Dialpuri, Jordan S, Agirre, Jon orcid.org/0000-0002-1086-0253, Cowtan, Kathryn D orcid.org/0000-0002-0189-1437 et al. (1 more author) (2024) NucleoFind:a deep-learning network for interpreting nucleic acid electron density. Nucleic Acids Research. ISSN 0305-1048
Abstract
Nucleic acid electron density interpretation after phasing by molecular replacement or other methods remains a difficult problem for computer programs to deal with. Programs tend to rely on time-consuming and computationally exhaustive searches to recognise characteristic features. We present NucleoFind, a deep-learning-based approach to interpreting and segmenting electron density. Using an electron density map from X-ray crystallography obtained after molecular replacement, the positions of the phosphate group, sugar ring and nitrogenous base group can be predicted with high accuracy. On average, 78% of phosphate atoms, 85% of sugar atoms and 83% of base atoms are positioned in predicted density after giving NucleoFind maps produced following successful molecular replacement. NucleoFind can use the wealth of context these predicted maps provide to build more accurate and complete nucleic acid models automatically.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Chemistry (York) |
Depositing User: | Pure (York) |
Date Deposited: | 28 Aug 2024 07:50 |
Last Modified: | 06 Feb 2025 00:13 |
Published Version: | https://doi.org/10.1093/nar/gkae715 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1093/nar/gkae715 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216531 |