Studies of multi-domain proteins and multi-protein machines necessitate a deeper understanding of dynamic structure and transient conformations. Therefore, conventional structure determination methods need to be integrated into dynamic detection approaches. We developed a hybrid method that integrates x-ray structure information into self-consistent distance networks based on single-molecule Förster resonance energy transfer (FRET) [1,2]. By analyzing time-correlated distance distributions globally, we can separate real protein dynamics and fluctuations from modelling uncertainties, and ultimately generate time-correlated structural ensembles. On top of that, we reveal correlated small- and large-scale intra-molecular fluctuations from time-resolved, color- and polarization-sensitive fluorescence measurements. We applied our approach to the heat shock protein Hsp90. The chaperone activates large sets of signal transduction proteins often assisted by co-chaperones [3]. These conformation- and nucleotide-dependent processes are not yet comprehensively understood. Our approach resembled the x-ray structure of Hsp90's closed conformation with an RMSD of 2.8 Å. Beyond that, we resolved the previously unknown dynamic open structure of this multidomain protein. The large-scale fluctuations on the lower millisecond timescale might be the basis of a general regulation mechanism. Finally, I want to show how FRET can be orthogonally integrated into cross-disciplinary platforms such as a microfluidics [4] and others, enabling the simultaneous detection of dynamic structure and low-affinity interactions. [1] Hellenkamp B, Wortmann P, Kandzia F, Zacharias M, Hugel T. Multidomain structure and correlated dynamics determined by self-consistent FRET networks. Nature Methods 14(2), 174-180 (2017) [2] Hellenkamp B, Schmid S et al. Precision and accuracy of single-molecule FRET measurements—a multi-laboratory benchmark study Nature Methods 15, 669-676 (2018) [3] Taipale, M et al. Quantitative Analysis of Hsp90-Client Interactions Reveals Principles of Substrate Recognition. Cell 140(5), 987-1001 (2012) [4] Hellenkamp B, Thurn J, Stadlmeier M, Hugel T., submitted