# Protein complex prediction with AlphaFold-Multimer

> While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically f...

## Metadata
- Authors: Richard Evans, M. E. O’Neill, Alexander Pritzel, Н. В. Антропова, Andrew Senior, Tim Green, Augustin Žídek, Russ Bates, Sam Blackwell, Jason Yim, Olaf Ronneberger, Sebastian W. Bodenstein, Michał Zieliński, Alex Bridgland, Anna Potapenko, Andrew Cowie, Kathryn Tunyasuvunakool, Rishub Jain, Ellen Clancy, Pushmeet Kohli, John Jumper, Demis Hassabis
- Journal: bioRxiv (Cold Spring Harbor Laboratory)
- Published: 2021-10-04
- DOI: https://doi.org/10.1101/2021.10.04.463034
- Citations: 3,937
- Source: OpenAlex

## 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
While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] ≥ 0.49) on 13 targets and high accuracy (DockQ ≥ 0.8) on 7 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,446 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ ≥ 0.23) in 70% of cases, and produce high accuracy predictions (DockQ ≥ 0.8) in 26% of cases, an improvement of +27 and +14 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric inter-faces we successfully predict the interface in 72% of cases, and produce high accuracy predictions in 36% of cases, an improvement of +8 and +7 percentage points respectively.

## Links
- DOI: https://doi.org/10.1101/2021.10.04.463034
- OpenAlex: https://openalex.org/W3202105508
- JSON API: https://science-database.com/api/v1/technology/protein-structure

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Paper ID: oa-W3202105508 | Hub: protein-structure