EnzymeMiner

EnzymeMiner identifies and prioritizes putative enzyme family members from genomic and protein sequence databases to support selection for experimental characterization.

Key Features:

• Automated screening and annotation: Automated analysis of diverse family members to detect and annotate putative enzyme homologs for downstream selection.
• Candidate prioritization: Ranks and prioritizes putative enzyme sequences for experimental characterization based on integrated sequence-derived metrics.
• Solubility prediction (SoluProt): Predicts recombinant solubility using the in-house SoluProt predictor employing machine learning to estimate likelihood of soluble expression in Escherichia coli.
• Sequence similarity network: Constructs sequence similarity networks to represent sequence identities and relationships among identified family members.
• Annotated output table: Generates an output table of annotated sequences to support selection of enzyme candidates.
• Customizable search queries: Accepts queries using Swiss-Prot sequences or user-supplied sequences with descriptions of essential residues to target specific families or subfamilies.

Scientific Applications:

• Discovery of novel biocatalysts: Identification and prioritization of candidate enzymes from large protein databases for biocatalysis research.
• Selection for experimental characterization: Prioritization of sequences most amenable to wet-lab expression and study, including those predicted soluble in Escherichia coli.
• Enzyme family exploration: Comparative analysis and mapping of enzyme family diversity, exemplified by application to the haloalkane dehalogenase family.

Methodology:

Automated screening and annotation of sequence homologs; SoluProt machine learning-based solubility prediction; construction of sequence similarity networks; integration of solubility predictions with similarity networks; query input via Swiss-Prot or custom sequences with essential residue descriptions.