# A pipeline for enzyme engineering
Enzyme-tk is a collection of tools for enzyme engineering, setup as interoperable modules that act on dataframes. These modules are designed to be imported into pipelines for specific function. For this reason, `steps` as each module is called (e.g. finding similar proteins with `BLAST` would be considered a step) are designed to be as light as possible. An example of a pipeline is the [annotate-e](https://github.com/ArianeMora/annotate-e) ` pipeline, this acts to annotate a fasta with an ensemble of methods (each is designated as an Enzyme-tk step).
**If you have any issues installing, let me know - this has been tested only on Linux/Ubuntu. Please post an issue!**
## Installation
## Install base package to import modules
```bash
pip install enzymetk
```
### Install only the specific requirements you need (recomended)
For this clone the repo and then install the requirements for the specific modules you use
```bash
git clone git@github.com:ArianeMora/enzyme-tk.git
cd enzymetk/conda_envs/ # would recommend looking at thes
# e.g. to install all from within that folder you would do
source install_all.sh
```
## Usage
If you have any issues at all just email me using my caltech email: `amora at caltech . edu`
This is a work-in progress! e.g. some tools (e.g. proteInfer and CLEAN) require extra data to be downloaded in order to run (like model weights.) I'm working on integrating these atm, buzz me if you need this!
Here are some of the tools that have been implemented to be chained together as a pipeline:
[boltz2](https://github.com/jwohlwend/boltz)
[mmseqs2](https://github.com/soedinglab/mmseqs2)
[foldseek](https://github.com/steineggerlab/foldseek)
[diamond](https://github.com/bbuchfink/diamond)
[proteinfer](https://github.com/google-research/proteinfer)
[CLEAN](https://github.com/tttianhao/CLEAN)
[chai](https://github.com/chaidiscovery/chai-lab/)
[chemBERTa2](https://github.com/seyonechithrananda/bert-loves-chemistry)
[SELFormer](https://github.com/HUBioDataLab/SELFormer)
[rxnfp](https://github.com/rxn4chemistry/rxnfp)
[clustalomega](http://www.clustal.org/omega/)
[CREEP](https://github.com/jsunn-y/CARE)
[esm](https://github.com/facebookresearch/esm)
[LigandMPNN](https://github.com/dauparas/LigandMPNN)
[vina](https://vina.scripps.edu/)
[Uni-Mol](https://github.com/deepmodeling/Uni-Mol)
[fasttree](https://morgannprice.github.io/fasttree/)
[Porechop](https://github.com/rrwick/Porechop)
[prokka](https://github.com/tseemann/prokka)
## Things to note
All the tools use the conda env of `enzymetk` by default.
If you want to use a different conda env, you can do so by passing the `env_name` argument to the constructor of the step.
For example:
```python
proteinfer = ProteInfer(env_name='proteinfer')
```
## Arguments
All the arguments are passed to the constructor of the step, the ones that are required are passed as arguments to the constructor and the ones that are optional are passed as a list to the `args` argument, this needs to be a list as one would normally pass arguments to a command line tool.
For example:
```python
proteinfer = ProteInfer(env_name='proteinfer', args=['--num_threads', '10'])
```
For those wanting to use specific arguments, check the individual tools for specifics.
## Steps
The steps are the main building blocks of the pipeline. They are responsible for executing the individual tools.
### BLAST
BLAST is a tool for searching a database of sequences for similar sequences. Here you can either pass a database that you have already created or pass the sequences as part of your dataframe and pass the label column (this needs to have two values: reference and query) reference refers to sequences that you want to search against and query refers to sequences that you want to search for.
Note you need to have installed the BLAST environment.
```python
id_col = 'Entry'
seq_col = 'Sequence'
label_col = 'label'
rows = [['AXE2_TALPU', 'query', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],
['AXE2_TALPU', 'reference', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],
['AXE2_GEOSE', 'reference', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR'],
['AXE7A_XYLR2', 'referece', 'MFNFAPKQTTEMKKLLFTLVFVLGSMATALAENYPYRADYLWLTVPNHADWLYKTGERAKVEVSFCLYGMPQNVEVAYEIGPDMMPATSSGKVTLKNGRAVIDMGTMKKPGFLDMRLSVDGKYQHHVKVGFSPELLKPYTKNPQDFDAFWKANLDEARKTPVSVSCNKVDKYTTDAFDCYLLKIKTDRRHSIYGYLTKPKKAGKYPVVLCPPGAGIKTIKEPMRSTFYAKNGFIRLEMEIHGLNPEMTDEQFKEITTAFDYENGYLTNGLDDRDNYYMKHVYVACVRAIDYLTSLPDWDGKNVFVQGGSQGGALSLVTAGLDPRVTACVANHPALSDMAGYLDNRAGGYPHFNRLKNMFTPEKVNTMAYYDVVNFARRITCPVYITWGYNDNVCPPTTSYIVWNLITAPKESLITPINEHWTTSETNYTQMLWLKKQVK'],
['A0A0B8RHP0_LISMN', 'reference', 'MKKLLFLGDSVTDAGRDFENDRELGHGYVKIIADQLEQEDVTVINRGVSANRVADLHRRIEADAISLQPDVVTIMIGINDTWFSFSRWEDTSVTAFKEVYRVILNRIKTETNAELILMEPFVLPYPEDRKEWRGDLDPKIGAVRELAAEFGATLIPLDGLMNALAIKHGPTFLAEDGVHPTKAGHEAIASTWLEFTK']]
df = pd.DataFrame(rows, columns=[id_col, label_col, seq_col])
df << (BLAST(id_col, seq_col, label_col) >> Save('tmp/blast_test.pkl'))
```
### ActiveSitePred
ActiveSitePred is a tool for predicting the active site of an enzyme. This returns a dataframe with the active site prediction for each sequence, and the probability of the active site. Note we use a zero index for the active site prediction while UniProt uses a one index.
```python
squidly_dir = '/disk1/share/software/AS_inference/' # This should be where you downloaded the data from zotero, there is a folder in there called AS_inference
num_threads = 1
id_col = 'Entry'
seq_col = 'Sequence'
rows = [['AXE2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR'],
['H7C0D0', 'XRAHREIKDIFYKAIQKRRQSQEKIDDILQTLLDATYKDGRPLTDDEVAGMLIGLLLAGQHTSSTTSAWMGFFLARDKTLQKKCYLEQKTVCGENLPPLTYDQLKDLNLLDRCIKETLRLRPPIMIMMRMARTPQTVAGYTIPPGHQDNPASGEKFAYVPFGAGRHRCIGENFAYVQIKTIWSTMLRLYEFDLIDGYFPTVNYTTMIHTPENPVIRYKRRSK']]
df = pd.DataFrame(rows, columns=[id_col, seq_col])
print(df)
df << (ActiveSitePred(id_col, seq_col, squidly_dir, num_threads) >> Save('tmp/squidly_as_pred.pkl'))
```
### Boltz2
Boltz2 is a model for predicting structures. Note you need docko installed as I run via that.
Below is an example using boltz with 4 threads, and uses a cofactor (intermediate in this case). Just set to be None for a single substrate version.
```
import sys
from enzymetk.dock_boltz_step import Boltz
from enzymetk.save_step import Save
import pandas as pd
import os
os.environ['MKL_THREADING_LAYER'] = 'GNU'
output_dir = 'tmp/'
num_threads = 4
id_col = 'Entry'
seq_col = 'Sequence'
substrate_col = 'Substrate'
intermediate_col = 'Intermediate'
rows = [['P0DP23_boltz_8999', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]'],
['P0DP24_boltz_p1', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]'],
['P0DP23_boltz_p2', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]'],
['P0DP24_boltz_p3', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]'],
['P0DP24_boltz_p4', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]']]
df = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col, intermediate_col])
df << (Boltz(id_col, seq_col, substrate_col, intermediate_col, f'{output_dir}', num_threads) >> Save(f'{output_dir}test.pkl'))
```
### Chai
Chai is a tool for predicting the structure of a protein and a ligand, this tool outputs the data to a new folder and creates directories based on the id that is passed. We return the paths to the specific structure for each id in the returned dataframe.
Requres the `docko` conda environment to be created.
```python
output_dir = 'tmp/'
num_threads = 1
id_col = 'Entry'
seq_col = 'Sequence'
substrate_col = 'Substrate'
rows = [['P0DP23', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC'],
['AXE2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC']]
df = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col])
print(df)
df << (Chai(id_col, seq_col, substrate_col, f'{output_dir}', num_threads) >> Save(f'{output_dir}test.pkl'))
```
### ChemBERTa
ChemBERTa2 encodes reactions and SMILES strings into a vector space. Note this requires the base environment, i.e. `enzymetk` conda env.
```python
from enzymetk.embedchem_chemberta_step import ChemBERT
from enzymetk.save_step import Save
output_dir = 'tmp/'
num_threads = 1
id_col = 'Entry'
seq_col = 'Sequence'
substrate_col = 'Substrate'
rows = [['P0DP23', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC'],
['P0DP24', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC']]
df = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col])
new_df = (df << (ChemBERT(id_col, substrate_col, num_threads) >> Save(f'{output_dir}chemberta.pkl')))
```
### CLEAN
CLEAN is a tool for predicting the EC number of an enzyme.
```python
output_dir = 'tmp/'
num_threads = 1
id_col = 'Entry'
seq_col = 'Sequence'
substrate_col = 'Substrate'
rows = [['P0DP23', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC'],
['AXE2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC']]
df = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col])
# This should be relative to the location of the script if you installed via the install_all.sh script
# Note you need to have downloaded their predictive models (ToDo )
clean_dir = 'software/CLEAN/app/'
df << (CLEAN(id_col, seq_col, clean_dir, num_threads=num_threads) >> Save(f'clean_missing_EC_seqs.pkl'))
```
### ClustalOmega
ClustalOmega is a tool for aligning a set of sequences. This gets installed to the system (expecting a linux machine) and added to the bash path. You need to have installed it first (check out the `conda_envs` directory in enzymetk.)
```python
from enzymetk.generate_msa_step import ClustalOmega
from enzymetk.save_step import Save
import pandas as pd
id_col = 'Entry'
seq_col = 'Sequence'
label_col = 'label'
rows = [['AXE2_TALPU', 'query', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],
['AXE2_TALPU', 'reference', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],
['AXE2_GEOSE', 'reference', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR'],
['AXE7A_XYLR2', 'referece', 'MFNFAPKQTTEMKKLLFTLVFVLGSMATALAENYPYRADYLWLTVPNHADWLYKTGERAKVEVSFCLYGMPQNVEVAYEIGPDMMPATSSGKVTLKNGRAVIDMGTMKKPGFLDMRLSVDGKYQHHVKVGFSPELLKPYTKNPQDFDAFWKANLDEARKTPVSVSCNKVDKYTTDAFDCYLLKIKTDRRHSIYGYLTKPKKAGKYPVVLCPPGAGIKTIKEPMRSTFYAKNGFIRLEMEIHGLNPEMTDEQFKEITTAFDYENGYLTNGLDDRDNYYMKHVYVACVRAIDYLTSLPDWDGKNVFVQGGSQGGALSLVTAGLDPRVTACVANHPALSDMAGYLDNRAGGYPHFNRLKNMFTPEKVNTMAYYDVVNFARRITCPVYITWGYNDNVCPPTTSYIVWNLITAPKESLITPINEHWTTSETNYTQMLWLKKQVK'],
['A0A0B8RHP0_LISMN', 'reference', 'MKKLLFLGDSVTDAGRDFENDRELGHGYVKIIADQLEQEDVTVINRGVSANRVADLHRRIEADAISLQPDVVTIMIGINDTWFSFSRWEDTSVTAFKEVYRVILNRIKTETNAELILMEPFVLPYPEDRKEWRGDLDPKIGAVRELAAEFGATLIPLDGLMNALAIKHGPTFLAEDGVHPTKAGHEAIASTWLEFTK']]
df = pd.DataFrame(rows, columns=[id_col, label_col, seq_col])
df << (ClustalOmega(id_col, seq_col) >> Save('tmp/clustalomega_test.pkl'))
```
### CREEP
CREEP is a tool for predicting the EC number of a reaction. At the moment it only supports reactions to EC however we are extending this to other modalities.
```python
from enzymetk.annotateEC_CREEP_step import CREEP
from enzymetk.save_step import Save
import pandas as pd
# CREEP expects you to have downloaded the data from the zotero page and put it in the data/CREEP folder
output_dir = 'tmp/'
df = pd.DataFrame({'EC number': ['1.1.1.1', '1.1.1.2'],
'Sequence': ['MALWMRLLPLLALLALWGPDPAAA', 'MALWMRLLPLLALLALWGPDPAAA'],
'Reaction': ['O=P(OC1=CC=CC=C1)(OC2=CC=CC=C2)OC3=CC=CC=C3>>O=P(O)(OC4=CC=CC=C4)OC5=CC=CC=C5.OC6=CC=CC=C6',
'O=P(OC1=CC=CC=C1)(OC2=CC=CC=C2)OC3=CC=CC=C3>>O=P(O)(OC4=CC=CC=C4)OC5=CC=CC=C5.OC6=CC=CC=C6']})
id_col = 'Entry'
reaction_col = 'Reaction'
df << (CREEP(id_col, reaction_col, CREEP_cache_dir='/disk1/share/software/CREEP/data/', CREEP_dir='/disk1/share/software/CREEP/',
modality='reaction', reference_modality='protein') >> Save(f'{output_dir}CREEP_test_protein.pkl'))
```
### EmbedESM
EmbedESM is a tool for embedding a set of sequences using ESM2.
```python
from enzymetk.embedprotein_esm_step import EmbedESM
from enzymetk.save_step import Save
import pandas as pd
id_col = 'Entry'
seq_col = 'Sequence'
label_col = 'ActiveSite'
rows = [['AXE2_TALPU', '10', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],
['AXE2_GEOSE', '1|2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR'],
['AXE7A_XYLR2', '1', 'MFNFAPKQTTEMKKLLFTLVFVLGSMATALAENYPYRADYLWLTVPNHADWLYKTGERAKVEVSFCLYGMPQNVEVAYEIGPDMMPATSSGKVTLKNGRAVIDMGTMKKPGFLDMRLSVDGKYQHHVKVGFSPELLKPYTKNPQDFDAFWKANLDEARKTPVSVSCNKVDKYTTDAFDCYLLKIKTDRRHSIYGYLTKPKKAGKYPVVLCPPGAGIKTIKEPMRSTFYAKNGFIRLEMEIHGLNPEMTDEQFKEITTAFDYENGYLTNGLDDRDNYYMKHVYVACVRAIDYLTSLPDWDGKNVFVQGGSQGGALSLVTAGLDPRVTACVANHPALSDMAGYLDNRAGGYPHFNRLKNMFTPEKVNTMAYYDVVNFARRITCPVYITWGYNDNVCPPTTSYIVWNLITAPKESLITPINEHWTTSETNYTQMLWLKKQVK'],
['A0A0B8RHP0_LISMN', '2', 'MKKLLFLGDSVTDAGRDFENDRELGHGYVKIIADQLEQEDVTVINRGVSANRVADLHRRIEADAISLQPDVVTIMIGINDTWFSFSRWEDTSVTAFKEVYRVILNRIKTETNAELILMEPFVLPYPEDRKEWRGDLDPKIGAVRELAAEFGATLIPLDGLMNALAIKHGPTFLAEDGVHPTKAGHEAIASTWLEFTK']]
df = pd.DataFrame(rows, columns=[id_col, label_col, seq_col])
df << (EmbedESM(id_col, seq_col, extraction_method='mean', tmp_dir='tmp/') >> Save('tmp/esm2_test.pkl'))
# You can also extract the active site embedding in addition to the mean embedding
df << (EmbedESM(id_col, seq_col, extraction_method='active_site', active_site_col='ActiveSite', tmp_dir='tmp/') >> Save('tmp/esm2_test_active_site.pkl'))
```
### FoldSeek
See: [FoldSeek](https://github.com/steineggerlab/foldseek)
FoldSeek does a similarity search against a database of structures, it runs in the `enzyme-tk` environment. Similarly to the diamond blast, you can either create databases yourself before hand using the
foldseek documentation or you can create a database on the fly by passing the dataframe with a column called `label` that has two values: `reference` and `query`.
If you pass a database, you need to pass the path to the database.
The columns expect a path to a pdb file i.e. the output from the `Chai` step.
```python
from enzymetk.similarity_foldseek_step import FoldSeek
from enzymetk.save_step import Save
import pandas as pd
# id_col: str, seq_col: str, proteinfer_dir: str,
output_dir = 'tmp/'
rows = [['tmp/P0DP24/chai/P0DP24_3.cif'],
['tmp/P0DP24/chai/P0DP24_1.cif']]
df = pd.DataFrame(rows, columns=['pdbs'])
# foldseek_dir: str, pdb_column_name: str, reference_database: str
pdb_column_name = 'pdbs'
# The foldseek database was created using the folldwing command in this location:
# foldseek databases PDB pdb tmp
reference_database = '/disk1/share/software/foldseek/structures/pdb/pdb'
df << (FoldSeek(pdb_column_name, reference_database) >> Save(f'{output_dir}pdb_files.pkl'))
```
### LigandMPNN
LigandMPNN is a tool for inpainting the sequence for a protein backbone that has been generated by a generative model.
See: [LigandMPNN](https://github.com/dauparas/LigandMPNN)
```python
from steps.inpaint_ligandMPNN_step import LigandMPNN
from steps.save_step import Save
import pandas as pd
# id_col: str, seq_col: str, proteinfer_dir: str,
# This needs to be the full path to the file since LigandMPNN requires the full path (otherwise it will save to the ligandmpnn directory)
output_dir = '/disk1/ariane/vscode/enzyme-tk/examples/tmp/'
# These have to be the full path to the file since LigandMPNN requires the full path.
rows = [['/disk1/ariane/vscode/enzyme-tk/examples/tmp/P0DP24/chai/P0DP24_3.cif'],
['/disk1/ariane/vscode/enzyme-tk/examples/tmp/P0DP24/chai/P0DP24_1.cif']]
df = pd.DataFrame(rows, columns=['pdbs'])
# foldseek_dir: str, pdb_column_name: str, reference_database: str
pdb_column_name = 'pdbs'
ligand_mpnn_dir = '/disk1/share/software/LigandMPNN/'
# See how you need to enclose the fixed residues in quotes make sure any spaces are closed in double quotes!
args = ['--fixed_residues', '"A19 A20 A21 A59 A60 A61 A90 A91 A92"', '--checkpoint_path_sc', f'{ligand_mpnn_dir}model_params/ligandmpnn_sc_v_32_002_16.pt']
df << (LigandMPNN(pdb_column_name, ligand_mpnn_dir, output_dir,args=args) >> Save(f'{output_dir}ligandmpnn_inpainted.pkl'))
```
### Proteinfer
Proteinfer is a tool for predicting the EC number of an enzyme.
```python
output_dir = 'tmp/'
num_threads = 1
id_col = 'Entry'
seq_col = 'Sequence'
substrate_col = 'Substrate'
rows = [['P0DP23', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC'],
['AXE2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC']]
df = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col])
# This should be relative to the location of the script if you installed via the install_all.sh script
# Note you need to have downloaded their predictive models (ToDo )
proteinfer_dir = 'software/proteinfer/'
df << (ProteInfer(id_col, seq_col, proteinfer_dir, num_threads=num_threads) >> Save(f'proteinfer.pkl'))
```
## Tools and references
Being a toolkit this is a collection of other tools, which means if you use any of these tools then cite the ones relevant to your work:
[mmseqs2](https://github.com/soedinglab/mmseqs2)
[foldseek](https://github.com/steineggerlab/foldseek)
[diamond](https://github.com/bbuchfink/diamond)
[proteinfer](https://github.com/google-research/proteinfer)
[CLEAN](https://github.com/tttianhao/CLEAN)
[chai](https://github.com/chaidiscovery/chai-lab/)
[chemBERTa2](https://github.com/seyonechithrananda/bert-loves-chemistry)
[SELFormer](https://github.com/HUBioDataLab/SELFormer)
[rxnfp](https://github.com/rxn4chemistry/rxnfp)
[clustalomega](http://www.clustal.org/omega/)
[CREEP](https://github.com/jsunn-y/CARE)
[esm](https://github.com/facebookresearch/esm)
[LigandMPNN](https://github.com/dauparas/LigandMPNN)
[vina](https://vina.scripps.edu/)
[Uni-Mol](https://github.com/deepmodeling/Uni-Mol)
[fasttree](https://morgannprice.github.io/fasttree/)
[Porechop](https://github.com/rrwick/Porechop)
[prokka](https://github.com/tseemann/prokka)
Raw data
{
"_id": null,
"home_page": "https://github.com/arianemora/enzyme-tk/",
"name": "enzymetk",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "enzymes, protein-engineering",
"author": "Ariane Mora",
"author_email": "ariane.n.mora@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/a6/92/c1d164b61834071fc882a15c070f0a287181ff8239c417d73a0944563a75/enzymetk-0.0.3.tar.gz",
"platform": null,
"description": "# A pipeline for enzyme engineering\n\nEnzyme-tk is a collection of tools for enzyme engineering, setup as interoperable modules that act on dataframes. These modules are designed to be imported into pipelines for specific function. For this reason, `steps` as each module is called (e.g. finding similar proteins with `BLAST` would be considered a step) are designed to be as light as possible. An example of a pipeline is the [annotate-e](https://github.com/ArianeMora/annotate-e) ` pipeline, this acts to annotate a fasta with an ensemble of methods (each is designated as an Enzyme-tk step). \n\n\n**If you have any issues installing, let me know - this has been tested only on Linux/Ubuntu. Please post an issue!**\n\n## Installation\n\n## Install base package to import modules\n\n```bash\npip install enzymetk\n```\n\n### Install only the specific requirements you need (recomended) \n\nFor this clone the repo and then install the requirements for the specific modules you use \n```bash\ngit clone git@github.com:ArianeMora/enzyme-tk.git\ncd enzymetk/conda_envs/ # would recommend looking at thes\n# e.g. to install all from within that folder you would do\nsource install_all.sh\n```\n\n## Usage\n\nIf you have any issues at all just email me using my caltech email: `amora at caltech . edu`\n\nThis is a work-in progress! e.g. some tools (e.g. proteInfer and CLEAN) require extra data to be downloaded in order to run (like model weights.) I'm working on integrating these atm, buzz me if you need this!\n\nHere are some of the tools that have been implemented to be chained together as a pipeline:\n\n[boltz2](https://github.com/jwohlwend/boltz)\n[mmseqs2](https://github.com/soedinglab/mmseqs2) \n[foldseek](https://github.com/steineggerlab/foldseek) \n[diamond](https://github.com/bbuchfink/diamond) \n[proteinfer](https://github.com/google-research/proteinfer) \n[CLEAN](https://github.com/tttianhao/CLEAN) \n[chai](https://github.com/chaidiscovery/chai-lab/) \n[chemBERTa2](https://github.com/seyonechithrananda/bert-loves-chemistry) \n[SELFormer](https://github.com/HUBioDataLab/SELFormer) \n[rxnfp](https://github.com/rxn4chemistry/rxnfp) \n[clustalomega](http://www.clustal.org/omega/) \n[CREEP](https://github.com/jsunn-y/CARE) \n[esm](https://github.com/facebookresearch/esm) \n[LigandMPNN](https://github.com/dauparas/LigandMPNN) \n[vina](https://vina.scripps.edu/) \n[Uni-Mol](https://github.com/deepmodeling/Uni-Mol) \n[fasttree](https://morgannprice.github.io/fasttree/) \n[Porechop](https://github.com/rrwick/Porechop) \n[prokka](https://github.com/tseemann/prokka) \n\n## Things to note\n\nAll the tools use the conda env of `enzymetk` by default.\n\nIf you want to use a different conda env, you can do so by passing the `env_name` argument to the constructor of the step.\n\nFor example:\n\n```python\nproteinfer = ProteInfer(env_name='proteinfer')\n```\n\n## Arguments\n\nAll the arguments are passed to the constructor of the step, the ones that are required are passed as arguments to the constructor and the ones that are optional are passed as a list to the `args` argument, this needs to be a list as one would normally pass arguments to a command line tool.\n\nFor example:\n\n```python\nproteinfer = ProteInfer(env_name='proteinfer', args=['--num_threads', '10'])\n```\nFor those wanting to use specific arguments, check the individual tools for specifics. \n\n## Steps\n\nThe steps are the main building blocks of the pipeline. They are responsible for executing the individual tools.\n\n### BLAST\n\nBLAST is a tool for searching a database of sequences for similar sequences. Here you can either pass a database that you have already created or pass the sequences as part of your dataframe and pass the label column (this needs to have two values: reference and query) reference refers to sequences that you want to search against and query refers to sequences that you want to search for.\n\nNote you need to have installed the BLAST environment.\n\n```python\nid_col = 'Entry'\nseq_col = 'Sequence'\nlabel_col = 'label'\nrows = [['AXE2_TALPU', 'query', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],\n ['AXE2_TALPU', 'reference', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],\n ['AXE2_GEOSE', 'reference', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR'], \n ['AXE7A_XYLR2', 'referece', 'MFNFAPKQTTEMKKLLFTLVFVLGSMATALAENYPYRADYLWLTVPNHADWLYKTGERAKVEVSFCLYGMPQNVEVAYEIGPDMMPATSSGKVTLKNGRAVIDMGTMKKPGFLDMRLSVDGKYQHHVKVGFSPELLKPYTKNPQDFDAFWKANLDEARKTPVSVSCNKVDKYTTDAFDCYLLKIKTDRRHSIYGYLTKPKKAGKYPVVLCPPGAGIKTIKEPMRSTFYAKNGFIRLEMEIHGLNPEMTDEQFKEITTAFDYENGYLTNGLDDRDNYYMKHVYVACVRAIDYLTSLPDWDGKNVFVQGGSQGGALSLVTAGLDPRVTACVANHPALSDMAGYLDNRAGGYPHFNRLKNMFTPEKVNTMAYYDVVNFARRITCPVYITWGYNDNVCPPTTSYIVWNLITAPKESLITPINEHWTTSETNYTQMLWLKKQVK'], \n ['A0A0B8RHP0_LISMN', 'reference', 'MKKLLFLGDSVTDAGRDFENDRELGHGYVKIIADQLEQEDVTVINRGVSANRVADLHRRIEADAISLQPDVVTIMIGINDTWFSFSRWEDTSVTAFKEVYRVILNRIKTETNAELILMEPFVLPYPEDRKEWRGDLDPKIGAVRELAAEFGATLIPLDGLMNALAIKHGPTFLAEDGVHPTKAGHEAIASTWLEFTK']]\ndf = pd.DataFrame(rows, columns=[id_col, label_col, seq_col])\ndf << (BLAST(id_col, seq_col, label_col) >> Save('tmp/blast_test.pkl'))\n```\n\n### ActiveSitePred\n\nActiveSitePred is a tool for predicting the active site of an enzyme. This returns a dataframe with the active site prediction for each sequence, and the probability of the active site. Note we use a zero index for the active site prediction while UniProt uses a one index.\n\n```python\nsquidly_dir = '/disk1/share/software/AS_inference/' # This should be where you downloaded the data from zotero, there is a folder in there called AS_inference\nnum_threads = 1\nid_col = 'Entry'\nseq_col = 'Sequence'\nrows = [['AXE2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR'], \n ['H7C0D0', 'XRAHREIKDIFYKAIQKRRQSQEKIDDILQTLLDATYKDGRPLTDDEVAGMLIGLLLAGQHTSSTTSAWMGFFLARDKTLQKKCYLEQKTVCGENLPPLTYDQLKDLNLLDRCIKETLRLRPPIMIMMRMARTPQTVAGYTIPPGHQDNPASGEKFAYVPFGAGRHRCIGENFAYVQIKTIWSTMLRLYEFDLIDGYFPTVNYTTMIHTPENPVIRYKRRSK']]\ndf = pd.DataFrame(rows, columns=[id_col, seq_col])\nprint(df)\ndf << (ActiveSitePred(id_col, seq_col, squidly_dir, num_threads) >> Save('tmp/squidly_as_pred.pkl'))\n\n```\n### Boltz2\n\nBoltz2 is a model for predicting structures. Note you need docko installed as I run via that.\n\nBelow is an example using boltz with 4 threads, and uses a cofactor (intermediate in this case). Just set to be None for a single substrate version.\n```\nimport sys\nfrom enzymetk.dock_boltz_step import Boltz\nfrom enzymetk.save_step import Save\nimport pandas as pd\nimport os\nos.environ['MKL_THREADING_LAYER'] = 'GNU'\n\noutput_dir = 'tmp/'\nnum_threads = 4\nid_col = 'Entry'\nseq_col = 'Sequence'\nsubstrate_col = 'Substrate'\nintermediate_col = 'Intermediate'\n\nrows = [['P0DP23_boltz_8999', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]'], \n ['P0DP24_boltz_p1', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]'],\n ['P0DP23_boltz_p2', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]'], \n ['P0DP24_boltz_p3', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]'],\n ['P0DP24_boltz_p4', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC', 'CC1=C(C2=CC3=C(C(=C([N-]3)C=C4C(=C(C(=N4)C=C5C(=C(C(=N5)C=C1[N-]2)C)C=C)C)C=C)C)CCC(=O)[O-])CCC(=O)[O-].[Fe]']]\ndf = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col, intermediate_col])\ndf << (Boltz(id_col, seq_col, substrate_col, intermediate_col, f'{output_dir}', num_threads) >> Save(f'{output_dir}test.pkl'))\n```\n\n### Chai\n\nChai is a tool for predicting the structure of a protein and a ligand, this tool outputs the data to a new folder and creates directories based on the id that is passed. We return the paths to the specific structure for each id in the returned dataframe.\n\nRequres the `docko` conda environment to be created.\n\n```python\noutput_dir = 'tmp/'\nnum_threads = 1\nid_col = 'Entry'\nseq_col = 'Sequence'\nsubstrate_col = 'Substrate'\nrows = [['P0DP23', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC'], \n ['AXE2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC']]\ndf = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col])\nprint(df)\ndf << (Chai(id_col, seq_col, substrate_col, f'{output_dir}', num_threads) >> Save(f'{output_dir}test.pkl'))\n\n```\n\n### ChemBERTa\n\nChemBERTa2 encodes reactions and SMILES strings into a vector space. Note this requires the base environment, i.e. `enzymetk` conda env.\n\n```python\nfrom enzymetk.embedchem_chemberta_step import ChemBERT\nfrom enzymetk.save_step import Save\n\noutput_dir = 'tmp/'\nnum_threads = 1\nid_col = 'Entry'\nseq_col = 'Sequence'\nsubstrate_col = 'Substrate'\nrows = [['P0DP23', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC'], \n ['P0DP24', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC']]\ndf = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col])\nnew_df = (df << (ChemBERT(id_col, substrate_col, num_threads) >> Save(f'{output_dir}chemberta.pkl')))\n```\n\n### CLEAN\n\nCLEAN is a tool for predicting the EC number of an enzyme.\n\n```python\n\noutput_dir = 'tmp/'\nnum_threads = 1\nid_col = 'Entry'\nseq_col = 'Sequence'\nsubstrate_col = 'Substrate'\nrows = [['P0DP23', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC'], \n ['AXE2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC']]\ndf = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col])\n# This should be relative to the location of the script if you installed via the install_all.sh script\n# Note you need to have downloaded their predictive models (ToDo )\nclean_dir = 'software/CLEAN/app/'\ndf << (CLEAN(id_col, seq_col, clean_dir, num_threads=num_threads) >> Save(f'clean_missing_EC_seqs.pkl'))\n\n\n```\n### ClustalOmega\n\nClustalOmega is a tool for aligning a set of sequences. This gets installed to the system (expecting a linux machine) and added to the bash path. You need to have installed it first (check out the `conda_envs` directory in enzymetk.)\n\n```python\nfrom enzymetk.generate_msa_step import ClustalOmega\nfrom enzymetk.save_step import Save\nimport pandas as pd\n\nid_col = 'Entry'\nseq_col = 'Sequence'\nlabel_col = 'label'\nrows = [['AXE2_TALPU', 'query', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],\n ['AXE2_TALPU', 'reference', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],\n ['AXE2_GEOSE', 'reference', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR'], \n ['AXE7A_XYLR2', 'referece', 'MFNFAPKQTTEMKKLLFTLVFVLGSMATALAENYPYRADYLWLTVPNHADWLYKTGERAKVEVSFCLYGMPQNVEVAYEIGPDMMPATSSGKVTLKNGRAVIDMGTMKKPGFLDMRLSVDGKYQHHVKVGFSPELLKPYTKNPQDFDAFWKANLDEARKTPVSVSCNKVDKYTTDAFDCYLLKIKTDRRHSIYGYLTKPKKAGKYPVVLCPPGAGIKTIKEPMRSTFYAKNGFIRLEMEIHGLNPEMTDEQFKEITTAFDYENGYLTNGLDDRDNYYMKHVYVACVRAIDYLTSLPDWDGKNVFVQGGSQGGALSLVTAGLDPRVTACVANHPALSDMAGYLDNRAGGYPHFNRLKNMFTPEKVNTMAYYDVVNFARRITCPVYITWGYNDNVCPPTTSYIVWNLITAPKESLITPINEHWTTSETNYTQMLWLKKQVK'], \n ['A0A0B8RHP0_LISMN', 'reference', 'MKKLLFLGDSVTDAGRDFENDRELGHGYVKIIADQLEQEDVTVINRGVSANRVADLHRRIEADAISLQPDVVTIMIGINDTWFSFSRWEDTSVTAFKEVYRVILNRIKTETNAELILMEPFVLPYPEDRKEWRGDLDPKIGAVRELAAEFGATLIPLDGLMNALAIKHGPTFLAEDGVHPTKAGHEAIASTWLEFTK']]\ndf = pd.DataFrame(rows, columns=[id_col, label_col, seq_col])\ndf << (ClustalOmega(id_col, seq_col) >> Save('tmp/clustalomega_test.pkl'))\n```\n\n### CREEP\n\nCREEP is a tool for predicting the EC number of a reaction. At the moment it only supports reactions to EC however we are extending this to other modalities. \n\n```python\nfrom enzymetk.annotateEC_CREEP_step import CREEP\nfrom enzymetk.save_step import Save\nimport pandas as pd\n\n# CREEP expects you to have downloaded the data from the zotero page and put it in the data/CREEP folder\noutput_dir = 'tmp/'\ndf = pd.DataFrame({'EC number': ['1.1.1.1', '1.1.1.2'], \n 'Sequence': ['MALWMRLLPLLALLALWGPDPAAA', 'MALWMRLLPLLALLALWGPDPAAA'], \n 'Reaction': ['O=P(OC1=CC=CC=C1)(OC2=CC=CC=C2)OC3=CC=CC=C3>>O=P(O)(OC4=CC=CC=C4)OC5=CC=CC=C5.OC6=CC=CC=C6',\n 'O=P(OC1=CC=CC=C1)(OC2=CC=CC=C2)OC3=CC=CC=C3>>O=P(O)(OC4=CC=CC=C4)OC5=CC=CC=C5.OC6=CC=CC=C6']})\nid_col = 'Entry'\nreaction_col = 'Reaction'\n\ndf << (CREEP(id_col, reaction_col, CREEP_cache_dir='/disk1/share/software/CREEP/data/', CREEP_dir='/disk1/share/software/CREEP/',\n modality='reaction', reference_modality='protein') >> Save(f'{output_dir}CREEP_test_protein.pkl'))\n```\n\n### EmbedESM\n\nEmbedESM is a tool for embedding a set of sequences using ESM2.\n\n```python\nfrom enzymetk.embedprotein_esm_step import EmbedESM\nfrom enzymetk.save_step import Save\nimport pandas as pd\n\nid_col = 'Entry'\nseq_col = 'Sequence'\nlabel_col = 'ActiveSite'\nrows = [['AXE2_TALPU', '10', 'MHSKFFAASLLGLGAAAIPLEGVMEKRSCPAIHVFGARETTASPGYGSSSTVVNGVLSAYPGSTAEAINYPACGGQSSCGGASYSSSVAQGIAAVASAVNSFNSQCPSTKIVLVGYSQGGEIMDVALCGGGDPNQGYTNTAVQLSSSAVNMVKAAIFMGDPMFRAGLSYEVGTCAAGGFDQRPAGFSCPSAAKIKSYCDASDPYCCNGSNAATHQGYGSEYGSQALAFVKSKLG'],\n ['AXE2_GEOSE', '1|2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR'], \n ['AXE7A_XYLR2', '1', 'MFNFAPKQTTEMKKLLFTLVFVLGSMATALAENYPYRADYLWLTVPNHADWLYKTGERAKVEVSFCLYGMPQNVEVAYEIGPDMMPATSSGKVTLKNGRAVIDMGTMKKPGFLDMRLSVDGKYQHHVKVGFSPELLKPYTKNPQDFDAFWKANLDEARKTPVSVSCNKVDKYTTDAFDCYLLKIKTDRRHSIYGYLTKPKKAGKYPVVLCPPGAGIKTIKEPMRSTFYAKNGFIRLEMEIHGLNPEMTDEQFKEITTAFDYENGYLTNGLDDRDNYYMKHVYVACVRAIDYLTSLPDWDGKNVFVQGGSQGGALSLVTAGLDPRVTACVANHPALSDMAGYLDNRAGGYPHFNRLKNMFTPEKVNTMAYYDVVNFARRITCPVYITWGYNDNVCPPTTSYIVWNLITAPKESLITPINEHWTTSETNYTQMLWLKKQVK'], \n ['A0A0B8RHP0_LISMN', '2', 'MKKLLFLGDSVTDAGRDFENDRELGHGYVKIIADQLEQEDVTVINRGVSANRVADLHRRIEADAISLQPDVVTIMIGINDTWFSFSRWEDTSVTAFKEVYRVILNRIKTETNAELILMEPFVLPYPEDRKEWRGDLDPKIGAVRELAAEFGATLIPLDGLMNALAIKHGPTFLAEDGVHPTKAGHEAIASTWLEFTK']]\ndf = pd.DataFrame(rows, columns=[id_col, label_col, seq_col])\ndf << (EmbedESM(id_col, seq_col, extraction_method='mean', tmp_dir='tmp/') >> Save('tmp/esm2_test.pkl'))\n# You can also extract the active site embedding in addition to the mean embedding\ndf << (EmbedESM(id_col, seq_col, extraction_method='active_site', active_site_col='ActiveSite', tmp_dir='tmp/') >> Save('tmp/esm2_test_active_site.pkl'))\n```\n\n### FoldSeek\n\nSee: [FoldSeek](https://github.com/steineggerlab/foldseek)\n\nFoldSeek does a similarity search against a database of structures, it runs in the `enzyme-tk` environment. Similarly to the diamond blast, you can either create databases yourself before hand using the \nfoldseek documentation or you can create a database on the fly by passing the dataframe with a column called `label` that has two values: `reference` and `query`.\nIf you pass a database, you need to pass the path to the database.\n\nThe columns expect a path to a pdb file i.e. the output from the `Chai` step.\n\n```python\nfrom enzymetk.similarity_foldseek_step import FoldSeek\nfrom enzymetk.save_step import Save\nimport pandas as pd\n\n# id_col: str, seq_col: str, proteinfer_dir: str,\noutput_dir = 'tmp/'\nrows = [['tmp/P0DP24/chai/P0DP24_3.cif'],\n ['tmp/P0DP24/chai/P0DP24_1.cif']]\ndf = pd.DataFrame(rows, columns=['pdbs'])\n# foldseek_dir: str, pdb_column_name: str, reference_database: str\npdb_column_name = 'pdbs'\n# The foldseek database was created using the folldwing command in this location:\n# foldseek databases PDB pdb tmp \nreference_database = '/disk1/share/software/foldseek/structures/pdb/pdb'\ndf << (FoldSeek(pdb_column_name, reference_database) >> Save(f'{output_dir}pdb_files.pkl'))\n\n```\n\n### LigandMPNN\n\nLigandMPNN is a tool for inpainting the sequence for a protein backbone that has been generated by a generative model.\n\nSee: [LigandMPNN](https://github.com/dauparas/LigandMPNN)\n\n```python\nfrom steps.inpaint_ligandMPNN_step import LigandMPNN\nfrom steps.save_step import Save\nimport pandas as pd\n\n# id_col: str, seq_col: str, proteinfer_dir: str,\n# This needs to be the full path to the file since LigandMPNN requires the full path (otherwise it will save to the ligandmpnn directory)\noutput_dir = '/disk1/ariane/vscode/enzyme-tk/examples/tmp/'\n# These have to be the full path to the file since LigandMPNN requires the full path.\nrows = [['/disk1/ariane/vscode/enzyme-tk/examples/tmp/P0DP24/chai/P0DP24_3.cif'],\n ['/disk1/ariane/vscode/enzyme-tk/examples/tmp/P0DP24/chai/P0DP24_1.cif']]\ndf = pd.DataFrame(rows, columns=['pdbs'])\n# foldseek_dir: str, pdb_column_name: str, reference_database: str\npdb_column_name = 'pdbs'\nligand_mpnn_dir = '/disk1/share/software/LigandMPNN/'\n# See how you need to enclose the fixed residues in quotes make sure any spaces are closed in double quotes!\nargs = ['--fixed_residues', '\"A19 A20 A21 A59 A60 A61 A90 A91 A92\"', '--checkpoint_path_sc', f'{ligand_mpnn_dir}model_params/ligandmpnn_sc_v_32_002_16.pt']\ndf << (LigandMPNN(pdb_column_name, ligand_mpnn_dir, output_dir,args=args) >> Save(f'{output_dir}ligandmpnn_inpainted.pkl'))\n\n```\n\n### Proteinfer\n\nProteinfer is a tool for predicting the EC number of an enzyme.\n\n```python\n\noutput_dir = 'tmp/'\nnum_threads = 1\nid_col = 'Entry'\nseq_col = 'Sequence'\nsubstrate_col = 'Substrate'\nrows = [['P0DP23', 'MALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAAMALWMRLLPLLALLALWGPDPAAA', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC'], \n ['AXE2', 'MKIGSGEKLLFIGDSITDCGRARPEGEGSFGALGTGYVAYVVGLLQAVYPELGIRVVNKGISGNTVRDLKARWEEDVIAQKPDWVSIMIGINDVWRQYDLPFMKEKHVYLDEYEATLRSLVLETKPLVKGIILMTPFYIEGNEQDPMRRTMDQYGRVVKQIAEETNSLFVDTQAAFNEVLKTLYPAALAWDRVHPSVAGHMILARAFLREIGFEWVRSR', 'CCCCC(CC)COC(=O)C1=CC=CC=C1C(=O)OCC(CC)CCCC']]\ndf = pd.DataFrame(rows, columns=[id_col, seq_col, substrate_col])\n# This should be relative to the location of the script if you installed via the install_all.sh script\n# Note you need to have downloaded their predictive models (ToDo )\nproteinfer_dir = 'software/proteinfer/'\ndf << (ProteInfer(id_col, seq_col, proteinfer_dir, num_threads=num_threads) >> Save(f'proteinfer.pkl'))\n```\n\n## Tools and references\nBeing a toolkit this is a collection of other tools, which means if you use any of these tools then cite the ones relevant to your work:\n\n[mmseqs2](https://github.com/soedinglab/mmseqs2) \n[foldseek](https://github.com/steineggerlab/foldseek) \n[diamond](https://github.com/bbuchfink/diamond) \n[proteinfer](https://github.com/google-research/proteinfer) \n[CLEAN](https://github.com/tttianhao/CLEAN) \n[chai](https://github.com/chaidiscovery/chai-lab/) \n[chemBERTa2](https://github.com/seyonechithrananda/bert-loves-chemistry) \n[SELFormer](https://github.com/HUBioDataLab/SELFormer) \n[rxnfp](https://github.com/rxn4chemistry/rxnfp) \n[clustalomega](http://www.clustal.org/omega/) \n[CREEP](https://github.com/jsunn-y/CARE) \n[esm](https://github.com/facebookresearch/esm) \n[LigandMPNN](https://github.com/dauparas/LigandMPNN) \n[vina](https://vina.scripps.edu/) \n[Uni-Mol](https://github.com/deepmodeling/Uni-Mol) \n[fasttree](https://morgannprice.github.io/fasttree/) \n[Porechop](https://github.com/rrwick/Porechop) \n[prokka](https://github.com/tseemann/prokka) \n\n\n",
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