Protein engineering via sequence-performance mapping.
Protein engineering via sequence-performance mapping.
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通过序列性能图谱进行蛋白质工程。
DOI：
10.1016/j.cels.2023.06.009
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发表时间：
2023
期刊：
影响因子：
9.3
通讯作者：
Hackel,BenjaminJ
中科院分区：
文献类型：
--
作者：
McConnell,Adam;Hackel,BenjaminJ
新的和改进的蛋白质的发现和进化增强了分子治疗、诊断和工业生物技术。发现和进化都需要有效的筛选和有效的文库，尽管它们的挑战不同，因为分别缺乏或存在具有所需功能的初始蛋白质变体。大量高通量技术（实验和计算）能够进行有效的筛选，以识别高性能的蛋白质变体。在合作中，需要对序列空间进行明智的搜索，以克服序列性能景观的巨大、稀疏和复杂性。在蛋白质工程历史轨迹的早期，这些元素与不同的方法相结合来识别最有效的序列：从大型随机组合库中选择与理性计算设计。这些观点的协同作用现已取得了实质性进展。组合文库的合理设计有助于序列空间的实验搜索，高通量、高完整性的实验数据为计算设计提供信息。在协作界面的核心，有效的蛋白质表征（而不仅仅是选择最佳变体）绘制了序列性能图谱。此类定量图谱阐明了蛋白质序列和性能（例如结合、催化效率、生物活性和可开发性）之间的复杂关系，从而推进基础蛋白质科学并促进蛋白质发现和进化。
Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies—experimental and computational—enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance—e.g., binding, catalytic efficiency, biological activity, and developability—thereby advancing fundamental protein science and facilitating protein discovery and evolution.