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.

<p align="center"><img src="https://github.com/mircare/Brewery/blob/master/diagram%20pipeline.png" alt="Pipeline of Brewery">Diagram of the pipeline we propose to gather and exploit deeper profiles.</p>

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;<br> 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<br> 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,<br> Bioinformatics, Oxford University Press; Mirko Torrisi and Gianluca Pollastri;<br> 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<br> Mirko Torrisi, Manaz Kaleel and Gianluca Pollastri; bioRxiv 289033; doi: https://doi.org/10.1101/289033.

License

This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

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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