Issue 47, 2022

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 PKCĪ±-C1A than for the PKCĪ“-C1B domain. Thus, this compound is expected to serve as the basis for developing PKC ligands with isozyme selectivity.

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

Supplementary files

Article information

Article type
Communication
Submitted
28 mar 2022
Accepted
17 máj 2022
First published
18 máj 2022

Chem. Commun., 2022,58, 6693-6696

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

J. Maki, A. Oshimura, C. Tsukano, R. C. Yanagita, Y. Saito, Y. Sakakibara and K. Irie, Chem. Commun., 2022, 58, 6693 DOI: 10.1039/D2CC01759H

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