Directed Evolution of a Genetically Encoded Indicator for Chloride.

IF 3.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

ACS Synthetic Biology Pub Date : 2025-03-20 DOI:10.1021/acssynbio.4c00818

Weicheng Peng, Jasmine N Tutol, Shelby M Phelps, Hiu Kam, Jacob K Lynd, Sheel C Dodani

引用次数: 0

Abstract

Inarguably, the green fluorescent protein (GFP) family is an exemplary model for protein engineering, accessing a range of unparalleled functions and utility in biology. The first variant to recognize and provide an optical output of chloride in living cells was serendipitously uncovered more than 25 years ago. Since then, researchers have actively expanded the potential of GFP indicators for chloride through site-directed and combinatorial site-saturation mutagenesis, along with chimeragenesis. However, to date, the power of directed evolution has yet to be unleashed. As a proof-of-concept, here, we use random mutagenesis paired with anion walking to engineer a chloride-insensitive fluorescent protein named OFPxm into a functional indicator named ChlorOFF. The sampled mutational landscape unveils an evolutionary convergent solution at one position in the anion binding pocket and nine other mutations across eight positions, of which only one has been previously linked to chloride sensing potential in the GFP family.

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氯基因编码指示物的定向进化。

毋庸置疑，绿色荧光蛋白（GFP）家族是蛋白质工程的典范模型，在生物学中具有一系列无与伦比的功能和用途。第一个在活细胞中识别并提供氯光输出的变体是在25年前偶然发现的。从那时起，研究人员通过位点定向和组合位点饱和诱变以及嵌合，积极扩大了氯化物GFP指标的潜力。然而，迄今为止，定向进化的力量还没有被释放出来。作为概念验证，在这里，我们使用随机诱变与阴离子行走配对，将名为OFPxm的氯化物不敏感荧光蛋白改造成名为ChlorOFF的功能指示剂。样本突变景观揭示了阴离子结合口袋中一个位置的进化趋同解决方案和8个位置的9个其他突变，其中只有一个先前与GFP家族中的氯化物感应电位有关。

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

ACS Synthetic Biology 生物-

CiteScore

8.00

自引率

10.60%

发文量

380

审稿时长

6-12 weeks

期刊介绍： The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.