An Accurate Method for Prediction of Protein-Ligand Binding Site on Protein Surface Using SVM and Statistical Depth Function.

Since proteins carry out their functions through interactions with other molecules, accurately identifying the protein-ligand binding site plays an important role in protein functional annotation and rational drug discovery In the past two decades, a lot of algorithms were present to predict the pro...

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Publicado en:BioMed Research International Vol. 2013; pp. 409658 - 409659
Autores principales: Wang, Kui, Gao, Jianzhao, Shen, Shiyi, Tuszynski, Jack A, Ruan, Jishou, Hu, Gang
Formato: Journal Article
Publicado: Wiley-Blackwell 2013
Acceso en línea:Ver este registro en EBSCOhost
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        atl: An Accurate Method for Prediction of Protein-Ligand Binding Site on Protein Surface Using SVM and Statistical Depth Function.
      aug:
        au:
          Wang, Kui
          Gao, Jianzhao
          Shen, Shiyi
          Tuszynski, Jack A
          Ruan, Jishou
          Hu, Gang
        affil: College of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China.
      sug:
        subj:
          Biochemical Phenomena
          Bioinformatics
          Proteins
          Software
          Algorithms
          Binding Sites
          Laboratory Chemicals
          Membrane Proteins Metabolism
          Molecular Structure
          Proteins Metabolism
          Sequence Analysis
          Surface Properties
      ab: Since proteins carry out their functions through interactions with other molecules, accurately identifying the protein-ligand binding site plays an important role in protein functional annotation and rational drug discovery In the past two decades, a lot of algorithms were present to predict the protein-ligand binding site. In this paper, we introduce statistical depth function to define negative samples and propose an SVM-based method which integrates sequence and structural information to predict binding site. The results show that the present method performs better than the existent ones. The accuracy, sensitivity, and specificity on training set are 77.55%, 56.15%, and 87.96%, respectively; on the independent test set, the accuracy, sensitivity, and specificity are 80.36%, 53.53%, and 92.38%, respectively.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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