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  • Naive prediction of protein backbone phi and psi dihedral angles using deep learning [Elektronski vir]
    Broz, Matic ; Jukič, Marko ; Bren, Urban
    Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant ... progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone φ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the φ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies.
    Source: Molecules [Elektronski vir]. - ISSN 1420-3049 (Vol. 28, iss. 20, [article no.] 7046, Okt. 2023, 19 str.)
    Type of material - e-article ; adult, serious
    Publish date - 2023
    Language - english
    COBISS.SI-ID - 168255235

    Link(s):

    Digital Library of the University of Maribor – DLUM
    DOI

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