Search for: All records

ABSTRACT Protein design is advancing toward quantitative modeling of enzyme function and stability. However, progress remains limited by the scarcity of standardized experimental datasets for training and benchmarking computational models. The Design to Data (D2D) program addresses this need by generating harmonized measurements of catalytic and stability parameters across an extensive β-glucosidase B (BglB) variant library. Here, we expand the D2D dataset with kinetic and thermal characterization of five single-point BglB variants and the wild-type (WT), including soluble expression, Michaelis-Menten constants (k_cat, K_M, andk_cat/K_M), and melting temperature (T_M,). Foldit Standalone was used to model the structural effects of the mutations. In this study, a weak but consistent association between Foldit total system energy (TSE) and T_Mwas observed, suggesting local energetic effects that may influence stability. Together with the broader D2D corpus, these data enhance the functional mapping of BglB and provide model-ready benchmarks for developing and evaluating data-driven predictors of enzyme activity and stability. 

ABSTRACT This study explores computational design predictions related to experimental enzyme behavior by analyzing seven single-point mutants of β-glucosidase B (BglB) fromPaenibacillus polymyxa: Y333F, A88E, L219Q, A408H, Y173L, E340S, and Y422F. Each mutation was modeled using Foldit Standalone, and mutant selections were based on predicted thermodynamic stability changes of interest. Six of the seven mutants in this set yielded soluble, expressed protein. Most variants had similar catalytic efficiency compared to the wild type with one exception. The melting temperatures for most variants were also similar to the wild type. Correlation analysis revealed weak but potentially informative relationships between predicted ΔTSE and (a) thermal stability and (b) catalytic efficiency. These results further support known limitations of TSE score as a tool for single point mutation design and add to a growing dataset being generated to build the next generation of functionally predictive protein models. 

Engaging computational tools for protein design is gaining traction in the enzyme engineering community. However, current design and modeling algorithms have limited functionality predictive capacities for enzymes due to limitations of the dataset in terms of size and data quality. This study aims to expand training datasets for improved algorithm development with the addition of five rationally designed single-point enzyme variants. β-glucosidase B variants were modeled in Foldit Standalone and then produced and assayed for thermal stability and kinetic parameters. Functional parameters: thermal stability (T_M) and Michaelis-Menten constants (k_cat, K_M, andk_cat/K_M) of five variants, V311D, Y166H, M221K, F248N, and Y166K, were added into the Design2Data database. As a case study, evaluation of this small mutant set finds mutational effect trends that both corroborate and contradict findings from larger studies examining the entire dataset. 

Abstract Noncanonical cofactor biomimetics (NCBs) such as nicotinamide mononucleotide (NMN + ) provide enhanced scalability for biomanufacturing. However, engineering enzymes to accept NCBs is difficult. Here, we establish a growth selection platform to evolve enzymes to utilize NMN + -based reducing power. This is based on an orthogonal, NMN + -dependent glycolytic pathway in Escherichia coli which can be coupled to any reciprocal enzyme to recycle the ensuing reduced NMN + . With a throughput of >10 6 variants per iteration, the growth selection discovers a Lactobacillus pentosus NADH oxidase variant with ~10-fold increase in NMNH catalytic efficiency and enhanced activity for other NCBs. Molecular modeling and experimental validation suggest that instead of directly contacting NCBs, the mutations optimize the enzyme’s global conformational dynamics to resemble the WT with the native cofactor bound. Restoring the enzyme’s access to catalytically competent conformation states via deep navigation of protein sequence space with high-throughput evolution provides a universal route to engineer NCB-dependent enzymes. 

Abstract Noncanonical redox cofactors are attractive low-cost alternatives to nicotinamide adenine dinucleotide (phosphate) (NAD(P) + ) in biotransformation. However, engineering enzymes to utilize them is challenging. Here, we present a high-throughput directed evolution platform which couples cell growth to the in vivo cycling of a noncanonical cofactor, nicotinamide mononucleotide (NMN + ). We achieve this by engineering the life-essential glutathione reductase in Escherichia coli to exclusively rely on the reduced NMN + (NMNH). Using this system, we develop a phosphite dehydrogenase (PTDH) to cycle NMN + with ~147-fold improved catalytic efficiency, which translates to an industrially viable total turnover number of ~45,000 in cell-free biotransformation without requiring high cofactor concentrations. Moreover, the PTDH variants also exhibit improved activity with another structurally deviant noncanonical cofactor, 1-benzylnicotinamide (BNA + ), showcasing their broad applications. Structural modeling prediction reveals a general design principle where the mutations and the smaller, noncanonical cofactors together mimic the steric interactions of the larger, natural cofactors NAD(P) + .