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Kinetic studies on strand displacement in de novo designed parallel heterodimeric coiled coils.

Mike C GrothW Mathis RinkNils F MeyerFranziska Thomas
Published in: Chemical science (2018)
Among the protein folding motifs, which are accessible by de novo design, the parallel heterodimeric coiled coil is most frequently used in bioinspired applications and chemical biology in general. This is due to the straightforward sequence-to-structure relationships, which it has in common with all coiled-coil motifs, and the heterospecificity, which allows control of association. Whereas much focus was laid on designing orthogonal coiled coils, systematic studies on controlling association, for instance by strand displacement, are rare. As a contribution to the design of dynamic coiled-coil-based systems, we studied the strand-displacement mechanism in obligate heterodimeric coiled coils to investigate the suitability of the dissociation constants (KD) as parameters for the prediction of the outcome of strand-displacement reactions. We use two sets of heterodimeric coiled coils, the previously reported N-A x B y and the newly characterized C-A x B y . Both comprise KD values in the μM to sub-nM regime. Strand displacement is explored by CD titration and a FRET-based kinetic assay and is proved to be an equilibrium reaction with half-lifes from a few seconds up to minutes. We could fit the displacement data by a competitive binding model, giving rate constants and overall affinities of the underlying association and dissociation reactions. The overall affinities correlate well with the ratios of KD values determined by CD-thermal denaturation experiments and, hence, support the dissociative mechanism of strand displacement in heterodimeric coiled coils. From the results of more than 100 different displacement reactions we are able to classify three categories of overall affinities, which allow for easy prediction of the equilibrium of strand displacement in two competing heterodimeric coiled coils.
Keyphrases
  • molecular dynamics simulations
  • molecular dynamics
  • single molecule
  • deep learning
  • small molecule
  • electronic health record
  • protein protein