CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
ACS Synth Biol. 2023 May 19;12(5):1461-1473. doi: 10.1021/acssynbio.2c00662. Epub 2023 Apr 17.
Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from , we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.
氧非依赖型、黄素单核苷酸基荧光蛋白(FbFPs)是一种有前途的替代绿色荧光蛋白的选择,可用于厌氧环境。深度突变扫描可对蛋白质序列-功能关系进行系统分析,但尚未应用于 FbFPs。本研究以 CreiLOV 为例,创建并分析了两个综合突变体文库:(1)覆盖所有 118 个残基的单残基、点饱和突变文库;(2)15 个残基中的 20 个突变的全组合代谢突变文库,其中突变和残基选择基于单点突变结果。值得注意的是,第二种文库对于研究更高阶的上位性至关重要,但在文献中代表性不足。通过优化的 FACS-seq 检测,2360 个可能的单点突变体中有 2185 个(>92.5%),184320 个可能的组合突变体中有 165428 个(>89.7%)能够可靠地分配到适合度值。我们构建了统计和机器学习模型来分析 CreiLOV 数据集,从而能够使用较低阶的突变数据准确预测高阶突变体的适合度。此外,我们还成功分离出荧光量子产率和热稳定性得到改善的 CreiLOV 变体。这项工作提供了新的经验数据和设计规则,用于工程化组合蛋白变体。