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  • Enhancing protein backbone angle prediction by using simpler models of deep neural networks

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    Author(s)
    Mataeimoghadam, Fereshteh
    Newton, MA Hakim
    Dehzangi, Abdollah
    Karim, Abdul
    Jayaram, B
    Ranganathan, Shoba
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Newton, MAHakim A.
    Karim, Abdul
    Mataeimoghadam, Fereshteh
    Year published
    2020
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    Abstract
    Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an overall trend to employ more and more complex neural networks and then to throw more and more features to the neural networks. While more features might add more predictive power to the neural network, we argue that redundant features could rather clutter the scenario and more ...
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    Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an overall trend to employ more and more complex neural networks and then to throw more and more features to the neural networks. While more features might add more predictive power to the neural network, we argue that redundant features could rather clutter the scenario and more complex neural networks then just could counterbalance the noise. From artificial intelligence and machine learning perspectives, problem representations and solution approaches do mutually interact and thus affect performance. We also argue that comparatively simpler predictors can more easily be reconstructed than the more complex ones. With these arguments in mind, we present a deep learning method named Simpler Angle Predictor (SAP) to train simpler DNN models that enhance protein backbone angle prediction. We then empirically show that SAP can significantly outperform existing state-of-the-art methods on well-known benchmark datasets: for some types of angles, the differences are 6–8 in terms of mean absolute error (MAE). The SAP program along with its data is available from the website https://gitlab.com/mahnewton/sap.
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    Journal Title
    Scientific Reports
    Volume
    10
    Issue
    1
    DOI
    https://doi.org/10.1038/s41598-020-76317-6
    Copyright Statement
    © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
    Subject
    Artificial Intelligence and Image Processing
    Science & Technology
    Multidisciplinary Sciences
    Science & Technology - Other Topics
    SECONDARY STRUCTURE PREDICTION
    REAL-VALUE PREDICTION
    Publication URI
    http://hdl.handle.net/10072/402535
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    • Journal articles

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