ParaFold for AlphaFold 2.3.1

.gitignore

@@ -0,0 +1 @@
+*.slurm

README.md

@@ -2,69 +2,77 @@
 <img src="./docs/parafoldlogo.png" width="400" >
 </div>
 
-# ParallelFold
+# ParaFold
 
 Author: Bozitao Zhong - [email protected]
 
-:station: We are adding new functions to ParallelFold, you can see our [Roadmap](https://trello.com/b/sAqBIxBC/parallelfold).
-
-:bookmark_tabs: Please cite our [paper](https://arxiv.org/abs/2111.06340) if you used ParallelFold (ParaFold) in you research. 
+:bookmark_tabs: Please cite our [paper](https://arxiv.org/abs/2111.06340) if you used ParaFold (ParallelFold) in you research. 
 
 ## Overview
 
+Recent change: **ParaFold now supports AlphaFold 2.3.1**
+
 This project is a modified version of DeepMind's [AlphaFold2](https://github.com/deepmind/alphafold) to achieve high-throughput protein structure prediction. 
 
 We have these following modifications to the original AlphaFold pipeline:
 
 - Divide **CPU part** (MSA and template searching) and **GPU part** (prediction model)
 
-**ParallelFold now supports AlphaFold 2.1.2**
-
 
 
 ## How to install 
 
 We recommend to install AlphaFold locally, and not using **docker**.
 
-For CUDA 11, you can refer to the [installation guide here](./docs/install.md).
+```bash
+# clone this repo
+git clone https://github.com/Zuricho/ParallelFold.git
 
-For CUDA 10.1, you can refer to the [installation guide here](./docs/install_cuda10.md).
+# Create a miniconda environment for ParaFold/AlphaFold
+# Recommend you to use python 3.8, version < 3.7 have missing packages, python versions newer than 3.8 were not tested
+conda create -n parafold python=3.8
 
+pip install py3dmol
+# openmm 7.7 is recommended (original alphafold using 7.5.1, but it is not supported now)
+conda install -c conda-forge openmm=7.7 pdbfixer
 
+# use pip3 to install most of packages
+pip3 install -r requirements.txt
 
-## Some detail information of modified files
+# install cuda and cudnn
+# cudatoolkit 11.3.1 matches cudnn 8.2.1
+conda install cudatoolkit=11.3 cudnn
 
-- `run_alphafold.py`: modified version of original `run_alphafold.py`, it has multiple additional functions like skipping featuring steps when exists `feature.pkl` in output folder
-- `run_alphafold.sh`: bash script to run `run_alphafold.py`
-- `run_figure.py`: this file can help you make figure for your system
+# downgrade jaxlib to the correct version, matches with cuda and cudnn version
+pip3 install --upgrade --no-cache-dir jax==0.3.25 jaxlib==0.3.25+cuda11.cudnn82 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
 
+# install packages for multiple sequence alignment
+conda install -c bioconda hmmer=3.3.2 hhsuite=3.3.0 kalign2=2.04
 
+chmod +x run_alphafold.sh
+```
 
-## How to run
 
-Visit the [usage page](./docs/usage.md) to know how to run
 
+## Some detail information of modified files
 
+- `run_alphafold.py`: modified version of original `run_alphafold.py`, it has multiple additional functions like skipping featuring steps when exists `feature.pkl` in output folder
+- `run_alphafold.sh`: bash script to run `run_alphafold.py`
+- `run_figure.py`: this file can help you make figure for your system
 
-## Functions
-
-You can using some flags to change prediction model for ParallelFold:
-
-`-r`: Skip AMBER refinement [Under repair]
 
-`-b`: Using benchmark mode - running JAX model for twice, and the second run can used for evaluate running time
 
-`-R`: Change the number of cycles in recycling
+## How to run
 
-**More functions are under development.**
+Visit the [usage page](./docs/usage.md) to know how to run
 
 
 
 ## What is this for
 
-ParallelFold can help you accelerate AlphaFold when you want to predict multiple sequences. After dividing the CPU part and GPU part, users can finish feature step by multiple processors. Using ParallelFold, you can run AlphaFold 2~3 times faster than DeepMind's procedure. 
+ParallelFold can help you accelerate AlphaFold when you want to predict multiple sequences. After dividing the CPU part and GPU part, users can finish feature step by multiple processors. Using ParaFold, you can run AlphaFold 2~3 times faster than DeepMind's procedure. 
 
-**If you have any question, please send GitHub issues**
+**If you have any question, please raise issues**
 
 
 

alphafold/common/residue_constants.py

@@ -120,7 +120,7 @@
 # 4,5,6,7: 'chi1,2,3,4-group'
 # The atom positions are relative to the axis-end-atom of the corresponding
 # rotation axis. The x-axis is in direction of the rotation axis, and the y-axis
-# is defined such that the dihedral-angle-definiting atom (the last entry in
+# is defined such that the dihedral-angle-defining atom (the last entry in
 # chi_angles_atoms above) is in the xy-plane (with a positive y-coordinate).
 # format: [atomname, group_idx, rel_position]
 rigid_group_atom_positions = {
@@ -772,10 +772,10 @@ def _make_rigid_transformation_4x4(ex, ey, translation):
 # and an array with (restype, atomtype, coord) for the atom positions
 # and compute affine transformation matrices (4,4) from one rigid group to the
 # previous group
-restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=np.int)
+restype_atom37_to_rigid_group = np.zeros([21, 37], dtype=int)
 restype_atom37_mask = np.zeros([21, 37], dtype=np.float32)
 restype_atom37_rigid_group_positions = np.zeros([21, 37, 3], dtype=np.float32)
-restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=np.int)
+restype_atom14_to_rigid_group = np.zeros([21, 14], dtype=int)
 restype_atom14_mask = np.zeros([21, 14], dtype=np.float32)
 restype_atom14_rigid_group_positions = np.zeros([21, 14, 3], dtype=np.float32)
 restype_rigid_group_default_frame = np.zeros([21, 8, 4, 4], dtype=np.float32)

alphafold/data/pipeline.py

@@ -117,7 +117,7 @@ def __init__(self,
                uniref90_database_path: str,
                mgnify_database_path: str,
                bfd_database_path: Optional[str],
-               uniclust30_database_path: Optional[str],
+               uniref30_database_path: Optional[str],
                small_bfd_database_path: Optional[str],
                template_searcher: TemplateSearcher,
                template_featurizer: templates.TemplateHitFeaturizer,
@@ -135,9 +135,9 @@ def __init__(self,
           binary_path=jackhmmer_binary_path,
           database_path=small_bfd_database_path)
     else:
-      self.hhblits_bfd_uniclust_runner = hhblits.HHBlits(
+      self.hhblits_bfd_uniref_runner = hhblits.HHBlits(
           binary_path=hhblits_binary_path,
-          databases=[bfd_database_path, uniclust30_database_path])
+          databases=[bfd_database_path, uniref30_database_path])
     self.jackhmmer_mgnify_runner = jackhmmer.Jackhmmer(
         binary_path=jackhmmer_binary_path,
         database_path=mgnify_database_path)
@@ -211,14 +211,14 @@ def process(self, input_fasta_path: str, msa_output_dir: str) -> FeatureDict:
           use_precomputed_msas=self.use_precomputed_msas)
       bfd_msa = parsers.parse_stockholm(jackhmmer_small_bfd_result['sto'])
     else:
-      bfd_out_path = os.path.join(msa_output_dir, 'bfd_uniclust_hits.a3m')
-      hhblits_bfd_uniclust_result = run_msa_tool(
-          msa_runner=self.hhblits_bfd_uniclust_runner,
+      bfd_out_path = os.path.join(msa_output_dir, 'bfd_uniref_hits.a3m')
+      hhblits_bfd_uniref_result = run_msa_tool(
+          msa_runner=self.hhblits_bfd_uniref_runner,
           input_fasta_path=input_fasta_path,
           msa_out_path=bfd_out_path,
           msa_format='a3m',
           use_precomputed_msas=self.use_precomputed_msas)
-      bfd_msa = parsers.parse_a3m(hhblits_bfd_uniclust_result['a3m'])
+      bfd_msa = parsers.parse_a3m(hhblits_bfd_uniref_result['a3m'])
 
     templates_result = self.template_featurizer.get_templates(
         query_sequence=input_sequence,

alphafold/data/templates.py

@@ -89,8 +89,8 @@ class LengthError(PrefilterError):
     'template_aatype': np.float32,
     'template_all_atom_masks': np.float32,
     'template_all_atom_positions': np.float32,
-    'template_domain_names': np.object,
-    'template_sequence': np.object,
+    'template_domain_names': object,
+    'template_sequence': object,
     'template_sum_probs': np.float32,
 }
 
@@ -1002,8 +1002,8 @@ def get_templates(
               (1, num_res, residue_constants.atom_type_num), np.float32),
           'template_all_atom_positions': np.zeros(
               (1, num_res, residue_constants.atom_type_num, 3), np.float32),
-          'template_domain_names': np.array([''.encode()], dtype=np.object),
-          'template_sequence': np.array([''.encode()], dtype=np.object),
+          'template_domain_names': np.array([''.encode()], dtype=object),
+          'template_sequence': np.array([''.encode()], dtype=object),
           'template_sum_probs': np.array([0], dtype=np.float32)
       }
     return TemplateSearchResult(

alphafold/data/tools/jackhmmer.py

@@ -167,10 +167,20 @@ def query(self,
             input_fasta_path: str,
             max_sequences: Optional[int] = None) -> Sequence[Mapping[str, Any]]:
     """Queries the database using Jackhmmer."""
+    return self.query_multiple([input_fasta_path], max_sequences)[0]
+
+  def query_multiple(
+      self,
+      input_fasta_paths: Sequence[str],
+      max_sequences: Optional[int] = None,
+    ) -> Sequence[Sequence[Mapping[str, Any]]]:
+    """Queries the database for multiple queries using Jackhmmer."""
     if self.num_streamed_chunks is None:
-      single_chunk_result = self._query_chunk(
-          input_fasta_path, self.database_path, max_sequences)
-      return [single_chunk_result]
+      single_chunk_results = []
+      for input_fasta_path in input_fasta_paths:
+        single_chunk_results.append([self._query_chunk(
+            input_fasta_path, self.database_path, max_sequences)])
+      return single_chunk_results
 
     db_basename = os.path.basename(self.database_path)
     db_remote_chunk = lambda db_idx: f'{self.database_path}.{db_idx}'
@@ -185,7 +195,7 @@ def query(self,
 
     # Download the (i+1)-th chunk while Jackhmmer is running on the i-th chunk
     with futures.ThreadPoolExecutor(max_workers=2) as executor:
-      chunked_output = []
+      chunked_outputs = [[] for _ in range(len(input_fasta_paths))]
       for i in range(1, self.num_streamed_chunks + 1):
         # Copy the chunk locally
         if i == 1:
@@ -197,9 +207,9 @@ def query(self,
 
         # Run Jackhmmer with the chunk
         future.result()
-        chunked_output.append(self._query_chunk(
-            input_fasta_path, db_local_chunk(i), max_sequences))
-
+        for fasta_index, input_fasta_path in enumerate(input_fasta_paths):
+          chunked_outputs[fasta_index].append(self._query_chunk(
+              input_fasta_path, db_local_chunk(i), max_sequences))
         # Remove the local copy of the chunk
         os.remove(db_local_chunk(i))
         # Do not set next_future for the last chunk so that this works even for
@@ -208,4 +218,4 @@ def query(self,
           future = next_future
         if self.streaming_callback:
           self.streaming_callback(i)
-    return chunked_output
+    return chunked_outputs

alphafold/model/all_atom_multimer.py

@@ -426,7 +426,7 @@ def torsion_angles_to_frames(
   chi3_frame_to_backb = chi2_frame_to_backb @ all_frames[:, 6]
   chi4_frame_to_backb = chi3_frame_to_backb @ all_frames[:, 7]
 
-  all_frames_to_backb = jax.tree_multimap(
+  all_frames_to_backb = jax.tree_map(
       lambda *x: jnp.concatenate(x, axis=-1), all_frames[:, 0:5],
       chi2_frame_to_backb[:, None], chi3_frame_to_backb[:, None],
       chi4_frame_to_backb[:, None])

alphafold/model/common_modules.py

@@ -128,3 +128,64 @@ def __call__(self, inputs):
 
     return output
 
+
+class LayerNorm(hk.LayerNorm):
+  """LayerNorm module.
+
+  Equivalent to hk.LayerNorm but with different parameter shapes: they are
+  always vectors rather than possibly higher-rank tensors. This makes it easier
+  to change the layout whilst keep the model weight-compatible.
+  """
+
+  def __init__(self,
+               axis,
+               create_scale: bool,
+               create_offset: bool,
+               eps: float = 1e-5,
+               scale_init=None,
+               offset_init=None,
+               use_fast_variance: bool = False,
+               name=None,
+               param_axis=None):
+    super().__init__(
+        axis=axis,
+        create_scale=False,
+        create_offset=False,
+        eps=eps,
+        scale_init=None,
+        offset_init=None,
+        use_fast_variance=use_fast_variance,
+        name=name,
+        param_axis=param_axis)
+    self._temp_create_scale = create_scale
+    self._temp_create_offset = create_offset
+
+  def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
+    is_bf16 = (x.dtype == jnp.bfloat16)
+    if is_bf16:
+      x = x.astype(jnp.float32)
+
+    param_axis = self.param_axis[0] if self.param_axis else -1
+    param_shape = (x.shape[param_axis],)
+
+    param_broadcast_shape = [1] * x.ndim
+    param_broadcast_shape[param_axis] = x.shape[param_axis]
+    scale = None
+    offset = None
+    if self._temp_create_scale:
+      scale = hk.get_parameter(
+          'scale', param_shape, x.dtype, init=self.scale_init)
+      scale = scale.reshape(param_broadcast_shape)
+
+    if self._temp_create_offset:
+      offset = hk.get_parameter(
+          'offset', param_shape, x.dtype, init=self.offset_init)
+      offset = offset.reshape(param_broadcast_shape)
+
+    out = super().__call__(x, scale=scale, offset=offset)
+
+    if is_bf16:
+      out = out.astype(jnp.bfloat16)
+
+    return out
+