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Open Access Publications from the University of California

Characterizing highly dynamic conformational states: the transcription bubble in RNAP-promoter open complex as an example

  • Author(s): Lerner, Eitan
  • Ingargiola, Antonino
  • Weiss, Shimon
  • et al.

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Bio-macromolecules carry out complicated functions through structural changes. To understand their mechanism of action, the structure of each step has to be characterized. While classical structural biology techniques allow the characterization of a few 'structural snapshots' along the enzymatic cycle (usually of stable conformations), they do not cover all (and often fast interconverting) structures in the ensemble, where each may play an important functional role. Recently, several groups have demonstrated that structures of different conformations in solution could be solved by measuring multiple distances between different pairs of residues using single-molecule Förster resonance energy transfer (smFRET) and using them as constrains for hybrid/integrative structural modeling. However, this approach is limited in cases where the conformational dynamics is faster than the technique's temporal resolution. In this study, we combine existing tools that elucidate sub-millisecond conformational dynamics together with hybrid/integrative structural modeling to study the conformational states of the transcription bubble in the bacterial RNA polymerase (RNAP)-promoter open complex (RPo). We measured microsecond alternating laser excitation (μsALEX)-smFRET of differently labeled lacCONS promoter dsDNA constructs. We used a combination of burst variance analysis (BVA), photon-by-photon hidden Markov modelling (H2MM) and the FRET-restrained positioning and screening (FPS) approach to identify two conformational states for RPo. The experimentally-derived distances of one conformational state match the known crystal structure of bacterial RPo. The experimentally-derived distances of the other conformational state have characteristics of a scrunched RPo. These findings support the hypothesis that sub-millisecond dynamics in the transcription bubble are responsible for transcription start site selection.

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