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Computational Design of DNA-Binding Proteins.

Summer Thyme1, Yifan Song2

  • 1Department of Molecular and Cellular Biology, Harvard University, 16 Divinity Ave BL 1020, Cambridge, MA, 02138, USA. sthyme@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
|April 21, 2016
PubMed
Summary

Computational protein design and directed evolution can engineer DNA-binding proteins. This chapter details Rosetta software methods for protein-DNA interface modulation and comparative modeling for enhanced protein engineering.

Keywords:
Computational designDirect readoutHomology modelIn silico predictionProtein–DNA interactionsRosettaSpecificity

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Area of Science:

  • Computational biology
  • Protein engineering
  • Bioinformatics

Background:

  • Accurate computational modeling is crucial for predicting sequence perturbation outcomes in protein-DNA interfaces.
  • Computational design can accommodate DNA target site substitutions.
  • Rosetta software is established for modulating DNA-binding protein specificity.

Purpose of the Study:

  • To detail Rosetta macromolecular modeling program's design methods for protein-DNA interactions.
  • To describe protocols for increasing computational design output diversity.
  • To present comparative modeling for exploring natural specificity modulation and engineering templates.

Main Methods:

  • Utilizing the Rosetta macromolecular modeling program for computational design.
  • Combining computational design with directed evolution for protein engineering.
  • Developing protocols to increase the diversity of designed outputs.
  • Building comparative models of protein-DNA complexes using homologous sequences.

Main Results:

  • Demonstrated success of Rosetta in modulating DNA-binding protein specificity.
  • Highlighted the synergy between computational design and directed evolution.
  • Provided methods to generate diverse computational design solutions.
  • Established comparative modeling as a tool to study natural specificity and guide engineering.

Conclusions:

  • Computational methods, particularly Rosetta, are effective for engineering protein-DNA interfaces.
  • Integrating computational design with directed evolution enhances protein engineering success rates.
  • Comparative modeling offers insights into natural specificity and aids future engineering efforts.