James Jessica, Towers Sebastian, Foerster Jakob, Steel Harrison
Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol. 2024 Dec 19;20(12):e1012695. doi: 10.1371/journal.pcbi.1012695. eCollection 2024 Dec.
Directed evolution can enable engineering of biological systems with minimal knowledge of their underlying sequence-to-function relationships. A typical directed evolution process consists of iterative rounds of mutagenesis and selection that are designed to steer changes in a biological system (e.g. a protein) towards some functional goal. Much work has been done, particularly leveraging advancements in machine learning, to optimise the process of directed evolution. Many of these methods, however, require DNA sequencing and synthesis, making them resource-intensive and incompatible with developments in targeted in vivo mutagenesis. Operating within the experimental constraints of established sorting-based directed evolution techniques (e.g. Fluorescence-Activated Cell Sorting, FACS), we explore approaches for optimisation of directed evolution that could in future be implemented without sequencing information. We then expand our methods to the context of emerging experimental techniques in directed evolution, which allow for single-cell selection based on fitness objectives defined from any combination of measurable traits. Finally, we explore these alternative strategies on the GB1 and TrpB empirical landscapes, demonstrating that they could lead to up to 19-fold and 7-fold increases respectively in the probability of attaining the global fitness peak.
定向进化能够在对生物系统潜在的序列-功能关系了解甚少的情况下，实现对其进行工程改造。典型的定向进化过程包括多轮迭代的诱变和筛选，旨在引导生物系统（如蛋白质）发生变化，朝着某个功能目标发展。人们已经开展了大量工作，特别是利用机器学习的进展来优化定向进化过程。然而，这些方法大多需要DNA测序和合成，这使得它们资源密集，并且与体内靶向诱变的发展不兼容。在既定的基于分选的定向进化技术（如荧光激活细胞分选，FACS）的实验限制范围内，我们探索了优化定向进化的方法，这些方法未来可以在没有测序信息的情况下实施。然后，我们将我们的方法扩展到定向进化中新兴实验技术的背景下，这些技术允许基于从任何可测量特征组合定义的适应性目标进行单细胞选择。最后，我们在GB1和TrpB经验景观上探索了这些替代策略，证明它们分别可以使达到全局适应性峰值的概率提高19倍和7倍。
