Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction problem (CSP), akin to Sudoku puzzles. We trained ProteinSolver on over 70,000,000 real protein sequences corresponding to over 80,000 structures. We show that our method rapidly designs new protein sequences and benchmark them in silico using energy-based scores, molecular dynamics, and structure prediction methods. As a proof-of-principle validation, we use ProteinSolver to generate sequences that match the structure of serum albumin, then synthesize the top-scoring design and validate it in vitro using circular dichroism. ProteinSolver is freely available at http://design.proteinsolver.org and https://gitlab.com/ostrokach/proteinsolver. A record of this paper’s transparent peer review process is included in the Supplemental Information.

蛋白质的结构和功能是由氨基酸在 3D 空间中的线性排列决定的。我们展示了深度图神经网络 ProteinSolver 可以通过将这一挑战表述为约束满足问题 (CSP)，类似于数独谜题，精确设计折叠成预定形状的序列。我们在超过 70,000,000 个真实蛋白质序列上训练了 ProteinSolver，这些序列对应于超过 80,000 个结构。我们表明，我们的方法可以快速设计新的蛋白质序列，并使用基于能量的评分、分子动力学和结构预测方法在计算机上对它们进行基准测试。作为原理验证验证，我们使用 ProteinSolver 生成与血清白蛋白结构匹配的序列，然后合成得分最高的设计并进行体外验证使用圆二色性。ProteinSolver 可在 http://design.proteinsolver.org 和 https://gitlab.com/ostrokach/proteinsolver 上免费获得。本文的透明同行评审过程的记录包含在补充信息中。