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.
Diagram 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
- Python3 (https://www.python.org/downloads/);
- NumPy (https://www.scipy.org/scipylib/download.html);
- HHblits (https://github.com/soedinglab/hh-suite/);
- uniprot20 (http://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/old-releases/uniprot20_2016_02.tgz).
Optionally (for more accurate predictions):
- PSI-BLAST (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/);
- 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.
