Disulfide Bond Prediction using Neural Network and Secondary Structure Information
In protein-folding prediction, the location of disulfide bonds can strongly reduce the search in the conformational space. Therefore the correct prediction of the disulfide connectivity starting from the protein residue sequence may also help in predicting its 3D structure. In this paper, we describe a method to predict disulfide connectivity in a protein given only the amino acid sequence, using neural network, and given input of symmetric flanking regions of N-terminus and C-terminus cystines augmented with residue secondary structure (helix, sheet, and coil) as well as evolutionary information. 252 protein sequences were selected from the SWISS-PROT database. From the results of 4-fold cross validation, we find that merging protein secondary structure allows us to obtain significant prediction accuracy improvements.
disulfide bonds neural network protein secondary structure
Ouyan Shi Huiyun Yang Chunquan Cai Jing Yang Xin Tian
Faculty of Basic Medicine Tianjin Medical University Tianjin, China Department of Biomedical Engineering Tianjin Medical University Tianjin, China Department of Neurosurgery General Hospital of Tianjin Medical University Tianjin, China
国际会议
上海
英文
656-659
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）