Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure
Author(s) -
Jiangning Song,
Zheng Yuan,
Hao Tan,
Thomas Huber,
Kevin Burrage
Publication year - 2007
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btm505
Subject(s) - sequence (biology) , feature (linguistics) , computational biology , computer science , disulfide bond , protein sequencing , feature vector , peptide sequence , sequence alignment , protein secondary structure , artificial intelligence , biology , genetics , biochemistry , gene , philosophy , linguistics
Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom