DeepMind AlphaFold2 (Solving the protein folding problem)

AlphaFold2, developed by DeepMind, crushed the competition at this years CASP 14. This is a competition that happens every two years whereby the challenge is to predict the protein structure from a amino acid sequence. This is the grand challenge referred to as "The protein folding problem".
So here is my take on the problem, what AlphaFold does, what needs improving compared to experimental techniques such as CryoEM and Xray crystallography and what the uses are - why should you care about the protein folding problem.

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TIMESTAMPS
Intro - 00:00
Protein folding problem - 02:00
Evolutionary covariation data - 07:00
AlphaFold - 10:00
Are structural biologists out of a job? - 14:30
Uses and applications - 17:20

REFERENCES:
High Accuracy Protein Structure Prediction Using Deep Learning

John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Kathryn Tunyasuvunakool, Olaf Ronneberger, Russ Bates, Augustin Žídek, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Anna Potapenko, Andrew J Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Martin Steinegger, Michalina Pacholska, David Silver, Oriol Vinyals, Andrew W Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis.
Protein folding problem - cience 23 Nov 2012:
Vol. 338, Issue 6110, pp. 1042-1046
DOI: 10.1126/science.1219021
Nature news; https://www.nature.com/articles/d4158...
Fitzpatrick, A., Falcon, B., He, S. et al. Cryo-EM structures of tau filaments from Alzheimer’s disease. Nature 547, 185–190 (2017). https://doi.org/10.1038/nature23002
Science Advances 27 Feb 2019:
Vol. 5, no. 2, eaav4322
DOI: 10.1126/sciadv.aav4322

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