Selenzyme

Selenzyme selects and ranks candidate enzymes to facilitate metabolic pathway design in synthetic biology by evaluating sequence- and function-related properties for constructing biosynthetic routes to target compounds.

Key Features:

• Guided decision-making process: Implements a stepwise decision workflow to shortlist optimal enzyme candidates for individual pathway steps.
• Sequence similarity and catalytic reaction matching: Evaluates candidates based on sequence similarity and compatibility with specified catalytic reactions.
• Phylogenetic distance assessment: Computes phylogenetic distances between source organisms and intended host species to inform host compatibility.
• Multiple sequence alignment and conserved region identification: Produces multiple alignments that highlight conserved regions across candidate sequences.
• Catalytic site and active region prediction: Identifies predicted catalytic sites and active regions within enzyme sequences.
• Physicochemical property prediction: Assesses properties including solubility and transmembrane regions relevant to expression and localization.
• Bespoke sequence selection for automated workflows: Generates sequence selections tailored for automated biofoundry workflows.
• Integration with SYNBIOCHEM pipeline: Operates as part of the SYNBIOCHEM pathway design stage for cohesive pipeline integration.

Scientific Applications:

• Metabolic pathway design for target compound biosynthesis: Supports selection of enzymes for constructing pathways to produce pharmaceuticals, biomaterials, and other chemicals.
• Host compatibility and heterologous expression planning: Informs enzyme selection based on phylogenetic distance and physicochemical predictions to improve compatibility with intended hosts.
• Automation and DBTL support in biofoundries: Supplies sequence selections and analyses suitable for incorporation into design-build-test-learn cycles and automated workflows.

Methodology:

Selenzyme queries existing databases and tools and evaluates candidate enzymes by sequence similarity, catalytic reaction matching, phylogenetic relationships, multiple sequence alignment for conserved regions, prediction of catalytic/active sites, and prediction of physicochemical properties such as solubility and transmembrane regions, producing sequence selections for automated workflows.