AlphaFold: a game-changer for structural biology

EMBL-EBI teams up with DeepMind to make breakthrough AI-powered protein structure predictions freely available to the world.

Proteins play many roles in the biological world. They transport nutrients, trigger chemical reactions, and build the structures that make up all living things. To fully understand what a protein does, scientists need to know its 3D structure, and this is no easy task.

Since the 1950s, scientists have used a range of methods to uncover hundreds of thousands of protein structures and made the data accessible through public archives. Building on these data,  DeepMind developed AlphaFold – an AI-powered system that can accurately predict millions of protein structures.

In 2021, EMBL-EBI and DeepMind entered into a collaboration to develop and release the AlphaFold Protein Structure Database  – an open platform where anyone can search, analyse, and download AlphaFold predictions. 

The beauty of AlphaFold is that it was trained using data from public resources – including the Protein Data Bank (PDB), UniProt, and MGnify, which are all co-hosted at EMBL-EBI. Building on EMBL-EBI’s decades of data management expertise, DeepMind was able to quickly provide public access to AlphaFold predictions and crosslink them to existing data resources, making it much easier to discover them. DeepMind and EMBL-EBI are working to extend the database to over 100 million predictions.

Many labs at EMBL and around the world have used the data to gain new insights into protein science:

• Jan Kosinski at EMBL Hamburg used AlphaFold and electron cryo-microscopy data to build a new model of the nuclear pore complex, exemplifying how AlphaFold can support experimental structural biology to understand subcellular architecture.
• Pedro Beltrao at EMBL-EBI and collaborators used AlphaFold to predict complex protein interactions, expanding understanding of human cell biology. 
• Jan Korbel at EMBL Heidelberg created a de novo protein design framework based on AlphaFold, which could help design new proteins for applications such as processing waste or degrading plastics.