Fe(2)OG

Fe(2)OG predicts and analyzes the function of putative non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenases (i2OGdd) in protein sequences, enabling inference of probable active-site geometries and functional annotation.

Key Features:

• HMM profile-based prediction: Uses Hidden Markov Model (HMM) profiles derived from laboratory-validated i2OGdd members to predict i2OGdd function.
• Active-site geometry prediction: Predicts probable active-site geometries characterized by a ferrous iron in a hexa-coordinated distorted octahedron with 2-oxoglutarate and a displaceable water molecule.
• Sequence resource: Provides a pre-compiled list of analyzed and searchable i2OGdd-like protein sequences, including entries of potential clinical relevance.
• Dual functionality: Integrates prediction and analysis capabilities corresponding to prior tools H2OGpred and DB2OG.
• Diversity-aware profiling: Applies HMM-derived signatures to accommodate taxonomic and functional diversity and variable active-site geometries among i2OGdd enzymes.

Scientific Applications:

• Functional annotation: Assigns likely biochemical functions to putative non-haem iron(II)- and 2-oxoglutarate-dependent dioxygenases in protein datasets.
• Active-site inference: Infers probable active-site configurations to support mechanistic and structural hypotheses about i2OGdd enzymes.
• Genome and metagenome mining: Identifies and characterizes novel i2OGdd members within genomic and metagenomic protein sequences.
• Clinical sequence identification: Flags i2OGdd-like sequences within the pre-compiled collection that may have clinical relevance.

Methodology:

Fe(2)OG employs Hidden Markov Model (HMM) profiles derived from laboratory-validated i2OGdd members to predict probable active-site geometries and annotate i2OGdd function, and provides a pre-compiled set of analyzed i2OGdd-like sequences.