Corpus ID: 227209430

Protein model quality assessment using rotation-equivariant, hierarchical neural networks

@article{Eismann2020ProteinMQ,
  title={Protein model quality assessment using rotation-equivariant, hierarchical neural networks},
  author={Stephan Eismann and Patricia Suriana and Bowen Jing and Raphael Townshend and Ron O. Dror},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.13557}
}
  • Stephan Eismann, Patricia Suriana, +2 authors Ron O. Dror
  • Published 2020
  • Computer Science, Biology
  • ArXiv
  • Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A major bottleneck involves selecting the most accurate structural model among a large pool of candidates, a task addressed in model quality assessment. Here, we present a novel deep learning approach to assess the quality of a protein model. Our network builds on a point-based representation of the atomic structure and… CONTINUE READING

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