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Computational Design of DNA-Binding Proteins

  • Summer ThymeEmail author
  • Yifan Song
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1414)

Abstract

Predicting the outcome of engineered and naturally occurring sequence perturbations to protein–DNA interfaces requires accurate computational modeling technologies. It has been well established that computational design to accommodate small numbers of DNA target site substitutions is possible. This chapter details the basic method of design used in the Rosetta macromolecular modeling program that has been successfully used to modulate the specificity of DNA-binding proteins. More recently, combining computational design and directed evolution has become a common approach for increasing the success rate of protein engineering projects. The power of such high-throughput screening depends on computational methods producing multiple potential solutions. Therefore, this chapter describes several protocols for increasing the diversity of designed output. Lastly, we describe an approach for building comparative models of protein–DNA complexes in order to utilize information from homologous sequences. These models can be used to explore how nature modulates specificity of protein–DNA interfaces and potentially can even be used as starting templates for further engineering.

Key words

Protein–DNA interactions Computational design Rosetta Specificity In silico prediction Direct readout Homology model 

Notes

Acknowledgements

The authors would like to thank Justin Ashworth, Phil Bradley, and Jim Havranek for their vast contributions to improving protein–DNA interface design, as well as the entire ROSETTA Commons community for contributions to the Rosetta code base. This work was supported by the US National Institutes of Health (#GM084433 and #RL1CA133832 to D.B.), the Foundation for the National Institutes of Health through the Gates Foundation Grand Challenges in Global Health Initiative, and the Howard Hughes Medical Institute.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Molecular and Cellular BiologyHarvard UniversityCambridgeUSA
  2. 2.Department of BiochemistryUniversity of WashingtonSeattleUSA

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