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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein

Renu Vyas, Sanket Bapat, Purva Goel

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 24, 2017
    PubMed
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    This study introduces a novel Genetic Programming approach to predict protein-protein interactions (PPIs) involved in diseases. The method accurately identifies cancer-related PPIs and distinguishes them from other disease-related interactions.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Systems Biology

    Background:

    • Protein-protein interactions (PPIs) are crucial for cellular functions and disease mechanisms.
    • Experimental PPI prediction methods are labor-intensive and prone to false positives.
    • Accurate prediction of disease-related PPIs is essential for understanding disease pathways.

    Purpose of the Study:

    • To develop a new computational method for predicting disease-related PPIs.
    • To assess the accuracy and generalization ability of the developed model.
    • To evaluate the model's capability in discriminating PPIs across different diseases.

    Main Methods:

    • Utilized Genetic Programming (GP) based Symbolic Regression (SR).
    • Constructed a predictive model using a dataset of 135 cancer-related PPI complexes.
    • Validated the model on a separate dataset of diabetes-related PPI complexes.

    Main Results:

    • Achieved a high correlation coefficient (CC) of 0.893.
    • Obtained low root mean square error (RMSE) and mean absolute percentage error (MAPE) values.
    • Demonstrated the model's effectiveness in discriminating cancer PPIs from other disease PPIs, indicated by significantly low CC values on the diabetes dataset.

    Conclusions:

    • The developed GP-SR model accurately predicts cancer-related PPI binding energy.
    • The model serves as an effective classifier for distinguishing cancer PPIs from those of other diseases.
    • This approach offers a promising computational tool for PPI analysis in disease research.