Biology subjects

Vater, A.

Publications and source records attributed to Vater, A..

6 recordsLinked to original sources

bioRxiv2026

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: Y333F, A88E, L219Q, A408H, Y173L, E340S, and Y422F

This study explores computational design predictions related to experimental enzyme behavior by analyzing seven single-point mutants of {beta}-glucosidase B (BglB) from Paenibacillus 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 {Delta}TSE 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.

biochemistry↗

bioRxiv2025

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: N160L, N160S, N160C, N160M, N160G

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 {beta}-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 (kcat, KM, and kcat/KM), and melting temperature (TM,). 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 TM was 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.

bioengineering↗

bioRxiv2025

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: L336M, L336A, L336S, L336H, D35E, D35W

Recent groundbreaking advances in protein structure prediction have significantly propelled the fields of protein design and engineering. However, improving computational enzyme modeling remains a key area of interest, with exciting opportunities to enhance predictive capabilities. Existing algorithms face a crucial hurdle - predicting enzyme stability and function with accuracy. To address this limitation, large sets of experimental data that capture enzyme structure-function are needed to guide the training and testing of next-gen tools. The Design 2 Data (D2D) program aims to build such a data set by engaging students from around the world to contribute standardized experimental data of enzyme variants. The flagship enzyme dataset of D2D, {beta}-glucosidase B (BglB), currently contains over 1300 records. The data consists of kinetic (kcat, KM, and kcat/KM), and thermal stability measures to expand the range of data available to train new computational algorithms. This paper explores six new single point mutants produced as part of this larger project and investigates variations in functional outcomes across the mutated sites. These data taken together with the larger and growing mutant library aim to deepen our understanding of the relationship between structure and function in BglB.

biochemistry↗

bioRxiv2023

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: V311D, F248N, Y166H, Y166K, M221K

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. {beta}-glucosidase B variants were modeled in Foldit Standalone and then produced and assayed for thermal stability and kinetic parameters. Functional parameters: thermal stability (TM) and Michaelis-Menten constants (kcat, KM, and kcat/KM) 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.

biochemistry↗

bioRxiv2020

Design to Data for mutants of β-Glucosidase B from Paenibacillus polymyxa: I45K, A357S, I20A, I20V, and I20E

The relatively small size and scope of most current datasets of biophysical mutation effects in enzymes limit the ability to develop data-driven algorithms enabling accurate generative modeling tools for designing novel enzyme function. Here, the Michaelis-Menten constants (kcat, KM, and kcat/KM) and thermal stability (TM) of five new mutations of {beta}-glucosidase B from Paenibacillus polymyxa (BglB) are characterized. Foldit software was used to create molecular models of the mutants, for which synthetic genes were constructed and the corresponding proteins produced and purified from E. coli. It was found that mutations that disrupted pre-existing hydrogen bonds near the active site had reduced expression in contrast to mutations at the same site that did not affect native hydrogen bonding. This is consistent with previous results showing the relationship between hydrogen bonding and enzyme functionality. These mutants contribute to a growing data set of >100 mutants that have been characterized for expression, kinetic, and thermal properties

biochemistry↗

bioRxiv2019

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

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 {beta}-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.

biochemistry↗