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Campo DC | Valor | Idioma |
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dc.contributor.author | MAK, J. K. | |
dc.contributor.author | BENDANDI, A. | |
dc.contributor.author | SALIM, J. A. | |
dc.contributor.author | MAZONI, I. | |
dc.contributor.author | MORAES, F. R. de | |
dc.contributor.author | BORRO, L. | |
dc.contributor.author | STÖRTZ, F. | |
dc.contributor.author | ROCCHIA, W. | |
dc.contributor.author | NESHICH, G. | |
dc.contributor.author | MINARY, P. | |
dc.date.accessioned | 2025-05-21T14:49:54Z | - |
dc.date.available | 2025-05-21T14:49:54Z | - |
dc.date.created | 2025-05-21 | |
dc.date.issued | 2025 | |
dc.identifier.citation | NAR Genomics and Bioinformatics, v. 7, n. 2, lqaf054, June 2025. | |
dc.identifier.uri | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1175827 | - |
dc.description | Despite advances in determining the factors influencing cleavage activity of a CRISPR–Cas9 single guide RNA (sgRNA) at an (off-)target DNA sequence, a comprehensive assessment of pertinent physico-chemical/structural descriptors is missing. In particular, studies have not yet directly exploited the information-rich internal protein 3D nanoenvironment of the sgRNA–(off-)target strand DNA pair, which we obtain by harvesting 634 980 residue-level features for CRISPR–Cas9 complexes. As a proof-of-concept study, we simulated the internal protein 3D nanoenvironment for all experimentally available single-base protospacer-adjacent motif-distal mutations for a given sgRNA–target strand pair. By determining the most relevant residue-level features for CRISPR–Cas9 off-target cleavage activity, we developed STING_CRISPR, a machine learning model delivering accurate predictive performance of off-target cleavage activity for the type of single-base mutations considered in this study. By interpreting STING_CRISPR, we identified four important Cas9 residue spatial hotspots and associated structural/physico-chemical descriptor classes influencing CRISPR–Cas9 (off-)target cleavage activity for the sgRNA–target strand pairs covered in this study. | |
dc.language.iso | eng | |
dc.rights | openAccess | |
dc.subject | Sequenciamento genético | |
dc.subject | Dinâmica molecular | |
dc.subject | Aprendizado de máquina | |
dc.subject | Simulação de nanoambiente de proteína | |
dc.subject | Machine learning | |
dc.title | Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 of f-tar get activity. | |
dc.type | Artigo de periódico | |
dc.subject.nalthesaurus | Computer simulation | |
dc.subject.nalthesaurus | Molecular dynamics | |
riaa.ainfo.id | 1175827 | |
riaa.ainfo.lastupdate | 2025-05-21 | |
dc.identifier.doi | https://doi.org/10.1093/nargab/lqaf054 | |
dc.contributor.institution | JEFFREY KELVIN MAK, UNIVERSITY OF OXFORD; ARTEMI BENDANDI, ISTITUTO ITALIANO DI TECNOLOGIA; JOSÉ AUGUSTO SALIM, UNIVERSIDADE ESTADUAL DE CAMPINAS; IVAN MAZONI, CNPTIA; FABIO ROGERIO DE MORAES, UNIVERSIDADE ESTADUAL PAULISTA "JÚLIO DE MESQUITA FILHO"; LUIZ BORRO, BEON CLARO; FLORIAN STÖRTZ, UNIVERSITY OF OXFORD; WALTER ROCCHIA, ISTITUTO ITALIANO DI TECNOLOGIA; GORAN NESIC, CNPTIA; PETER MINARY, UNIVERSITY OF OXFORD. | |
Aparece nas coleções: | Artigo em periódico indexado (CNPTIA)![]() ![]() |
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AP-Learning-utilize-2025.pdf | 2.85 MB | Adobe PDF | ![]() Visualizar/Abrir |