The commissioning of X-ray Free-Electron Lasers with their super-powerful (by many orders of magnitude exceeding the brightness of modern synchrotrons) and ultra-short (less than 100 fs) pulse is an experimental breakthrough that allows us to expect to obtain diffraction patterns from individual biological particles and then determine their structure. The most serious limitation of its applicability, to date, is the need to prepare a sample of the object under study in the form of a single crystal, which is caused by the extremely low intensity of rays scattered by a single molecule. X-ray diffraction analysis is the main experimental approach to determining the atomic structure of biological macromolecules and their complexes. Moreover, a major advantage of GML is its ability to retrieve single particles in the presence of structural variability. The single-particle identification for this experimental dataset was compared with previously published results and it was found that GML covers a wide photon-count range, outperforming other single-particle identification methods. Third, an experimental dataset collected at the Stanford Linear Accelerator Center. These modified datasets were used to quantify the predictive limits of our approach. Second, simulated test datasets that were modified to reflect different levels of photon counts and background noise.

We created a training dataset and classified three types of test datasets: First, a noise-free simulated test dataset, which gave near perfect separation.

This method uses supervised Geometric Machine Learning (GML) to extract low-dimensional feature vectors from a training dataset, fuse test datasets into the feature space of training datasets, and separate the data into binary distributions of “single particles” and “non-single particles.” As a proof of principle, we tested simulated and experimental datasets of the Coliphage PR772 virus. Here, we present a method that allows for efficient identification of single particles in very large XFEL datasets, operates at low signal levels, and is tolerant to background. However, high-resolution structures can only be obtained from a sufficiently large number of diffraction patterns of individual molecules, so-called single particles.

This method has the advantage that the samples do not require crystallization and can be examined at room temperature. A promising new route for structural biology is single-particle imaging with an X-ray Free-Electron Laser (XFEL).

Software cdi cheetah power