PDF(Pubmed)
Abstract：
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 Chlamydomonas reinhardtii, 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.
摘要：
不依赖氧气,基于黄素单核苷酸的荧光蛋白（FbFP）是厌氧环境中绿色荧光蛋白的有希望的替代品。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FbFP。专注于来自莱茵衣藻的CreiLOV，我们创建并分析了两个综合突变集合：(1)单残基，覆盖所有118个残基的位点饱和诱变文库;和(2)在15个残基的20个突变中的完整组合诱变文库，其中突变和残基选择基于单定点诱变结果。值得注意的是,第二类图书馆对于研究高阶上位性是必不可少的，但在文献中代表性不足。使用优化的FACS-seq测定，2,360个可能的单位点突变体中的2,185个（>92.5％）和184,320个可能的组合突变体中的165,428个（>89.7％）被可靠地分配了适应度值。我们构建了统计和机器学习模型来分析CreiLOV数据集，使用低阶诱变数据实现高阶突变体的准确适应度预测。此外,我们成功地分离了具有改进的荧光量子产率和热稳定性的CreiLOV变体。这项工作为工程组合蛋白质变体提供了新的经验数据和设计规则。
