Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking
Jeppesen, Mads LU and André, Ingemar LU
(2023)
In Nature Communications
14(1).
- Abstract
AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic – tetrahedral, octahedral and icosahedral – symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 27 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.72. 21 had TM-scores of above 0.9 and were categorized as acceptable- to high-quality according... (More)
AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic – tetrahedral, octahedral and icosahedral – symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 27 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.72. 21 had TM-scores of above 0.9 and were categorized as acceptable- to high-quality according to DockQ. The resulting models are energetically optimized and can be used for detailed studies of intermolecular interactions in higher-order symmetrical assemblies. The results demonstrate how explicit treatment of structural symmetry can significantly expand the size and complexity of AlphaFold predictions.
(Less)
- author
- Jeppesen, Mads
LU
and André, Ingemar
LU
- organization
- publishing date
- 2023-12
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Nature Communications
- volume
- 14
- issue
- 1
- article number
- 8283
- publisher
- Nature Publishing Group
- external identifiers
-
- pmid:38092742
- scopus:85179645815
- ISSN
- 2041-1723
- DOI
- 10.1038/s41467-023-43681-6
- language
- English
- LU publication?
- yes
- id
- b26264da-713f-40bc-9746-d80e47fbcc05
- date added to LUP
- 2024-01-09 14:44:27
- date last changed
- 2024-04-24 10:40:40
@article{b26264da-713f-40bc-9746-d80e47fbcc05,
abstract = {{<p>AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic – tetrahedral, octahedral and icosahedral – symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 27 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.72. 21 had TM-scores of above 0.9 and were categorized as acceptable- to high-quality according to DockQ. The resulting models are energetically optimized and can be used for detailed studies of intermolecular interactions in higher-order symmetrical assemblies. The results demonstrate how explicit treatment of structural symmetry can significantly expand the size and complexity of AlphaFold predictions.</p>}},
author = {{Jeppesen, Mads and André, Ingemar}},
issn = {{2041-1723}},
language = {{eng}},
number = {{1}},
publisher = {{Nature Publishing Group}},
series = {{Nature Communications}},
title = {{Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking}},
url = {{http://dx.doi.org/10.1038/s41467-023-43681-6}},
doi = {{10.1038/s41467-023-43681-6}},
volume = {{14}},
year = {{2023}},
}