Krusemark Casey J, Tilmans Nicolas P, Brown Patrick O, Harbury Pehr B
Department of Biochemistry, Stanford University, Stanford, California, United States of America.
Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America.
PLoS One. 2016 Aug 10;11(8):e0154765. doi: 10.1371/journal.pone.0154765. eCollection 2016.
The first demonstration that macromolecules could be evolved in a test tube was reported twenty-five years ago. That breakthrough meant that billions of years of chance discovery and refinement could be compressed into a few weeks, and provided a powerful tool that now dominates all aspects of protein engineering. A challenge has been to extend this scientific advance into synthetic chemical space: to enable the directed evolution of abiotic molecules. The problem has been tackled in many ways. These include expanding the natural genetic code to include unnatural amino acids, engineering polyketide and polypeptide synthases to produce novel products, and tagging combinatorial chemistry libraries with DNA. Importantly, there is still no small-molecule analog of directed protein evolution, i.e. a substantiated approach for optimizing complex (≥ 10^9 diversity) populations of synthetic small molecules over successive generations. We present a key advance towards this goal: a tool for genetically-programmed synthesis of small-molecule libraries from large chemical alphabets. The approach accommodates alphabets that are one to two orders of magnitude larger than any in Nature, and facilitates evolution within the chemical spaces they create. This is critical for small molecules, which are built up from numerous and highly varied chemical fragments. We report a proof-of-concept chemical evolution experiment utilizing an outsized genetic code, and demonstrate that fitness traits can be passed from an initial small-molecule population through to the great-grandchildren of that population. The results establish the practical feasibility of engineering synthetic small molecules through accelerated evolution.
25年前，有报道称首次证明了大分子能够在试管中进化。这一突破意味着数十亿年的偶然发现和优化过程可以被压缩到几周内，并且提供了一种强大的工具，如今该工具主导着蛋白质工程的各个方面。一个挑战是将这一科学进展扩展到合成化学领域：实现非生物分子的定向进化。人们已通过多种方式解决了这个问题。这些方式包括扩展天然遗传密码以纳入非天然氨基酸、改造聚酮化合物和多肽合成酶以生产新产物，以及用DNA标记组合化学文库。重要的是，目前仍然没有蛋白质定向进化的小分子类似物，即一种经过验证的方法，用于在连续几代中优化合成小分子的复杂（多样性≥10^9）群体。我们朝着这个目标取得了一项关键进展：一种用于从大型化学字母表中对小分子文库进行基因编程合成的工具。该方法适用的字母表比自然界中的任何字母表都大1至2个数量级，并促进在它们所创建的化学空间内的进化。这对于由众多且高度多样的化学片段构成的小分子来说至关重要。我们报告了一项利用超大遗传密码的概念验证化学进化实验，并证明适应度性状可以从最初的小分子群体传递到该群体的曾孙代。这些结果确立了通过加速进化对合成小分子进行工程改造的实际可行性。
