Computational framework for predictive biodegradation.

Area of Science:

• Environmental chemistry
• Computational biology
• Biotechnology

Background:

• Increasing release of anthropogenic chemicals poses risks to ecosystems and human health.
• Predicting biodegradability and engineering alternative degradation routes for xenobiotics is crucial.
• Existing methods for predicting biodegradation pathways are limited.

Purpose of the Study:

• To develop and validate a computational framework (BNICE) for predicting novel biodegradation pathways of xenobiotics.
• To expand known biodegradation reaction networks by generating new compounds and reactions.
• To assess the thermodynamic feasibility of novel biodegradation pathways for potential metabolic engineering.

Main Methods:

• Application of the BNICE framework to diverse xenobiotic compounds (4-chlorobiphenyl, phenanthrene, gamma-hexachlorocyclohexane, 1,2,4-trichlorobenzene).
• Comparison of BNICE predictions with experimentally determined biodegradation routes.
• In-depth pathway and thermodynamic analyses of novel reactions, particularly for 1,2,4-trichlorobenzene.

Main Results:

• BNICE successfully reproduced known biodegradation pathways for the tested compounds.
• The framework generated novel compounds and reactions, expanding existing biodegradation networks.
• Novel biodegradation pathways for 1,2,4-trichlorobenzene were identified as thermodynamically feasible.

Conclusions:

• BNICE is a powerful tool for predicting novel biodegradation pathways of xenobiotics.
• The generated pathways offer thermodynamically feasible alternatives to known routes.
• BNICE provides valuable insights for metabolic engineering strategies aimed at degrading persistent environmental pollutants.