Application of neural networks for recognition of conformational changes in protein structure by x-ray diffractograms of its single molecules on the example of bacteriorhodopsin photocycle

Free electron lasers are becoming increasingly powerful and affordable installations for determining the structure of proteins. Conformational rearrangements are characteristic of the vast majority of proteins, but the transience of transformations often does not allow obtaining crystals of sufficient size. The solution could be to obtain X-ray diffraction patterns from single molecules, but in this case it is not possible to determine their orientation, which makes it impossible to restore the structure by modern methods. The paper considers the applicability of a number of neural network architectures to the recognition of the conformational state of a protein based on diffraction data on single bacteriorhodopsin molecules.