Accurate structure prediction of biomolecular interactions with AlphaFold 3.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 38718835.
- Also identified by DOI 10.1038/s41586-024-07487-w and PMC identifier 11168924.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
The introduction of AlphaFold 2<sup>1</sup> has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design<sup>2-6</sup>. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.3<sup>7,8</sup>. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
Medical subject headings
- Deep Learning
- Ligands
- Models, Molecular
- Proteins
- Software