We describe a new generation of algorithms capable of mapping the structure and conformations of macromolecules and their complexes from large ensembles of heterogeneous snapshots, and demonstrate the feasibility of determining both discrete and cont
inuous macromolecular conformational spectra. These algorithms naturally incorporate conformational heterogeneity without resort to sorting and classification, or prior knowledge of the type of heterogeneity present. They are applicable to single-particle diffraction and image datasets produced by X-ray lasers and cryo-electron microscopy, respectively, and particularly suitable for systems not easily amenable to purification or crystallization.
We demonstrate that a common-line method can assemble a 3D oversampled diffracted intensity distribution suitable for high-resolution structure solution from a set of measured 2D diffraction patterns, as proposed in experiments with an X-ray free ele
ctron laser (XFEL) (Neutze {it et al.}, 2000). Even for a flat Ewald sphere, we show how the ambiguities due to Friedels Law may be overcome. The method breaks down for photon counts below about 10 per detector pixel, almost 3 orders of magnitude higher than expected for scattering by a 500 kDa protein with an XFEL beam focused to a 0.1 micron diameter spot. Even if 10**3 orientationally similar diffraction patterns could be identified and added to reach the requisite photon count per pixel, the need for about 10**6 orientational classes for high-resolution structure determination suggests that about ~ 10**9 diffraction patterns must be recorded. Assuming pulse and read-out rates of 100 Hz, such measurements would require ~ 10**7 seconds, i.e. several months of continuous beam time.
