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State-of-the-art ab initio prediction of 1D protein structure annotations
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State-of-the-art ab initio prediction of 1D protein structure annotations

The web server of Brewery is available at
The train and test sets are available at

Porter 5 (state-of-the-art SS predictor) is available at


Protein Structure Annotations; Essentials of Bioinformatics, Volume I. Springer Nature
Mirko Torrisi and Gianluca Pollastri; doi:

Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction, Scientific Reports, Nature Publishing Group
Mirko Torrisi, Manaz Kaleel and Gianluca Pollastri; doi:

PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning; Proteins, Springer Nature
Mirko Torrisi, Manaz Kaleel and Gianluca Pollastri; toll-free link:

Porter 5: fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes
Mirko Torrisi, Manaz Kaleel and Gianluca Pollastri; bioRxiv 289033; doi:


$ git clone --depth 1 && rm -rf Brewery/.git


  1. Python3 (;
  2. NumPy (;
  3. HHblits (;
  4. uniprot20 (

Optionally (for more accurate predictions):

  1. PSI-BLAST (;
  2. UniRef90 (

How to run Brewery with/without PSI-BLAST

# To exploit HHblits only (for fast and accurate predictions)
$ python3 Brewery/ -i Brewery/example/2FLGA.fasta --cpu 4 --fast

# To exploit both PSI-BLAST and HHblits (for very accurate predictions)
$ python3 Brewery/ -i Brewery/example/2FLGA.fasta --cpu 4

How to run Brewery on multiple sequences

# To split a FASTA file with multiple sequences (Optionally)
$ python3 Brewery/ many_sequences.fasta

# To predict all the fasta files in a given directory (Fastas)
$ python3 Brewery/ -i Fastas/ --cpu 4 --fast

# To run multiple predictions in parallel (using a total of 8 cores)
$ python3 Brewery/ -i Fastas/ --cpu 4 --parallel 2 --fast

All the parameters in the next section are valid.

How to visualize the help of Brewery

$ python3 Brewery/ --help
usage: [-h] [-input fasta_file] [--cpu CPU] [--fast] [--noSS]
                  [--noTA] [--noSA] [--noCD] [--distill] [--setup]

This is the standalone of Brewery5. Run it on a FASTA file to predict its
Secondary Structure in 3- and 8-classes (Porter5), Solvent Accessibility in 4
classes (PaleAle5), Torsional Angles in 14 classes (Porter+5) and Contact
Density in 4 classes (BrownAle).

optional arguments:
  -h, --help         show this help message and exit
  -input fasta_file  FASTA file containing the protein to predict
  --cpu CPU          How many cores to perform this prediction
  --fast             Use only HHblits (skip PSI-BLAST)
  --noSS             Skip Secondary Structure prediction with Porter5
  --noTA             Skip Torsional Angles prediction with Porter+5
  --noSA             Skip Solvent Accessibility prediction with PaleAle5
  --noCD             Skip Contact Density prediction with BrownAle5
  --distill          Generate useful outputs for 3D protein structure
  --setup            Initialize Brewery5 from scratch (it is recommended when
                     there has been any change involving PSI-BLAST, HHblits,
                     Brewery itself, etc).

E.g., run Brewery on 4 cores: python3 -i example/2FLGA --cpu 4

Use the docker image

# Set-up docker image
$ docker pull mircare/brewery

# set the absolute PATHs for databases and query sequences (stored locally)
$ docker run --name brewery -v /**PATH_to_uniprot20_2016_02**:/uniprot20 \
-v /**PATH_to_UniRef90_optional**:/uniref90 -v /**PATH_to_fasta_to_predict**:/Brewery/query \
--cap-add IPC_LOCK mircare/brewery sleep infinity &

# How to run a prediction using 5 cores and HHblits only
$ docker exec brewery python3 -i query/2FLGA.fasta --cpu 5 --fast

Performances of Secondary Structure Predictors in 3 classes

Method Q3 per AA SOV'99 per AA Q3 per protein SOV'99 per protein
Brewery 83.81% 80.41% 84.32% 81.05%
SPIDER 3 83.15% 79.43% 83.42% 79.79%
Brewery HHblits only 83.06% 79.49% 83.68% 80.26%
SSpro 5.1 with templates 82.58% 78.54% 83.94% 80.29%
PSIPRED 4.01 81.88% 77.36% 82.48% 78.22%
RaptorX-Property 81.86% 78.08% 82.57% 78.99%
Porter 4 81.66% 78.05% 82.29% 78.61%
SSpro 5.1 ab initio 81.17% 76.87% 81.10% 76.92%
DeepCNF 81.04% 76.74% 81.16% 76.99%

Reference: Table 1 in

Performances of Secondary Structure Predictors in 8 classes

Method Q8 per AA SOV8_refine per AA Q8 per protein SOV8_refine per protein
Brewery 73.02% 72.09% 73.92% 72.64%
SSpro 5.1 with templates 71.91% 70.72% 74.46% 73.45%
Brewery HHblits only 71.8% 71.16% 72.83% 71.74%
RaptorX-Property 70.74% 69.65% 71.78% 70.03%
DeepCNF 69.76% 68.5% 70.14% 68.06%
SSpro 5.1 ab initio 68.85% 67.54% 69.27% 67.91%

Reference: Table 2 in

Performances of Solvent Accessibility Predictors in up to 4 classes

Method Q2 per AA Q3 per AA Q4 per AA
ACCpro 5.1 with templates 80.5% N.A. N.A.
Brewery 80.48% 66.41% 56.46%
PaleAle 4 78.21% N.A. 52.53%
SPIDER 3 77.91% 61.19% 49.01%
ACCpro 5.1 ab initio 76.6% N.A. N.A.
RaptorX-Property N.A. 63.25% N.A.

Performances of Torsion Angles Predictors in 14 classes

Method Q14 per AA Q14 per protein
Brewery 69.93% 70.59%
SPIDER 3 66.58% 66.27%
Porter+ 64.73% 66%

Performances of Contact Density Predictors in 4 classes

Method Q4 per AA Q4 per protein
Brewery 50.01% 48%
BrownAle 46.5% N.A.


This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Email us at gianluca[dot]pollastri[at]ucd[dot]ie if you wish to use it for purposes not permitted by the CC BY-NC-SA 4.0.

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