データ融合によるタンパク質切断解析および疾患との関連性発見
| Project/Area Number |
15F15788
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 外国 |
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| Research Field |
Life / Health / Medical informatics
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| Research Institution | Kyoto University |
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Principal Investigator |
阿久津 達也 京都大学, 化学研究所, 教授 (90261859)
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| Co-Investigator(Kenkyū-buntansha) |
MARINI SIMONE 京都大学, 化学研究所, 外国人特別研究員
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| Project Period (FY) |
2015-11-09 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2016: ¥200,000 (Direct Cost: ¥200,000)
Fiscal Year 2015: ¥500,000 (Direct Cost: ¥500,000)
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| Keywords | caspase / protease / data fution / bioinformatics / Protein cleavage / Data fusion / Machine learning, |
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| Outline of Annual Research Achievements |
The problem of protease-protein target prediction has been extensively studied in Bioinformatics. However, existing algorithms are either very specific (i.e. they work only with specific proteins or protein families, such as Caspases) or solely based on the primary structure, therefore very prone to provide false positives (i.e. non-cleaving pairs wrongly labeled as cleaving). Our work consisted in the design of a protease-protein target algorithm, wrapping up the general protein cleavage machinery, through the application of data fusion.
We extracted up to 9000 pairs of cleaving, wet lab tested protease-protein target pairs from the MEROPS database. Beside the use of protein similarity (BLAST), the model was designed by fuse relevant, but directly related cleavage information. By harvesting publicly available data bases such as KEGG, BioGRID, STRING, Domine and Interpro, we included domain, pathway, gene and protein knowledge to our model. By assessing our model on test data, not involved in the training and tuning phase, we showed how it outperforms state-of-the-art software for the protease cleavage target prediction. Unlike state-of-the-art approaches, this algorithm is general and not dedicated to specific proteases, therefore it can be used to explore poorly-studied proteases, where for example secondary and tertiary structure are completely unknown.
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| Research Progress Status |
28年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
28年度が最終年度であるため、記入しない。
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Report
(2 results)
Research Products
(7 results)
