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Research Article
Jisna Vellara Antony
NITC: National Institute of Technology Calicut
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5210-9583
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Knowledge about protein structure assignment enriches the structural and functional understanding of proteins. Accurate and reliable structure assignment data is crucial for secondary structure prediction systems. Since the '80s various methods based on hydrogen bond analysis and atomic coordinate geometry, followed by Machine Learning, have been employed in protein structure assignment. However, the assignment process becomes challenging when missing atoms are present in protein files. Our model develops a multi-class classifier program named DLFSA for assigning protein Secondary Structure Elements(SSE) using Convolutional Neural Networks(CNN). A fast and efficient GPU based parallel procedure extracts fragments from protein files. The model implemented in this work is trained with a subset of protein fragments and achieves 88.1% and 82.5% train and test accuracy, respectively. Our model uses only Cα coordinates for secondary structure assignments. The model is successfully tested on a few full-length proteins also. Results from the fragment-based studies demonstrate the feasibility of applying deep learning solutions for structure assignment problems.
Keywords
Protein Structure Assignment, Deep Learning, Fragment Library Creation, Convolutional Neural Networks, Protein Fragments, Protein Secondary Structures, Multi-Class Classifier
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View the published article at Journal of Molecular Modeling.
Version 1
Posted 09 Mar, 2021
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Received 03 Mar, 2021
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First submitted
On 21 Jan, 2021
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Molecular Biology
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See the published version of this preprint at Journal of Molecular Modeling.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Jisna Vellara Antony
NITC: National Institute of Technology Calicut
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5210-9583
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Journal of Molecular Modeling.
Version 1
Posted 09 Mar, 2021
No community comments so far
Reviews received
Received 03 Mar, 2021
Reviewers invited
Invitations sent on 02 Mar, 2021
First submitted
On 21 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Molecular Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Molecular Modeling.
Knowledge about protein structure assignment enriches the structural and functional understanding of proteins. Accurate and reliable structure assignment data is crucial for secondary structure prediction systems. Since the '80s various methods based on hydrogen bond analysis and atomic coordinate geometry, followed by Machine Learning, have been employed in protein structure assignment. However, the assignment process becomes challenging when missing atoms are present in protein files. Our model develops a multi-class classifier program named DLFSA for assigning protein Secondary Structure Elements(SSE) using Convolutional Neural Networks(CNN). A fast and efficient GPU based parallel procedure extracts fragments from protein files. The model implemented in this work is trained with a subset of protein fragments and achieves 88.1% and 82.5% train and test accuracy, respectively. Our model uses only Cα coordinates for secondary structure assignments. The model is successfully tested on a few full-length proteins also. Results from the fragment-based studies demonstrate the feasibility of applying deep learning solutions for structure assignment problems.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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