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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...

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Autores principales: Kozlovskii, Igor, Popov, Petr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
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.
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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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