2021 Fiscal Year Final Research Report

Unified comprehension of chemical communication by natural PKC ligands and development of new medicinal seeds

Planned Research

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Project Area	Frontier research of chemical communications
Project/Area Number	17H06405
Research Category	Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
Allocation Type	Single-year Grants
Review Section	Science and Engineering
Research Institution	Kyoto University
Principal Investigator	Irie Kazuhiro 京都大学, 農学研究科, 教授 (00168535)
Co-Investigator(Kenkyū-buntansha)	柳田亮香川大学, 農学部, 准教授 (10598121) 塚野千尋京都大学, 農学研究科, 准教授 (70524255) 村上一馬京都大学, 農学研究科, 准教授 (80571281)
Project Period (FY)	2017-06-30 – 2022-03-31
Keywords	プロテインキナーゼＣ / 発がんプロモーター / ホルボールエステル / アプリシアトキシン / 機械学習 / アロタケタール / HIV / アルツハイマー病
Outline of Final Research Achievements	Protein kinase (PKC) isozymes involved in cell surface signal transduction are one of the targets of intractable diseases such as cancer, Alzheimer's disease, and HIV infection. 10-Me-Aplog-1, the simplified analog of proinflammatory aplysiatoxin, is a potent PKC ligand with little tumor-promoting and proinflammatory activities. We showed that 10-Me-Aplog-1 becomes a promising medicinal lead for the above-mentioned intractable diseases through activation of PKCα and δ. In addition, its further structural modification paved the way for development of PKC isozyme-selective ligands. On the other hand, new PKC ligands were searched by machine-learning, and the alotaketals isolated from marine sponge were identified as new PKC ligand candidates. Their simplified analogs designed by the docking simulation against PKCδ-C1B were synthesized to be assayed for PKC isozyme surrogate binding. One of the ligands exhibited a higher affinity for PKCα-C1A than for PKCδ-C1B.
Free Research Field	天然物有機化学、生物有機化学
Academic Significance and Societal Importance of the Research Achievements	本研究代表者らが開発した10-Me-Aplog-1のPKCアイソザイムを介した化学シグナルを理解するとともに，本化合物が，がん，アルツハイマー病，HIV感染症などの難治性疾患に対する治療薬シードになりうることを明らかにした．複雑な天然物を適切に単純化することにより，必要な活性のみを抽出した新規医薬品シーズを得るという新しい方法論を提示できた．さらに機械学習により新規PKCリガンドとしてアロタケタール類を同定し，ドッキングシミュレーションにより設計・合成した複数の化合物の中から，PKCアイソザイム選択性を有する新規リガンドを開発できた点も、新薬の効率的な開発に繋がる意義のある研究と考えている。