4.7 Article

Improved Modeling of Side-Chain-Base Interactions and Plasticity in Protein-DNA Interface Design

Journal

JOURNAL OF MOLECULAR BIOLOGY
Volume 419, Issue 3-4, Pages 255-274

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmb.2012.03.005

Keywords

computational modeling and specificity redesign; sequence recovery; LAGLIDADG homing endonucleases; protein-DNA interaction conservation; experimental benchmarks for computation

Funding

  1. National Science Foundation
  2. US National Institutes of Health [GM084433, RL1CA133832, GM088277]
  3. Foundation for the National Institutes of Health through the Gates Foundation Grand Challenges in Global Health Initiative
  4. Howard Hughes Medical Institute

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Combinatorial sequence optimization for protein design requires libraries of discrete side-chain conformations. The discreteness of these libraries is problematic, particularly for long, polar side chains, since favorable interactions can be missed. Previously, an approach to loop remodeling where protein backbone movement is directed by side-chain rotamers predicted to form interactions previously observed in native complexes (termed motifs) was described. Here, we show how such motif libraries can be incorporated into combinatorial sequence optimization protocols and improve native complex recapitulation. Guided by the motif rotamer searches, we made improvements to the underlying energy function, increasing recapitulation of native interactions. To further test the methods, we carried out a comprehensive experimental scan of amino acid preferences in the I-AniI protein-DNA interface and found that many positions tolerated multiple amino acids. This sequence plasticity is not observed in the computational results because of the fixed-backbone approximation of the model. We improved modeling of this diversity by introducing DNA flexibility and reducing the convergence of the simulated annealing algorithm that drives the design process. In addition to serving as a benchmark, this extensive experimental data set provides insight into the types of interactions essential to maintain the function of this potential gene therapy reagent. Published by Elsevier Ltd.

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