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  • Secondary structure specific simpler prediction models for protein backbone angles

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    Newton528716-Published.pdf (1.242Mb)
    File version
    Version of Record (VoR)
    Author(s)
    Newton, MA Hakim
    Mataeimoghadam, Fereshteh
    Zaman, Rianon
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Mataeimoghadam, Fereshteh
    Zaman, Rianon
    Newton, MAHakim A.
    Year published
    2022
    Metadata
    Show full item record
    Abstract
    MOTIVATION: Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of ...
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    MOTIVATION: Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories of secondary structures they belong to. In this paper, we propose to train separate deep learning models for each category of secondary structures. Machine learning methods strive to achieve generality over the training examples and consequently loose accuracy. In this work, we explicitly exploit classification knowledge to restrict generalisation within the specific class of training examples. This is to compensate the loss of generalisation by exploiting specialisation knowledge in an informed way. RESULTS: The new method named SAP4SS obtains mean absolute error (MAE) values of 15.59, 18.87, 6.03, and 21.71 respectively for four types of backbone angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Consequently, SAP4SS significantly outperforms existing state-of-the-art methods SAP, OPUS-TASS, and SPOT-1D: the differences in MAE for all four types of angles are from 1.5 to 4.1% compared to the best known results. AVAILABILITY: SAP4SS along with its data is available from https://gitlab.com/mahnewton/sap4ss .
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    Journal Title
    BMC Bioinformatics
    Volume
    23
    Issue
    1
    DOI
    https://doi.org/10.1186/s12859-021-04525-6
    Copyright Statement
    © The Author(s), 2021. 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
    Biological sciences
    Information and computing sciences
    Mathematical sciences
    Science & Technology
    Life Sciences & Biomedicine
    Biochemical Research Methods
    Biotechnology & Applied Microbiology
    Mathematical & Computational Biology
    Publication URI
    http://hdl.handle.net/10072/411983
    Collection
    • Journal articles

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