PSIONplus^m

This is a stand-alone version of PSIONplus^m for accurate multi-label prediction of ion channels and their types.

Webserver

You can also use our server at:
https://yanglab.nankai.edu.cn/PSIONplusm/

The software need to install:

1.SPINE-X

Download the source code of Real SPINE-X and install it.
https://sparks-lab.org/downloads/

2.PSIPRED 3.3

Download the PSIPRED from:
http://bioinfadmin.cs.ucl.ac.uk/downloads/psipred/old_versions/

3.DISOPRED

Download the DISOPRED 3.16 from:
http://bioinfadmin.cs.ucl.ac.uk/downloads/DISOPRED/

4.PSIBLAST and BLAST

Download the PSIBLAST 2.2.6 from
https://ftp.ncbi.nih.gov/blast/executables/

5.Run PSIPRED,DISOPRED, SPINE X, PSIBLAST

Take 'example.fasta' for example,

1. run DISOPRED and get the result named target.diso. Copy the target.diso into ./output/example/
2. run PSIBLAST and get the result named target.pssm. Copy the target.pssm into ./output/example/
3. run SPINE-X and get the result named target.spXout. Copy the target.spXout into ./output/example/spXout
4. run PSIPRED and get the result named target.ss. Copy the target.ss into ./output/example/

Change the Parameters

1.open the pred.py
2.change the NCBIdir="/home/XXX/XXX/blast/bin" with your psiblast path.
3.change the db="/library/nr/nr" with your nr database path.

Run the PSIONplus^m

Usage: pred.py -i fastafile -m model(1/2/3/4/5)
Options: 1 PSIONplus: discrimination for ion channel and non-ion channel (ION)
2 PSIONplus: discrimination for voltage-gated and ligand-gated channel (VLG)
3 PSIONplus: discrimination for four types ion channel (VGS)
4 PSIONplusm: sequential prediction for single-label prediction
5 PSIONplusm: sequential prediciton for multi-label prediction

Example

python pred.py -i example.fasta -m 2
### output is ./output/example/output.VLG.psionplus.predict

Contact

If you have any questions, please contact gaojz AT nankai.edu.cn

Reference

Gao J, Cui W, Sheng Y, Ruan J, Kurgan L.PSIONplus: Accurate Sequence-Based Predictor of Ion Channels and Their Types,PLoS One, 2016, 11(4):e0152964.

Gao J, Miao Z, Zhang Z, Wei H, Kurgan L. Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment. Current Drug Targets. 2019;20(5):579–592. doi:10.2174/1389450119666181022153942

David T. Jones, Domenico Cozzetto, DISOPRED3: precise disordered region predictions with annotated protein-binding activity, Bioinformatics, Volume 31, Issue 6, 15 March 2015, Pages 857–863, https://doi.org/10.1093/bioinformatics/btu744

Faraggi E, Zhang T, Yang Y, Kurgan L, Zhou Y. SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. Journal of computational chemistry. 2012; 33(3):259–67. Epub 2011/11/03. doi: 10.1002/jcc.21968 PMID: 22045506; PubMed Central PMCID: PMC3240697.

Jones, D.T. (1999) Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292:195-202.

Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.Nucleic Acids Res. 1997 Sep 1;25(17):3389-402.