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Effectiveness of in silico Engineering of the β‐glucosidase B Enzyme
Author(s) -
Boulanger Kyle R.,
Hall Bonnie L.
Publication year - 2018
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2018.32.1_supplement.796.19
Subject(s) - in silico , computational biology , function (biology) , computer science , mutant , process (computing) , protein engineering , enzyme , biological system , data mining , biology , biochemistry , genetics , gene , operating system
Enzymes are used in a wide range of commercial and medical applications, and it is desirable for these enzymes to have maximum efficiency. A common approach to engineering a more efficient enzyme is generating random mutations and selecting those that increase enzyme activity in vitro . In silico design utilizes molecular modeling software to more rapidly predict structure‐function relationships, thus identifying more efficient enzymes without massive in vitro screening efforts. The drawback with these predictions is a lack of accuracy, yielding correct results as low as ten percent of the time (Carlin, 2016 ). To improve the algorithms used for molecular modeling, a large pool of enzyme mutants would need to be analyzed both in silico and simultaneously characterized kinetically under consistent experimental conditions. We are characterizing the BglB protein, as it is easily overexpressed and its efficiency can be measured using a simple colorimetric assay. We have engineered a pool of BglB mutants in silico using FoldIt, and compared the computational predictions with the in vitro kinetic results. Based on k cat and K m values for the various BglB mutants, we found FoldIt was able to correctly predict structure‐function relationships for BglB approximately 40% of the time. The remaining 60% of the time, the software predictions did not match the in vitro results. These BglB data, as a part of the larger SEEK database, can help refine the algorithms used in the FoldIt modeling software. Hopefully, molecular modeling will eventually be able to accurately predict structure‐function results in silico , saving both time and money in the enzyme engineering process. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .