Prediction of protein functional sites by multivariate analysis of amino acid sequences
| Project/Area Number |
63480514
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
生物物性学
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
KANEHISA Minoru Kyoto University, Institute for Chemical Research, Professor, 化学研究所, 教授 (70183275)
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| Project Period (FY) |
1988 – 1989
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| Project Status |
Completed (Fiscal Year 1989)
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| Budget Amount *help |
¥6,000,000 (Direct Cost: ¥6,000,000)
Fiscal Year 1989: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1988: ¥5,000,000 (Direct Cost: ¥5,000,000)
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| Keywords | Functional prediction of proteins / Multivariate analysis / Expert system / 判別分析 / クラスター分析 / アミノ酸指標 |
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| Research Abstract |
In order to predict functional sites of proteins from their amino acid sequences, we have developed multivariate analysis and other methods and constructed databases for prediction. The starting point of our multivariate analysis method is to represent the amino acid sequence by a series of numerical values reflecting various biophysicochemical aspects of amino acid residues. For this purpose, we organized a database of amino acid indices by collecting published data for hydrophobicity and other properties. Since many of the reported indices were highly correlated, we performed a cluster analysis for grouping. Then, using discriminant analysis, we designed procedures to select important variables characterizing functional sites from a set of numerous variables defined from amino acid sequence data. The procedures were applied to the prediction of protein secondary structure segments and also to the prediction of glycosylation and phosphorylation sites. For the prediction of antigenicity determining sites, we organized a database with cross references of published peptide fragments and corresponding entries of the NBRF protein sequence database. However, our variable selection procedure did not produce satisfactory prediction. Because it was a severe limitation to represent sequence characteristics only by variables for multivariate analysis, we investigated more flexible methods. Thus, we applied an artificial intelligence method and developed an expert system. An expert system is more advantageous because it can incorporate various observations including results from multivariate analysis methods. We investigated the problem of predicting protein translocation sites in cells with this expert system approach. In summary, the multivariate analysis methods developed here are useful tools by themselves, but they can be more effective when combined with other approaches. Expert systems seem most suitable for practical applications.
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Report
(3 results)
Research Products
(21 results)
