An AlphaFold model of Tmem81, a membrane protein involved in the fusion of egg and sperm.Credit: Google DeepMind/EMBL-EBI (CC-BY-4.0)
For nearly a decade, Andrea Pauli, a biochemist at the Research Institute of Molecular Pathology in Vienna, has been trying to work out how sperm and egg get together.
In 2018, her laboratory found a protein on the surface of zebrafish (Danio rerio) eggs, called Bouncer, that was essential for fertilization. But Pauli’s team and others struggled to show how Bouncer recognized sperm cells. Then a revolution happened.
AlphaFold reveals how sperm and egg hook up in intimate detail
Five years ago, in late November 2020, researchers at London-based Google DeepMind unveiled AlphaFold2. The artificial intelligence tool for predicting protein structures generated stunningly accurate 3D models that, in some cases, were indistinguishable from experimental maps, dominating a long-running structure-prediction challenge. The first version of AlphaFold was announced in 2018, but its predictions weren’t nearly as good as its successor, which limited its impact.
The 2021 release of AlphaFold2’s code and a database that has swelled to hundreds of millions of predicted structures mean that scientists can now get a reliable prediction for almost any protein.
“Having models for anything has had a huge impact,” says Janet Thornton, a bioinformatician at the European Bioinformatics Institute in Hinxton, UK, part of the European Molecular Biology Laboratory (EMBL-EBI). “It’s like the second coming of structural biology.”
Rapid discovery
For Pauli’s team, the software shone a light on a path they might otherwise never have found. The model predicted that a protein, called Tmem81, stabilizes a complex of two other sperm proteins, creating a pocket for Bouncer to bind to1. Experiments backed up the tool’s predictions. AlphaFold “speeds up discovery”, says Pauli. “We use it for every project.”
Source: OpenAlex/Google DeepMind
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