Acta Biochimica et Biophysica Sinica

【Acta Biochimica et Biophysica Sinica】介绍
英文名称:Acta Biochimica et Biophysica Sinica

现用刊名:Acta Biochimica et Biophysica Sinica

CA 化学文摘(美)(2009)
SCI 科学引文索引(美)(2009)
CBST 科学技术文献速报(日)(2009)

  1. Local Sequence Information-based Support Vector Machine to Classify Voltage-gated Potassium Channels

  2. Acta Biochimica et Biophysica Sinica,(2006)38(6): 363-371.

    LiXia Liu  , Menglong Li*  , Fuyuan Tan  , MinChun Lu  , Ke Long Wang  , Yanzhi Guo  , Zhining Wen  , Lin JIANG 


    In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv). In this paper, a new method based on the local sequence information of Kv channels is introduced to classify Kv channels. Six transmembrane domains of a Kv channel protein are used to define a protein, and the dipeptide composition technique is used to transform an amino acid sequence to a numerical sequence. A Kv channel protein is represented by a vector with 2000 elements, and a support vector machine algorithm is applied to classify Kv channels. This method shows good performance with averages of total accuracy (Acc), sensitivity (SE), specificity (SP), reliability (R) and Matthews correlation coefficient (MCC) of 98.0%, 89.9%, 100%, 0.95 and 0.94 respectively. The results indicate that the local sequence information-based method is better than the global sequence information-based method to classify Kv channels.

  3. Fast Fourier Transform-based Support Vector Machine for Prediction of G-protein Coupled Receptor Subfamilies

  4. Acta Biochimica et Biophysica Sinica, 2005, 37, 759–766

    Lin JIANG  , Menglong Li


    Although the sequence information on G-protein coupled receptors (GPCRs) continues to
    grow, many GPCRs remain orphaned (i.e. ligand specificity unknown) or poorly characterized with little
    structural information available, so an automated and reliable method is badly needed to facilitate the identification
    of novel receptors. In this study, a method of fast Fourier transform-based support vector machine
    has been developed for predicting GPCR subfamilies according to protein’s hydrophobicity. In classifying
    Class B, C, D and F subfamilies, the method achieved an overall Matthew’s correlation coefficient and
    accuracy of 0.95 and 93.3%, respectively, when evaluated using the jackknife test. The method achieved an
    accuracy of 100% on the Class B independent dataset. The results show that this method can classify GPCR
    subfamilies as well as their functional classification with high accuracy. A web server implementing the
    prediction is available at