AlphaFold2 examples from CASP 14
From Proteopedia
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*<span class="text-gray">'''Gray''': Shortest sidechain nitrogen to sidechain oxygen distance 4.4 to 4.8 Å.</span> | *<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. | *–: Shortest sidechain nitrogen to sidechain oxygen distance 6 to 16 Å. | ||
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| + | |+ Table III. Cation-Pi Prediction Accuracy | ||
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| + | !7JX6 !! 7JTL !! AlphaFold2 !! 2nd Best | ||
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| + | | R101:Y46+Y108 (AB) || R101:Y46+Y108 (AB) || || | ||
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| + | | K44:F108 (B) || K44:F108 (AB) || || | ||
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==References== | ==References== | ||
<references /> | <references /> | ||
Revision as of 01:07, 1 March 2021
This page is under construction. Eric Martz 01:03, 22 February 2021 (UTC)
Prediction of protein structures from amino acid sequences, theoretical 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 CASP, please see 2020: CASP 14. Below are illustrated some examples of predictions from that competition.
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ORF8 Sidechain Accuracy
Table I gives RMSD values for all atoms, which is one indication of sidechain accuracy. Another is prediction of salt bridges and cation-pi interactions.
| 7JX6 | 7JTL | AlphaFold2 | 2nd Best |
|---|---|---|---|
| R101:D112 (2) | R101:D113 (2) | R86:D98 | R86:D98 |
| R115:D119 (2) | R115:D119 (2) | – | R100:E4 |
| K44:E59 (1 + 1) | K44:E59 (2) | K29:E44 | – |
| – | – | – | K78:E77 |
- Bridges in the same row are identical (except for red residues). Subtract 15 from the sequence numbers in the X-ray structures for the equivalent sequence numbers in the predictions.
- 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.
| 7JX6 | 7JTL | AlphaFold2 | 2nd Best |
|---|---|---|---|
| R101:Y46+Y108 (AB) | R101:Y46+Y108 (AB) | ||
| K44:F108 (B) | K44:F108 (AB) | ||
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:http://dx.doi.org/10.1038/s41586-019-1923-7
- ↑ CASP14: what Google DeepMind’s AlphaFold 2 really achieved, and what it means for protein folding, biology and bioinformatics, a blog post by Carlos Outeir al Rubiera, December 3, 2020.
- ↑ Flower TG, Buffalo CZ, Hooy RM, Allaire M, Ren X, Hurley JH. Structure of SARS-CoV-2 ORF8, a rapidly evolving immune evasion protein. Proc Natl Acad Sci U S A. 2021 Jan 12;118(2). pii: 2021785118. doi:, 10.1073/pnas.2021785118. PMID:33361333 doi:http://dx.doi.org/10.1073/pnas.2021785118
- ↑ 4.0 4.1 Summary and Classifications of Domains for CASP 14.
- ↑ Flower TG, Buffalo CZ, Hooy RM, Allaire M, Ren X, Hurley JH. Structure of SARS-CoV-2 ORF8, a rapidly evolving immune evasion protein. Proc Natl Acad Sci U S A. 2021 Jan 12;118(2). pii: 2021785118. doi:, 10.1073/pnas.2021785118. PMID:33361333 doi:http://dx.doi.org/10.1073/pnas.2021785118
- ↑ 6.0 6.1 6.2 Alignment by Swiss-PdbViewer's iterative magic fit. This starts with a sequence alignment-guided structural alignment, and then selects subsets of the structures to minimize the RMSD. Eight intermediate structures were generated by the Theis Morph Server by linear interpolation.
- ↑ Cuff AL, Sillitoe I, Lewis T, Clegg AB, Rentzsch R, Furnham N, Pellegrini-Calace M, Jones D, Thornton J, Orengo CA. Extending CATH: increasing coverage of the protein structure universe and linking structure with function. Nucleic Acids Res. 2011 Jan;39(Database issue):D420-6. doi: 10.1093/nar/gkq1001. , Epub 2010 Nov 19. PMID:21097779 doi:http://dx.doi.org/10.1093/nar/gkq1001
- ↑ Holm L. DALI and the persistence of protein shape. Protein Sci. 2020 Jan;29(1):128-140. doi: 10.1002/pro.3749. Epub 2019 Nov 5. PMID:31606894 doi:http://dx.doi.org/10.1002/pro.3749
- ↑ Using Swiss-PdbViewer's Fit from Selection with 102 residues selected from each structure, followed by Improve Fit.
- ↑ Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013 Apr;30(4):772-80. doi: 10.1093/molbev/mst010. Epub 2013 Jan, 16. PMID:23329690 doi:http://dx.doi.org/10.1093/molbev/mst010
- ↑ Structural alignment by Dali. Interpolation by the Yale Morph2 Server. Homogenization method: homology modeling. No minimization. This produced a 9-model file where model 1 was 7jx6, and models 2-9 were interpolations. 5a2f residues 28-133 were added as model 10 (black in the molecular scene).
- ↑ The interpretation of Dali's result to mean that ORF8 does not have a novel fold was kindly confirmed by Liisa Holm, personal communication to Eric Martz.
- ↑ Download AlphaFold2's predicted structure for ORF8 from T1064TS427_1-D1.pdb.
- ↑ Alignment by Swiss-PdbViewer's magic fit. This is a sequence alignment-guided structural alignment. Eight intermediate structures were generated by the Theis Morph Server by linear interpolation.
- ↑ For all targets in CASP 14, the top two servers were QUARK and Zhang-server (which were not significantly different at a Z-score sum of 62.9), followed by Zhang-CEthreader (55.9) and BAKER-ROSETTASERVER (55.3).
