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 the N-terminal domain 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 superposed with RMSD 3.2 Å. Swiss-PdbViewer obtained RMSD 1.95 Å for 48 alpha carbons<ref name="fitselimprov">Using Swiss-PdbViewer's ''Fit from Selection'' with 102 residues selected from each structure, followed by ''Improve Fit''.</ref>. Dali reported the identity as 6% in its structure-based sequence alignment. Sequence alignment by MAFFT<ref name="mafft">PMID: 23329690</ref> obtained 18% sequence identity using more and larger gaps. <scene name='87/875686/Dali_5a2f_vs_7jx6_yale/2'>The structural similarity between Dali's top hit and 7jx6</scene><ref name="yale">Structural superposition by Dali. Interpolation by the [http://www2.molmovdb.org/wiki/info/index.php/Morph2_Server 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).</ref> is not as close as for AlphaFold2's prediction, but is closer than the 2nd best prediction (see Table I below). Dali's top hit has a single disulfide bond (compare with Table I). In conclusion, '''ORF8 does not have a novel fold'''<ref name="holm">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 [[User:Eric Martz|Eric Martz]].</ref>.
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 the N-terminal domain 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 superposed with RMSD 3.2 Å. Swiss-PdbViewer obtained RMSD 1.95 Å for 48 alpha carbons<ref name="fitselimprov">Using Swiss-PdbViewer's ''Fit from Selection'' with 102 residues selected from each structure, followed by ''Improve Fit''.</ref>. Dali reported the identity as 6% in its structure-based sequence alignment. Sequence alignment by MAFFT<ref name="mafft">PMID: 23329690</ref> obtained 18% sequence identity using more and larger gaps. <scene name='87/875686/Dali_5a2f_vs_7jx6_yale/2'>The structural similarity between Dali's top hit and 7jx6</scene><ref name="yale">Structural superposition by Dali. Interpolation by the [http://www2.molmovdb.org/wiki/info/index.php/Morph2_Server 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).</ref> is not as close as for AlphaFold2's prediction, but is closer than the 2nd best prediction (see Table I below). In conclusion, '''ORF8 does not have a novel fold'''<ref name="holm">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 [[User:Eric Martz|Eric Martz]], February, 2021.</ref>.


===AlphaFold2 Prediction for ORF8===
===AlphaFold2 Prediction for ORF8===
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| AlphaFold2 || 87 || 3 || 2.58<br>'''1.25''' || 92/92 (100%)<br>'''83/92* (90%)''' || 3.23<br>'''1.91''' || 747/748 (100%)<br>'''679/748 (91%)'''
| AlphaFold2 || 87 || 3 || 2.58<br>'''1.25''' || 92/92 (100%)<br>'''83/92* (90%)''' || 3.23<br>'''1.91''' || 747/748 (100%)<br>'''679/748 (91%)'''
|-
|-
| Dali top hit [[5a2f]] || 53<ref name="gdt_ts" /> || na || 3.2<br>'''1.95''' || 92/92 (100%)<br>'''48/92 (52%)''' || na || na
| Dali top hit<ref name="nnf">See [[#ORF8 is not a novel fold]].</ref> [[5a2f]] || 53<ref name="gdt_ts" /> || na || 3.2<br>'''1.95''' || 92/92 (100%)<br>'''48/92 (52%)''' || na || na
|-
|-
| 2nd Best* || 43 || 0 || 5.33<br>'''1.71''' || 92/92 (100%)<br>'''38/92 (41%)''' || 6.54<br>'''5.86''' || 747/748 (100%)<br>'''324/748 (43%)'''
| 2nd Best* || 43 || 0 || 5.33<br>'''1.71''' || 92/92 (100%)<br>'''38/92 (41%)''' || 6.54<br>'''5.86''' || 747/748 (100%)<br>'''324/748 (43%)'''

Revision as of 00:18, 4 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

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 7jx6 and 3afc.
  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

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[2]. See instructions for empirical models. CASP 14 reported GDT_TS 86.96 for the AlphaFold2 prediction, while the AS2TS server calculated GDT_TS 86.41.

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
  2. ↑ 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:https://dx.doi.org/10.1093/nar/gkg571

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