AlphaFold2 Model Refinement Using Structure Decoys
Document Type
Conference Paper
Publication Date
2023
DOI
10.1145/3584371.3612952
Publication Title
Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
Pages
108 (1-7)
Conference Name
14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, September 3-6, 2023, Houston, Texas
Abstract
AlphaFold2-predicted protein structures inform many modeling techniques in structural biology, including the interpretation of cryogenic electron microscopy (cryo-EM) maps. However, the accuracy of the AlphaFold2 prediction, the quality of the experimental cryo-EM data, and the reliability of the model's alignment with density may affect the accuracy of the interpretation. In this work, we explored a new refinement approach by generating unbiased structural decoys from the AlphaFold2 model via 3DRobot or elastic network model (ENM)-based ModeHunter. Our hope was that some of the decoys would resemble the true structure, and consequently, that the refinement problem could then be reduced to selecting the correct decoy from the decoy set that most closely resembles the experimental cryo-EM map. We explored a map/model pair from the structure of a lipid-preserved respiratory supercomplex, where AlphaFold2 previously struggled (TM-score: 0.52). In this specific case, we observed that the inherent bias of 3DRobot toward compact decoys limited the AlphaFold2 model enhancement (best decoy TM-score: 0.53), whereas ENM is capable of producing extended decoys that significantly improve the accuracy of the AlphaFold2 model (best decoy TM-score: 0.68).
Rights
© 2023 Copyright held by the owner/author. All rights reserved.
"ACM treats links as citations (references to objects) rather than as incorporations (embedding of objects). Permission is not needed to create links to citations in The ACM Digital Library or Online Guide to Computing Literature. ACM encourages the widespread distribution of links to the definitive Version of Records of its copyrighted works in the ACM Digital Library and does not require that authors obtain prior permission to include such links in their new works.
However, someone who creates a work or a service whose pattern of links substantially duplicates an ACM-copyrighted volume or issue should get prior permission from ACM. One example: the creator of "A Table of Contents for the Current Issue of TODS" -- consisting of citations and active links to author-versions of the works in the latest issue of TODS -- needs ACM permission because that creator is reproducing an ACM-copyrighted work. If all the links in the "Table of Contents" pointed to the ACM-held definitive Version of Records, ACM would normally give permission because then the new work advertises an ACM work. To avoid misunderstandings, consult with ACM before duplicating an ACM work via links.
If an author wishes to embed a copyrighted object---rather than a link---in a new work, that author needs to obtain the copyright holder's permission."
Original Publication Citation
Alshammari, M., He, J., & Wriggers, W. (2023) AlphaFold2 model refinement using structure decoys. In Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (Article 108). Association for Computing Machinery. https://doi.org/10.1145/3584371.3612952
Repository Citation
Alshammari, M., He, J., & Wriggers, W. (2023) AlphaFold2 model refinement using structure decoys. In Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (Article 108). Association for Computing Machinery. https://doi.org/10.1145/3584371.3612952
ORCID
0000-0002-1386-5447 (Alshammari), 0000-0001-5326-3152 (Wriggers)