AlphaFold2 examples from CASP 14: Difference between revisions

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===ORF8 is not a novel fold===
===ORF8 is not a novel fold===
Less than 2% of new [[empirically-determined structures]] have novel folds; that is, folds not aready represented in the [[PDB]]<ref name="cath2011">PMID: 21097779</ref>. When chain A of [[7jx6]] was submitted to Dali<ref name="dali2020">PMID: 31606894</ref> (February, 2021), the top hit was one of the two domains in [[5a2f]], the CD166 human cell surface receptor involved in activation of T lymphocytes. The Z-score was 7.1, and 88 alpha carbons aligned with RMSD 3.2 Å. See Table I below for further analysis.
Less than 2% of new [[empirically-determined structures]] have novel folds; that is, folds not aready represented in the [[PDB]]<ref name="cath2011">PMID: 21097779</ref>. When chain A of [[7jx6]] was submitted to Dali<ref name="dali2020">PMID: 31606894</ref> (February, 2021), the top hit was one of the two domains in [[5a2f]], the CD166 human cell surface receptor involved in activation of T lymphocytes. The Z-score was 7.1, and 88 alpha carbons aligned with RMSD 3.2 Å. Dali reported the sequence identity as 6%.


===AlphaFold2 Prediction for ORF8===
===AlphaFold2 Prediction for ORF8===

Revision as of 21:37, 28 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, documented by the bi-annual prediction competition empirical models, please see 7jtl. Below are illustrated some examples of predictions from that competition.

Drag the structure with the mouse to rotate

References

  1. ↑ 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

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