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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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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Protein-Protein Interaction Sites Prediction Using Batch Normalization Based CNNs and Oversampling Method

Changkun Jiang, Weipeng Lv, Jianqiang Li

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    This study introduces a new computational model using convolutional neural networks (CNNs) and Borderline-SMOTE to accurately predict protein-protein interaction (PPI) sites, overcoming data imbalance challenges for better drug discovery.

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    Area of Science:

    • Computational biology
    • Bioinformatics
    • Machine learning in proteomics

    Background:

    • Protein-protein interactions (PPIs) are crucial for understanding protein functions and developing therapeutics.
    • Experimental identification of PPI sites is costly and time-consuming.
    • Accurate prediction of PPI sites is hindered by imbalanced datasets.

    Purpose of the Study:

    • To develop a novel computational model for predicting protein-protein interaction sites.
    • To address the challenge of sample imbalance in PPI site prediction.
    • To improve the accuracy and stability of PPI site prediction models.

    Main Methods:

    • A hybrid model combining Convolutional Neural Networks (CNNs) with Batch Normalization.
    • Application of the Borderline-SMOTE oversampling technique to mitigate data imbalance.
    • Feature extraction using a sliding window approach to capture residue context.

    Main Results:

    • Achieved high prediction accuracies of 88.6%, 89.9%, and 86.7% on three public datasets.
    • Demonstrated superior performance compared to existing state-of-the-art methods.
    • Ablation experiments confirmed the significant contribution of Batch Normalization to model generalization and stability.

    Conclusions:

    • The proposed CNN-based model effectively predicts protein-protein interaction sites, outperforming current methods.
    • Batch Normalization enhances model generalization and prediction stability.
    • This approach offers a more efficient and accurate alternative for PPI site identification, aiding drug discovery efforts.