Journal article
Prediction of the disulphide bridges in proteins using SVM
International journal of bioinformatics research and applications, v 3(2)
2007
PMID: 18048190
Abstract
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.
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1 citations in Scopus
Details
- Title
- Prediction of the disulphide bridges in proteins using SVM
- Creators
- Aiguo Du - Georgia State UniversityXiaohua Hu - Drexel UniversityYi Pan - Georgia State University
- Publication Details
- International journal of bioinformatics research and applications, v 3(2)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-34250864962
- Other Identifier
- 991019173520304721