PMID:17942444

Citation	Song, J, Yuan, Z, Tan, H, Huber, T and Burrage, K (2007) Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure. Bioinformatics 23:3147-54
Abstract	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.
Links	PubMed Online version:10.1093/bioinformatics/btm505
Keywords	Algorithms; Amino Acid Sequence; Artificial Intelligence; Binding Sites; Computer Simulation; Disulfides/chemistry; Models, Chemical; Models, Molecular; Molecular Sequence Data; Pattern Recognition, Automated/methods; Protein Binding; Protein Interaction Mapping/methods; Protein Structure, Secondary; Sequence Alignment/methods; Sequence Analysis, Protein/methods
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