Google Deepmind just broke chemistry\u2026<\/p>\n
They open-sourced a model that can invent completely new biology from scratch.<\/p>\n
It\u2019s called \u201cAlphaProtein Novo\u201d<\/p>\n
For decades, scientists have dreamed of designing enzymes\u2014nature\u2019s tiny molecular machines\u2014from scratch to perform chemistry that doesn\u2019t exist in the natural world. Traditionally, protein engineers have had to start with enzymes that already exist in nature and tweak them, but finding the right natural starting point is often difficult, and there\u2019s no guarantee nature has an enzyme for the job you want done.<\/p>\n
A new approach called de novo design aims to build enzymes from first principles, like designing a tool for a specific task rather than adapting whatever happens to be in the toolbox. Until now, however, designed enzymes haven\u2019t been good enough to be practically useful.<\/p>\n
Enter AlphaProtein Novo (AP Novo), a machine-learning pipeline that changes the game. The researchers show, for the first time, that designing enzymes from scratch can actually beat searching through nature\u2019s existing enzyme diversity when it comes to tackling hard chemistry problems.<\/p>\n
Using AP Novo, the team designed entirely new enzymes for two impressive tasks. First, they created \u201cnitrene transferases\u201d\u2014enzymes that don\u2019t exist in nature\u2014to build piperidine, a molecular building block important in many medicines, with exceptional precision in producing the desired form of the molecule. Second, they designed enzymes that can break down DEHP, a harmful environmental pollutant, under harsh conditions that would destroy natural enzymes.<\/p>\n
They also achieved top-tier performance on two well-studied benchmark reactions. By analyzing what made these designs work, they discovered that two strategies were key: using predictions from AlphaFold 3 (an AI system that predicts protein structures) to guide designs based on chemical mechanisms, and scoring candidate enzyme scaffolds by looking at how ensembles of related sequences behave.<\/p>\n