How to Install and Uninstall disulfinder Package on Ubuntu 21.10 (Impish Indri)
Last updated: November 26,2024
1. Install "disulfinder" package
This is a short guide on how to install disulfinder on Ubuntu 21.10 (Impish Indri)
$
sudo apt update
Copied
$
sudo apt install
disulfinder
Copied
2. Uninstall "disulfinder" package
Here is a brief guide to show you how to uninstall disulfinder on Ubuntu 21.10 (Impish Indri):
$
sudo apt remove
disulfinder
Copied
$
sudo apt autoclean && sudo apt autoremove
Copied
3. Information about the disulfinder package on Ubuntu 21.10 (Impish Indri)
Package: disulfinder
Architecture: amd64
Version: 1.2.11-10
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: 691
Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), disulfinder-data
Filename: pool/universe/d/disulfinder/disulfinder_1.2.11-10_amd64.deb
Size: 238896
MD5sum: e465b109c8e016c05c584b4f64bc8189
SHA1: f5cc5e4aee957f60e75df2d1b3e097a461d34e86
SHA256: acebf55e1fb5cb684c07200c6a97f12e4590ae36baff10a3208ba0746411d517
SHA512: 2cc320adff40ab75109d61753763e76801c67fef75e59b4342e1d2472db3823005f0c65f223561a67b9bd2d391aaac2251fbdc9bedff8d0049fb6a219a871036
Homepage: https://disulfind.dsi.unifi.it/
Description-en: cysteines disulfide bonding state and connectivity predictor
'disulfinder' is for predicting the disulfide bonding state of cysteines
and their disulfide connectivity starting from sequence alone. Disulfide
bridges play a major role in the stabilization of the folding process for
several proteins. Prediction of disulfide bridges from sequence alone is
therefore useful for the study of structural and functional properties
of specific proteins. In addition, knowledge about the disulfide bonding
state of cysteines may help the experimental structure determination
process and may be useful in other genomic annotation tasks.
.
'disulfinder' predicts disulfide patterns in two computational stages:
(1) the disulfide bonding state of each cysteine is predicted by a
BRNN-SVM binary classifier; (2) cysteines that are known to participate
in the formation of bridges are paired by a Recursive Neural Network
to obtain a connectivity pattern.
Description-md5: 5f70380c76687c70f279559ee87b7d0e
Architecture: amd64
Version: 1.2.11-10
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: 691
Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), disulfinder-data
Filename: pool/universe/d/disulfinder/disulfinder_1.2.11-10_amd64.deb
Size: 238896
MD5sum: e465b109c8e016c05c584b4f64bc8189
SHA1: f5cc5e4aee957f60e75df2d1b3e097a461d34e86
SHA256: acebf55e1fb5cb684c07200c6a97f12e4590ae36baff10a3208ba0746411d517
SHA512: 2cc320adff40ab75109d61753763e76801c67fef75e59b4342e1d2472db3823005f0c65f223561a67b9bd2d391aaac2251fbdc9bedff8d0049fb6a219a871036
Homepage: https://disulfind.dsi.unifi.it/
Description-en: cysteines disulfide bonding state and connectivity predictor
'disulfinder' is for predicting the disulfide bonding state of cysteines
and their disulfide connectivity starting from sequence alone. Disulfide
bridges play a major role in the stabilization of the folding process for
several proteins. Prediction of disulfide bridges from sequence alone is
therefore useful for the study of structural and functional properties
of specific proteins. In addition, knowledge about the disulfide bonding
state of cysteines may help the experimental structure determination
process and may be useful in other genomic annotation tasks.
.
'disulfinder' predicts disulfide patterns in two computational stages:
(1) the disulfide bonding state of each cysteine is predicted by a
BRNN-SVM binary classifier; (2) cysteines that are known to participate
in the formation of bridges are paired by a Recursive Neural Network
to obtain a connectivity pattern.
Description-md5: 5f70380c76687c70f279559ee87b7d0e