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

doi:10.1039/d2cc01759h

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

Department of Biosciences and Informatics

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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 language	English
Pages (from-to)	6693-6696
Number of pages	4
Journal	Chemical Communications
Volume	58
Issue number	47
DOIs	https://doi.org/10.1039/d2cc01759h
Publication status	Published - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1039/d2cc01759h

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title = "AI and computational chemistry-accelerated development of an alotaketal analogue with conventional PKC selectivity",

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.",

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T1 - AI and computational chemistry-accelerated development of an alotaketal analogue with conventional PKC selectivity

AU - Maki, Jumpei

AU - Oshimura, Asami

AU - Tsukano, Chihiro

AU - Yanagita, Ryo C.

AU - Saito, Yutaka

AU - Sakakibara, Yasubumi

AU - Irie, Kazuhiro

PY - 2022/5/18

Y1 - 2022/5/18

N2 - 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.

AB - 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.

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JO - Chemical Communications

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

Abstract

ASJC Scopus subject areas

UN SDGs

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