Title:	Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 of f-tar get activity.
Authors:	MAK, J. K. BENDANDI, A. SALIM, J. A. MAZONI, I. MORAES, F. R. de BORRO, L. STÖRTZ, F. ROCCHIA, W. NESHICH, G. MINARY, P.
Affiliation:	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.
Date Issued:	2025
Citation:	NAR Genomics and Bioinformatics, v. 7, n. 2, lqaf054, June 2025.
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.
NAL Thesaurus:	Computer simulation Molecular dynamics
Keywords:	Sequenciamento genético Dinâmica molecular Aprendizado de máquina Simulação de nanoambiente de proteína Machine learning
DOI:	https://doi.org/10.1093/nargab/lqaf054
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CNPTIA)