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dc.contributor.authorMAK, J. K.
dc.contributor.authorBENDANDI, A.
dc.contributor.authorSALIM, J. A.
dc.contributor.authorMAZONI, I.
dc.contributor.authorMORAES, F. R. de
dc.contributor.authorBORRO, L.
dc.contributor.authorSTÖRTZ, F.
dc.contributor.authorROCCHIA, W.
dc.contributor.authorNESHICH, G.
dc.contributor.authorMINARY, P.
dc.date.accessioned2025-05-21T14:49:54Z-
dc.date.available2025-05-21T14:49:54Z-
dc.date.created2025-05-21
dc.date.issued2025
dc.identifier.citationNAR Genomics and Bioinformatics, v. 7, n. 2, lqaf054, June 2025.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1175827-
dc.descriptionDespite 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.isoeng
dc.rightsopenAccess
dc.subjectSequenciamento genético
dc.subjectDinâmica molecular
dc.subjectAprendizado de máquina
dc.subjectSimulação de nanoambiente de proteína
dc.subjectMachine learning
dc.titleLearning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 of f-tar get activity.
dc.typeArtigo de periódico
dc.subject.nalthesaurusComputer simulation
dc.subject.nalthesaurusMolecular dynamics
riaa.ainfo.id1175827
riaa.ainfo.lastupdate2025-05-21
dc.identifier.doihttps://doi.org/10.1093/nargab/lqaf054
dc.contributor.institutionJEFFREY 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.
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