Development of T-cell receptor ligand identification technology using deep learning
| Project/Area Number |
18K07180
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 49070:Immunology-related
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| Research Institution | Tokyo Medical University (2019-2022)
Sapporo Medical University (2018) |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | T cell receptor / molecular modeling / 腫瘍免疫 / 構造解析 / ペプチド／抗原提示分子複合体 / 分子モデリング / 細胞受容体 / T細胞受容体 / T細胞 / ディープラーニング / 免疫 |
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| Outline of Final Research Achievements |
Using various combinations of known TCR/pHLA structural analysis data and docking simulations, we found that certain binding conditions were true for TCR-recognized peptide antigen-presenting molecule pairs, but not for non-recognized TCR/pHLA pairs. By scoring the presence or absence of binding conditions, we found that the TCR-recognized peptide antigen-presenting molecule pairs scored higher. Binding evaluation using structurally unknown TCR/pHLA pairs showed higher scores only for pairs with TCR-recognizable pHLA. It was also found that more accurate TCR/pHLA binding decisions can be made by performing exhaustive docking simulations.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成功によって、個人個人が有するT細胞がどんな抗原を認識するかがわかれば、疾患との関連性を解析し、治療の標的を的確に同定し最適な個別化免疫治療が可能となる。また将来罹患する可能性のあるがん種、感染症、アレルギー疾患、自己免疫疾患を発症前にそのリスクを予測する研究へと発展させることも可能となる。それによって個別化予防医学が発達し、医療費の削減に貢献する。また新たな産業あるいは免疫予防学といった新しい学問を創出する基盤技術となりうる。
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Report
(6 results)
Research Products
(6 results)
