deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction
Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. Reconstruction of atomic details...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/14/11/1448 |
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| author | Justyna D. Kryś Maksymilian Głowacki Piotr Śmieja Dominik Gront |
| author_facet | Justyna D. Kryś Maksymilian Głowacki Piotr Śmieja Dominik Gront |
| author_sort | Justyna D. Kryś |
| collection | DOAJ |
| description | Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. Reconstruction of atomic details may also be required in the case of some experimental methods, like electron microscopy, which may provide C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-only structures. In this contribution, we present a new method for recovery of all backbone atom positions from just the C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> coordinates. Our approach, called deepBBQ, uses a deep convolutional neural network to predict a single internal coordinate per peptide plate, based on C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> trace geometric features, and then proceeds to recalculate the cartesian coordinates based on the assumption that the peptide plate atoms lie in the same plane. Extensive comparison with similar programs shows that our solution is accurate and cost-efficient. The deepBBQ program is available as part of the open-source bioinformatics toolkit Bioshell and is free for download and the documentation is available online. |
| format | Article |
| id | doaj-art-18adcceb0b8943b690b3e95db110e454 |
| institution | OA Journals |
| issn | 2218-273X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-18adcceb0b8943b690b3e95db110e4542025-08-20T02:08:02ZengMDPI AGBiomolecules2218-273X2024-11-011411144810.3390/biom14111448deepBBQ: A Deep Learning Approach to the Protein Backbone ReconstructionJustyna D. Kryś0Maksymilian Głowacki 1Piotr Śmieja 2Dominik Gront3Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, PolandFaculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, PolandFaculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, PolandFaculty of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, PolandCoarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. Reconstruction of atomic details may also be required in the case of some experimental methods, like electron microscopy, which may provide C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-only structures. In this contribution, we present a new method for recovery of all backbone atom positions from just the C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> coordinates. Our approach, called deepBBQ, uses a deep convolutional neural network to predict a single internal coordinate per peptide plate, based on C<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> trace geometric features, and then proceeds to recalculate the cartesian coordinates based on the assumption that the peptide plate atoms lie in the same plane. Extensive comparison with similar programs shows that our solution is accurate and cost-efficient. The deepBBQ program is available as part of the open-source bioinformatics toolkit Bioshell and is free for download and the documentation is available online.https://www.mdpi.com/2218-273X/14/11/1448atomic reconstructionstructural bioinformaticsdeep learningcoarse-grained modelingconvolutional neural network |
| spellingShingle | Justyna D. Kryś Maksymilian Głowacki Piotr Śmieja Dominik Gront deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction Biomolecules atomic reconstruction structural bioinformatics deep learning coarse-grained modeling convolutional neural network |
| title | deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction |
| title_full | deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction |
| title_fullStr | deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction |
| title_full_unstemmed | deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction |
| title_short | deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction |
| title_sort | deepbbq a deep learning approach to the protein backbone reconstruction |
| topic | atomic reconstruction structural bioinformatics deep learning coarse-grained modeling convolutional neural network |
| url | https://www.mdpi.com/2218-273X/14/11/1448 |
| work_keys_str_mv | AT justynadkrys deepbbqadeeplearningapproachtotheproteinbackbonereconstruction AT maksymiliangłowacki deepbbqadeeplearningapproachtotheproteinbackbonereconstruction AT piotrsmieja deepbbqadeeplearningapproachtotheproteinbackbonereconstruction AT dominikgront deepbbqadeeplearningapproachtotheproteinbackbonereconstruction |