, inp) < diff_cutoff for s in current_tuple))
-
-  def _all_tokens_not_used_before() -> bool:
-    return all((s not in tokens_used_in_answer for s in current_tuple))
-
-  def dfs(level, accession_id, diff_cutoff=diff_cutoff) -> None:
-    if level == len(accession_id_lists) - 1:
-      if _all_tokens_not_used_before():
-        all_accession_tuples.append(list(current_tuple))
-        for s in current_tuple:
-          tokens_used_in_answer.add(s)
-      return
-
-    if level == -1:
-      new_list = accession_id_lists[level+1]
-    else:
-      new_list = [(_calc_id_diff(accession_id, s), s) for
-                  s in accession_id_lists[level+1]]
-      new_list = sorted(new_list)
-      new_list = [s for d, s in new_list]
-
-    for s in new_list:
-      if (_matches_all_in_current_tuple(s, diff_cutoff) and
-          s not in tokens_used_in_answer):
-        current_tuple.append(s)
-        dfs(level + 1, s)
-        current_tuple.pop()
-  dfs(-1, '')
-  return all_accession_tuples
-
-
-def _accession_row(msa_df: pd.DataFrame, accession_id: bytes) -> pd.Series:
-  matched_df = msa_df[msa_df.msa_uniprot_accession_identifiers == accession_id]
-  return matched_df.iloc[0]
-
-
-def _match_rows_by_genetic_distance(
-    this_species_msa_dfs: List[pd.DataFrame],
-    cutoff: int = 20) -> List[List[int]]:
-  """Finds MSA sequence pairings across chains within a genetic distance cutoff.
-
-  The genetic distance between two sequences is approximated by taking the
-  difference in their UniProt accession ids.
-
-  Args:
-    this_species_msa_dfs: a list of dataframes containing MSA features for
-      sequences for a specific species. If species is missing for a chain, the
-      dataframe is set to None.
-    cutoff: the genetic distance cutoff.
-
-  Returns:
-    A list of lists, each containing M indices corresponding to paired MSA rows,
-    where M is the number of chains.
-  """
-  num_examples = len(this_species_msa_dfs)  # N
-
-  accession_id_lists = []  # M
-  match_index_to_chain_index = {}
-  for chain_index, species_df in enumerate(this_species_msa_dfs):
-    if species_df is not None:
-      accession_id_lists.append(
-          list(species_df.msa_uniprot_accession_identifiers.values))
-      # Keep track of which of the this_species_msa_dfs are not None.
-      match_index_to_chain_index[len(accession_id_lists) - 1] = chain_index
-
-  all_accession_id_matches = _find_all_accession_matches(
-      accession_id_lists, cutoff)  # [k, M]
-
-  all_paired_msa_rows = []  # [k, N]
-  for accession_id_match in all_accession_id_matches:
-    paired_msa_rows = []
-    for match_index, accession_id in enumerate(accession_id_match):
-      # Map back to chain index.
-      chain_index = match_index_to_chain_index[match_index]
-      seq_series = _accession_row(
-          this_species_msa_dfs[chain_index], accession_id)
-
-      if (seq_series.msa_similarity > SEQUENCE_SIMILARITY_CUTOFF or
-          seq_series.gap > SEQUENCE_GAP_CUTOFF):
-        continue
-      else:
-        paired_msa_rows.append(seq_series.msa_row)
-    # If a sequence is skipped based on sequence similarity to the respective
-    # target sequence or a gap cuttoff, the lengths of accession_id_match and
-    # paired_msa_rows will be different. Skip this match.
-    if len(paired_msa_rows) == len(accession_id_match):
-      paired_and_non_paired_msa_rows = np.array([-1] * num_examples)
-      matched_chain_indices = list(match_index_to_chain_index.values())
-      paired_and_non_paired_msa_rows[matched_chain_indices] = paired_msa_rows
-      all_paired_msa_rows.append(list(paired_and_non_paired_msa_rows))
-  return all_paired_msa_rows
-
-
 def _match_rows_by_sequence_similarity(this_species_msa_dfs: List[pd.DataFrame]
                                        ) -> List[List[int]]:
   """Finds MSA sequence pairings across chains based on sequence similarity.
@@ -322,8 +175,8 @@ def _match_rows_by_sequence_similarity(this_species_msa_dfs: List[pd.DataFrame]
   return all_paired_msa_rows
 
 
-def pair_sequences(examples: List[pipeline.FeatureDict],
-                   prokaryotic: bool) -> Dict[int, np.ndarray]:
+def pair_sequences(examples: List[pipeline.FeatureDict]
+                   ) -> Dict[int, np.ndarray]:
   """Returns indices for paired MSA sequences across chains."""
 
   num_examples = len(examples)
@@ -365,22 +218,6 @@ def pair_sequences(examples: List[pipeline.FeatureDict],
                   isinstance(species_df, pd.DataFrame)]) > 600):
       continue
 
-    # In prokaryotes (and some eukaryotes), interacting genes are often
-    # co-located on the chromosome into operons. Because of that we can assume
-    # that if two proteins' intergenic distance is less than a threshold, they
-    # two proteins will form an an interacting pair.
-    # In most eukaryotes, a single protein's MSA can contain many paralogs.
-    # Two genes may interact even if they are not close by genomic distance.
-    # In case of eukaryotes, some methods pair MSA sequences using sequence
-    # similarity method.
-    # See Jinbo Xu's work:
-    # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030867/#B28.
-    if prokaryotic:
-      paired_msa_rows = _match_rows_by_genetic_distance(this_species_msa_dfs)
-
-      if not paired_msa_rows:
-        continue
-    else:
     paired_msa_rows = _match_rows_by_sequence_similarity(this_species_msa_dfs)
     all_paired_msa_rows.extend(paired_msa_rows)
     all_paired_msa_rows_dict[species_dfs_present].extend(paired_msa_rows)

alphafold/data/pipeline.py

@@ -57,7 +57,6 @@ def make_msa_features(msas: Sequence[parsers.Msa]) -> FeatureDict:
 
   int_msa = []
   deletion_matrix = []
-  uniprot_accession_ids = []
   species_ids = []
   seen_sequences = set()
   for msa_index, msa in enumerate(msas):
@@ -72,8 +71,6 @@ def make_msa_features(msas: Sequence[parsers.Msa]) -> FeatureDict:
       deletion_matrix.append(msa.deletion_matrix[sequence_index])
       identifiers = msa_identifiers.get_identifiers(
           msa.descriptions[sequence_index])
-      uniprot_accession_ids.append(
-          identifiers.uniprot_accession_id.encode('utf-8'))
       species_ids.append(identifiers.species_id.encode('utf-8'))
 
   num_res = len(msas[0].sequences[0])
@@ -83,8 +80,6 @@ def make_msa_features(msas: Sequence[parsers.Msa]) -> FeatureDict:
   features['msa'] = np.array(int_msa, dtype=np.int32)
   features['num_alignments'] = np.array(
       [num_alignments] * num_res, dtype=np.int32)
-  features['msa_uniprot_accession_identifiers'] = np.array(
-      uniprot_accession_ids, dtype=np.object_)
   features['msa_species_identifiers'] = np.array(species_ids, dtype=np.object_)
   return features
 

alphafold/data/pipeline_multimer.py

@@ -231,7 +231,6 @@ class DataPipeline:
     msa = msa.truncate(max_seqs=self._max_uniprot_hits)
     all_seq_features = pipeline.make_msa_features([msa])
     valid_feats = msa_pairing.MSA_FEATURES + (
-        'msa_uniprot_accession_identifiers',
         'msa_species_identifiers',
     )
     feats = {f'{k}_all_seq': v for k, v in all_seq_features.items()
@@ -240,8 +239,7 @@ class DataPipeline:
 
   def process(self,
               input_fasta_path: str,
-              msa_output_dir: str,
-              is_prokaryote: bool = False) -> pipeline.FeatureDict:
+              msa_output_dir: str) -> pipeline.FeatureDict:
     """Runs alignment tools on the input sequences and creates features."""
     with open(input_fasta_path) as f:
       input_fasta_str = f.read()
@@ -278,9 +276,7 @@ class DataPipeline:
     all_chain_features = add_assembly_features(all_chain_features)
 
     np_example = feature_processing.pair_and_merge(
-        all_chain_features=all_chain_features,
-        is_prokaryote=is_prokaryote,
-    )
+        all_chain_features=all_chain_features)
 
     # Pad MSA to avoid zero-sized extra_msa.
     np_example = pad_msa(np_example, 512)

docker/run_docker.py

@@ -45,12 +45,6 @@ flags.DEFINE_list(
     'multiple sequences, then it will be folded as a multimer. Paths should be '
     'separated by commas. All FASTA paths must have a unique basename as the '
     'basename is used to name the output directories for each prediction.')
-flags.DEFINE_list(
-    'is_prokaryote_list', None, 'Optional for multimer system, not used by the '
-    'single chain system. This list should contain a boolean for each fasta '
-    'specifying true where the target complex is from a prokaryote, and false '
-    'where it is not, or where the origin is unknown. These values determine '
-    'the pairing method for the MSA.')
 flags.DEFINE_string(
     'output_dir', '/tmp/alphafold',
     'Path to a directory that will store the results.')
@@ -224,10 +218,6 @@ def main(argv):
       '--logtostderr',
   ])
 
-  if FLAGS.is_prokaryote_list:
-    command_args.append(
-        f'--is_prokaryote_list={",".join(FLAGS.is_prokaryote_list)}')
-
   client = docker.from_env()
   container = client.containers.run(
       image=FLAGS.docker_image_name,

notebooks/AlphaFold.ipynb

@@ -231,12 +231,6 @@
         "input_sequences = (sequence_1, sequence_2, sequence_3, sequence_4,\n",
         "                   sequence_5, sequence_6, sequence_7, sequence_8)\n",
         "\n",
-        "#@markdown If folding a complex target and all the input sequences are\n",
-        "#@markdown prokaryotic then set `is_prokaryotic` to `True`. Set to `False`\n",
-        "#@markdown otherwise or if the origin is unknown.\n",
-        "\n",
-        "is_prokaryote = False  #@param {type:\"boolean\"}\n",
-        "\n",
         "MIN_SINGLE_SEQUENCE_LENGTH = 16\n",
         "MAX_SINGLE_SEQUENCE_LENGTH = 2500\n",
         "MAX_MULTIMER_LENGTH = 2500\n",
@@ -426,7 +420,6 @@
         "  # Construct the all_seq features only for heteromers, not homomers.\n",
         "  if model_type_to_use == notebook_utils.ModelType.MULTIMER and len(set(sequences)) \u003e 1:\n",
         "    valid_feats = msa_pairing.MSA_FEATURES + (\n",
-        "        'msa_uniprot_accession_identifiers',\n",
         "        'msa_species_identifiers',\n",
         "    )\n",
         "    all_seq_features = {\n",
@@ -450,7 +443,7 @@
         "  all_chain_features = pipeline_multimer.add_assembly_features(all_chain_features)\n",
         "\n",
         "  np_example = feature_processing.pair_and_merge(\n",
-        "      all_chain_features=all_chain_features, is_prokaryote=is_prokaryote)\n",
+        "      all_chain_features=all_chain_features)\n",
         "\n",
         "  # Pad MSA to avoid zero-sized extra_msa.\n",
         "  np_example = pipeline_multimer.pad_msa(np_example, min_num_seq=512)"

run_alphafold.py

@@ -49,12 +49,6 @@ flags.DEFINE_list(
     'multiple sequences, then it will be folded as a multimer. Paths should be '
     'separated by commas. All FASTA paths must have a unique basename as the '
     'basename is used to name the output directories for each prediction.')
-flags.DEFINE_list(
-    'is_prokaryote_list', None, 'Optional for multimer system, not used by the '
-    'single chain system. This list should contain a boolean for each fasta '
-    'specifying true where the target complex is from a prokaryote, and false '
-    'where it is not, or where the origin is unknown. These values determine '
-    'the pairing method for the MSA.')
 
 flags.DEFINE_string('data_dir', None, 'Path to directory of supporting data.')
 flags.DEFINE_string('output_dir', None, 'Path to a directory that will '
@@ -162,8 +156,7 @@ def predict_structure(
     model_runners: Dict[str, model.RunModel],
     amber_relaxer: relax.AmberRelaxation,
     benchmark: bool,
-    random_seed: int,
-    is_prokaryote: Optional[bool] = None):
+    random_seed: int):
   """Predicts structure using AlphaFold for the given sequence."""
   logging.info('Predicting %s', fasta_name)
   timings = {}
@@ -176,15 +169,9 @@ def predict_structure(
 
   # Get features.
   t_0 = time.time()
-  if is_prokaryote is None:
   feature_dict = data_pipeline.process(
       input_fasta_path=fasta_path,
       msa_output_dir=msa_output_dir)
-  else:
-    feature_dict = data_pipeline.process(
-        input_fasta_path=fasta_path,
-        msa_output_dir=msa_output_dir,
-        is_prokaryote=is_prokaryote)
   timings['features'] = time.time() - t_0
 
   # Write out features as a pickled dictionary.
@@ -324,22 +311,6 @@ def main(argv):
   if len(fasta_names) != len(set(fasta_names)):
     raise ValueError('All FASTA paths must have a unique basename.')
 
-  # Check that is_prokaryote_list has same number of elements as fasta_paths,
-  # and convert to bool.
-  if FLAGS.is_prokaryote_list:
-    if len(FLAGS.is_prokaryote_list) != len(FLAGS.fasta_paths):
-      raise ValueError('--is_prokaryote_list must either be omitted or match '
-                       'length of --fasta_paths.')
-    is_prokaryote_list = []
-    for s in FLAGS.is_prokaryote_list:
-      if s in ('true', 'false'):
-        is_prokaryote_list.append(s == 'true')
-      else:
-        raise ValueError('--is_prokaryote_list must contain comma separated '
-                         'true or false values.')
-  else:  # Default is_prokaryote to False.
-    is_prokaryote_list = [False] * len(fasta_names)
-
   if run_multimer_system:
     template_searcher = hmmsearch.Hmmsearch(
         binary_path=FLAGS.hmmsearch_binary_path,
@@ -423,7 +394,6 @@ def main(argv):
 
   # Predict structure for each of the sequences.
   for i, fasta_path in enumerate(FLAGS.fasta_paths):
-    is_prokaryote = is_prokaryote_list[i] if run_multimer_system else None
     fasta_name = fasta_names[i]
     predict_structure(
         fasta_path=fasta_path,
@@ -433,8 +403,7 @@ def main(argv):
         model_runners=model_runners,
         amber_relaxer=amber_relaxer,
         benchmark=FLAGS.benchmark,
-        random_seed=random_seed,
-        is_prokaryote=is_prokaryote)
+        random_seed=random_seed)
 
 
 if __name__ == '__main__':