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Cited by 38Advanced NMR Techniques and ApplicationsProtein Structure and DynamicsMolecular spectroscopy and chirality

Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction

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Klukowski, Piotr, Riek, Roland, Güntert, Peter·2023·Science Advances
DOI 10.1126/sciadv.adi9323PMC PMC10664993
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Abstract

Chemical shift assignment is vital for nuclear magnetic resonance (NMR)–based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)–4D NMR spectra. Here, we present an integrative approach that combines ARTINA with AlphaFold and UCBShift, enabling chemical shift assignment with reduced experimental data, increased accuracy, and enhanced robustness for larger systems, as presented in a comprehensive study with more than 5000 automated assignment calculations on 89 proteins. We demonstrate that five 3D spectra yield more accurate assignments (92.59%) than pure ARTINA runs using all experimentally available NMR data (on average 10 3D spectra per protein, 91.37%), considerably reducing the required measurement time. We also showcase automated assignments of only 15 N-labeled samples, and report improved assignment accuracy in larger synthetic systems of up to 500 residues.

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References 36 · Cited by 38

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Authors
Klukowski, P., Riek, R., & Güntert, P.
Affiliations
  • ETH Zurich (CH)
  • Goethe University Frankfurt (DE)
Corresponding author
  • Klukowski, Piotr
  • Riek, Roland
  • Güntert, Peter
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2023-11-24
Volume
9
Issue
47
Pages
eadi9323-eadi9323
ISSN
2375-2548 (ISSN-L)
DOI
10.1126/sciadv.adi9323
PMC
PMC10664993
OpenAlex
W4388922790
Cited by
38
References
36
Citations by year
2026: 12 · 2025: 12 · 2024: 14
FWCI
3.50
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Science Advances (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection
Funders
  • European Commission (HORIZON2020, 891690)
  • Japan Society for the Promotion of Science (23K05660, 20K06508)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced NMR Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Chemistry › Spectroscopy › Molecular spectroscopy and chirality
Keywords
chemical shift assignment, protein NMR, AlphaFold, deep learning
MeSH terms
Deep Learning, Algorithms, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Proteins

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