CRISPR/Cas System
Also known as: CRISPR, CRISPR-Cas
An adaptive immune system in prokaryotes repurposed as a programmable genome editing tool that uses guide RNAs to direct Cas nucleases to specific DNA targets.
CRISPR/Cas System is a prokaryotic adaptive immune mechanism that has been engineered into a versatile genome editing platform, using a short guide RNA to direct the Cas nuclease to create targeted double-strand breaks in DNA 1.
How It Works
In nature, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) loci store fragments of past viral invaders as spacer sequences between repeated DNA elements. When a matching virus is encountered again, the spacer is transcribed into a CRISPR RNA (crRNA) that associates with a Cas protein to form a surveillance complex. The complex scans incoming DNA for sequences complementary to the crRNA, and upon binding, the Cas nuclease cleaves the foreign DNA.
The breakthrough for genome engineering came with the simplification of the Type II system (Cas9), where a single guide RNA (sgRNA) — a fusion of the crRNA and tracrRNA — directs Cas9 to any genomic target adjacent to a protospacer adjacent motif (PAM). The resulting double-strand break is repaired by the cell’s own machinery via non-homologous end joining (NHEJ, often introducing insertions or deletions) or homology-directed repair (HDR, enabling precise edits when a donor template is supplied).
Beyond simple knockouts, catalytically dead Cas9 (dCas9) has been repurposed for transcriptional activation (CRISPRa), repression (CRISPRi), epigenetic editing, base editing, and prime editing — vastly expanding the synthetic biology toolkit.
Computational Considerations
Guide RNA design tools such as CHOPCHOP, CRISPRscan, and Benchling use machine learning models trained on empirical editing data to predict on-target cleavage efficiency and score potential off-target sites across the genome 2. These algorithms consider sequence features, chromatin accessibility, and thermodynamic properties to rank candidate guides.
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Computational tools score guide RNA candidates for on-target efficiency and off-target risk using machine learning models trained on large-scale editing outcome datasets.
Related Terms
References
- Jinek M, Chylinski K, Fonfara I, et al.. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity . Science (2012) DOI
- Doench JG, Fusi N, Sullender M, et al.. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9 . Nature Biotechnology (2016) DOI