Facilitating crRNA Design by Integrating DNA Interaction Features of CRISPR‐Cas12a System
The CRISPR-Cas12a system has gained significant attention as a rapid nucleic acid diagnostic tool due to its crRNA-guided trans-cleavage activity. Accurately predicting the activity of different targets is significant to facilitate the crRNA availability but remains challenging. In this study, a novel approach is presented that combines molecular dynamics simulations and neural network modeling to predict the trans-cleavage activity. Unlike conventional tools that rely solely on the base sequences, our method integrated sequence features and molecular interaction features of DNA in the CRISPR-Cas12a system, significantly improving prediction accuracy. Through feature importance analysis, key sequence features that influence Cas12a trans-cleavage activity are identified. Additionally, a crRNA-DNA library with over 23 456 feature sequences from representative viruses and bacteria is established, and validated the high predictive accuracy of the model (Pearson's r = 0.9328) by screening crRNAs from reference targets. This study offers new insights into the molecular interactions of Cas12a/crRNA-DNA and provides a reliable framework for optimizing crRNA design, facilitating the application of the CRISPR-Cas12a in rapid nucleic acid diagnostics.
Authors
- Hongmei Wang (ORCID: https://orcid.org/0000-0002-7565-9040)
- Qun Wu (ORCID: https://orcid.org/0000-0001-6756-196X)
- Zhihao Yao (ORCID: https://orcid.org/0000-0001-8344-1546)
- Yao Nie (ORCID: https://orcid.org/0000-0001-8065-7640)
- Kaiyu He (ORCID: https://orcid.org/0000-0003-3373-7618)
- Liu Wang (ORCID: https://orcid.org/0000-0001-8252-0170)
- Yan Xu
- Wanglu Li
Institutions
- Jiangnan University (CN)
- ZheJiang Academy of Agricultural Sciences (CN)
- Ministry of Agriculture and Rural Affairs (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1002/advs.202501269
- Citations
- 6
- Primary Topic
- CRISPR and Genetic Engineering
- Type
- article
- Field-Weighted Citation Impact
- 4.84
Funders
- National Natural Science Foundation of China
- Jiangnan University