ARTINA: Difference between revisions

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ATNOS is a software for automated NOESY peak picking and NOE signal identification in homonuclear 2D and heteronuclear-resolved 3D [<sup>1</sup>H,<sup>1</sup>H]-NOESY spectra during de novo protein structure determination by NMR. By incorporating the analysis of the raw NMR data into the process of automated de novo protein structure determination, ATNOS enables direct feedback between the protein structure, the NOE assignments and the experimental NOESY spectra.
Using only NMR spectra and the protein sequence as input, ARTINA delivers signal positions, resonance assignments, and structures strictly without human intervention. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements.


== Availability ==
== Availability ==


* ARTINA is available in the [[NMRtist]] web platform.
* ARTINA is available within the [https://nmrtist.org/ NMRtist] online platform for automated biomolecular NMR spectra analysis and within [https://nmrtist.bruker.com/ NMRtist@Bruker].


== References ==
== References ==

Latest revision as of 11:18, 25 January 2026

Artificial intelligence for NMR applications


Using only NMR spectra and the protein sequence as input, ARTINA delivers signal positions, resonance assignments, and structures strictly without human intervention. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements.

Availability

  • ARTINA is available within the NMRtist online platform for automated biomolecular NMR spectra analysis and within NMRtist@Bruker.

References

  • Klukowski, P., Riek, R. & Güntert, P. Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA. Nat. Commun. 13, 6151 (2022)
  • Klukowski, P., Riek, R. & Güntert, P. Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction. Sci. Adv. 9, eadi9323 (2023)