Design to Data for mutants of β-glucosidase B fromPaenibacillus polymyxa: Q22T, W123R, F155G, Y169M, W438D, V401A

Abstract

A key goal of protein engineering is to accurately model the stability and catalytic activity of enzymes. However, the limitations of functional predictive abilities pose a major challenge for modeling algorithm design, and can be attributed to the lack of large data sets quantifying the functional properties of enzymes. Here, the thermal stability (TM) and Michaelis-Menten constants (kcat, KM, and kcat/KM) of six new variants of the β-glucosidase B (BglB) protein are quantitatively characterized. Molecular stability of the enzyme variants were hypothesized using the Foldit software and BglB was synthesized in E. coli cells. Testing was done through a colorimetric kinetic assay and thermal stability fluorescence-based protein unfolding assay. Results from the assays suggest that all mutations, with the exception of variant Y169M, all experienced reduced catalytic efficiency compared to the wildtype. Assay results indicate that variant W123R is more thermally stable compared to the wildtype, while the differences in thermal stability between the other variants, and the wildtype are negligible. The collected thermal stability and catalytic efficiency data has been added to a data set with the aim of improving Rosetta algorithms for modeling and predicting the functional interactions between biomolecules through a machine learning algorithm and facilitate the precise engineering of protein catalysts.

Design to Data for mutants of β-glucosidase B fromPaenibacillus polymyxa:L171M, H178M, M221L, E406W, N160E, F415M

“…Another recent BioRxiv publication showed the opposite trend. 16 Contrasting these two findings exemplifies how small data sets evaluated in isolation like these were susceptible to variations that might be misleading and supporting the need for deeper investigation.…”

IntEnzyDB: an Integrated Structure–Kinetics Enzymology Database

“…Data Collection. The kinetics data in IntEnzyDB were extracted from BRENDA, 12 Sabio-RK, 13 ProtaBank, 29 and Design2Data, 4 the structure data from the PDB, 11 and the sequence data from UniProt. 10 The enzyme kinetics table contains EC number, UniProtKB entry, organism, substrate, experimental temperature, and mutational information.…”

“…These data cover thousands of Enzyme Commission (EC) classes that span seven enzyme types (i.e., oxidoreductases, transferases, hydrolases, lyases, isomerases, ligases, and translocases). In addition, databases have been established to annotate enzyme functions based on their structural, chemical, and metabolic relevance (e.g., EzCatDB, M-CSA, KEGG, FunCat, Reactcome, and MetaCyc); to map enzyme sequence, structure, and function relationships (e.g., PDBSWS, SFLD, FunTree, IntEnz, ExploreEnz, and ExPASy); to classify enzymes based structural and functional superfamilies (e.g., CATH and SCOP , ); and to store designed enzymes (e.g., ProtaBank and Design2Data).…”

“…As a holy grail challenge in modern chemical sciences, developing new enzyme catalysts provides solutions to transform chemically challenging reactions, expand substrate scope, control complex reaction selectivity, treat metabolic disorders, and degrade inert environmental wastes and pollutants . Data-driven modeling methods have been extensively leveraged to innovate the approaches for enzyme catalyst discovery.…”

Development of a Broadly Accessible, Computationally Guided Biochemistry Course-Based Undergraduate Research Experience

“…Over the last year, four early adopter institutions have been incubating and building out the D2D CURE that involves students in an investigation of the sequence–structure–function relationship of enzymes for which the data is relevant to a community of protein modeling stakeholders. − Using the lens of protein biochemistry and the Design2Data (D2D) workflow, students in the CURE have the opportunity to expand their knowledge and develop skills by using computational modeling tools to design novel enzyme variants, after which they move into the wet lab to synthesize novel genes encoding the designed mutant, express and purify the corresponding protein, and finally biophysically characterize them (Figure ).…”