How to Install and Uninstall disulfinder Package on Ubuntu 20.10 (Groovy Gorilla)
Last updated: November 07,2024
1. Install "disulfinder" package
Please follow the guidelines below to install disulfinder on Ubuntu 20.10 (Groovy Gorilla)
$
sudo apt update
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$
sudo apt install
disulfinder
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2. Uninstall "disulfinder" package
Please follow the steps below to uninstall disulfinder on Ubuntu 20.10 (Groovy Gorilla):
$
sudo apt remove
disulfinder
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the disulfinder package on Ubuntu 20.10 (Groovy Gorilla)
Package: disulfinder
Architecture: amd64
Version: 1.2.11-8build1
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: 661
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), disulfinder-data
Filename: pool/universe/d/disulfinder/disulfinder_1.2.11-8build1_amd64.deb
Size: 243284
MD5sum: b3cdfd5207cb340bb8c2d223b35d6885
SHA1: 414e5d42b3d97f0a1f7ab113fdfd86064a34658f
SHA256: 8076feea44ed5b949990bd91f21fc34c61a7db9c130e32b83ceb748d87fb5123
SHA512: 3b241588ac2c0f4820dcbb3f6285f729755392ec2e25498ba5dbf07717fe3413364a812b421f6d62e9b7ad0e6e90fb3bc34e7eeea79d1e94a30a688748e610ff
Homepage: http://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: 1.2.11-8build1
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: 661
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), disulfinder-data
Filename: pool/universe/d/disulfinder/disulfinder_1.2.11-8build1_amd64.deb
Size: 243284
MD5sum: b3cdfd5207cb340bb8c2d223b35d6885
SHA1: 414e5d42b3d97f0a1f7ab113fdfd86064a34658f
SHA256: 8076feea44ed5b949990bd91f21fc34c61a7db9c130e32b83ceb748d87fb5123
SHA512: 3b241588ac2c0f4820dcbb3f6285f729755392ec2e25498ba5dbf07717fe3413364a812b421f6d62e9b7ad0e6e90fb3bc34e7eeea79d1e94a30a688748e610ff
Homepage: http://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