disulfinder 1.2.11-4 source package in Ubuntu
Changelog
disulfinder (1.2.11-4) unstable; urgency=medium * Included patch to solve 'FTBFS with clang' (Closes: #741559) * Patched out 'unrecognized escape sequences' -- Laszlo Kajan <email address hidden> Sun, 07 Sep 2014 09:56:52 +0200
Upload details
- Uploaded by:
- Debian Med
- Uploaded to:
- Sid
- Original maintainer:
- Debian Med
- Architectures:
- any all
- Section:
- science
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| disulfinder_1.2.11-4.dsc | 2.1 KiB | ebecad964e2a01b52eab2e9d2e566524d4363ff4d78f44a60a71c43a1caf8be9 |
| disulfinder_1.2.11.orig.tar.gz | 2.4 MiB | d653aaac3ea26e9f6106a371de4f43d05b781a8b8b690c97820e4b27ea9d3495 |
| disulfinder_1.2.11-4.debian.tar.xz | 5.2 KiB | 29337c4fa73fd92e259a17534d816079f2d976ac17bfceaff68bd035aa03b983 |
Available diffs
- diff from 1.2.11-3 to 1.2.11-4 (3.0 KiB)
No changes file available.
Binary packages built by this source
- disulfinder: No summary available for disulfinder in ubuntu wily.
No description available for disulfinder in ubuntu wily.
- disulfinder-data: No summary available for disulfinder-data in ubuntu vivid.
No description available for disulfinder-data in ubuntu vivid.
- disulfinder-dbgsym: debug symbols for package disulfinder
'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.
