title: Prediction of the disulphide bonding state of cysteines in proteins using conditional random fields.
abstract: The formation of disulphide bonds between cysteines plays a major role in protein folding, structure, function and evolution. Many computational approaches have been used to predict the disulphide bonding state ofcysteines. In our work, we developed a novel method based on Conditional Random Fields (CRFs) to predict the disulphide bonding state from protein primary sequence, predicted secondary structures and predicted relative solvent accessibilities (all-state information). Our experiments obtain 84% accuracy, 88% precision and 94% recall, using all-state information. However, our results show essentially identical results when using protein sequence and predicted relative solvent accessibilities in the absence of secondary structure.
authors.affiliation: Faculty of Science, Bioinformatics Research Laboratory, Chiang Mai University, 50200, Thailand. Satruncmu@hotmail.com
authors.forename: Watshara
authors.initials: W
authors.lastname: Shoombuatong
authors.forename: Patrinee
authors.initials: P
authors.lastname: Traisathit
authors.forename: Sukon
authors.initials: S
authors.lastname: Prasitwattanaseree
authors.forename: Chatchai
authors.initials: C
authors.lastname: Tayapiwatana
authors.forename: Robert
authors.initials: R
authors.lastname: Cutler
authors.forename: Jeerayut
authors.initials: J
authors.lastname: Chaijaruwanich
mesh_terms: D000465:Algorithms; D003545:Cysteine / Q000737:chemistry*; D030562:Databases, Protein; D004220:Disulfides / Q000737:chemistry*; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment
chemical_list: D004220:Disulfides; D011506:Proteins; D003545:Cysteine