title: Bayesian orientation estimate and structure information from sparse single-molecule x-ray diffraction images.
abstract: We developed a Bayesian method to extract macromolecular structure information from sparse single-molecule x-ray free-electron laser diffraction images. The method addresses two possible scenarios. First, using a "seed" structural model, the molecular orientation is determined for each of the provided diffraction images, which are then averaged in three-dimensional reciprocal space. Subsequently, the real space electron density is determined using a relaxed averaged alternating reflections algorithm. In the second approach, the probability that the "seed" model fits to the given set of diffraction images as a whole is determined and used to distinguish between proposed structures. We show that for a given x-ray intensity, unexpectedly, the achievable resolution increases with molecular mass such that structure determination should be more challenging for small molecules than for larger ones. For a sufficiently large number of recorded photons (>200) per diffraction image an M^{1/6} scaling is seen. Using synthetic diffraction data for a small glutathione molecule as a challenging test case, successful determination of electron density was demonstrated for 20000 diffraction patterns with random orientations and an average of 82 elastically scattered and recorded photons per image, also in the presence of up to 50% background noise. The second scenario is exemplified and assessed for three biomolecules of different sizes. In all cases, determining the probability of a structure given set of diffraction patterns allowed successful discrimination between different conformations of the test molecules. A structure model of the glutathione tripeptide was refined in a Monte Carlo simulation from a random starting conformation. Further, effective distinguishing between three differently arranged immunoglobulin domains of a titin molecule and also different states of a ribosome in a tRNA translocation process was demonstrated. These results show that the proposed method is robust and enables structure determination from sparse and noisy x-ray diffraction images of single molecules spanning a wide range of molecular masses.
authors.affiliation: Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany.
authors.forename: Michał
authors.initials: M
authors.lastname: Walczak
authors.affiliation: Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany.
authors.forename: Helmut
authors.initials: H
authors.lastname: Grubmüller
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D064211:Connectin / Q000737:chemistry; D004583:Electrons; D005978:Glutathione / Q000737:chemistry; D016013:Likelihood Functions; D046911:Macromolecular Substances / Q000737:chemistry*; D008956:Models, Chemical; D009010:Monte Carlo Method; D017785:Photons; D011336:Probability; D012343:RNA, Transfer / Q000737:chemistry; D012270:Ribosomes / Q000737:chemistry; D014178:Translocation, Genetic; D014618:Vacuum; D014961:X-Ray Diffraction / Q000379:methods*
chemical_list: D064211:Connectin; D046911:Macromolecular Substances; D012343:RNA, Transfer; D005978:Glutathione