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

Wai Shun Mak, Xiao Kang Wang, Rigoberto Arenas, Youtian Cui, Steve Bertolani, Wen Qiao Deng, Ilias Tagkopoulos, David K. Wilson, Justin B. Siegel

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)3834-3843
Number of pages10
JournalBiochemistry
Volume59
Issue number40
DOIs
StatePublished - Oct 13 2020

ASJC Scopus subject areas

  • Biochemistry

Fingerprint Dive into the research topics of 'Discovery, Design, and Structural Characterization of Alkane-Producing Enzymes across the Ferritin-like Superfamily'. Together they form a unique fingerprint.

Cite this