2018 Fiscal Year Final Research Report
Understanding and modelizing of selective GPCR-G-protein coupling mechanism
| Project/Area Number |
16K00403
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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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| Research Field |
Life / Health / Medical informatics
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| Research Institution | Aoyama Gakuin University |
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Principal Investigator |
SUWA MAKIKO 青山学院大学, 理工学部, 教授 (30242241)
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| Co-Investigator(Kenkyū-buntansha) |
池田 修己 国立研究開発法人産業技術総合研究所, 人工知能研究センター, 主任研究員 (20415772)
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| Research Collaborator |
Manaka Tomomi
Kasado Risako
Horiguchi Wataru
Kawamura Mayu
Shinozaki Ryuji
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Keywords | Ｇタンパク質共役型受容体 / Ｇタンパク質 / 結合選択性予測 / シグナル伝達パスウェイ / アドレナリン受容体構造 / 分子動力学計算 / 機械学習 |
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| Outline of Final Research Achievements |
We collected combination data of ligand-GPCR-G protein from literature and DB, and converted them into 934 vectors using physico-chemical quantity. In addition, as a result of MD calculation of the structure substituted by 16 kinds of G proteins and 9 kinds of AR based on Gs & β2 adrenergic receptor (AR) complex structure (3SN6), it is characterized between identical G protein coupling receptors, the interaction profiles were obtained, from which the regions contributing to the selective coupling of G proteins were identified. The amino acids in this region described by physico-chemical quantities were added to 934 vectors, and a G-protein coupling selectivity prediction program was created by using machine learning method (SVM). This program can distinguish 16 kinds of coupling G protein species with high accuracy (cross-validation: 94.4%, objective test: 95.0%).
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| Free Research Field |
バイオインフォマティクス、生物物理
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| Academic Significance and Societal Importance of the Research Achievements |
Gタンパク共役型受容体（GPCR）は、細胞外からリガンド分子と結合し、構造変化することで細胞内側のGタンパク質を選択的に活性化させ、後の伝達経路につなげる。本研究はこれら一連のメカニズムを解明することを第一の目的とし、このメカニズムを再現するシグナル伝達経路予測システムの構築を最終目的とする。本研究の結果は、例えば生命現象の予測、制御、副作用の少ない薬剤のデザイン,GPCRが関与する疾病のメカニズム解明につながる。つまりGPCRのシグナル伝達のメカニズムを網羅的かつ統合的に理解する道筋ができ、細胞内での生命現象を予測して創薬支援となる基盤が確立できると考える。
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