Learning to utilize internal protein 3D nanoenvironment descriptors in predicting CRISPR–Cas9 of f-target activity.
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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.
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Sequenciamento genético, Dinâmica molecular, Aprendizado de máquina, Simulação de nanoambiente de proteína, Machine learning, Computer simulation, Molecular dynamics