杨黎



【杨黎】个人简历
生日:1983.07
籍贯:重庆
毕业院校:长江师范学院
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兴趣爱好:
旅游
主要科研方向

膜蛋白的功能分类






近期发表文章
  • Using the augmented Chou‘s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach.

  • Journal of Theoretical Biology,(2009)366–372

    Yuhong Zeng  , Yanzhi Guo  , Rongquan Xiao  , Li Yang  , LeZheng Yu  , Menglong Li

    Abstract:  

    The submitochondria location of a mitochondrial protein is very important for further understanding the structure and function of this protein. Hence, it is of great practical significance to develop an automated and reliable method for timely identifying the submitochondria locations of novel mitochondrial proteins. In this study, a sequence-based algorithm combining the augmented Chou's pseudo amino acid composition (Chou's PseAA) based on auto covariance (AC) is developed to predict protein submitochondria locations and membrane protein types in mitochondria inner membrane. The model fully considers the sequence-order effects between residues a certain distance apart in the sequence by AC combined with eight representative descriptors for both common proteins and membrane proteins. As a result of jackknife cross-validation tests, the method for submitochondria location prediction yields the accuracies of 91.8%, 96.4% and 66.1% for inner membrane, matrix, and outer membrane, respectively. The total accuracy is 89.7%. When predicting membrane protein types in mitochondria inner membrane, the method achieves the prediction performance with the accuracies of 98.4%, 64.3% and 86.7% for multi-pass inner membrane, single-pass inner membrane, and matrix side inner membrane, where the total accuracy is 93.6%. The overall performance of our method is better than the achievements of the previous studies. So our method can be an effective supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://chemlab.scu.edu.cn/Predict_subMITO/index.htm.



  • Using auto covariance method for functional discrimination of membrane proteins based on evolution information

  • Amino Acids,(2010) 38:1497–1503

    Li Yang  , Yizhou Li  , Rongquan Xiao  , Yuhong Zeng  , JiaMin Xiao  , Fuyuan Tan  , Menghui Chen

    Abstract:  

    Membrane transporters are critical in living cells. Therefore, the discrimination of the types of membrane proteins based on their functions is of great importance both for helping genome annotation and providing a supplementary role to experimental researchers to gain insight into membrane proteins’ function. There are a lot of computational methods to facilitate the identification of the functional types of membrane proteins. However, in these methods, the local sequence environment was not integrated into the constructed model. In this study, we described a new strategy to predict the functional types of membrane proteins using a model based on auto covariance and position-specific scoring matrix. The novelty of the presented approach is considering the distribution of different positions of functional conservation sites in protein sequences. Thereby, this model adequately takes into account the long-range correlation between such sites during sequential evolution. Fivefold cross-validation test shows that this method greatly improves the prediction
    accuracy and achieves an acceptable prediction accuracy of 87.51%. The result indicates that the current approach might be an effective tool for predicting the functional
    types of membrane proteins only using the primary sequences. The code and dataset used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/
    predict_membrane.zip.