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Structure-based deep learning for binding site detection in nucleic acid macromolecules
Structure-based drug design (SBDD) targeting nucleic acid macromolecules, particularly RNA, is a gaining momentum research direction that already resulted in several FDA-approved compounds. Similar to proteins, one of the critical components in SBDD for RNA is the correct identification of the bindi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633674/ https://www.ncbi.nlm.nih.gov/pubmed/34859211 http://dx.doi.org/10.1093/nargab/lqab111 |
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author | Kozlovskii, Igor Popov, Petr |
author_facet | Kozlovskii, Igor Popov, Petr |
author_sort | Kozlovskii, Igor |
collection | PubMed |
description | Structure-based drug design (SBDD) targeting nucleic acid macromolecules, particularly RNA, is a gaining momentum research direction that already resulted in several FDA-approved compounds. Similar to proteins, one of the critical components in SBDD for RNA is the correct identification of the binding sites for putative drug candidates. RNAs share a common structural organization that, together with the dynamic nature of these molecules, makes it challenging to recognize binding sites for small molecules. Moreover, there is a need for structure-based approaches, as sequence information only does not consider conformation plasticity of nucleic acid macromolecules. Deep learning holds a great promise to resolve binding site detection problem, but requires a large amount of structural data, which is very limited for nucleic acids, compared to proteins. In this study we composed a set of ∼2000 nucleic acid-small molecule structures comprising ∼2500 binding sites, which is ∼40-times larger than previously used one, and demonstrated the first structure-based deep learning approach, BiteNet(N), to detect binding sites in nucleic acid structures. BiteNet(N) operates with arbitrary nucleic acid complexes, shows the state-of-the-art performance, and can be helpful in the analysis of different conformations and mutant variants, as we demonstrated for HIV-1 TAR RNA and ATP-aptamer case studies. |
format | Online Article Text |
id | pubmed-8633674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86336742021-12-01 Structure-based deep learning for binding site detection in nucleic acid macromolecules Kozlovskii, Igor Popov, Petr NAR Genom Bioinform Standard Article Structure-based drug design (SBDD) targeting nucleic acid macromolecules, particularly RNA, is a gaining momentum research direction that already resulted in several FDA-approved compounds. Similar to proteins, one of the critical components in SBDD for RNA is the correct identification of the binding sites for putative drug candidates. RNAs share a common structural organization that, together with the dynamic nature of these molecules, makes it challenging to recognize binding sites for small molecules. Moreover, there is a need for structure-based approaches, as sequence information only does not consider conformation plasticity of nucleic acid macromolecules. Deep learning holds a great promise to resolve binding site detection problem, but requires a large amount of structural data, which is very limited for nucleic acids, compared to proteins. In this study we composed a set of ∼2000 nucleic acid-small molecule structures comprising ∼2500 binding sites, which is ∼40-times larger than previously used one, and demonstrated the first structure-based deep learning approach, BiteNet(N), to detect binding sites in nucleic acid structures. BiteNet(N) operates with arbitrary nucleic acid complexes, shows the state-of-the-art performance, and can be helpful in the analysis of different conformations and mutant variants, as we demonstrated for HIV-1 TAR RNA and ATP-aptamer case studies. Oxford University Press 2021-11-26 /pmc/articles/PMC8633674/ /pubmed/34859211 http://dx.doi.org/10.1093/nargab/lqab111 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Standard Article Kozlovskii, Igor Popov, Petr Structure-based deep learning for binding site detection in nucleic acid macromolecules |
title | Structure-based deep learning for binding site detection in nucleic acid macromolecules |
title_full | Structure-based deep learning for binding site detection in nucleic acid macromolecules |
title_fullStr | Structure-based deep learning for binding site detection in nucleic acid macromolecules |
title_full_unstemmed | Structure-based deep learning for binding site detection in nucleic acid macromolecules |
title_short | Structure-based deep learning for binding site detection in nucleic acid macromolecules |
title_sort | structure-based deep learning for binding site detection in nucleic acid macromolecules |
topic | Standard Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633674/ https://www.ncbi.nlm.nih.gov/pubmed/34859211 http://dx.doi.org/10.1093/nargab/lqab111 |
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