Catalyzed relaxation of a metastable DNA fuel

Practically all of life's molecular processes, from chemical synthesis to replication, involve enzymes that carry out their functions through the catalytic transformation of metastable fuels into waste products. Catalytic control of reaction rates will prove to be as useful and ubiquitous in nucleic-acid-based engineering as it is in biology. Here we report a metastable DNA "fuel" and a corresponding DNA "catalyst" that improve upon the original hybridization-based catalyst system (Turberfield et al. Phys. Rev. Lett. 90, 118102-1118102-4) by more than 2 orders of magnitude. This is achieved by identifying and purifying a fuel with a kinetically trapped metastable configuration consisting of a "kissing loop" stabilized by flanking helical domains; the catalyst strand acts by opening a helical domain and allowing the complex to relax to its ground state by a multistep pathway. The improved fuel/catalyst system shows a roughly 5000-fold acceleration of the uncatalyzed reaction, with each catalyst molecule capable of turning over in excess of 40 substrates. With k_(cat)/K_M ≈ 10^7/M/min, comparable to many protein enzymes and ribozymes, this fuel system becomes a viable component enabling future DNA-based synthetic molecular machines and logic circuits. As an example, we designed and characterized a signal amplifier based on the fuel-catalyst system. The amplifier uses a single strand of DNA as input and releases a second strand with unrelated sequence as output. A single input strand can catalytically trigger the release of more than 10 output strands

Decoherence in ballistic mesoscopic interferometers

We provide a theoretical explanation for two recent experiments on decoherence of Aharonov-Bohm oscillations in two- and multi-terminal ballistic rings. We consider decoherence due to charge fluctuations and emphasize the role of charge exchange between the system and the reservoir or nearby gates. A time-dependent scattering matrix approach is shown to be a convenient tool for the discussion of decoherence in ballistic conductors.Comment: 11 pages, 3 figures. To appear in a special issue on "Quantum Computation at the Atomic Scale" in the Turkish Journal of Physic

Fast differentiable DNA and protein sequence optimization for molecular design

Designing DNA and protein sequences with improved function has the potential to greatly accelerate synthetic biology. Machine learning models that accurately predict biological fitness from sequence are becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, this method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. Here, we build on a previously proposed straight-through approximation method to optimize through discrete sequence samples. By normalizing nucleotide logits across positions and introducing an adaptive entropy variable, we remove bottlenecks arising from overly large or skewed sampling parameters. The resulting algorithm, which we call Fast SeqProp, achieves up to 100-fold faster convergence compared to previous versions of activation maximization and finds improved fitness optima for many applications. We demonstrate Fast SeqProp by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor.Comment: All code available at http://www.github.com/johli/seqprop; Moved example sequences from Suppl to new Figure 2, Added new benchmark comparison to Section 4.3, Moved some technical comparisons to Suppl, Added new Methods sectio

Enzyme-free nucleic acid logic circuits

Biological organisms perform complex information processing and control tasks using sophisticated biochemical circuits, yet the engineering of such circuits remains ineffective compared with that of electronic circuits. To systematically create complex yet reliable circuits, electrical engineers use digital logic, wherein gates and subcircuits are composed modularly and signal restoration prevents signal degradation. We report the design and experimental implementation of DNA-based digital logic circuits. We demonstrate AND, OR, and NOT gates, signal restoration, amplification, feedback, and cascading. Gate design and circuit construction is modular. The gates use single-stranded nucleic acids as inputs and outputs, and the mechanism relies exclusively on sequence recognition and strand displacement. Biological nucleic acids such as microRNAs can serve as inputs, suggesting applications in biotechnology and bioengineering

Robust Digital Molecular Design of Binarized Neural Networks

Molecular programming - a paradigm wherein molecules are engineered to perform computation - shows great potential for applications in nanotechnology, disease diagnostics and smart therapeutics. A key challenge is to identify systematic approaches for compiling abstract models of computation to molecules. Due to their wide applicability, one of the most useful abstractions to realize is neural networks. In prior work, real-valued weights were achieved by individually controlling the concentrations of the corresponding "weight" molecules. However, large-scale preparation of reactants with precise concentrations quickly becomes intractable. Here, we propose to bypass this fundamental problem using Binarized Neural Networks (BNNs), a model that is highly scalable in a molecular setting due to the small number of distinct weight values. We devise a noise-tolerant digital molecular circuit that compactly implements a majority voting operation on binary-valued inputs to compute the neuron output. The network is also rate-independent, meaning the speed at which individual reactions occur does not affect the computation, further increasing robustness to noise. We first demonstrate our design on the MNIST classification task by simulating the system as idealized chemical reactions. Next, we map the reactions to DNA strand displacement cascades, providing simulation results that demonstrate the practical feasibility of our approach. We perform extensive noise tolerance simulations, showing that digital molecular neurons are notably more robust to noise in the concentrations of chemical reactants compared to their analog counterparts. Finally, we provide initial experimental results of a single binarized neuron. Our work suggests a solid framework for building even more complex neural network computation

Electron-phonon scattering in quantum point contacts

We study the negative correction to the quantized value $2e^2/h$ of the conductance of a quantum point contact due to the backscattering of electrons by acoustic phonons. The correction shows activated temperature dependence and also gives rise to a zero-bias anomaly in conductance. Our results are in qualitative agreement with recent experiments studying the 0.7 feature in the conductance of quantum point contacts.Comment: 4 pages, no figure

Synthesis and Interactions of 7-Deoxy-, 10-Deacetoxy, and 10-Deacetoxy-7-Deoxypaclitaxel with NCI/ADR-RES Cancer Cells and Bovine Brain Microvessel Endothelial Cells

Please note that this is an author-produced PDF of an article accepted for publication following peer review. The publisher version is available on its site.7-Deoxypaclitaxel, 10-deacetoxypaclitaxel and 10-deacetoxy-7-deoxypaclitaxel were prepared and evaluated for their ability to promote assembly of tubulin into microtubules, their cytotoxicity against NCI/ADR-RES cells and for their interactions with Pglycoprotein in bovine brain microvessel endothelial cells. The three compounds were essentially equivalent to paclitaxel in cytotoxicity against NCI/ADR-RES cells. They also appeared to interact with P-glycoprotein in the endothelial cells with the two 10-deacetoxy compounds having less interaction than paclitaxel and 7-deoxypaclitaxel. ©2000 Elsevier Science Ltd. All rights reserved

Enzyme-Free Nucleic Acid Dynamical Systems

An important goal of synthetic biology is to create biochemical control systems with the desired characteristics from scratch. Srinivas et al. describe the creation of a biochemical oscillator that requires no enzymes or evolved components, but rather is implemented through DNA molecules designed to function in strand displacement cascades. Furthermore, they created a compiler that could translate a formal chemical reaction network into the necessary DNA sequences that could function together to provide a specified dynamic behavior

Paclitaxel Succinate Analogs: Anionic Introduction as a Strategy to Impart Blood Brain Barrier Permeability

A focused library of TX-67 (C10 hemi-succinate) analogs have been prepared including regioisomeric, functional group, and one-carbon homologs. These were prepared to investigate TX-67’s lack of interaction with P-glycoprotein (Pgp). Tubulin stabilization ability, cytotoxicity, and Pgp interactions were evaluated. All carboxylic acid analogs had no apparent interactions with Pgp whereas the ester variants of the same compounds displayed characteristics of Pgp substrates. Furthermore, it is demonstrated that hydrogen-bonding properties were significant with respect to Pgp interactions. This anionic introduction strategy may allow for delivery of paclitaxel into the CNS as well as establishing a new method for delivery of other, non-CNS permeable drugs

A DNA-Based Archival Storage System

Abstract Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up. Using DNA to archive data is an attractive possibility because it is extremely dense, with a raw limit of 1 exabyte/mm 3 (10 9 GB/mm 3 ), and long-lasting, with observed half-life of over 500 years. This paper presents an architecture for a DNA-based archival storage system. It is structured as a key-value store, and leverages common biochemical techniques to provide random access. We also propose a new encoding scheme that offers controllable redundancy, trading off reliability for density. We demonstrate feasibility, random access, and robustness of the proposed encoding with wet lab experiments involving 151 kB of synthesized DNA and a 42 kB randomaccess subset, and simulation experiments of larger sets calibrated to the wet lab experiments. Finally, we highlight trends in biotechnology that indicate the impending practicality of DNA storage for much larger datasets