Discovery, Design, and Structural Characterization of Alkane-Producing Enzymes across the Ferritin-like Superfamily

To complement established
rational and evolutionary protein design
approaches, significant efforts are being made to utilize computational
modeling and the diversity of naturally occurring protein sequences.
Here, we combine structural biology, genomic mining, and computational
modeling to identify structural features critical to aldehyde deformylating
oxygenases (ADOs), an enzyme family that has significant implications
in synthetic biology and chemoenzymatic synthesis. Through these efforts,
we discovered latent ADO-like function across the ferritin-like superfamily
in various species of Bacteria and Archaea. We created a machine learning
model that uses protein structural features to discriminate ADO-like
activity. Computational enzyme design tools were then utilized to
introduce ADO-like activity into the small subunit of Escherichia
coli class I ribonucleotide reductase. The integrated approach
of genomic mining, structural biology, molecular modeling, and machine
learning has the potential to be utilized for rapid discovery and
modulation of functions across enzyme families.