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Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks

Biomolecules (Basel, Switzerland), 2022-06, Vol.12 (6), p.841 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 2218-273X ;EISSN: 2218-273X ;DOI: 10.3390/biom12060841 ;PMID: 35740966

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  • Title:
    Automated Protein Secondary Structure Assignment from Cα Positions Using Neural Networks
  • Author: Saqib, Mohammad N. ; Kryś, Justyna D. ; Gront, Dominik
  • Subjects: Accuracy ; Algorithms ; Amino acids ; Automation ; Computer applications ; deep learning ; Hydrogen bonds ; machine learning ; multi-class classifier ; Neural networks ; Peptides ; protein secondary structure ; Protein structure ; protein structure prediction ; Proteins ; Secondary structure
  • Is Part Of: Biomolecules (Basel, Switzerland), 2022-06, Vol.12 (6), p.841
  • Description: The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E (Strand), or C (Coil, also known as Loop). When particular atoms are absent from an input protein structure, the procedure becomes more complicated, especially when only the alpha carbon locations are known. Various techniques have been tested and applied to this problem during the last forty years. The application of machine learning techniques is the most recent trend. This contribution presents the HECA classifier, which uses neural networks to assign protein secondary structure types. The technique exclusively employs Cα coordinates. The Keras (TensorFlow) library was used to implement and train the neural network model. The BioShell toolkit was used to calculate the neural network input features from raw coordinates. The study’s findings show that neural network-based methods may be successfully used to take on structure assignment challenges when only Cα trace is available. Thanks to the careful selection of input features, our approach’s accuracy (above 97%) exceeded that of the existing methods.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2218-273X
    EISSN: 2218-273X
    DOI: 10.3390/biom12060841
    PMID: 35740966
  • Source: PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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