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 70: Line 70:
|-
|-
!7JX6 !! 7JTL !! AlphaFold2 !! 2nd Best
!7JX6 !! 7JTL !! AlphaFold2 !! 2nd Best
|-
| R101:D112 (2) || R101:D113 (2) || R86:D98 || R86:D98
|}
|}
 
*Bridges in the same row are identical. Sequence numbers in the predictions are 15 less than those in the X-ray structures.
*'''Black''': Shortest sidechain nitrogen to sidechain oxygen distance ≤4.0 Å.
*<span class="text-gray">'''Gray''': Shortest sidechain nitrogen to sidechain oxygen distance 4.4 to 4.8 Å.</span>
*–: Shortest sidechain nitrogen to sidechain oxygen distance 6 to 16 Å.


==References==
==References==
<references />
<references />

Revision as of 00:37, 1 March 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

ORF8 Sidechain Accuracy

Table I gives RMSD values for all atoms, which is one indication of sidechain accuracy. Another is prediction of 7jx6 and 3afc.

Table II. Salt Bridge Prediction Accuracy
7JX6 7JTL AlphaFold2 2nd Best
R101:D112 (2) R101:D113 (2) R86:D98 R86:D98
  • Bridges in the same row are identical. Sequence numbers in the predictions are 15 less than those in the X-ray structures.
  • Black: Shortest sidechain nitrogen to sidechain oxygen distance ≤4.0 Å.
  • Gray: Shortest sidechain nitrogen to sidechain oxygen distance 4.4 to 4.8 Å.
  • –: Shortest sidechain nitrogen to sidechain oxygen distance 6 to 16 Å.

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