Protein engineering via sequence-performance mapping.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Cell Systems Pub Date : 2023-08-16 Epub Date: 2023-07-25 DOI:10.1016/j.cels.2023.06.009
Adam McConnell, Benjamin J Hackel
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引用次数: 0

Abstract

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.

Abstract Image

通过序列性能映射的蛋白质工程。
新的和改进的蛋白质的发现和进化为分子治疗、诊断和工业生物技术提供了力量。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。大量的高通量实验和计算技术使高效的筛选能够识别高性能的蛋白质变体。在合作伙伴关系中,需要对序列空间进行知情搜索,以克服序列性能景观的巨大性、稀疏性和复杂性。在蛋白质工程的历史轨迹早期,这些元素与识别最具性能序列的不同方法相一致:从大型随机组合库中选择与合理的计算设计。这些观点的协同作用现已取得实质性进展。组合库的合理设计有助于序列空间的实验搜索,而高通量、高完整性的实验数据为计算设计提供了信息。在协作界面的核心,高效的蛋白质表征(而不仅仅是选择最佳变体)绘制了序列性能图。这种定量图谱阐明了蛋白质序列与性能之间的复杂关系,例如结合、催化效率、生物活性和可开发性,从而推进了基础蛋白质科学,促进了蛋白质的发现和进化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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