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A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad

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We developed DREAM, a deep learning tool for designing synthetic enhancers with predictable activity. Our engineered enhancers are highly potent and function across diverse species, advancing gene regulation applications.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioengineering

Background:

  • Enhancers are crucial for gene expression and cell identity.
  • Identifying sequence determinants of enhancer activity is challenging.
  • Current methods lack precision in synthetic enhancer design.

Purpose of the Study:

  • To introduce DREAM, a deep learning framework for designing synthetic enhancers.
  • To predict enhancer activity based on DNA sequence.
  • To engineer potent and versatile cis-regulatory elements.

Main Methods:

  • Developed the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework.
  • Utilized deep learning to analyze enhancer screening data.
  • Applied DREAM for sequence-based enhancer activity prediction and design.

Main Results:

  • Engineered synthetic enhancers 3.6-fold more potent than the strongest Drosophila enhancer.
  • Demonstrated conserved functionality of designed enhancers across species.
  • Successfully designed silencers and cell line-specific enhancers using DREAM.

Conclusions:

  • DREAM provides an interpretable approach for synthetic cis-regulatory element design.
  • The framework learned conserved enhancer regulatory grammar.
  • DREAM offers a versatile platform for gene therapy and biosynthetic engineering applications.