Document Type
Article
Publication Date
2022
DOI
10.1017/qrd.2022.13
Publication Title
QRB Discovery
Volume
3
Pages
e16 (1-11)
Abstract
Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing structure prediction method at CASP14) on cryo-EM refinement using the Phenix refinement suite for AlphaFold2 models. To study the robustness of model refinement at a lower resolution of interest, we introduced hybrid maps (i.e. experimental cryo-EM maps) filtered to lower resolutions by real-space convolution. The AlphaFold2 models were refined to attain good accuracies above 0.8 TM scores for 9 of the 13 cryo-EM maps. TM scores improved for AlphaFold2 models refined against all 13 cryo-EM maps of better than 4.5 Å resolution, 8 hybrid maps of 6 Å resolution, and 3 hybrid maps of 8 Å resolution. The results show that it is possible (at least with the Phenix protocol) to extend the refinement success below 4.5 Å resolution. We even found isolated cases in which resolution lowering was slightly beneficial for refinement, suggesting that highresolution cryo-EM maps might sometimes trap AlphaFold2 models in local optima.
Original Publication Citation
Alshammari, M., Wriggers, W., Sun, J., & He, J. (2022). Refinement of AlphaFold2 models against experimental and hybrid cryo-EM density maps. QRB Discovery, 3, 1-11, Article e16. https://doi.org/10.1017/qrd.2022.13
Repository Citation
Alshammari, M., Wriggers, W., Sun, J., & He, J. (2022). Refinement of AlphaFold2 models against experimental and hybrid cryo-EM density maps. QRB Discovery, 3, 1-11, Article e16. https://doi.org/10.1017/qrd.2022.13
ORCID
0000-0002-1386-5447 (Alshammari), 0000-0001-5326-3152 (Wriggers)
Included in
Amino Acids, Peptides, and Proteins Commons, Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Computer Sciences Commons
Comments
© The Authors 2022.
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0) licence, which permits re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited.