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Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index

Author

Listed:
  • Gabriele Orlando

    (ULB-VUB
    Vrije Universiteit Brussel)

  • Daniele Raimondi

    (KU Leuven)

  • Wim Vranken

    (ULB-VUB
    Vrije Universiteit Brussel
    VIB)

Abstract

Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as the movements it performs. Due to their intrinsically multi-faceted nature, CS are difficult to interpret and visualize. Classical approaches for the analysis of CS aim to extract specific protein-related properties, thus discarding a large amount of information that cannot be directly linked to structural features of the protein. Here we propose an autoencoder-based method, called ShiftCrypt, that provides a way to analyze, compare and interpret CS in their native, multidimensional space. We show that ShiftCrypt conserves information about the most common structural features. In addition, it can be used to identify hidden similarities between diverse proteins and peptides, and differences between the same protein in two different binding states.

Suggested Citation

  • Gabriele Orlando & Daniele Raimondi & Wim Vranken, 2019. "Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10322-w
    DOI: 10.1038/s41467-019-10322-w
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    Cited by:

    1. Gabriele Orlando & Daniele Raimondi & Ramon Duran-Romaña & Yves Moreau & Joost Schymkowitz & Frederic Rousseau, 2022. "PyUUL provides an interface between biological structures and deep learning algorithms," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

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