Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort^1,2,3,4, the structures of around 100,000 unique proteins have been determined⁵, but this represents a small fraction of the billions of known protein sequences^6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’⁸—has been an important open research problem for more than 50 years⁹. Despite recent progress^{10,11,12,13,14}, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)¹⁵, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

蛋白质对生命至关重要，了解它们的结构可以促进对其功能的机械理解。通过大量的实验工作^1,2,3,4，大约 100,000 个独特蛋白质的结构已被确定⁵，但这仅代表数十亿已知蛋白质序列的一小部分^6,7。确定单一蛋白质结构需要数月至数年的艰苦努力，因此结构覆盖率受到瓶颈。需要精确的计算方法来解决这一差距并实现大规模结构生物信息学。仅根据蛋白质的氨基酸序列来预测蛋白质将采用的三维结构（“蛋白质折叠问题” ⁸的结构预测部分）50 多年来一直是一个重要的开放研究问题⁹。尽管最近取得了进展^{10,11,12,13,14}，但现有方法远远达不到原子精度，特别是当没有同源结构可用时。在这里，我们提供了第一种计算方法，即使在不知道类似结构的情况下，也可以以原子精度定期预测蛋白质结构。我们在具有挑战性的第 14 次蛋白质结构预测关键评估 (CASP14) 中验证了完全重新设计的基于神经网络的模型 AlphaFold 15 ，证明了在大多数情况下与实验结构相媲美的准确性，并且大大^优于其他方法。最新版本 AlphaFold 的基础是一种新颖的机器学习方法，该方法将蛋白质结构的物理和生物学知识结合起来，利用多序列比对，将其融入深度学习算法的设计中。