Pattern Generation with Nucleic Acid Chemical Reaction Networks.

Biological and biochemical systems are manifestations of chemical reaction networks (CRNs). The ability to design and engineer such networks may allow the construction of artificial systems that are as complex as those seen in biology, opening the way to translational possibilities including adaptive materials. One venue for progress is the design of autonomous systems capable of pattern generation; however many synthetic CRNs, such as the Belousov-Zhabotinsky reaction, cannot be rewired to encode more complex interactions and thus lack the capacity for more detailed engineering algorithms. In contrast, DNA is an information-rich molecule with predictable and reliable base-pairing interactions and well-studied kinetics, and the use of DNA has greatly enabled the rational design of much more complex synthetic CRNs. Recent advances in the DNA computing field include circuits for pattern transformation, an example of self-organization. An arsenal of tools for designing DNA circuits to implement various CRNs has been developed by DNA nanotechnologists, including software to reliably program strand-displacement nucleic acid circuits. In addition, DNA walkers can be used to create CRNs with controlled diffusivity, while DNA gels similarly represent a new medium for implementing CRNs that may ultimately lead to the development of smart materials. As we will argue, future endeavors in nucleic acid-based pattern generation will be most greatly advanced by harnessing well-known enzymatic processes to serve as generators and amplifiers. Once nucleic acid computing tools are further developed to expedite the design process of pattern generation, we anticipate a transition from proof-of-concept curiosities to application-driven inquiries.

Developing predictive hybridization models for phosphorothioate oligonucleotides using high-resolution melting.

The ability to predict nucleic acid hybridization energies has been greatly enabling for many applications, but predictive models require painstaking experimentation, which may limit expansion to non-natural nucleic acid analogues and chemistries. We have assessed the utility of dye-based, high-resolution melting (HRM) as an alternative to UV-Vis determinations of hyperchromicity in order to more quickly acquire parameters for duplex stability prediction. The HRM-derived model for phosphodiester (PO) DNA can make comparable predictions to previously established models. Using HRM, it proved possible to develop predictive models for DNA duplexes containing phosphorothioate (PS) linkages, and we found that hybridization stability could be predicted as a function of sequence and backbone composition for a variety of duplexes, including PS:PS, PS:PO, and partially modified backbones. Individual phosphorothioate modifications destabilize helices by around 0.12 kcal/mol on average. Finally, we applied these models to the design of a catalytic hairpin assembly circuit, an enzyme-free amplification method used for nucleic acid-based molecular detection. Changes in PS circuit behavior were consistent with model predictions, further supporting the addition of HRM modeling and parameters for PS oligonucleotides to the rational design of nucleic acid hybridization.

Recovery of Information Stored in Modified DNA with an Evolved Polymerase.

DNA is increasingly being explored as an alternative medium for digital information storage, but the potential information loss from degradation and associated issues with error during reading challenge its wide-scale implementation. To address this, we propose an atomic-scale encoding standard for DNA, where information is encoded in degradation-resistant analogues of natural nucleic acids (xNAs). To better enable this approach, we used directed evolution to create a polymerase capable of transforming 2'-O-methyl templates into double-stranded DNA. Starting from a thermophilic, error-correcting reverse transcriptase, RTX, we evolved an enzyme (RTX-Ome v6) that relies on a fully functional proofreading domain to correct mismatches on DNA, RNA, and 2'-O-methyl templates. In addition, we implemented a downstream analysis strategy that accommodates deletions that arise during phosphoramidite synthesis, the most common type of synthesis error. By coupling and integrating new chemistries, enzymes, and algorithms, we further enable the large-scale use of nucleic acids for information storage.

Modeling spatial correlation of DNA deformation: DNA allostery in protein binding.

We report a study of DNA deformations using a coarse-grained mechanical model and quantitatively interpret the allosteric effects in protein-DNA binding affinity. A recent single-molecule study (Kim et al. Science 2013, 339, 816) showed that when a DNA molecule is deformed by specific binding of a protein, the binding affinity of a second protein separated from the first protein is altered. Experimental observations together with molecular dynamics simulations suggested that the origin of the DNA allostery is related to the observed deformation of DNA's structure, in particular, the major groove width. To unveil and quantify the underlying mechanism for the observed major groove deformation behavior related to the DNA allostery, here we provide a simple but effective analytical model where DNA deformations upon protein binding are analyzed and spatial correlations of local deformations along the DNA are examined. The deformation of the DNA base orientations, which directly affect the major groove width, is found in both an analytical derivation and coarse-grained Monte Carlo simulations. This deformation oscillates with a period of 10 base pairs with an amplitude decaying exponentially from the binding site with a decay length lD ≈10 base pairs as a result of the balance between two competing terms in DNA base-stacking energy. This length scale is in agreement with that reported from the single-molecule experiment. Our model can be reduced to the worm-like chain form at length scales larger than lP but is able to explain DNA's mechanical properties on shorter length scales, in particular, the DNA allostery of protein-DNA interactions.