Biology subjects

Siegel, J. B.

Publications and source records attributed to Siegel, J. B..

4 recordsLinked to original sources

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↗

bioRxiv2019

Evaluating molecular modeling tools for thermal stability using an independently generated dataset

Engineering proteins to enhance thermal stability is a widely utilized approach for creating industrially relevant biocatalysts. Computational tools that guide these engineering efforts remain an active area of research with new data sets and develop algorithms. To aid in these efforts, we are reporting an expansion of our previously published data set of mutants for a {beta}-glucosidase to include both measures of TM and {Delta}{Delta}G, to complement the previously reported measures of T50 and kinetic constants (kcat and KM). For a set of 51 mutants, we found that T50 and TM are moderately correlated with a Pearson correlation coefficient (PCC) of 0.58, indicated the two methods capture different physical features. The performance of predicted stability using five computational tools are also evaluated on the 51 mutants dataset, none of which are found to be strong predictors of the observed changes in T50, TM, or {Delta}{Delta}G. Furthermore, the ability of the five algorithms to predict the production of isolatable soluble protein is examined, which revealed that Rosetta {Delta}{Delta}G, ELASPIC, and DeepDDG are capable of predicting if a mutant could be produced and isolated as a soluble protein. These results further highlight the need for new algorithms for predicting modest, yet important, changes in thermal stability as well as a new utility for current algorithms for prescreening designs for the production of soluble mutants.

biophysics↗

bioRxiv2019

Design to Data for mutants of β-glucosidase B from Paenibacillus polymyxa: M319C, T431I, and K337D

The use of computational tools has become an increasingly popular tool for engineering protein function. While there are numerous examples of computational tools enabling the design of novel protein functions, there remains room for improvement in both prediction accuracy and success. To improve algorithms for functional and stability predictions, we have initiated the development of a data set designed to be used for training new computational algorithms for enzyme design. To date our dataset is composed of over 129 mutants with associated expression levels, kinetic data, and thermal stability for the enzyme {beta}-glucosidase B (BglB) from Paenibacillus polymyxa. In this study, we introduced three new variants (M319C, T431I, and K337D) to our existing dataset with the goal of cultivating a larger dataset to train new design algorithms and more broadly explore structure-function relationships in BglB.

biochemistry↗

bioRxiv2019

Computationally Guided Design of Dipeptidyl Peptidase-4 Inhibitors

The Type 2 Diabetes Mellitus (T2DM) epidemic undoubtedly creates a need for the development of new pharmaceuticals. With the goal of generating new therapeutics for this disease, computational studies were conducted to design novel dipeptidyl peptidase-4 (DPP-4) inhibitors. Two candidates, generated by chemical intuition-driven design and bioisosteric replacement, were found to have better docking scores than anagliptin, a currently available diabetes medication.

pharmacology and toxicology↗