PT  - JOURNAL ARTICLE
AU  - Peishan Huang
AU  - Stephanie C. Contreras
AU  - Eliana Bloomfield
AU  - Kristine Schmitz
AU  - Augustine Arredondo
AU  - Justin B. Siegel
TI  - Design to Data for mutants of β-glucosidase B from <em>Paenibacillus polymyxa</em>: M319C, T431I, and K337D
AID  - 10.1101/839027
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 839027
4099  - http://biorxiv.org/content/early/2019/11/12/839027.short
4100  - http://biorxiv.org/content/early/2019/11/12/839027.full
AB  - 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 β-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.