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Related Concept Videos

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Context influences on TALE-DNA binding revealed by quantitative profiling.

Julia M Rogers1, Luis A Barrera2, Deepak Reyon3

  • 11] Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA [2] Committee on Higher Degrees in Biophysics, Harvard University, Cambridge, Massachusetts 02138, USA.

Nature Communications
|June 13, 2015
PubMed
Summary
This summary is machine-generated.

Transcription activator-like effector (TALE) proteins have a complex DNA-binding code. A new computational model, SIFTED, predicts TALE DNA-binding specificity and potential off-target sites for improved genome engineering.

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Transcription activator-like effector (TALE) proteins are crucial for genome engineering due to their DNA-targeting capabilities.
  • The existing TALE DNA-binding code, while useful, has limitations in predicting off-target binding.
  • Off-target binding of TALEs is a significant challenge in precise genome engineering applications.

Purpose of the Study:

  • To comprehensively investigate TALE-DNA interactions and identify factors influencing binding specificity.
  • To develop a predictive computational model for TALE DNA-binding specificity.
  • To create a tool for predicting potential genomic off-target sites for enhanced TALE design.

Main Methods:

  • Quantitative assessment of DNA-binding specificities for 21 TALE proteins using custom protein-binding microarrays (PBMs).
  • Analysis of binding data to identify influences of protein context on TALE-DNA interactions.
  • Development of the Specificity Inference For TAL-Effector Design (SIFTED) computational model.

Main Results:

  • Protein context significantly influences TALE DNA-binding, indicating the canonical code is insufficient.
  • PBM data revealed complexities beyond the simple TALE recognition code.
  • The SIFTED model accurately predicts TALE DNA-binding specificity based on experimental data.

Conclusions:

  • The canonical TALE DNA-binding code does not fully explain binding interactions.
  • SIFTED provides a powerful computational approach to predict TALE specificity and off-target binding.
  • SIFTED is available as a web tool to aid in designing more precise TALE-based genome engineering strategies.