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Design of Synthetic Surrogates for the Macrolactone Linker Motif in Coibamide A.

ABSTRACT: A marine cyanobacterial cyclic depsipeptide, coibamide A (CbA), inhibits the mammalian protein secretory pathway by blocking the Sec61 translocon, which is an emerging drug target for cancer and other chronic diseases. In our previous structure-activity relationship study of CbA, the macrolactone ester linker was replaced with alkyl/alkenyl surrogates to provide synthetically accessible macrocyclic scaffolds. To optimize the cellular bioactivity profile of CbA analogues, novel lysine mimetics having β- and ε-methyl groups have now been designed and synthesized by a stereoselective route. A significant increase in cytotoxicity was observed upon introduction of these two methyl groups, corresponding to the d-MeAla α-methyl and MeThr β-methyl of CbA. All synthetic products retained the ability to inhibit secretion of a model Sec61 substrate. Tandem evaluation of secretory function inhibition in living cells and cytotoxicity was an effective strategy to assess the impact of structural modifications to the linker for ring closure.

SUBMITTER: Suzuki R

PROVIDER: S-EPMC10578308 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Design of Synthetic Surrogates for the Macrolactone Linker Motif in Coibamide A.

Suzuki Rikito R Mattos Daphne R DR Kitamura Takashi T Tsujioka Rina R Kobayashi Kazuya K Inuki Shinsuke S Ohno Hiroaki H Ishmael Jane E JE McPhail Kerry L KL Oishi Shinya S

ACS medicinal chemistry letters 20230919 10

A marine cyanobacterial cyclic depsipeptide, coibamide A (CbA), inhibits the mammalian protein secretory pathway by blocking the Sec61 translocon, which is an emerging drug target for cancer and other chronic diseases. In our previous structure-activity relationship study of CbA, the macrolactone ester linker was replaced with alkyl/alkenyl surrogates to provide synthetically accessible macrocyclic scaffolds. To optimize the cellular bioactivity profile of CbA analogues, novel lysine mimetics ha ...[more]

PMID: 37849553

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Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Dataset Information

Design of Synthetic Surrogates for the Macrolactone Linker Motif in Coibamide A.

Publications

Design of Synthetic Surrogates for the Macrolactone Linker Motif in Coibamide A.

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