Simple topological properties predict functional misannotations in a metabolic network.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Sequence database misannotations impede automated gene function annotation.
• Sequence-independent methods are crucial for identifying misannotated gene products.
• Metabolic network analysis can reveal errors in automated genome annotations, such as dead-end reactions.

Purpose of the Study:

• To develop and validate a sequence-independent computational tool for identifying misannotated enzyme functions.
• To assess the accuracy and generalizability of a machine-learning approach using metabolic network topology.

Main Methods:

• A random forest machine-learning model was trained using topological features of metabolic networks.
• The model was trained on curated sets of correct and incorrect enzyme assignments.
• Performance was evaluated using 5-fold cross-validation and testing on unseen enzyme superfamilies.

Main Results:

• The random forest model achieved up to 86% accuracy in predicting enzyme annotation validity.
• The classifier demonstrated successful extrapolation to enzyme superfamilies not present in the training data.
• Application to automated genome annotations yielded ~60% accuracy, with annotation quality inversely correlated with phylogenetic distance to model organisms.

Conclusions:

• Machine learning utilizing metabolic network topology is an effective strategy for detecting misannotated enzyme functions.
• This approach enhances the reliability of automated genome annotations and metabolic models.
• The method shows promise for improving the quality of genomic data across diverse species.