Multimeric structure of a subfamily III haloalkane dehalogenase-like enzyme solved by combination of cryo-EM and x-ray crystallography.

Haloalkane dehalogenase (HLD) enzymes employ an SN 2 nucleophilic substitution mechanism to erase halogen substituents in diverse organohalogen compounds. Subfamily I and II HLDs are well-characterized enzymes, but the mode and purpose of multimerization of subfamily III HLDs are unknown. Here we probe the structural organization of DhmeA, a subfamily III HLD-like enzyme from the archaeon Haloferax mediterranei, by combining cryo-electron microscopy (cryo-EM) and x-ray crystallography. We show that full-length wild-type DhmeA forms diverse quaternary structures, ranging from small oligomers to large supramolecular ring-like assemblies of various sizes and symmetries. We optimized sample preparation steps, enabling three-dimensional reconstructions of an oligomeric species by single-particle cryo-EM. Moreover, we engineered a crystallizable mutant (DhmeAΔGG ) that provided diffraction-quality crystals. The 3.3 Å crystal structure reveals that DhmeAΔGG forms a ring-like 20-mer structure with outer and inner diameter of ~200 and ~80 Å, respectively. An enzyme homodimer represents a basic repeating building unit of the crystallographic ring. Three assembly interfaces (dimerization, tetramerization, and multimerization) were identified to form the supramolecular ring that displays a negatively charged exterior, while its interior part harboring catalytic sites is positively charged. Localization and exposure of catalytic machineries suggest a possible processing of large negatively charged macromolecular substrates.

Tools for computational design and high-throughput screening of therapeutic enzymes.

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.