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CRISPR–Cas interference is mediated by Cas effector nucleases that are either components of multisubunit complexes—in class 1 CRISPR–Cas systems—or domains of a single protein—in class 2 systems1–3. Here we show that the subtype III-E effector Cas7-11 is a single-protein effector in the class 1 CRISPR–Cas systems originating from the fusion of a putative Cas11 domain and multiple Cas7 subunits that are derived from subtype III-D. Cas7-11 from Desulfonema ishimotonii (DiCas7-11), when expressed in Escherichia coli, has substantial RNA interference effectivity against mRNAs and bacteriophages. Similar to many class 2 effectors—and unique among class 1 systems—DiCas7-11 processes pre-CRISPR RNA into mature CRISPR RNA (crRNA) and cleaves RNA at positions defined by the target:spacer duplex, without detectable non-specific activity. We engineered Cas7-11 for RNA knockdown and editing in mammalian cells. We show that Cas7-11 has no effects on cell viability, whereas other RNA-targeting tools (such as short hairpin RNAs and Cas13) show substantial cell toxicity4,5. This study illustrates the evolution of a single-protein effector from multisubunit class 1 effector complexes, expanding our understanding of the diversity of CRISPR systems. Cas7-11 provides the basis for new programmable RNA-targeting tools that are free of collateral activity and cell toxicity. Cas7-11—the fusion of a putative Cas11 domain and four Cas7 subunits—cleaves RNA without detectable non-specific activity and, when optimized for RNA knockdown and editing in mammalian cells, has no effects on cell viability.

Nanoformulations are transforming our capacity to effectively deliver and treat a myriad of conditions. However, many nanoformulation approaches still suffer from high production complexity and low drug loading. One potential solution relies on harnessing co-assembly of drugs and small molecular excipients to facilitate nanoparticle formation through solvent exchange without the need for chemical synthesis, generating nanoparticles with up to 95% drug loading. However, there is currently no understanding which of the millions of possible combinations of small molecules can result in the formation of these nanoparticles. Here we report the development of a high-throughput screening platform coupled to machine learning to enable the rapid evaluation of such nanoformulations. Our platform identified 101 novel self-assembling drug nanoparticles from 2.1 million pairings derived from 788 candidate drugs with one of 2686 excipients, spanning treatments for multiple diseases and often harnessing well-known food additives, vitamins, or approved drugs as carrier materials – with potential for accelerated approval and translation. Given their long-term stability and potential for clinical impact, we further characterize novel sorafenib-glycyrrhizin and terbinafine-taurocholic acid nanoparticles ex vivo and in vivo. We anticipate that this platform could accelerate the development of safer and more efficacious nanoformulations with high drug loadings for a wide range of therapeutics.