AI and computational chemistry-accelerated development of an alotaketal analogue with conventional PKC selectivity

Jumpei Maki, Asami Oshimura, Chihiro Tsukano, Ryo C. Yanagita, Yutaka Saito, Yasubumi Sakakibara, Kazuhiro Irie

Research output: Contribution to journalArticlepeer-review

Abstract

The protein kinase C (PKC) family consists of ten isozymes and is a potential target for treating cancer, Alzheimer's disease, and HIV infection. Since known natural PKC agonists have little selectivity among the PKC isozymes, a new scaffold is needed to develop PKC ligands with remarkable isozyme selectivity. Taking advantage of machine-learning and computational chemistry approaches, we screened the PubChem database to select sesterterpenoids alotaketals as potential PKC ligands, then designed and synthesized alotaketal analogues with a different ring system and stereochemistry from the natural products. The analogue exhibited a one-order higher affinity for PKCα-C1A than for the PKCδ-C1B domain. Thus, this compound is expected to serve as the basis for developing PKC ligands with isozyme selectivity.

Original languageEnglish
Pages (from-to)6693-6696
Number of pages4
JournalChemical Communications
Volume58
Issue number47
DOIs
Publication statusPublished - 2022 May 18

ASJC Scopus subject areas

  • Catalysis
  • Electronic, Optical and Magnetic Materials
  • Ceramics and Composites
  • Chemistry(all)
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Materials Chemistry

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