# HG changeset patch
# User galaxy-australia
# Date 1663106652 0
# Node ID 3bd420ec162d4a6a1c50b4b0ba963692227914bb
# Parent ca90d17ff51b51a34f124c171fff94347ddf5f71
planemo upload for repository https://github.com/usegalaxy-au/tools-au commit 7726c3cba165bdc8fc6366ec0ce6596e55657468
diff -r ca90d17ff51b -r 3bd420ec162d alphafold.xml
--- a/alphafold.xml Fri Aug 19 00:29:16 2022 +0000
+++ b/alphafold.xml Tue Sep 13 22:04:12 2022 +0000
@@ -2,7 +2,7 @@
- AI-guided 3D structural prediction of proteins2.1.2
- 1
+ 2topic_0082
@@ -32,6 +32,9 @@
python3 '$__tool_directory__/validate_fasta.py' input.fasta
--min_length \${ALPHAFOLD_AA_LENGTH_MIN:-0}
--max_length \${ALPHAFOLD_AA_LENGTH_MAX:-0}
+#if $multimer:
+--multimer
+#end if
> alphafold.fasta &&
## env vars -------------------------------
@@ -46,18 +49,32 @@
--data_dir \${ALPHAFOLD_DB:-/data}
--uniref90_database_path \${ALPHAFOLD_DB:-/data}/uniref90/uniref90.fasta
--mgnify_database_path \${ALPHAFOLD_DB:-/data}/mgnify/mgy_clusters_2018_12.fa
---pdb70_database_path \${ALPHAFOLD_DB:-/data}/pdb70/pdb70
---template_mmcif_dir \${ALPHAFOLD_DB:-/data}/pdb_mmcif/mmcif_files
+--template_mmcif_dir \${ALPHAFOLD_DB:-/data}/pdb_mmcif/mmcif_files
--obsolete_pdbs_path \${ALPHAFOLD_DB:-/data}/pdb_mmcif/obsolete.dat
--max_template_date=\$DATE
--bfd_database_path \${ALPHAFOLD_DB:-/data}/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt
--uniclust30_database_path \${ALPHAFOLD_DB:-/data}/uniclust30/uniclust30_2018_08/uniclust30_2018_08
+
## Param introduced in AlphaFold v2.1.2:
--use_gpu_relax=\${ALPHAFOLD_USE_GPU:-True}
+
+#if $multimer:
+--model_preset=multimer
+--pdb_seqres_database_path=\${ALPHAFOLD_DB:-/data}/pdb_seqres/pdb_seqres.txt
+--uniprot_database_path=\${ALPHAFOLD_DB:-/data}/uniprot/uniprot.fasta
+##--num_multimer_predictions_per_model=1 ## introduced alphafold>=2.2.0
+
+#else
+--pdb70_database_path \${ALPHAFOLD_DB:-/data}/pdb70/pdb70
+#end if
&&
## Generate additional outputs ------------
-python3 '$__tool_directory__/gen_extra_outputs.py' output/alphafold $output_plddts &&
+python3 '$__tool_directory__/gen_extra_outputs.py' output/alphafold $output_plddts
+#if $multimer:
+--multimer
+#end if
+&&
## HTML output
mkdir -p '${ html.files_path }' &&
@@ -67,24 +84,34 @@
## This is a (hacky) fix for a bug that has appeared in multiple Pulsar servers.
## The working directory ends up two levels deep and the visualization html page
## fails to load the PDB files as static assets.
-[ -d working ] && cp -r working/* .
+(([ -d working ] && cp -r working/* .) || true)
]]>
-
+
-
+
-
+
+
+
+
+
@@ -161,13 +188,14 @@
**What it does**
- | AlphaFold v2.0: AI-guided 3D structure prediction of proteins
+ | AlphaFold v2.1: AI-guided 3D structure prediction of proteins
|
*What is AlphaFold?*
| AlphaFold is a program which uses neural networks to predict the tertiary (3D) structure of proteins. AlphaFold accepts an amino acid sequence (in Fasta format), then will 'fold' that sequence into a 3D model.
- | NOTE: AlphaFold has a number of versions - this tool uses AlphaFold v2.0.
+ |
+ | **NOTE: AlphaFold has numerous versions - this tool uses AlphaFold v2.1.2.**
|
*What makes AlphaFold different?*
@@ -179,7 +207,7 @@
*Downstream analysis*
- | Obtaining a protein fold is the first step in many analyses.
+ | Obtaining a protein structure prediction is the first step in many analyses.
| The 3D models created by AlphaFold can be used in downstream analysis, including the following:
|
@@ -192,24 +220,27 @@
Proteins associate in many biological processes, including intracellular signalling pathways and protein complex formation.
To predict these interactions, other programs may ingest 3D models predicted by AlphaFold. Proprietary softwares include `GOLD `_ and `SeeSAR `_, but many `free and open-source options `_ are available such as `AutoDock `_ and `SwissDock `_.
+ | Protein complex interactions are also commonly observed with AlphaFold's multimer prediction mode.
+ |
+ |
+
**Input**
*Amino acid sequence*
- | AlphaFold accepts a **single amino acid sequence** in FASTA format.
+ | AlphaFold monomer (default) accepts a **single amino acid sequence** in FASTA format.
| You can choose to input either a file from your Galaxy history or paste a sequence into a text box.
- | Please paste only a single sequence - we can only process a single sequence per job.
- | Multiple sequences will return an error.
+ | If you choose the ``multimer`` option, you can supply a FASTA file containing **multiple sequences** to be folded concurrently into a multimer.
+ |
|
**Outputs**
*Visualization*
- | An interactive 3D graphic of the best predicted molecular structures.
- | This output can be opened in Galaxy to give a visual impression of the results, with different structural representations to choose from.
- | Open the "Visualization" history output by clicking on the "view data" icon:
- |
+ An interactive 3D graphic of the best predicted molecular structures.
+ This output can be opened in Galaxy to give a visual impression of the results, with different structural representations to choose from.
+ Open the "Visualization" history output by clicking on the "view data" icon:
.. image:: https://github.com/usegalaxy-au/galaxy-local-tools/blob/1a8d3e8daa7ccc5a345ca377697735ab95ed0666/tools/alphafold/static/img/alphafold-visualization.png?raw=true
:height: 520
@@ -225,15 +256,24 @@
*Model confidence scores (optional)*
- | This optional output produces a file which describes the confidence scores for each model (based on `pLDDTs `_) which may be useful for downstream analysis.
- | Model confidence scores are also included as a column in the default PDB output.
+ | This optional output produces a file which describes the confidence scores for each model (based on `pLDDTs `_, or the ``iptm+ptm`` score if run in multimer mode) which may be useful for downstream analysis.
+ | Model confidence scores are also included as a column (replacing ``bFactor``) in the default PDB output.
+ |
+ |
+
+ **AlphaFold configuration**
+
+ | We have configured AlphaFold to run with the parameters suggested by default on `AlphaFold's GitHub `_.
+ | This means that it runs against the full database with Amber relaxation, with ``max_template_date`` set to today's date. If there are additonal parameters that you would like to interact with, please `send a support request to Galaxy AU `_, or open an issue on `our GitHub `_.
+ |
|
**External Resources**
- We recommend checking out the
+ We HIGHLY recommend checking out the
`Alphafold Protein Structure Database `_,
- which contains predicted sequences for thousands of Human proteins. See also:
+ which contains pre-computed structures for over 200 million known proteins.
+ See also:
- `Google Deepmind's article on AlphaFold `_
- `AlphaFold source code on GitHub `_
@@ -241,5 +281,6 @@
]]>
https://doi.org/10.1038/s41586-021-03819-2
+ https://doi.org/10.1101/2021.10.04.463034
diff -r ca90d17ff51b -r 3bd420ec162d gen_extra_outputs.py
--- a/gen_extra_outputs.py Fri Aug 19 00:29:16 2022 +0000
+++ b/gen_extra_outputs.py Tue Sep 13 22:04:12 2022 +0000
@@ -1,10 +1,17 @@
-
+"""Generate additional output files not produced by AlphaFold."""
import json
import pickle
import argparse
from typing import Any, Dict, List
+# Keys for accessing confidence data from JSON/pkl files
+# They change depending on whether the run was monomer or multimer
+CONTEXT_KEY = {
+ 'monomer': 'plddts',
+ 'multimer': 'iptm+ptm',
+}
+
class Settings:
"""parses then keeps track of program settings"""
@@ -12,23 +19,31 @@
self.workdir = None
self.output_confidence_scores = True
self.output_residue_scores = False
+ self.is_multimer = False
def parse_settings(self) -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
- "workdir",
- help="alphafold output directory",
+ "workdir",
+ help="alphafold output directory",
type=str
- )
+ )
parser.add_argument(
"-p",
"--plddts",
- help="output per-residue confidence scores (pLDDTs)",
+ help="output per-residue confidence scores (pLDDTs)",
+ action="store_true"
+ )
+ parser.add_argument(
+ "--multimer",
+ help="parse output from AlphaFold multimer",
action="store_true"
)
args = parser.parse_args()
self.workdir = args.workdir.rstrip('/')
self.output_residue_scores = args.plddts
+ self.is_multimer = False
+ self.is_multimer = args.multimer
class ExecutionContext:
@@ -42,8 +57,13 @@
@property
def model_pkls(self) -> List[str]:
- return [f'{self.settings.workdir}/result_model_{i}.pkl'
- for i in range(1, 6)]
+ ext = '.pkl'
+ if self.settings.is_multimer:
+ ext = '_multimer.pkl'
+ return [
+ f'{self.settings.workdir}/result_model_{i}{ext}'
+ for i in range(1, 6)
+ ]
@property
def model_conf_score_output(self) -> str:
@@ -56,18 +76,28 @@
class FileLoader:
"""loads file data for use by other classes"""
+
def __init__(self, context: ExecutionContext):
self.context = context
+ @property
+ def confidence_key(self) -> str:
+ """Return the correct key for confidence data."""
+ if self.context.settings.is_multimer:
+ return CONTEXT_KEY['multimer']
+ return CONTEXT_KEY['monomer']
+
def get_model_mapping(self) -> Dict[str, int]:
data = self.load_ranking_debug()
- return {name: int(rank) + 1
+ return {name: int(rank) + 1
for (rank, name) in enumerate(data['order'])}
def get_conf_scores(self) -> Dict[str, float]:
data = self.load_ranking_debug()
- return {name: float(f'{score:.2f}')
- for name, score in data['plddts'].items()}
+ return {
+ name: float(f'{score:.2f}')
+ for name, score in data[self.confidence_key].items()
+ }
def load_ranking_debug(self) -> Dict[str, Any]:
with open(self.context.ranking_debug, 'r') as fp:
@@ -76,11 +106,14 @@
def get_model_plddts(self) -> Dict[str, List[float]]:
plddts: Dict[str, List[float]] = {}
model_pkls = self.context.model_pkls
- for i in range(5):
+ for i in range(len(model_pkls)):
pklfile = model_pkls[i]
with open(pklfile, 'rb') as fp:
data = pickle.load(fp)
- plddts[f'model_{i+1}'] = [float(f'{x:.2f}') for x in data['plddt']]
+ plddts[f'model_{i+1}'] = [
+ float(f'{x:.2f}')
+ for x in data['plddt']
+ ]
return plddts
@@ -94,13 +127,13 @@
scores = self.loader.get_conf_scores()
ranked = list(scores.items())
ranked.sort(key=lambda x: x[1], reverse=True)
- return {f'model_{mapping[name]}': score
+ return {f'model_{mapping[name]}': score
for name, score in ranked}
def gen_residue_scores(self) -> Dict[str, List[float]]:
mapping = self.loader.get_model_mapping()
model_plddts = self.loader.get_model_plddts()
- return {f'model_{mapping[name]}': plddts
+ return {f'model_{mapping[name]}': plddts
for name, plddts in model_plddts.items()}
@@ -114,7 +147,7 @@
with open(outfile, 'w') as fp:
for model, score in data.items():
fp.write(f'{model}\t{score}\n')
-
+
def write_residue_scores(self, data: Dict[str, List[float]]) -> None:
outfile = self.context.plddt_output
model_plddts = list(data.items())
@@ -133,23 +166,20 @@
settings.parse_settings()
context = ExecutionContext(settings)
loader = FileLoader(context)
-
+
# generate & write outputs
generator = OutputGenerator(loader)
writer = OutputWriter(context)
-
+
# confidence scores
conf_scores = generator.gen_conf_scores()
writer.write_conf_scores(conf_scores)
-
+
# per-residue plddts
if settings.output_residue_scores:
residue_scores = generator.gen_residue_scores()
writer.write_residue_scores(residue_scores)
-
+
if __name__ == '__main__':
main()
-
-
-