ecapriotti/ddgun repository overview

⁠DDGun

⁠An untrained method for predicting the stability change upon mutation

For information and bugs contact:
emidio.capriotti (at) gmail.com

⁠Ludovica Montanucci, Emidio Capriotti and Piero Fariselli 2019.

⁠DDGun scripts are released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

The container includes the following packages and databases licensed by other researchers:

• HHblits
• dsspcmbi
• uniclust30_2018_08
  
  

⁠USAGE

Before running the code, the hhblits database needs to be uncompressed with the following command:

tar -xzvf /home/bass/ddgun/data/uniclust30_2018_08.tar.gz --directory /home/bass/ddgun/data

1. Run DDGun 3D:

   ./ddgun_3d.py test/1aar.pdb A test/1aar.muts #PDBFILE CHAIN VARIANT S_DDG[3D] T_DDG[3D] STABILITY[3D] 1aar.pdb A K6A 0.4 0.4 Increase 1aar.pdb A K6E -0.0 -0.0 Neutral 1aar.pdb A T7Q -0.3 -0.3 Decrease 1aar.pdb A T9A,G10A -0.1,-0.1 -0.1 Decrease 1aar.pdb A Y59W -0.4 -0.4 Decrease

2. Run DDGun Seq:

   ./ddgun_seq.py test/1aar.pdb.A.fasta test/1aar.muts #SEQFILE VARIANT S_DDG[SEQ] T_DDG[SEQ] STABILITY[SEQ] 1aarA.fasta K6A 0.3 0.3 Increase 1aarA.fasta K6E 0.0 0.0 Neutral 1aarA.fasta T7Q -0.6 -0.6 Decrease 1aarA.fasta T9A,G10A -0.7,-0.6 -0.6 Decrease 1aarA.fasta Y59W -0.6 -0.6 Decrease

   
   

⁠CITATIONS

• Montanucci L, Capriotti E, Frank Y, Ben-Tal N, Fariselli P. (2019). DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC Bioinformatics. 20 (Suppl 14): 335. PMID:31266447
• Steinegger M, Meier M, Mirdita M, Vöhringer H, Haunsberger S J, and Söding J (2019) HH-suite3 for fast remote homology detection and deep protein annotation, BMC Bioinformatics, 473. doi: 10.1186/s12859-019-3019-7: PMID:31521110.
  
  

⁠DISCLAIMER

Maintainers have no responsibility for any damage resulting from the use of this tool.