From CASP13 to the Nobel Prize: DeepMind's AlphaFold journey in revolutionizing protein structure prediction and beyond

Four years ago, at the 14th Critical Assessment of Structure Prediction (CASP14), John Moult made a historic announcement that the long-standing challenge of Protein Structure Prediction— a problem that had confounded scientists for over five decades—had been "solved" for single protein chains. Supporting this groundbreaking statement was a plot depicting the median Global Distance Test (GDT) across 87 out of 92 domains, where AlphaFold2, developed by DeepMind, achieved an unprecedented score of 92.4. The bar chart not only underscored AlphaFold2’ s remarkable performance—standing out prominently among other methods—but also revealed a level of accuracy that exceeded all prior expectations. In the years since this breakthrough, DeepMind's team has made significant strides. The AlphaFold Database now hosts approximately 214 million structures for various model organisms, covering nearly the entire genome. Research continues to explore multiple facets of protein science, including the prediction of multi-chain protein complex structures and the impact of missense mutations on protein function. The open availability of this extensive database and the suite of AlphaFold2 algorithms has catalysed remarkable advancements in protein biology and bioinformatics. This review will begin by revisiting DeepMind's early efforts in CASP13, detailing the architecture and the remarkable progress that led to their breakthrough of AlphaFold2 in CASP14 (2020). It will then delve into two main areas: (1) AlphaFold’s contributions to the scientific community across various fields over the past four years, and (2) the latest improvements, enhancements, and achievements by DeepMind, including AlphaFold3 and the Nobel Prize in Chemistry.