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Abstract CRISPR–Cas9 (clustered regularly interspaced short palindromic repeats–CRISPR-associated protein 9) has been revolutionizing genome engineering, and in-depth understanding of mechanisms governing its DNA discrimination is critical for continuing technology advances. An arginine-rich bridge helix (BH) connecting the nuclease lobe and the recognition lobe, which is conserved across the Cas9 family, exists in a helix–loop–helix conformation in the apo wild-type protein but converts to a long contiguous helix in the Cas9/RNA binary complex. In this work, distances measured with spin labels were utilized to investigate BH’s conformational transitions in the solution state upon single-guide RNA (sgRNA) binding, which is a critical early event preceding DNA binding and cleavage. Analyses show that sgRNA binding drives BH conformational changes in the wild-type SpyCas9 (SpyCas9WT) as well as in two BH-loop variants, SpyCas92Pro and SpyCas92Ala. Each Cas9–sgRNA binary complex, however, exhibits distinct BH features that reveal mutation-specific effects on helical integrity versus side-chain interactions. In addition, the BH conformational variations can be correlated to the observed changes in the mismatch cleavage profiles of the Cas9 variants. The work represents the first use of distances measured by site-directed spin labeling to investigate Cas9 protein conformational changes in the solution state and advances our understanding on the structure–dynamic–function relationship governing DNA target discrimination by Cas9.
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BoostMEC: predicting CRISPR-Cas9 cleavage efficiency through boosting models
Abstract Background In the CRISPR-Cas9 system, the efficiency of genetic modifications has been found to vary depending on the single guide RNA (sgRNA) used. A variety of sgRNA properties have been found to be predictive of CRISPR cleavage efficiency, including the position-specific sequence composition of sgRNAs, global sgRNA sequence properties, and thermodynamic features. While prevalent existing deep learning-based approaches provide competitive prediction accuracy, a more interpretable model is desirable to help understand how different features may contribute to CRISPR-Cas9 cleavage efficiency. Results We propose a gradient boosting approach, utilizing LightGBM to develop an integrated tool, BoostMEC (Boosting Model for Efficient CRISPR), for the prediction of wild-type CRISPR-Cas9 editing efficiency. We benchmark BoostMEC against 10 popular models on 13 external datasets and show its competitive performance. Conclusions BoostMEC can provide state-of-the-art predictions of CRISPR-Cas9 cleavage efficiency for sgRNA design and selection. Relying on direct and derived sequence features of sgRNA sequences and based on conventional machine learning, BoostMEC maintains an advantage over other state-of-the-art CRISPR efficiency prediction models that are based on deep learning through its ability to produce more interpretable feature insights and predictions.
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- Award ID(s):
- 1764421
- PAR ID:
- 10427889
- Date Published:
- Journal Name:
- BMC Bioinformatics
- Volume:
- 23
- Issue:
- 1
- ISSN:
- 1471-2105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1186/s12859-022-04998-z
