OligoPred: a web-server for predicting homo-oligomeric proteins by incorporating discrete wavelet transform into Chou’s pseudo amino acid composition. In vivo, some proteins exist as monomers (single polypeptide chains) and others as oligomers. Not like monomers, oligomers are composed of two or more chains (subunits) that are associated with each other through non-covalent interactions and, occasionally, through disulfide bonds. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. However, with the dramatic increase in the number of protein sequences submitted to the public data bank, it is important for both basic research and drug discovery research to acquire the possible knowledge about homo-oligomeric attributes of their interested proteins in a timely manner. In this paper, a high-throughput method, combined support vector machines with discrete wavelet transform, has been developed to predict the protein homo-oligomers. The total accuracy obtained by the re-substitution test, jackknife test and independent dataset test are 99.94%, 96.17% and 96.18%, respectively, showing that the proposed method of extracting feature from the protein sequences is effective and feasible for predicting homo-oligomers. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Li, Yao-Wang; Li, Bo: Characterization of structure-antioxidant activity relationship of peptides in free radical systems using QSAR models: key sequence positions and their amino acid properties (2013)
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- Zhou, Xuan; Li, Zhanchao; Dai, Zong; Zou, Xiaoyong: Predicting promoters by pseudo-trinucleotide compositions based on discrete wavelets transform (2013)
- Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui: Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection (2012)
- Mei, Suyu: Multi-kernel transfer learning based on Chou’s PseAAC formulation for protein submitochondria localization (2012)