Corsano, A. | bioRxiv Science

bioRxiv2025

Split Cas12a protospacer engineering enables ultra-specific, PAM-free detection

ABSTRACTCRISPR-Cas12a is a programmable, RNA-guided endonuclease that has revolutionized biotechnology, with applications in genome engineering and diagnostics. To induce nuclease activity, Cas12a must first interact with the target dsDNA duplex by associating with a short protospacer adjacent motif (PAM) in the sequence. In this study we have split this target duplex to create PAM-proximal and PAM-distal duplex regions, which has allowed us to regulate trans-cleavage activity when these regions are included in combination or separately. These observations on Cas12a activity led to hypotheses into the related functional mechanisms, which we have tested and that have highlighted DNA/protein interactions during Cas12a complex assembly that were not otherwise apparent. Selective destabilization of the nucleic acid complexes appears to drive greater reliance on the Cas12a protein for complex stability. We have exploited this to provide significant improvements in both structural selectivity and nucleotide specificity in PAM-proximal and PAM-distal duplex regions, respectively. The result is an architecture that shows promise as a PAM-free ultra-specific platform to resolve single nucleotide polymorphisms. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/667643v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@b1771dorg.highwire.dtl.DTLVardef@103197aorg.highwire.dtl.DTLVardef@7dd393org.highwire.dtl.DTLVardef@cd812d_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2023

Simple and rewireable biomolecular building blocks for DNA machine-learning algorithms

Deep learning algorithms, such as neural networks, enable the processing of complex datasets with many related variables, and have applications in disease diagnosis, cell profiling, and drug discovery. Beyond its use in electronic computers, neural networks have been implemented using programmable biomolecules such as DNA. This confers unique advantages such as greater portability, ability to operate without electricity, and direct analysis of patterns of biomolecules in solution. Analogous to past bottlenecks in electronic computers, the computing power of DNA-based neural networks is limited by the ability to add more computing units, i.e. neurons. This limitation exists because current architectures require many nucleic acids to model a single neuron. Each addition of a neuron to the network compounds existing problems such as long assembly times, high background signal, and cross-talk between components. Here we test three strategies to solve this limitation and improve the scalability of DNA-based neural networks: (i) enzymatic synthesis to generate high-purity neurons, (ii) spatial patterning of neuron clusters based on their network position, and (iii) encoding neuron connectivity on a universal single-stranded DNA backbone. We show that neurons implemented via these strategies activate quickly, with high signal-to-background ratio, and respond to varying input concentrations and weights. Using this neuron design, we implemented basic neural network motifs such as cascading, fan-in, and fan-out circuits. Since this design is modular, easy to synthesize, and compatible with multiple neural network architectures, we envision it will help scale DNA-based neural networks in a variety of settings. This will enable portable computing power for applications such as portable diagnostics, compact data storage, and autonomous decision making for lab-on-a-chips.

bioengineering↗