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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Compact Genetic Algorithm-Based Feature Selection for Sequence-Based Prediction of Dengue-Human Protein Interactions.

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    Researchers developed a new computational model to predict Dengue Virus (DENV)-human protein-protein interactions (PPIs). This method identifies potential drug targets to combat dengue infection, a significant global health concern.

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    Area of Science:

    • Virology
    • Bioinformatics
    • Computational Biology

    Background:

    • Dengue Virus (DENV) infection is a major global health issue, causing millions of infections and thousands of deaths annually.
    • Despite its prevalence, many Dengue Virus-human protein-protein interactions (PPIs) crucial for understanding infection mechanisms remain undiscovered.

    Purpose of the Study:

    • To develop and validate a computational model for predicting novel Dengue Virus-human protein-protein interactions (PPIs).
    • To identify potential therapeutic targets for developing effective anti-dengue drugs.

    Main Methods:

    • Utilized sequence-based features of human and DENV proteins, including amino acid composition, dipeptide composition, conjoint triad, and pseudo amino acid composition.
    • Employed a Compact Genetic Algorithm (CGA) with Learning Vector Quantization (LVQ) for efficient feature subset selection.
    • Applied a weighted Random Forest (RF) classifier for predicting DENV-human PPIs, achieving superior performance over other methods.
    • Validated predicted interactions through literature mining, Gene Ontology (GO) assessment, and KEGG Pathway enrichment analysis.

    Main Results:

    • Successfully predicted 1013 novel PPIs between 335 human proteins and 10 DENV proteins.
    • The proposed model effectively integrates diverse sequence-based features for accurate PPI prediction.
    • Validated interactions provide a robust foundation for further experimental investigation.

    Conclusions:

    • The developed computational model offers a powerful tool for identifying DENV-human PPIs, accelerating the discovery of new therapeutic targets.
    • This study significantly contributes to the field of anti-dengue drug discovery by highlighting potential interaction pathways.