| Title: | Naive prediction of protein backbone phi and psi dihedral angles using deep learning |
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| Authors: | ID Broz, Matic (Author)
ID Jukič, Marko (Author)
ID Bren, Urban (Author) |
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| Files: |  molecules-28-07046.pdf (3,60 MB)
MD5: 9667B704BA6EECBE99A508438ACE2A15
 https://www.mdpi.com/1420-3049/28/20/7046
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FKKT - Faculty of Chemistry and Chemical Engineering
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| Abstract: | 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. |
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| Keywords: | protein structure prediction, backbone dihedral angles, deep neural network, fully connected neural network, FCNN, protein secondary structure prediction |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 01.09.2023 |
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| Article acceptance date: | 09.10.2023 |
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| Publication date: | 12.10.2023 |
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| Publisher: | MDPI |
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| Year of publishing: | 2023 |
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| Number of pages: | 19 str. |
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| Numbering: | Vol. 28, iss. 20, [article no.] 7046 |
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| PID: | 20.500.12556/DKUM-86438  |
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| UDC: | 54 |
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| ISSN on article: | 1420-3049 |
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| COBISS.SI-ID: | 168255235 |
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| DOI: | 10.3390/molecules28207046 |
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| Publication date in DKUM: | 01.12.2023 |
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| Views: | 597 |
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| Downloads: | 191 |
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| Metadata: |    |
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| Categories: | Misc.
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