AlphaFold2 examples from CASP 14: Difference between revisions
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The quality of predictions for the structure of ORF8 are judged by comparison with X-ray crystallographic [[empirical models]] which were not available to the groups making predictions. Shortly after the CASP 14 competition (summer 2020), two X-ray crystal structures were reported for ORF8: [[7jtl]] released August 26, 2020, and [[7jx6]], released September 23, 2020. The [[resolution|resolutions]] are 2.0 and 1.6 Å respectively, and both have worse than average [[Rfree]] values. | The quality of predictions for the structure of ORF8 are judged by comparison with X-ray crystallographic [[empirical models]] which were not available to the groups making predictions. Shortly after the CASP 14 competition (summer 2020), two X-ray crystal structures were reported for ORF8: [[7jtl]] released August 26, 2020, and [[7jx6]], released September 23, 2020. The [[resolution|resolutions]] are 2.0 and 1.6 Å respectively, and both have worse than average [[Rfree]] values. | ||
{{Template:Green links zoom}} | {{Template:Green links zoom}} | ||
<scene name='87/875686/Morf_lin_7jx6_imf_7jtl/3'>The two X-ray structures agree very well</scene>. See TABLE below for [https://en.wikipedia.org/wiki/Root-mean-square_deviation_of_atomic_positions RMSD] values. | <scene name='87/875686/Morf_lin_7jx6_imf_7jtl/3'>The two X-ray structures agree very well</scene>. The only substantial disagreement is for an external loop, sequence range 47-58. See the TABLE below for [https://en.wikipedia.org/wiki/Root-mean-square_deviation_of_atomic_positions RMSD] values. | ||
===AlphaFold2 Prediction for ORF8=== | ===AlphaFold2 Prediction for ORF8=== | ||
Revision as of 00:57, 24 February 2021
This page is under construction. Eric Martz 01:03, 22 February 2021 (UTC)
Prediction of protein structures from amino acid sequences, homology modeling, has been extremely challenging. In 2020, breakthrough success was achieved by AlphaFold2[1], a project of DeepMind. For an overview of this breakthrough, verified by the bi-annual prediction competition empirical models, please see 7jtl. Below are illustrated some examples of predictions from that competition.
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References
- ↑ Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Zidek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D. Improved protein structure prediction using potentials from deep learning. Nature. 2020 Jan;577(7792):706-710. doi: 10.1038/s41586-019-1923-7. Epub 2020 Jan, 15. PMID:31942072 doi:https://dx.doi.org/10.1038/s41586-019-1923-7