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Prediction of Protein Supersecondary Structures Based On the Artificial Neural Network Method

  1. Author:
    Sun, Z. R.
    Rao, X. Q.
    Peng, L. W.
    Xu, D.

  2. Author Address

    Xu D NCI FREDERICK CANC RES & DEV CTR SAIC MATH BIOL LAB FREDERICK, MD 21702 USA NCI FREDERICK CANC RES & DEV CTR SAIC MATH BIOL LAB FREDERICK, MD 21702 USA TSING HUA UNIV DEPT BIOL SCI & BIOTECHNOL STATE KEY LAB BIOMEMBRANE & MEMBRANE ENGN BEIJING 100084 PEOPLES REPUBLIC OF CHINA
    1. Year: 1997
  2. Journal: Protein Engineering
    1. 10
    2. 7
    3. Pages: 763-769
  4. Type of Article: Article
  1. Abstract:

    The sequence patterns of 11 types of frequently occurring connecting peptides, which lead to a classification of supersecondary moths, were studied. A database of protein supersecondary motifs was set up, An artificial neural network method, i,e, the back propagation neural network, was applied to the predictions of the supersecondary moths from protein sequences, The prediction correctness ratios are higher than 70%, and many of them vary from 75 to 82%, These results are useful for the further study of the relationship between the structure and function of proteins, It may also provide some important information about protein design and the prediction of protein tertiary structure. [References: 27]

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