Deep mutational scanning is a foundational tool for addressing the functional consequences of large numbers of mutants, but a more efficient and accessible method for construction of user-defined mutagenesis libraries is needed. Here we present nicking mutagenesis, a robust, single-day, one-pot saturation mutagenesis method performed on routinely prepped plasmid dsDNA. The method can be used to produce comprehensive or single- or multi-site saturation mutagenesis libraries.

Proteins are marginally stable, and an understanding of the sequence determinants for improved protein solubility is highly desired. For enzymes, it is well known that many mutations that increase protein solubility decrease catalytic activity. These competing effects frustrate efforts to design and engineer stable, active enzymes without laborious high-throughput activity screens. To address the trade-off between enzyme solubility and activity, we performed deep mutational scanning using two different screens/selections that purport to gauge protein solubility for two full-length enzymes. We assayed a TEM-1 beta-lactamase variant and levoglucosan kinase (LGK) using yeast surface display (YSD) screening and a twin-arginine translocation pathway selection. We then compared these scans with published experimental fitness landscapes. Results from the YSD screen could explain 37% of the variance in the fitness landscapes for one enzyme. Five percent to 10% of all single missense mutations improve solubility, matching theoretical predictions of global protein stability. For a given solubility-enhancing mutation, the probability that it would retain wild-type fitness was correlated with evolutionary conservation and distance to active site, and anticorrelated with contact number. Hybrid classification models were developed that could predict solubility-enhancing mutations that maintain wild-type fitness with an accuracy of 90%. The downside of using such classification models is the removal of rare mutations that improve both fitness and solubility. To reveal the biophysical basis of enhanced protein solubility and function, we determined the crystallographic structure of one such LGK mutant. Beyond fundamental insights into trade-offs between stability and activity, these results have potential biotechnological applications.

Comprehensive sequence-function mapping involves detailing the fitness contribution of every possible single mutation to a gene by comparing the abundance of each library variant before and after selection for the phenotype of interest. Deep sequencing of library DNA allows frequency reconstruction for tens of thousands of variants in a single experiment, yet short read lengths of current sequencers makes it challenging to probe genes encoding full-length proteins. Here we extend the scope of sequence-function maps to entire protein sequences with a modular, universal sequence tiling method. We demonstrate the approach with both growth-based selections and FACS screening, offer parameters and best practices that simplify design of experiments, and present analytical solutions to normalize data across independent selections. Using this protocol, sequence-function maps covering full sequences can be obtained in four to six weeks. Best practices introduced in this manuscript are fully compatible with, and complementary to, other recently published sequence-function mapping protocols.

In exponentially growing bacteria, expression of heterologous protein impedes cellular growth rates. Quantitative understanding of the relationship between expression and growth rate will advance our ability to forward engineer bacteria, important for metabolic engineering and synthetic biology applications. Recently, a work described a scaling model based on optimal allocation of ribosomes for protein translation. This model quantitatively predicts a linear relationship between microbial growth rate and heterologous protein expression with no free parameters. With the aim of validating this model, we have rigorously quantified the fitness cost of gene expression by using a library of synthetic constitutive promoters to drive expression of two separate proteins (eGFP and amiE) in E. coli in different strains and growth media. In all cases, we demonstrate that the fitness cost is consistent with the previous findings. We expand upon the previous theory by introducing a simple promoter activity model to quantitatively predict how basal promoter strength relates to growth rate and protein expression. We then estimate the amount of protein expression needed to support high flux through a heterologous metabolic pathway and predict the sizable fitness cost associated with enzyme production. This work has broad implications across applied biological sciences because it allows for prediction of the interplay between promoter strength, protein expression, and the resulting cost to microbial growth rates.

CD19-targeted chimeric antigen receptor (CAR) T-cells (CAR19s) show remarkable efficacy in the treatment of relapsed/refractory acute lymphocytic leukemia and Non-Hodgkin’s lymphoma. However, the use of CAR T-cell therapy against CD19-negative hematological cancers and solid tumors has been challenging. We propose CD19-fusion proteins (CD19-FPs) to leverage the benefits of CAR19s while retargeting this validated cellular therapy to alternative tumor antigens. We demonstrate the ability of a fusion of CD19 extracellular domain (ECD) and a human epidermal growth factor receptor 2 (HER2) single-chain antibody fragment to retarget CAR19s to kill HER2+ CD19– tumor cells. To enhance the modularity of this technology, we engineered a more robust CD19 ECD via deep mutational scanning with yeast display and flow cytometric selections for improved protease resistance and anti-CD19 antibody binding. These enhanced CD19 ECDs significantly increase, and in some cases recover, fusion protein expression while maintaining target antigen affinity. Importantly, CD19-FPs retarget CAR19s to kill tumor cells expressing multiple distinct antigens, including HER2, CD20, EGFR, BCMA, and Clec12A as N- or C-terminal fusions and linked to both antibody fragments and fibronectin ligands. This study provides fundamental insights into CD19 sequence–function relationships and defines a flexible and modular platform to retarget CAR19s to any tumor antigen.

Synthetic metabolic pathways often suffer from low specific productivity, and new methods that quickly assess pathway functionality for many thousands of variants are urgently needed. Here we present an approach that enables the rapid and parallel determination of sequence effects on flux for complete gene-encoding sequences. We show that this method can be used to determine the effects of over 8000 single point mutants of a pyrolysis oil catabolic pathway implanted in Escherichia coli. Experimental sequence-function data sets predicted whether fitness-enhancing mutations to the enzyme levoglucosan kinase resulted from enhanced catalytic efficiency or enzyme stability. A structure of one design incorporating 38 mutations elucidated the structural basis of high fitness mutations. One design incorporating 15 beneficial mutations supported a 15-fold improvement in growth rate and greater than 24-fold improvement in enzyme activity relative to the starting pathway. This technique can be extended to improve a wide variety of designed pathways.

The B-cell surface protein CD19 is present throughout the cell life cycle and is uniformly expressed in leukemias, making it a target for chimeric antigen receptor engineered immune cell therapy. Identifying the sequence dependence of the binding of CD19 to antibodies empowers fundamental study and more tailored development of CD19targeted therapeutics. To identify the antibody-binding epitopes on CD19, we screened a comprehensive single-site saturation mutation library of the human CD19 extracellular domain to identify mutations detrimental to binding FMC63the dominant CD19 antibody used in chimeric antigen receptor developmentas well as 4G7-2E3 and 3B10, which have been used in various types of CD19 research and development. All three antibodies had partially overlapping, yet distinct, epitopes near the published epitope of antibody B43. The FMC63 conformational epitope spans spatially adjacent, but genetically distant, loops in exons 3 and 4. The 3B10 epitope is a linear peptide sequence that binds CD19 with 440 pM affinity. Along with their primary goal of epitope mapping, the mutational tolerance data also empowered additional CD19 variant design and analysis. A designed CD19 variant with all N-linked glycosylation sites removed successfully bound antibody in the yeast display context, which provides a lead for aglycosylated applications. Screening for thermally stable variants identified mutations to guide further CD19 stabilization for fusion protein applications and revealed evolutionary affinity−stability tradeoffs. These fundamental insights into CD19 sequence−function relationships enhance our understanding of antibody-mediated CD19-targeted therapeutics.

Background: Levoglucosan kinase converts the cellulosic biomass pyrolysis product levoglucosan to the common metabolite glucose 6-phosphate.Results: Crystallographic structures demonstrate two metal binding sites and an alternate binding arrangement for levoglucosan. Conclusion:The unusual mode of levoglucosan binding provides a rationale for the high K m of LGK for its substrate. Significance: These results provide a greater understanding of the underlying biochemistry of anhydrosugar processing.