The three-dimensional structure of a protein — as determined by its primary amino acid sequence — ultimately dictates how it can interact with other molecules and therefore governs its function, which could be, for example, catalysing a chemical transformation, transporting a molecule or providing structure. The capacity to accurately characterize a protein’s 3D structure directly from its primary sequence could help scientists predict its functional output and enable the design of ligands to selectively modulate protein activity. Likewise, being able to design new proteins built with selected functions of interest could potentially afford new biological tools and therapeutics and serve as a starting point for biomaterials.

The Critical Assessment of Protein Structure Prediction (CASP) — a biennial global competition with the goal of finding solutions to predicting protein structure — has featured works from all laureates of this year’s prize. DeepMind’s first iteration of AlphaFold applied a type of AI called ‘deep learning’ to predict the distance between pairs of amino acids within a protein using both genetic and structural data. This first version outperformed others in the 2018 CASP13 for prediction accuracy, but it was the second iteration AlphaFold2 — taking the top spot in the 2020 CASP14 — that became a real game changer.

蛋白质的三维结构（由其一级氨基酸序列决定）最终决定了它如何与其他分子相互作用，从而控制其功能，例如，催化化学转化、运输分子或提供结构。直接从蛋白质的一级序列准确表征蛋白质 3D 结构的能力可以帮助科学家预测其功能输出，并使配体设计能够选择性地调节蛋白质活性。同样，能够设计具有选定感兴趣功能的新蛋白质可能会提供新的生物工具和治疗方法，并作为生物材料的起点。

蛋白质结构预测的关键评估 （CASP） 是一项两年一度的全球竞赛，旨在寻找预测蛋白质结构的解决方案，展出了今年奖项的所有获奖者的作品。DeepMind 的 AlphaFold 第一次迭代应用了一种称为“深度学习”的 AI，使用遗传和结构数据来预测蛋白质内氨基酸对之间的距离。第一个版本在预测准确性方面优于 2018 年 CASP13 中的其他版本，但第二次迭代 AlphaFold2 — 在 2020 年 CASP14 中占据榜首 — 真正改变了游戏规则。