| Project/Area Number |
20700419
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Medical systems
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| Research Institution | Shizuoka Cancer Center Research Institute |
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Principal Investigator |
NAKAMURA Yoji Shizuoka Cancer Center Research Institute, 中央水産研究所・水産遺伝子解析センター, 研究員 (90463182)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2009: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2008: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 医療情報システム / 免疫治療 / アルゴリズム / 癌 / 蛋白質 / 免疫学 / バイオインフォマティクス / タンパク質 / パイオインフォマティクス |
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| Research Abstract |
I developed an integrated database of immunoglobulin (Ig or antibody) and T-cell receptor (TCR) data reported in cancer studies (the Cancer-related Immunological Gene Database [CIG-DB]). This database is designed as a platform to explore public human and murine Ig/TCR genes sequenced in cancer studies, and a total of 2,081 cancer-related Ig and TCR entries are tabularized. The CIG-DB is equipped with search engines for amino acid sequences and MEDLINE references, sequence analysis tools, and a 3D viewer. This database is accessible without charge or registration at http://www.scchr-cigdb.jp/, and the search results are freely downloadable.
In addition, I developed an in silico docking simulation assay system of binding affinity between HLA-A24 protein and A24-restricted peptides using two softwares, AutoDock and MODELLER, and a crystal structure of HLA-A24 protein in the Protein Data Bank (PDB). I compared the current assay system with the previous method in terms of the prediction capability using MHC stabilization and peptide-stimulated CTL induction assays for carcinoembryonic antigen (CEA) and other HLA-A24 peptides. The result suggested that the current in silico assay system have potential advantages in efficiency of epitope prediction over the previous method.
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