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An Enhanced Protein Fold Recognition for Low Similarity Datasets Using Convolutional and Skip-Gram Features With Deep

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    Area of Science:

    • Structural biology
    • Computational biology
    • Bioinformatics

    Background:

    • Protein fold recognition is crucial for understanding protein structure and function.
    • Existing machine learning methods often struggle to exceed 80% accuracy on benchmark datasets.
    • Accurate fold recognition aids in predicting tertiary structures and biological functions.

    Purpose of the Study:

    • To develop an effective deep neural network model for enhanced protein fold recognition.
    • To extract novel global and local features using Convolutional (Conv) and SkipXGram bi-gram (SXGbg) techniques.
    • To achieve state-of-the-art accuracy in protein fold classification.

    Main Methods:

    • Extraction of global and local features using proposed Convolutional (Conv) and SkipXGram bi-gram (SXGbg) techniques.
    • Implementation of a deep neural network for fold recognition.
    • Evaluation on benchmark datasets including SCOPe_2.07, DD, EDD, and TG.

    Main Results:

    • Achieved 91.4% fold accuracy on a low similarity (< 25%) SCOPe_2.07 dataset.
    • Obtained 85.9% (DD), 95.8% (EDD), and 88.8% (TG) accuracies on benchmark datasets.
    • Outperformed state-of-the-art models by significant margins (5-30%) across datasets.

    Conclusions:

    • The proposed deep neural network model demonstrates superior performance in protein fold recognition.
    • This work sets a new benchmark, achieving >85% accuracy on all tested datasets.
    • The novel feature extraction techniques contribute to improved accuracy in structural biology tasks.