AlphaFold2 examples from CASP 14: Difference between revisions
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|+ ORF8 Alignments With Chain A of [[7jx6]] | |+ ORF8 Alignments With Chain A of [[7jx6]] | ||
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! Model || Cα RMSD, Å || Cα Aligned || RMSD Including<br>Sidechains, Å || Atoms Aligned | ! Model || GDT_TS || Cα RMSD, Å || Cα Aligned || RMSD Including<br>Sidechains, Å || Atoms Aligned | ||
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| [[7jtl]]:A || 0.66 || 87/102 (85%) || 4.3 || 829/918 (90%) | | [[7jtl]]:A || || 0.66 || 87/102 (85%) || 4.3<br>1.58 || 829/918 (90%)<br>709/918 (77%) | ||
|- | |- | ||
| AlphaFold2 || 1.25 || 83/92* (90%) || 3.23<br>1.21 || 727/728 (100%)<br>336/728 (46%) | | AlphaFold2 || 87 || 1.25 || 83/92* (90%) || 3.23<br>1.21 || 727/728 (100%)<br>336/728 (46%) | ||
|} | |} | ||
Revision as of 23:46, 23 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