How to Install and Uninstall concavity Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: November 26,2024

1. Install "concavity" package

Please follow the step by step instructions below to install concavity on Ubuntu 21.04 (Hirsute Hippo)

$ sudo apt update $ sudo apt install concavity

2. Uninstall "concavity" package

Please follow the instructions below to uninstall concavity on Ubuntu 21.04 (Hirsute Hippo):

$ sudo apt remove concavity $ sudo apt autoclean && sudo apt autoremove

3. Information about the concavity package on Ubuntu 21.04 (Hirsute Hippo)

Package: concavity
Architecture: amd64
Version: 0.1+dfsg.1-5
Priority: extra
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 980
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5)
Suggests: conservation-code, pymol
Filename: pool/universe/c/concavity/concavity_0.1+dfsg.1-5_amd64.deb
Size: 247548
MD5sum: 989531affb71edf70732b98c58b5fe8f
SHA1: 411c46a4da3d0d18c903e1c1610755e0628a8a32
SHA256: 04386fbb8c86ef627918d511b4a6aff4757772e15f06f94bb17ea16c937b11d1
SHA512: 80d64931c39e891fed6ec2fcb6f6b4a0aa537344e8e76ce187ef60c54cfc6e8b40031cd7e0c9950b6d676af94b971742026192ee79c20b6812269ff10ac7981f
Homepage: https://compbio.cs.princeton.edu/concavity/
Description-en: predictor of protein ligand binding sites from structure and conservation
ConCavity predicts protein ligand binding sites by combining evolutionary
sequence conservation and 3D structure.
.
ConCavity takes as input a PDB format protein structure and optionally
files that characterize the evolutionary sequence conservation of the chains
in the structure file.
.
The following result files are produced by default:
* Residue ligand binding predictions for each chain (*.scores).
* Residue ligand binding predictions in a PDB format file (residue
scores placed in the temp. factor field, *_residue.pdb).
* Pocket prediction locations in a DX format file (*.dx).
* PyMOL script to visualize the predictions (*.pml).
Description-md5: 218f0855db86d2d89a15960056332c1e