Prediction of amyloid fibril-forming segments based on a support vector machine
Background: Amyloid fibrillar aggregates of proteins or polypeptides are known to be associated with many human diseases. Recent studies suggest that this aggregation is triggered by short protein regions. Thus, identifying these short peptides is critical for understanding diseases and finding potential therapeutic targets.Results: We propose a method, named Pafig (Prediction of amyloid fibril-forming segments)based on support vector machines, to identify the hexpeptides associated with amyloid fibrillar aggregates. The features of Pafig were obtained by a two-round selection from AAindex.Using a 10-fold cross validation test on Hexpepset dataset, Pafig performed well with regards to overall accuracy of 81% and Matthews correlation coefficient of 0.63. Pafig was used to predict the potential fibril-forming hexpeptides in all of the 64,000,000 hexpeptides. As a resuit, approximately 5.08% of hexpeptides showed a high aggregation propensity. In the predicted fibril-forming hexpeptides, the amino acids -alanine, phenylalanine, isoleucine, leucine and valine occurred at the higher frequencies and the amino acids -aspartic acid, glutamic acid, histidine, lysine, arginine and praline, appeared with lower frequencies.Conclusions: The performance of Pafig indicates that it is a powerful tool for identifying the hexpeptides associated with fibrillar aggregates and will be useful for large-scale analysis of proteomic data.
Jian Tian Ningfeng Wu Jun Guo Yunliu Fan
Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
国际会议
The 7th Asia-Pacific Bioinformatics Conference(第七届亚太生物信息学大会)
北京
英文
513-522
2009-01-01（万方平台首次上网日期，不代表论文的发表时间）