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A Protocol for Computer-Based Protein Structure and Function Prediction
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TANGO: A GO-Term Embedding Based Method for Protein Semantic Similarity Prediction.

Hongxiao Wang, Hao Zheng, Danny Z Chen

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 14, 2022
    PubMed
    Summary

    TANGO enhances protein semantic similarity prediction by incorporating Gene Ontology (GO) taxonomy and term importance. This novel framework improves biological discovery by better representing protein attributes and their relationships.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Protein semantic similarity (PSS) is crucial for biological discovery.
    • Existing methods often use Gene Ontology (GO) graphs for protein attribute representation.
    • These methods overlook GO term taxonomy and differential term contributions.

    Purpose of the Study:

    • To develop a novel framework, TANGO, for quantitatively predicting PSS.
    • To address limitations in current methods by incorporating GO term taxonomy and term importance.
    • To improve the accuracy of protein similarity measurement for enhanced biological insights.

    Main Methods:

    • TANGO utilizes a TAxoNomy-aware embedding module encoding topological distances within the GO graph hierarchy.
    • An aggreGatiOn module mines GO term concept dependencies and annotation correlations.
    • This approach generates more accurate GO term and protein embeddings.

    Main Results:

    • TANGO significantly outperforms existing methods on two PSS metrics across multiple datasets.
    • The framework effectively captures taxonomic relationships and varying contributions of GO terms.
    • Improved protein embeddings lead to more precise semantic similarity measurements.

    Conclusions:

    • TANGO offers a more accurate approach to predicting protein semantic similarity.
    • The framework's ability to leverage GO term taxonomy and importance enhances biological discovery.
    • This work provides a valuable tool for bioinformatics and computational biology research.