This tutorial presents the first steps in the NMRtist system. It guides through account and project creation, data upload, and submission of an exemplary structure calculation job. You can go through the tutorial with your own data, or use one of our example datasets. We highly recommend doing this tutorial before making the first application call.
Artificial Intelligence for NMR Applications (ARTINA) is a deep learning-based approach to fully automated NMR protein structure determination. The method takes as input only NMR spectra and the protein sequence, and delivers automatically: peak lists, shift assignments, distance restraints, and the structure.
This video tutorial introduces beginners to the NMRtist system, guiding them through the process of submitting an automated protein structure determination job, and showcasing representative results from such a job.
Feb. 2, 2023, 8:39 p.m.
Our manuscript (application note), presenting the NMRtist platform, has been accepted for publication in Bioinformatics (early access version, https://doi.org/10.1093/bioinformatics/btad066).
Dec. 21, 2022, midnight
Since the release of the platform in February 2022, NMRtist analysed 4 368 2D/3D/4D NMR spectra, completed 1 100 automated chemical shift assignment and 444 automated structure determination jobs.
Dec. 20, 2022, midnight
Between 06.2022 and 01.2023, we presented ARTINA and NMRtist at several NMR events, including: Chianti Workshop (Principina Terra, Italy), EUROMAR (Utrecht, The Netherlands), EMBO Practical Course (Basel, Switzerland), EMBO Lecture Course (Berhampur, India), Biomolecular NMR: Advanced Tools, Machine Learning (Gothenburg, Sweden), and ICMRBS (Boston, USA).
Oct. 19, 2022, midnight
Our manuscript, presenting the ARTINA workflow for rapid assignment and structure determination, has been published in Nature Communications (https://doi.org/10.1038/s41467-022-33879-5).
Oct. 2, 2021, midnight
NMRtist was presented at the Biomolecular NMR: Advanced Tools workshop (29.09-01.10 2021). All participants of the training, supervised by Prof. Peter Güntert and Dr. Piotr Klukowski, submitted datasets to the platform, obtaining automatically determined structures and/or assignments.