@article{liuResolvingPreferredOrientation2023,
  title = {Resolving the {{Preferred Orientation Problem}} in {{CryoEM Reconstruction}} with {{Self-Supervised Deep Learning}}},
  author = {Liu, Yun-Tao and Hu, Jason and Zhou, Z Hong},
  year = {2023},
  month = aug,
  journal = {Microscopy and Microanalysis},
  volume = {29},
  number = {Supplement\_1},
  pages = {1918--1919},
  issn = {1431-9276},
  doi = {10.1093/micmic/ozad067.991},
  urldate = {2024-06-13},
  abstract = {Cryogenic electron microscopy (cryoEM) is a crucial technique for imaging biological macromolecules in their native state, enabling the determination of structures at atomic resolution. However, one major challenge encountered in cryoEM is the so-called ``preferred orientation'' problem. This problem arises due to specific regions of macromolecules preferentially adhering to the air-water interface or substrate support, which results in missing, or non-uniformly distributed, views of the macromolecule [1]. As a consequence, 3D reconstructions of the macromolecule with preferred orientation exhibit artifacts such as skewed secondary structures (alpha helices and beta sheets), broken peptide or nucleotide chains, distorted protein sidechain or nucleotide base densities, making atomic modeling from the cryoEM reconstructions difficult.},
  file = {C\:\\Users\\shervinnia\\Zotero\\storage\\FYFT54Y9\\Liu et al. - 2023 - Resolving the Preferred Orientation Problem in Cry.pdf;C\:\\Users\\shervinnia\\Zotero\\storage\\9MAN2AHM\\7228117.html}
}

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