Prediction of the disulphide bridges in proteins using SVM.
Disulphide bonds link distant portions of protein chains and provide strong structural constraints in the form of long-range interactions. Prediction and knowledge of disulphide bond connectivity is important in reducing the search space of protein conformation. In this research, we present an effective way to predict disulphide bridges by Support Vector Machine (SVM). The SVM encoding was based on experimental results on the binding motifs of protein disulphide isomerases. The physical-chemical characteristics of the flanking sequences and the linear distance between the concerned cysteine pairs were also included in the encoding. An overall pair wise accuracy of 92% was obtained for the SP39 dataset.
Department of Computer Science, Georgia State University, 30303 Atlanta, GA, USA. adu@student.gsu.edu
Aiguo
A
Du
Xiaohua
X
Hu
Yi
Y
Pan
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D004220:Disulfides / Q000737:chemistry*; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment
D004220:Disulfides; D011506:Proteins