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Updated: Sep 11, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Optimizing Global Network Alignment With a Genetic Algorithm: Leveraging Pre-Trained Embeddings for Protein Sequences

Warith Eddine Djeddi, Sadok Ben Yahia, Gayo Diallo

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    GA2Vec aligns multiple protein-protein interaction (PPI) networks globally using genetic algorithms and protein sequence embeddings. This novel method enhances accuracy and efficiency in understanding complex biological networks.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Network Science

    Background:

    • Protein-protein interaction (PPI) networks are crucial for understanding cellular processes.
    • Existing methods for aligning PPI networks face challenges in balancing accuracy and computational efficiency.
    • Identifying cross-species network similarities and predicting protein complexes are key objectives.

    Purpose of the Study:

    • To introduce GA2Vec, a novel approach for globally aligning multiple PPI networks in a many-to-many fashion.
    • To leverage advanced embedding techniques for reconstructing weighted PPI networks and incorporating functional similarity.
    • To optimize network alignment using a genetic algorithm for improved accuracy and efficiency.

    Main Methods:

    • GA2Vec utilizes vector embeddings from ProtBERT, ESM-2, and ProtT5-XL-UniRef50 for protein sequences.
    • Gene Ontology (GO) term embeddings from Anc2vec are incorporated to capture functional similarity.
    • A genetic algorithm refines candidate clusters generated by community detection algorithms using a fitness function based on embedding similarities.

    Main Results:

    • GA2Vec achieves robust global network alignment across diverse biological networks (eukaryotic, prokaryotic, SARS-CoV, virus-host).
    • Demonstrates effective alignment between SARS-CoV-2 and SARS-CoV-1 PPI networks.
    • Balances key metrics including F1 score, cluster interaction quality (CIQ), internal cluster quality (ICQ), consistent clusters, and sensitivity.

    Conclusions:

    • GA2Vec offers an effective and adaptable solution for global PPI network alignment.
    • The method successfully integrates sequence and functional information for enhanced network analysis.
    • GA2Vec provides a robust framework for comparative network biology and understanding virus-host interactions.