Transmembrane nanostructures like ion channels and transporters perform key biological functions by controlling flow of molecules across lipid bilayers. Much work has gone into engineering artificial nanopores and applications in selective gating of molecules, label-free detection/sensing of biomolecules and DNA sequencing have shown promise. Here, we use DNA origami to create a synthetic 9 nm wide DNA nanopore, controlled by programmable, lipidated flaps and equipped with a size-selective gating system for the translocation of macromolecules. Successful assembly and insertion of the nanopore into lipid bilayers are validated by transmission electron microscopy (TEM), while selective translocation of cargo and the pore mechanosensitivity are studied using optical methods, including single-molecule, total internal reflection fluorescence (TIRF) microscopy. Size-specific cargo translocation and oligonucleotide-triggered opening of the pore are demonstrated showing that the DNA nanopore can function as a real-time detection system for external signals, offering potential for a variety of highly parallelized sensing applications.

Combinatorial high throughput methodologies are central for both screening and discovery in synthetic biochemistry and biomedical sciences. They are, however, often reliant on large scale analyses and thus limited by long running time and excessive materials cost. We herein present Single PARticle Combinatorial multiplexed Liposome fusion mediated by DNA (SPARCLD), for the parallelized, multi-step and nondeterministic fusion of individual zeptoliter nanocontainers. We observed directly the efficient (>93%), and leakage free stochastic fusion sequences for arrays of surface tethered target liposomes with six freely diffusing populations of cargo liposomes, each functionalized with individual lipidated ssDNA (LiNA) and fluorescent barcoded by distinct ratio of chromophores. The stochastic fusion results in distinct permutation of fusion sequences for each autonomous nanocontainer. Real-time TIRF imaging allowed the direct observation of >16000 fusions and 566 distinct fusion sequences accurately classified using machine learning.The high-density arrays of surface tethered target nanocontainers ~42,000 containers per mm 2 offers entire combinatorial multiplex screens using only picograms of material.

Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET’s capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.