AlphaFold2 examples from CASP 14

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First, SARS-CoV-2 ORF8, a 92-residue FM domain where '''AlphaFold2's GDT_TS was 87, and the second best was 43''' (by the group of Xian Ming Pan)<ref name="t1064">For SARS-CoV-2 ORF8, at the [https://predictioncenter.org/casp14/results.cgi?view=tb-sel CASP 14 Table Browser], check T1064-D1 and press ''Show Results''.</ref>, the largest difference between 1st and 2nd predictions among the FM targets. It is further unusual because two independently-determined X-ray crystallographic structures were subsequently published. Inspiration for this case came from the discussion by Rubiera<ref name="rubiera">[https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/ 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.</ref>.
First, SARS-CoV-2 ORF8, a 92-residue FM domain where '''AlphaFold2's GDT_TS was 87, and the second best was 43''' (by the group of Xian Ming Pan)<ref name="t1064">For SARS-CoV-2 ORF8, at the [https://predictioncenter.org/casp14/results.cgi?view=tb-sel CASP 14 Table Browser], check T1064-D1 and press ''Show Results''.</ref>, the largest difference between 1st and 2nd predictions among the FM targets. It is further unusual because two independently-determined X-ray crystallographic structures were subsequently published. Inspiration for this case came from the discussion by Rubiera<ref name="rubiera">[https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/ 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.</ref>.
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Second, the '''longest domain in the FM category, 404 residues'''. This domain is part of the 2,180-residue RNA polymerase of a phage prevalent in the human gut<ref name="T1037">PMID: 33208949</ref>. AlphaFold2 achieved GDT_TS of 88, and the second best prediction, 63 (by Seok-refine). Among the 14 FM targets, the second-longest has 276 residues, the median 132, and the shortest, 92.
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Second, the '''longest domain in the FM category, 404 residues'''. This domain is part of the 2,180-residue RNA polymerase of a phage prevalent in the human gut<ref name="T1037">PMID: 33208949</ref>. Eight of the CASP 14 target domains are parts of this protein, [[6vr4]]. For the 404-residue domain, AlphaFold2 achieved GDT_TS of 88, and the second best prediction, 63 (by Seok-refine). Among the 14 FM targets, the second-longest has 276 residues, the median 132, and the shortest, 92.
==SARS-CoV-2 ORF8==
==SARS-CoV-2 ORF8==

Revision as of 20:36, 6 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.

Drag the structure with the mouse to rotate

Contents

ORF8 Sidechain Accuracy

AlphaFold2's predictions for sidechain positions seem fairly good, while sidechain positions in the 2nd best prediction seem poor. This conclusion is based on three types of observations:

  1. Table I gives RMSD values for all atoms, which is one indication of sidechain accuracy.
  2. Prediction of salt bridges and cation-pi interactions.
  3. Visualization of the distributions of charges on the surfaces.

Salt Bridges and Cation-Pi Interactions

  • AlphaFold2's prediction was correct for 4/5 interactions, with one incorrect interaction.
    • AlphaFold2's prediction was correct for one of two salt bridges, and predicted no incorrect salt bridges.
    • AlphaFold2's prediction was correct for three of three cation-pi interactions, but predicted one incorrect interaction.
  • The 2nd best prediction was correct for 1/5 interactions, with 2 incorrect interactions.
    • The 2nd best prediction was correct for one of two salt bridges, but predicted two incorrect salt bridges.
    • The 2nd best prediction failed to predict any of the three cation-pi interactions, predicting zero interactions.
Table II. Salt Bridge Prediction Accuracy
7JX6 7JTL AlphaFold2 2nd Best
R101:D112 (AB) R101:D113 (AB) R86:D98 R86:D98
R115:D119 (AB) R115:D119 (AB) R100:E4
K44:E59 (AB) K44:E59 (AB) 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.
  • (AB): The two chains in each X-ray model.
  • Italics: erroneous prediction.
Table III. Cation-Pi Prediction Accuracy
7JX6 7JTL AlphaFold2 2nd Best
R101:Y46+Y108 (AB) R101:Y46+Y108 (AB) R86:Y31+Y96
K44:F108 (B) K44:F108 (AB) K29:F93
K79:F105
  • All interactions listed are deemed energetically significant by the CaPTURE Server.
  • Interactions in the same row are identical. Subtract 15 from the sequence numbers in the X-ray structures for the equivalent sequence numbers in the predictions.
  • Italics: erroneous prediction.
  • The 2nd best prediction has no cation-pi interactions.
  • (AB): The two chains in each X-ray model.

Visualization of Surface Charge Distributions

Image:Orf8-casp14-charges.png

GDT_TS Calculations

GDT_TS values for predictions are taken from CASP 14 results. GDT_TS values for 7JTL and 5A2F vs. 7JX6 chain A were calculated using the AS2TS server of Adam Zemla[20]. See instructions for Calculating GDT_TS. CASP 14 reported GDT_TS 86.96 for the AlphaFold2 prediction, while the AS2TS server calculated GDT_TS 86.41 vs. 7jx6 chain A, and 88.59 vs. 7JTL chain A.

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:http://dx.doi.org/10.1038/s41586-019-1923-7
  2. For SARS-CoV-2 ORF8, at the CASP 14 Table Browser, check T1064-D1 and press Show Results.
  3. 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.
  4. Drobysheva AV, Panafidina SA, Kolesnik MV, Klimuk EI, Minakhin L, Yakunina MV, Borukhov S, Nilsson E, Holmfeldt K, Yutin N, Makarova KS, Koonin EV, Severinov KV, Leiman PG, Sokolova ML. Structure and function of virion RNA polymerase of a crAss-like phage. Nature. 2020 Nov 18. pii: 10.1038/s41586-020-2921-5. doi:, 10.1038/s41586-020-2921-5. PMID:33208949 doi:http://dx.doi.org/10.1038/s41586-020-2921-5
  5. 5.0 5.1 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. 6.0 6.1 Summary and Classifications of Domains for CASP 14.
  7. 7.0 7.1 7.2 7.3 Superposition by Swiss-PdbViewer's iterative magic fit. This starts with a sequence alignment-guided structural superposition, and then superposes subsets of the structures to minimize the RMSD. Eight intermediate structures were generated by the Theis Morph Server by linear interpolation.
  8. 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
  9. 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
  10. Using Swiss-PdbViewer's Fit from Selection with 102 residues selected from each structure, followed by Improve Fit.
  11. 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
  12. Structural superposition 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).
  13. 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, February, 2021.
  14. Download AlphaFold2's predicted structure for ORF8 from T1064TS427_1-D1.pdb.
  15. 15.0 15.1 See #GDT_TS Calculations.
  16. See #ORF8 is not a novel fold.
  17. Superposition by Swiss-PdbViewer's magic fit. This is a sequence alignment-guided structural superposition. Eight intermediate structures were generated by the Theis Morph Server by linear interpolation.
  18. Superposition by Swiss-PdbViewer's Explore Fragment Alternate Fits, which does not use sequence information. Eight intermediate structures were generated by the Theis Morph Server by linear interpolation.
  19. 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).
  20. Zemla A. LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res. 2003 Jul 1;31(13):3370-4. doi: 10.1093/nar/gkg571. PMID:12824330 doi:http://dx.doi.org/10.1093/nar/gkg571

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