Molecular data storage is an attractive alternative for dense and durable information storage, which is sorely needed to deal with the growing gap between information production and the ability to store data. DNA is a clear example of effective archival data storage in molecular form. In this Review, we provide an overview of the process, the state of the art in this area and challenges for mainstream adoption. We also survey the field of in vivo molecular memory systems that record and store information within the DNA of living cells, which, together with in vitro DNA data storage, lie at the growing intersection of computer systems and biotechnology.

Molecular digital data storage using DNA

DNA is an emerging medium for digital data and its adoption can be accelerated by synthesis processes specialized for storage applications. Here, we describe a de novo enzymatic synthesis strategy designed for data storage which harnesses the template-independent polymerase terminal deoxynucleotidyl transferase (TdT) in kinetically controlled conditions. Information is stored in transitions between non-identical nucleotides of DNA strands. To produce strands representing user-defined content, nucleotide substrates are added iteratively, yielding short homopolymeric extensions whose lengths are controlled by apyrase-mediated substrate degradation. With this scheme, we synthesize DNA strands carrying 144 bits, including addressing, and demonstrate retrieval with streaming nanopore sequencing. We further devise a digital codec to reduce requirements for synthesis accuracy and sequencing coverage, and experimentally show robust data retrieval from imperfectly synthesized strands. This work provides distributive enzymatic synthesis and information-theoretic approaches to advance digital information storage in DNA. Adoption of DNA as a data storage medium could be accelerated with specialized synthesis processes and codecs. The authors describe TdT-mediated DNA synthesis in which data is stored in transitions between non-identical nucleotides and the use of synchronization markers to provide error tolerance.

/pdf/terminator-free-template-independent-enzymatic-dna-synthesis-4t8wbafscx.pdf

Terminator-free template-independent enzymatic DNA synthesis for digital information storage

The density and long-term stability of DNA make it an appealing storage medium, particularly for long-term data archiving. Existing DNA storage technologies involve the synthesis and sequencing of multiple nominally identical molecules in parallel, resulting in information redundancy. We report the development of encoding and decoding methods that exploit this redundancy using composite DNA letters. A composite DNA letter is a representation of a position in a sequence that consists of a mixture of all four DNA nucleotides in a predetermined ratio. Our methods encode data using fewer synthesis cycles. We encode 6.4 MB into composite DNA, with distinguishable composition medians, using 20% fewer synthesis cycles per unit of data, as compared to previous reports. We also simulate encoding with larger composite alphabets, with distinguishable composition deciles, to show that 75% fewer synthesis cycles are potentially sufficient. We describe applicable error-correcting codes and inference methods, and investigate error patterns in the context of composite DNA letters. Toward more storage for less synthesis using a six-letter composite DNA alphabet.

Data storage in DNA with fewer synthesis cycles using composite DNA letters

Biocatalysis harnesses enzymes to make valuable products. This green technology is used in countless applications from bench scale to industrial production and allows practitioners to access complex organic molecules, often with fewer synthetic steps and reduced waste. The last decade has seen an explosion in the development of experimental and computational tools to tailor enzymatic properties, equipping enzyme engineers with the ability to create biocatalysts that perform reactions not present in nature. By using (chemo)-enzymatic synthesis routes or orchestrating intricate enzyme cascades, scientists can synthesize elaborate targets ranging from DNA and complex pharmaceuticals to starch made in vitro from CO2-derived methanol. In addition, new chemistries have emerged through the combination of biocatalysis with transition metal catalysis, photocatalysis, and electrocatalysis. This review highlights recent key developments, identifies current limitations, and provides a future prospect for this rapidly developing technology. Description Editor’s summary As nature’s catalysts, enzymes are adaptable, specific, and interoperable. There have been important advances in the identification of transformations catalyzed by enzymes, and new methods have been developed for identifying, engineering, or creating de novo suitable enzymes for a wide range of applications. In a review, Buller et al. collect this recent progress in biocatalysis research and look ahead toward increasing the use of computational tools, accelerating design test cycles, and expanding chemistry beyond what is familiar in nature. Engineered enzymes provide a green chemistry solution to the synthesis of complex organic molecules and can be used in the degradation of waste plastic and chemicals. —Michael A. Funk Engineered enzymes provide a green chemistry solution for synthesis of complex molecules. BACKGROUND Biocatalysis is an approach to synthetic chemistry in which enzymes carry out chemical reactions. Historically, enzymes from natural sources have been used to break down oils and proteins in laundry detergents, produce semisynthetic antibiotics, and create simple chiral precursors for the pharmaceutical industry. In the past 5 years, the number of available protein sequences has increased by a staggering 20-fold, accelerating the discovery of enzymes with useful activities and properties. Directed evolution, the cornerstone of our ability to tailor enzymatic properties, is allowing researchers to tailor enzymes for the synthesis of complex molecules, the modification of biological therapeutics, and the breakdown of plastic waste. Machine learning–driven protein-structure prediction, coupled with advances in automation and high-throughput screening, is further advancing our ability to create enzymes with desired function. The ability to add nonbiological catalytic elements to enzymes means that enzyme engineers no longer have to rely solely on natural catalytic machinery. Scientists have thus gained the capacity to redesign, reimagine, and repurpose enzymes. Illustrative applications include enzyme cascades to manufacture the antivirals islatravir and molnupiravir, biocatalysts that can generate and control radicals, and enzymes that exploit photocatalysis to affect stereocontrolled C–C couplings, hydroaminations, or Diels-Alder reactions. ADVANCES The past 5 years have witnessed a surge in the development of data-driven tools enabling the accelerated discovery, engineering, and deployment of enzymes for applications in chemistry, medicine, and food technology. By leveraging enzymes’ ability to control the environment of a chemical reaction, scientists have developed successful platforms for the construction of complex small molecules from suitable starting materials or even CO2. In addition to their use in (chemo)-enzymatic cascade reactions, precisely tailored biocatalysts can manufacture new therapeutic modalities, such as antisense oligonucleotide therapeutics, and bioconjugates. DNA synthesis, which still relies on phosphoramidite chemistry, is being reinvented with template-independent deoxynucleotidyl transferases to make the process faster and cheaper. Machine learning, already dominating protein-structure prediction and design, is finding applications in enzyme engineering, including improvement of functions such as enantioselectivity, activity, and stability. OUTLOOK In the decade ahead, biocatalysis research and applications will continue to profit from advances in data mining, machine learning, and DNA reading and writing. The combinatorial design of enzymes and the ease with which new enzyme variants can be experimentally generated lends itself to train data-intense machine-learning algorithms. Ideally, the sequence-function data of variants screened in a directed-evolution campaign could be used to predict which variants to evaluate next. Machine learning works best with clean and reproducible data, mandating the standardization and reporting of data and the further development of experimental techniques, including molecular biology methods, automation, and high-throughput screening assays. The toolbox of drug discovery is expanding beyond traditional small molecules (molecular weights <500 g/mol) to include RNA therapeutics, protein degraders, cyclopeptides, antibody drug conjugates, and gene therapy. Consequently, the synthetic complexity of lead molecules and clinical candidates is increasing with a growing percentage of chiral and beyond-rule-of-5 molecules. Enzymatic synthesis will play a key role beyond its present impact in small-molecule drug discovery and development. The discovery of new enzyme families and the rational design of new enzyme functions will expand the toolbox of available biocatalysts. Development and deployment of retrosynthetic tools containing enzymatic reactions will be an important step toward democratizing biocatalysis and making it available to the nonexpert. Fueled by these innovations, nature’s catalysts will be profitably used to address current challenges, including the fight against diseases, provision of affordable clean energy, and reduction of industrial and consumer waste. The accelerating development of biocatalysis. In silico and experimental tools developed in the last decade allow the fast creation of tailored enzymes. In addition to the concise synthesis of complex small molecules in elegant (chemo)-enzymatic cascades, newly accessible applications include enzymatic DNA synthesis, the generation of therapeutic oligonucleotides, and up- and recycling of plastic waste. [Figure created with Canva]

From nature to industry: Harnessing enzymes for biocatalysis

Measuring biological data across time and space is critical for understanding complex biological processes and for various biosurveillance applications However, such data are often inaccessible or difficult to directly obtain Less invasive, more robust and higher-throughput biological recording tools are needed to profile cells and their environments DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biological events over time across living cells and populations Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods Quantitative characterization, modelling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas

DNA-based memory devices for recording cellular events.

Oligonucleotides are almost exclusively synthesized using the nucleoside phosphoramidite method, even though it is limited to the direct synthesis of ∼200 mers and produces hazardous waste. Here, we describe an oligonucleotide synthesis strategy that uses the template-independent polymerase terminal deoxynucleotidyl transferase (TdT). Each TdT molecule is conjugated to a single deoxyribonucleoside triphosphate (dNTP) molecule that it can incorporate into a primer. After incorporation of the tethered dNTP, the 3' end of the primer remains covalently bound to TdT and is inaccessible to other TdT-dNTP molecules. Cleaving the linkage between TdT and the incorporated nucleotide releases the primer and allows subsequent extension. We demonstrate that TdT-dNTP conjugates can quantitatively extend a primer by a single nucleotide in 10-20 s, and that the scheme can be iterated to write a defined sequence. This approach may form the basis of an enzymatic oligonucleotide synthesizer.

/pdf/de-novo-dna-synthesis-using-polymerase-nucleotide-conjugates-5b2rj9iskm.pdf

De novo DNA synthesis using polymerase-nucleotide conjugates

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De novo DNA synthesis using polymerase-nucleotide conjugates