Network-based function prediction and interactomics: the case for metabolic enzymes.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput sequencing generates vast amounts of DNA data, necessitating efficient methods for protein functional annotation.
• Traditional functional annotation relies heavily on amino-acid sequence similarity, which has limitations as homology detection reaches its maximum.
• Homologous proteins can diverge in function, and pairwise comparisons fail to detect distant evolutionary relationships.

Purpose of the Study:

• To review the current state of network-based approaches for protein function prediction.
• To highlight the challenges and successes of these computational methods.
• To emphasize the potential of network-based strategies for annotating uncharacterized proteins.

Main Methods:

• Exploiting protein context within cellular networks for functional inference.
• Integrating diverse types of functional interactions to enhance prediction accuracy and coverage.
• Building upon traditional homology-based approaches with network analysis.

Main Results:

• Network-based methods provide complementary insights to homology-driven approaches.
• These techniques can address the 'moonlighting' phenomenon, assigning multiple functions to proteins.
• Integration of diverse interactions boosts confidence and coverage in functional predictions.

Conclusions:

• Network-based functional inference is crucial for understanding uncharacterized proteins in the era of big data.
• These methods improve our understanding of protein interplay and cellular function.
• The time is opportune to leverage network-based approaches for genomic database enrichment.