For over 60 years, the synthetic production of new DNA sequences has helped researchers understand and engineer biology. Here we summarize methods and caveats for the de novo synthesis of DNA, with ...particular emphasis on recent technologies that allow for large-scale and low-cost production. In addition, we discuss emerging applications enabled by large-scale de novo DNA constructs, as well as the challenges and opportunities that lie ahead.
Next-Generation Digital Information Storage in DNA Church, George M.; Gao, Yuan; Kosuri, Sriram
Science (American Association for the Advancement of Science),
09/2012, Letnik:
337, Številka:
6102
Journal Article
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Digital information is accumulating at an astounding rate, straining our ability to store and archive it. DNA is among the most dense and stable information media known. The development of new ...technologies in both DNA synthesis and sequencing make DNA an increasingly feasible digital storage medium. We developed a strategy to encode arbitrary digital information in DNA, wrote a 5.27-megabit book using DNA microchips, and read the book by using next-generation DNA sequencing.
We describe an autonomous DNA nanorobot capable of transporting molecular payloads to cells, sensing cell surface inputs for conditional, triggered activation, and reconfiguring its structure for ...payload delivery. The device can be loaded with a variety of materials in a highly organized fashion and is controlled by an aptamer-encoded logic gate, enabling it to respond to a wide array of cues. We implemented several different logical AND gates and demonstrate their efficacy in selective regulation of nanorobot function. As a proof of principle, nanorobots loaded with combinations of antibody fragments were used in two different types of cell-signaling stimulation in tissue culture. Our prototype could inspire new designs with different selectivities and biologically active payloads for cell-targeting tasks.
We present an approach for engineering evolving DNA barcodes in living cells. A homing guide RNA (hgRNA) scaffold directs the Cas9-hgRNA complex to the DNA locus of the hgRNA itself. We show that ...this homing CRISPR-Cas9 system acts as an expressed genetic barcode that diversifies its sequence and that the rate of diversification can be controlled in cultured cells. We further evaluate these barcodes in cell populations and show that they can be used to record lineage history and that the barcode RNA can be amplified in situ, a prerequisite for in situ sequencing. This integrated approach will have wide-ranging applications, such as in deep lineage tracing, cellular barcoding, molecular recording, dissecting cancer biology, and connectome mapping.
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting ...molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub—halicin—that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.
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•A deep learning model is trained to predict antibiotics based on structure•Halicin is predicted as an antibacterial molecule from the Drug Repurposing Hub•Halicin shows broad-spectrum antibiotic activities in mice•More antibiotics with distinct structures are predicted from the ZINC15 database
A trained deep neural network predicts antibiotic activity in molecules that are structurally different from known antibiotics, among which Halicin exhibits efficacy against broad-spectrum bacterial infections in mice.
RNA-guided Cas9 nucleases derived from clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems have dramatically transformed our ability to edit the genomes of diverse ...organisms. We believe tools and techniques based on Cas9, a single unifying factor capable of colocalizing RNA, DNA and protein, will grant unprecedented control over cellular organization, regulation and behavior. Here we describe the Cas9 targeting methodology, detail current and prospective engineering advances and suggest potential applications ranging from basic science to the clinic.
Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein ...into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.
Engineering cells to produce valuable metabolic products is hindered by the slow and laborious methods available for evaluating product concentration. Consequently, many designs go unevaluated, and ...the dynamics of product formation over time go unobserved. In this work, we develop a framework for observing product formation in real time without the need for sample preparation or laborious analytical methods. We use genetically encoded biosensors derived from small-molecule responsive transcription factors to provide a fluorescent readout that is proportional to the intracellular concentration of a target metabolite. Combining an appropriate biosensor with cells designed to produce a metabolic product allows us to track product formation by observing fluorescence. With individual cells exhibiting fluorescent intensities proportional to the amount of metabolite they produce, high-throughput methods can be used to rank the quality of genetic variants or production conditions. We observe production of several renewable plastic precursors with fluorescent readouts and demonstrate that higher fluorescence is indeed an indicator of higher product titer. Using fluorescence as a guide, we identify process parameters that produce 3-hydroxypropionate at 4.2 g/L, 23-fold higher than previously reported. We also report, to our knowledge, the first engineered route from glucose to acrylate, a plastic precursor with global sales of $14 billion. Finally, we monitor the production of glucarate, a replacement for environmentally damaging detergents, and muconate, a renewable precursor to polyethylene terephthalate and nylon with combined markets of $51 billion, in real time, demonstrating that our method is applicable to a wide range of molecules.
Adeno-associated virus (AAV) capsids can deliver transformative gene therapies, but our understanding of AAV biology remains incomplete. We generated the complete first-order AAV2 capsid fitness ...landscape, characterizing all single-codon substitutions, insertions, and deletions across multiple functions relevant for in vivo delivery. We discovered a frameshifted gene in the VP1 region that expresses a membrane-associated accessory protein that limits AAV production through competitive exclusion. Mutant biodistribution revealed the importance of both surface-exposed and buried residues, with a few phenotypic profiles characterizing most variants. Finally, we algorithmically designed and experimentally verified a diverse in vivo targeted capsid library with viability far exceeding random mutagenesis approaches. These results demonstrate the power of systematic mutagenesis for deciphering complex genomes and the potential of empirical machine-guided protein engineering.
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