Dataset Information

Design and synthesis of a cell-permeable synthetic transcription factor mimic.

ABSTRACT: Synthetic molecules capable of activating the expression of specific genes are of great interest as tools for biological research and, potentially, as a novel class of pharmaceutical agents. It has been demonstrated previously that such synthetic transcription factor mimics (STFMs) can be constructed by connecting a sequence-specific DNA-binding module to a molecule capable of binding to the transcriptional machinery via a suitable linker. These chimeras mimic the two basic properties of native transcription factors, which are able to recognize a promoter sequence specifically and to recruit the transcriptional machinery to that promoter. However, none of the compounds of this type reported to date have been shown to function in living cells. We report here the first example of a cell-permeable STFM that activates the transcription of a reporter gene in mammalian cells. The compound is composed of a cell-permeable coactivator-binding peptoid fused to a DNA-binding hairpin polyamide. The peptoid was identified by screening a combinatorial library of approximately 50,000 compounds for binding to the KIX domain of the CREB-binding protein (CBP), a mammalian transcription coactivator. When incubated with cultured HeLa cells carrying a luciferase reporter plasmid bearing several hairpin polyamide-binding sites, a 5-fold increase in luciferase expression was observed. These experiments set the stage for the identification of hairpin polyamide-peptoid conjugates that are targeted to native genes.

SUBMITTER: Xiao X

PROVIDER: S-EPMC2518654 | biostudies-other | 2007 Jul-Aug

REPOSITORIES: biostudies-other

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Design and synthesis of a cell-permeable synthetic transcription factor mimic.

Xiao Xiangshu X Yu Peng P Lim Hyun-Suk HS Sikder Devanjan D Kodadek Thomas T

Journal of combinatorial chemistry 20070527 4

Synthetic molecules capable of activating the expression of specific genes are of great interest as tools for biological research and, potentially, as a novel class of pharmaceutical agents. It has been demonstrated previously that such synthetic transcription factor mimics (STFMs) can be constructed by connecting a sequence-specific DNA-binding module to a molecule capable of binding to the transcriptional machinery via a suitable linker. These chimeras mimic the two basic properties of native ...[more]

PMID: 17530904

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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 and synthesis of a cell-permeable synthetic transcription factor mimic.

Publications

Design and synthesis of a cell-permeable synthetic transcription factor mimic.

Similar Datasets

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