Prediction of enzymatic pathways by integrative pathway mapping

Area of Science:

• Biochemistry
• Systems Biology
• Structural Biology

Background:

• The functions of many proteins, particularly enzymes, remain unknown.
• Enzyme function is typically understood through substrates, products, and their role in metabolic networks.

Purpose of the Study:

• To present a novel computational method for predicting the functions of orphan enzymes.
• To organize orphan enzymes into linear metabolic pathways using computational analysis.

Main Methods:

• Developed an integrative pathway mapping method.
• Utilized structural and network restraints derived from virtual screening, chemoinformatics, genomic context, and ligand-binding data.
• Predicted metabolic pathways by satisfying these restraints.

Main Results:

• Successfully predicted the L-gulonate catabolic pathway in *Haemophilus influenzae* Rd KW20.
• Validated the computational predictions through experimental enzymology, crystallography, and metabolomics.
• Demonstrated the method's effectiveness in assigning functions to orphan enzymes.

Conclusions:

• The integrative pathway mapping method provides a robust approach to determine orphan enzyme functions.
• This method bridges structural biology and systems biology by analyzing molecular networks.
• The approach is extensible to various molecular networks beyond metabolic pathways.