A method for in silico identification of SNAIL/SLUG DNA binding potentials to the E-box sequence using molecular dynamics and evolutionary conserved amino acids.

Abstract

Binding of transcription factors to DNA is a dynamic process allowing for spatial- and sequence-specificity. Many methods for determination of DNA-protein structures do not allow for identification of dynamics of the search process, but provide only a single snapshot of the most stable binding. In order to better understand the dynamics of DNA binding as a protein encounters its cognate site, we have created a computer-based DNA scanning array macro that sequentially inserts a high affinity DNA consensus binding site at all possible locations in a predicted protein-DNA interface. We show, using short molecular dynamic simulations at each location in the interface, that energy minimized states and decreased movement of evolutionary conserved amino acids can be readily observed and used to predict the consensus binding site. The macro was applied to SNAIL class C2H2 zinc finger family proteins. The analysis suggests that (1) SNAIL binds to the E-box in multiple states during its encounter with its cognate site; (2) several different amino acids contribute to the E-box binding in each state; (3) the linear array of zinc fingers contributes differentially to overall folding and base-pair recognition; and (4) each finger may be specialized for stability and sequence specificity. Moreover, the macromolecular movement observed using this dynamic approach may allow the NH2-terminal finger to bind without sequence specificity yet result in higher binding energy. This macro and overall approach could be applicable to many evolutionary conserved transcription factor families and should help to better elucidate the varied mechanisms used for DNA sequence-specific binding.

DOI: 10.1007/s00894-013-1876-y

Cite this paper

@article{Prokop2013AMF, title={A method for in silico identification of SNAIL/SLUG DNA binding potentials to the E-box sequence using molecular dynamics and evolutionary conserved amino acids.}, author={Jeremy W. Prokop and Yuanjie Liu and Amy Milsted and Hongzhuang Peng and Frank J. Rauscher}, journal={Journal of molecular modeling}, year={2013}, volume={19 9}, pages={3463-9} }