Exploring structure space to understand stability Understanding the determinants of protein stability is challenging because native proteins have conformations that are optimized for function. Proteins designed without functional bias could give insight into how structure determines stability, but this requires a large sample size. Rocklin et al. report a high-throughput protein design and characterization method that allows them to measure thousands of miniproteins (see the Perspective by Woolfson et al.). Iterative rounds of design and characterization increased the design success rate from 6 to 47%, which provides insight into the balance of forces that determine protein stability. Science, this issue p. 168; see also p. 133 Thousands of computationally designed proteins quantify the global determinants of miniprotein stability. Proteins fold into unique native structures stabilized by thousands of weak interactions that collectively overcome the entropic cost of folding. Although these forces are “encoded” in the thousands of known protein structures, “decoding” them is challenging because of the complexity of natural proteins that have evolved for function, not stability. We combined computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay to measure folding and stability for more than 15,000 de novo designed miniproteins, 1000 natural proteins, 10,000 point mutants, and 30,000 negative control sequences. This analysis identified more than 2500 stable designed proteins in four basic folds—a number sufficient to enable us to systematically examine how sequence determines folding and stability in uncharted protein space. Iteration between design and experiment increased the design success rate from 6% to 47%, produced stable proteins unlike those found in nature for topologies where design was initially unsuccessful, and revealed subtle contributions to stability as designs became increasingly optimized. Our approach achieves the long-standing goal of a tight feedback cycle between computation and experiment and has the potential to transform computational protein design into a data-driven science.

中文翻译：

---

使用大规模并行设计、合成和测试对蛋白质折叠进行全局分析

探索结构空间以了解稳定性 了解蛋白质稳定性的决定因素具有挑战性，因为天然蛋白质具有针对功能优化的构象。没有功能偏差的蛋白质设计可以深入了解结构如何决定稳定性，但这需要大样本量。罗克林等人。报告了一种高通量蛋白质设计和表征方法，使他们能够测量数千种微型蛋白质（参见 Woolfson 等人的观点）。设计和表征的迭代轮次将设计成功率从 6% 提高到 47%，从而深入了解决定蛋白质稳定性的力的平衡。科学，这个问题 p。168; 另见 p. 133 数以千计的计算设计的蛋白质量化了微型蛋白质稳定性的全局决定因素。蛋白质折叠成独特的天然结构，由数千种共同克服折叠熵成本的弱相互作用稳定。尽管这些力被“编码”在数以千计的已知蛋白质结构中，但“解码”它们具有挑战性，因为天然蛋白质的复杂性是为了功能而不是稳定性而进化的。我们结合计算蛋白质设计、下一代基因合成和高通量蛋白酶敏感性测定来测量超过 15,000 种从头设计的微型蛋白质、1000 种天然蛋白质、10,000 种点突变体和 30,000 种阴性对照序列的折叠和稳定性。该分析在四个基本折叠中确定了 2500 多个稳定设计的蛋白质——这个数字足以使我们能够系统地检查序列如何决定未知蛋白质空间中的折叠和稳定性。设计和实验之间的迭代将设计成功率从 6% 提高到 47%，产生了稳定的蛋白质，这与自然界中发现的拓扑最初设计不成功的蛋白质不同，并且随着设计变得越来越优化，揭示了对稳定性的微妙贡献。我们的方法实现了计算和实验之间紧密反馈循环的长期目标，并有可能将计算蛋白质设计转变为数据驱动的科学。产生了稳定的蛋白质，这与自然界中发现的那些最初设计不成功的拓扑结构不同，并且随着设计变得越来越优化，揭示了对稳定性的微妙贡献。我们的方法实现了计算和实验之间紧密反馈循环的长期目标，并有可能将计算蛋白质设计转变为数据驱动的科学。产生了稳定的蛋白质，这与自然界中发现的那些最初设计不成功的拓扑结构不同，并且随着设计变得越来越优化，揭示了对稳定性的微妙贡献。我们的方法实现了计算和实验之间紧密反馈循环的长期目标，并有可能将计算蛋白质设计转变为数据驱动的科学。