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Minikonferens: Machine Learning in NMR and Structural Biology

Forskning

Svenskt NMR-centrum anordnar en minikonferens om användande av maskininlärning i NMR och strukturbiologi.

Konferens
Datum
27 sept 2023
Tid
09:40 - 15:20

Ämnen

•    AlphaFold for protein structure and dynamics
•    Conformational ensembles of the human IDP proteome
•    NUS spectra by neural networks
•    Non-stationary NMR
•    NMR spectral analysis by machine-learning techniques
•    Advanced database searching

Plats& Adress: Svenskt NMR-centrum, Göteborgs universitet,  Medicinaregatan 16A, 41390 Göteborg och över Zoom:

https://gu-se.zoom.us/j/63448914100?pwd=MmI5WTl3MkdRbTF1TGo4eDhScm5CQT09 Passcode: 991893


Organisatörer: Vladislav Orekhov, Göran Karlsson; Inst. för kemi och molekylärbiologi & Svenskt NMR-centrum, Göteborgs universitet; Ilya Kuprov, University of Southampton


Registrering och kontakt: via e-mail till bionmr.snc@gmail.com

 

Program Mini-conference 27th of Sept 2023

09.40 – 10.00 Kaffe

Session I, Chair: Göran Karlsson (University of Gothenburg, Sweden)


10.00 – 10.30 Title TBA. Simon Bruderer, (Bruker Biospin, Switzerland)

10.30 – 11.00 The functional dynamics of K-Ras and its G12C and G12D oncogenic mutants. Rafael Bruschweiler, (Ohio State University, USA)

11.00 – 11.30 Predicting alternate conformational states with AlphaFold2 and sequence clustering. Hannah K Wayment-Steele (Brandeis University, USA)

11.30 – 12.00 Magnetstein. Quantitative NMR mixture analysis robust to low-resolution, disturbed lineshapes, and peak shifts. Krzysztof Kazimierczuk (University of Warsaw, Poland)

12.00 – 13.20 Lunch

Session II, Chair: Krzysztof Kazimierczuk (University of Warsaw, Poland)


13.20 – 13.50 Title TBA. Ilya Kuprov, (University of Southampton, UK)

13.50 – 14.20 Beyond traditional NMR signal processing with Deep Learning. Vladislav Orekhov, (University of Gothenburg, Sweden)

14.20– 14.50 Conformational ensembles of the intrinsically disordered proteome. Giulio Tesei, (Copenhagen University, Danmark)

14.50– 15.20 Using deep learning to unleash the full potential of NMR spectroscopy. D. Flemming Hansen, (UCL, London, UK)