AlphaFold2 examples from CASP 14: Difference between revisions

From Proteopedia
Jump to navigationJump to search
Eric Martz (talk | contribs)
No edit summary
Eric Martz (talk | contribs)
No edit summary
Line 53: Line 53:


===Top Prediction by an Automated Server===
===Top Prediction by an Automated Server===
Among predictions by automated servers for all ~100 CASP 14 targets, the top ranking server was QUARK from the Yang Zhang group (Univ. Michigan). For ORF8, the Zhang-TBM server made the best server prediction with a '''GDT_TS of 27'''. (The prediction by QUARK was almost as good, GDT_TS 26.) The prediction has the '''two chain termini not parallel, and the amino terminus is not a beta strand''', differing in both respects from the X-ray model. Also, '''no disulfide bonds''' are predicted.
Among predictions by automated servers for all ~100 CASP 14 targets, the top ranking server was QUARK from the Yang Zhang group (Univ. Michigan). For ORF8, the Zhang-TBM server made the best server prediction with a '''GDT_TS of 27'''. (The prediction by QUARK was almost as good, GDT_TS 26.) The prediction has the '''two chain termini not parallel, and the amino terminus is not a beta strand''', differing in both respects from the X-ray model. Also, '''no disulfide bonds''' are predicted. The structural alignment is very poor and is not shown.


===Baker Rosetta Server Prediction for ORF8===
===Baker Rosetta Server Prediction for ORF8===

Revision as of 23:14, 25 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

Proteopedia Page Contributors and Editors (what is this?)

Eric Martz