# Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking

> 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 o...

## Metadata
- Authors: Mads Gravers Jeppesen, Ingemar André
- Journal: Nature Communications
- Published: 2023-12-13
- DOI: https://doi.org/10.1038/s41467-023-43681-6
- Citations: 44
- Source: OpenAlex
- Access: Open Access

## Technology Hub
- Hub: Protein Structure Prediction
- Discipline: Biochemistry / AI
- Hub URL: https://science-database.com/technology/protein-structure
- Hub llms.txt: https://science-database.com/technology/protein-structure/llms.txt

## 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 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.

## Links
- DOI: https://doi.org/10.1038/s41467-023-43681-6
- OpenAlex: https://openalex.org/W4389686140
- PDF: https://www.nature.com/articles/s41467-023-43681-6.pdf
- JSON API: https://science-database.com/api/v1/technology/protein-structure

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Paper ID: oa-W4389686140 | Hub: protein-structure