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PWC PWC

Porter 5

Light, fast and high quality prediction of protein secondary structure in 3 and 8 classes

The web server, train and test sets of Porter 5 are available at http://distilldeep.ucd.ie/porter/.
The docker container is available at https://hub.docker.com/r/mircare/porter5 (HOWTO).

See https://github.com/mircare/Brewery to predict more protein structure annotations, and download COVID-19 predictions.

Pipeline of BreweryDiagram of the pipeline we propose to gather and exploit deeper profiles.

Setup

$ git clone https://github.com/mircare/Porter5/ --depth 1 && rm -rf Porter5/.git

Requirements

  1. Python3 (https://www.python.org/downloads/);
  2. NumPy (https://www.scipy.org/scipylib/download.html);
  3. HHblits (https://github.com/soedinglab/hh-suite/);
  4. uniprot20 (http://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/old-releases/uniprot20_2016_02.tgz).

Optionally (for more accurate predictions):

  1. PSI-BLAST (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/);
  2. UniRef90 (ftp://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref90/uniref90.fasta.gz).

How to run Porter 5

# For fast and accurate predictions (exploiting HHblits only)
$ python3 Porter5/Porter5.py -i Porter5/example/2FLGA.fasta --cpu 4 --fast

# For very accurate predictions (exploiting both HHblits and PSI-BLAST)
$ python3 Porter5/Porter5.py -i Porter5/example/2FLGA.fasta --cpu 4

How to run Porter 5 on multiple sequences

# To split a FASTA file with multiple sequences (Optional)
$ python3 Porter5/split_fasta.py many_sequences.fasta

# To predict all the fasta files in a given directory (Fastas)
$ python3 Porter5/multiple_fasta.py -i Fastas/ --cpu 4 --fast

# To run multiple predictions in parallel (using a total of 8 cores)
$ python3 Porter5/multiple_fasta.py -i Fastas/ --cpu 4 --parallel 2 --fast

Use the docker image

# Set-up docker image
$ docker pull mircare/porter5

# set the absolute PATHs for databases and query sequences (stored locally)
$ docker run --name porter5 -v /**PATH_to_uniprot20_2016_02**:/uniprot20 \
-v /**PATH_to_UniRef90_optional**:/uniref90 -v /**PATH_to_fasta_to_predict**:/Porter5/query \
--cap-add IPC_LOCK mircare/porter5 sleep infinity &

# How to run a prediction using 5 cores and HHblits only
$ docker exec porter5 python3 Porter5.py -i query/2FLGA.fasta --cpu 5 --fast

Performances in 3 states on large independent test set

Method Q3 per AA SOV'99 per AA Q3 per protein SOV'99 per protein
Porter 5 83.81% 80.41% 84.32% 81.05%
SPIDER 3 83.15% 79.43% 83.42% 79.79%
Porter 5 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%

Calculated with http://dna.cs.miami.edu/SOV/.

Performances in 8 states on large independent test set in

Method Q8 per AA SOV8'99 per AA Q8 per protein SOV8'99 per protein
Porter 5 73.02% 69.91% 73.92% 70.76%
SSpro 5.1 with templates 71.91% 68.68% 74.46% 71.74%
Porter 5 HHblits only 71.8% 68.87% 72.83% 69.79%
RaptorX-Property 70.74% 67.59% 71.78% 68.36%
DeepCNF 69.76% 66.42% 70.14% 66.44%
SSpro 5.1 ab initio 68.85% 65.33% 69.27% 65.97%

Calculated with http://dna.cs.miami.edu/SOV/.

Citation

If you use Porter 5, please cite our Scientific Reports paper:

@article{torrisi_porter_2019,
	title = {Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction},
	volume = {9},
	issn = {2045-2322},
	doi = {10.1038/s41598-019-48786-x},
	journal = {Scientific Reports},
	author = {Torrisi, Mirko and Kaleel, Manaz and Pollastri, Gianluca},
	month = aug,
	year = {2019}
}

References

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: https://doi.org/10.1038/s41598-019-48786-x.

Protein Structure Annotations; Essentials of Bioinformatics, Volume I. Springer Nature
Mirko Torrisi and Gianluca Pollastri; doi: https://doi.org/10.1007/978-3-030-02634-9_10.

Brewery: Deep Learning and deeper profiles for the prediction of 1D protein structure annotations,
Bioinformatics, Oxford University Press; Mirko Torrisi and Gianluca Pollastri;
Toll-free link: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa204/5811232?guestAccessKey=9a73ae2a-2cb6-4fe1-b333-a4f3261f02cf.

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: https://doi.org/10.1101/289033.

License

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.

Creative Commons License

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Fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes

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