Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence¹. This problem is of fundamental importance as the structure of a protein largely determines its function²; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures³. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force⁴ that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction⁵ (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores⁶ of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined⁷.

^{蛋白质结构预测可用于根据氨基酸序列1}确定蛋白质的三维形状。这个问题至关重要，因为蛋白质的结构在很大程度上决定了它的功能²；然而，蛋白质结构可能难以通过实验确定。最近通过利用遗传信息取得了相当大的进展。可以通过分析同源序列的共变来推断哪些氨基酸残基是接触的，这有助于预测蛋白质结构³. 在这里，我们展示了我们可以训练一个神经网络来准确预测残基对之间的距离，这比接触预测传达了更多关于结构的信息。利用这些信息，我们构建了一个平均力⁴的势能，可以准确地描述蛋白质的形状。我们发现，可以通过简单的梯度下降算法优化产生的潜力，从而无需复杂的采样程序即可生成结构。由此产生的系统名为 AlphaFold，即使对于同源序列较少的序列，也能实现高精度。在最近的蛋白质结构预测⁵ (CASP13) 的关键评估中——对该领域状态的盲目评估——AlphaFold 创建了高精度结构（带有模板建模 (TM) 分数^{43 个免费建模领域中的 24 个获得 0.7 或更高的6}个），而使用采样和联系信息的次优方法仅在 43 个领域中的 14 个实现了这样的准确性。AlphaFold 代表了蛋白质结构预测的一个相当大的进步。我们希望这种提高的准确性能够深入了解蛋白质的功能和故障，特别是在没有通过实验确定同源蛋白质结构的情况下⁷。