Feng Zhang lab’s recent work published in Science introduces a novel algorithm, fast locality-sensitive hashing-based clustering (FLSHclust), designed for mining large sequencing databases. This approach aims to address the limitations of existing methods in discovering rare protein families due to the exponential growth of databases. The research focused on systematically enumerating CRISPR-linked gene modules across all available sequencing data. CRISPR-Cas systems, known for their role in microbial immune defense and as the basis for genome editing technologies, have recently been linked to diverse biochemical activities like transposition and protease activity. The study hypothesizes that many more enzymatic activities associated with CRISPR systems could be discovered, some of which might be rare in existing databases.

Using FLSHclust, the researchers identified 188 previously unknown CRISPR-associated systems, including several rare ones. They experimentally characterized four new systems: a type IV system with an HNH nuclease domain, demonstrating a specific interference mechanism; two type I systems with HNH domains for precise DNA cleavage, which showed potential for genome editing in human cells; and candidate type VII systems indicating RNA targeting origins from type III-E CRISPR systems. Additionally, they discovered new effector and adaptation components, associations of Mu transposons with CRISPR systems, and proteins related to type V systems. They also noted a potential anti-CRISPR mechanism involving Cas9 and other non-CRISPR repeat arrays.

The study underscores the potential of FLSHclust in efficiently clustering millions of sequences and highlights the discovery of diverse biochemical activities linked to RNA-guided mechanisms in CRISPR systems, opening avenues for biotechnological advancements.

Reference: Altae-Tran H, Kannan S, Suberski AJ, Mears KS, Demircioglu FE, Moeller L, Kocalar S, Oshiro R, Makarova KS, Macrae RK, Koonin EV, Zhang F. Uncovering the functional diversity of rare CRISPR-Cas systems with deep terascale clustering. Science. 2023 Nov 24;382(6673):eadi1910. doi: 10.1126/science.adi1910. Epub 2023 Nov 23. PMID: 37995242.
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