mircare/microbrewery repository overview

$ git clone https://github.com/mircare/Microbrewery/ --depth 1 && rm -rf Microbrewery/.git

$ python3 Microbrewery/Microbrewery.py -i Microbrewery/example/2FLGA.fasta --cpu 4 

# To split a FASTA file containing multiple sequences
$ python3 Microbrewery/split_fasta.py many_sequences.fasta

# To predict all the fasta files in a given directory, e.g. DIRNAME
$ python3 Microbrewery/multiple_fasta.py -i DIRNAME/ --cpu 4 --fast

$ python3 Microbrewery/Microbrewery.py --help
usage: Microbrewery.py [-h] [-input fasta_file] [--noSS] [--noTA] [--noSA]
                       [--noCD] [--setup]

This is the standalone of Microbrewery. Run it on a FASTA file to predict its
Secondary Structure in 3- and 8-classes (Porter5), Solvent Accessibility in 4
classes (PaleAle5), Structural Motifs in 14 classes (Porter+5) and Contact
Density in 4 classes (BrownAle5).

optional arguments:
  -h, --help         show this help message and exit
  -input fasta_file  FASTA file containing the protein to predict
  --noSS             Skip Secondary Structure prediction
  --noTA             Skip Structural Motifs prediction
  --noSA             Skip Solvent Accessibility prediction
  --noCD             Skip Contact Density prediction
  --setup            Initialize Microbrewery from scratch (e.g., required when
                     it is moved).

E.g., python3 Microbrewery.py -i example/2FLGA.fasta

# Set the absolute PATHs to the query sequences
$ docker run --name microbrewery -v /**PATH_to_fasta_to_predict**:/Microbrewery/query \
--cap-add IPC_LOCK mircare/microbrewery sleep infinity &

# Run Microbrewery
$ docker exec microbrewery python3 Microbrewery.py -i query/2FLGA.fasta

@article{torrisi_brewery_2020,
	title = {Brewery: Deep Learning and deeper profiles for the prediction of 1D protein structure annotations},
	doi = {10.1093/bioinformatics/btaa204},
	journal = {Bioinformatics},
	author = {Torrisi, Mirko and Pollastri, Gianluca}
}