250 research outputs found

    Kinetic Control of Nucleic Acid Strand Displacement Reactions

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    Nucleic acids are information-dense, programmable polymers that can be engineered into primers, probes, molecular motors, and signal amplification circuits for computation, diagnostic, and therapeutic purposes. Signal amplification circuits increase the signal-to-noise ratio of target nucleic acids in the absence of enzymes and thermal cycling. Amplification is made possible via toehold mediated strand displacement – a process where one nucleic acid strand binds to a nucleation site on a complementary helix, which then displaces one of the two strands in a nucleic acid complex. When compared to polymerase chain reactions (PCR), the sensitivity and stability of toehold-mediated strand displacement reactions suffer from circuit leakage – reactions of the system in the absence of an initiator. Presented here, from a materials science and engineering perspective, defect engineering has improved the leakage performance of model strand displacement systems made from DNA. Engineered defects used in this study included mismatched base pairs and alternative nucleic acids – both of which are known to impact the stability of hybridization. To identify sources of leakage in a model signal amplification circuit, availability was defined as the probability that a DNA base (A.T.C.G) was unpaired at equilibrium. This design metric was calculated using NUPACK, a thermodynamic modeling tool. To further understand the relationship between leakage rates and secondary structures, mutual availability was defined as the sum of all pairwise products of the availabilities of the corresponding bases in solution. This thermodynamic analysis yielded rational design principles for how to minimize leakage by as much as 4-fold by site-specifically introducing mismatched base pairs into DNA duplex regions. To further reduce leakage, chemically modified locked nucleic acids (LNAs) were site-specifically introduced into a model DNA strand displacement system. Briefly described, LNAs are geometrically restricted RNA analogues with enhanced thermo-mechanical stability towards their complement base. When compared to a DNA control with identical sequences, the leakage exhibited by a hybrid DNA/LNA system was reduced from 1.48 M-1s-1 (for the DNA system) to 0.03 M-1s-1. In addition, the signal-to-noise ratio increased ~50-fold for a similar hybrid system. This research provides insight into the sources of leakage in DNA strand-displacement systems, as well as how to maximize strand-displacement performance via the selective introduction of hybridization defects. Rational design of future nucleic acid signal amplification circuits will lead to broader applications in a variety of fields that range from DNA computation to point-of-care diagnostics and therapeutics

    BIOMOLECULE INSPIRED DATA SCIENCE

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    BIOMOLECULE INSPIRED DATA SCIENC

    Genomic Methods for Bacterial Infection Identification

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    Hospital-acquired infections (HAIs) have high mortality rates around the world and are a challenge to medical science due to rapid mutation rates in their pathogens. A new methodology is proposed to identify bacterial species causing HAIs based on sets of universal biomarkers for next-generation microarray designs (i.e., nxh chips), rather than a priori selections of biomarkers. This method allows arbitrary organisms to be classified based on readouts of their DNA sequences, including whole genomes. The underlying models are based on the biochemistry of DNA, unlike traditional edit-distance based alignments. Furthermore, the methodology is fairly robust to genetic mutations, which are likely to reduce accuracy. Standard machine learning methods (neural networks, self-organizing maps, and random forests) produce results to identify HAIs on nxh chips that are very competitive, if not superior, to current standards in the field. The potential feasibility of translating these techniques to a clinical test is also discussed

    Monte Carlo simulation studies of DNA hybridization and DNA-directed nanoparticle assembly

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    A coarse-grained lattice model of DNA oligonucleotides is proposed to investigate how fundamental thermodynamic processes are encoded by the nucleobase sequence at the microscopic level, and to elucidate the general mechanisms by which single-stranded oligonucleotides hybridize to their complements either in solution or when tethered to nanoparticles. Molecular simulations based on a high-coordination cubic lattice are performed using the Monte Carlo method. The dependence of the model's thermal stability on sequence complementarity is shown to be qualitatively consistent with experiment and statistical mechanical models. From the analysis of the statistical distribution of base-paired states and of the associated free-energy landscapes, two general hybridization scenarios are found. For sequences that do not follow a two-state process, hybridization is weakly cooperative and proceeds in multiple sequential steps involving stable intermediates with increasing number of paired bases. In contrast, sequences that conform to two-state thermodynamics exhibit moderately rough landscapes, in which multiple metastable intermediates appear over broad free-energy barriers. These intermediates correspond to duplex species that bridge the configurational and energetic gaps between duplex and denatured states with minimal loss of conformational entropy, and lead to a strongly cooperative hybridization. Remarkably, two-state thermodynamic signatures are generally observed in both scenarios. The role of cooperativity in the assembly of nanoparticles tethered with model DNA oligonucleotides is similarly addressed with the Monte Carlo method, where nanoparticles are represented as finely discretized hard-core spheres on a cubic lattice. The energetic and structural mechanisms of self-assembling are investigated by simulating the aggregation of small "satellite" particles from the bulk onto a large "core" particle. A remarkable enhancement of the system's thermal stability is attained by increasing the number of strands per satellite particle available to hybridize with those on the core particle. This cooperative process is driven by the formation of multiple bridging duplexes under favorable conditions of reduced translational entropy and the resultant energetic compensation; this behavior rapidly weakens above a certain threshold of linker strands per satellite particle. Cooperativity also enhances the structural organization of the assemblies by systematically narrowing the radial distribution of the satellite particles bound the core

    The development of aptamer-based probes for the detection of TB antigens ESAT-6.CFP-10 potential TB diagnostic tools

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    Includes abstract.Includes bibliographical references.Lack of point-of-care (PoC) diagnostic tools for TB hinders control of the disease, particularly in resource-limited, high HIV and TB prevalence countries. Therefore, there is a need for simple, rapid, accurate, and affordable PoC diagnostics to detect active TB early enough for opportune intervention. To develop TB detection probes that will constitute such diagnostics, our research group recently isolated DNA aptamers that bind to a putative marker for active TB; the ESAT-6.CFP-10 heterodimer. Aptamers are highly specific artificial mimics of antibodies that have shown great prospects in diagnostic applications. The aim of this study was to characterise the anti-ESAT-6.CFP-10 aptamers, and to optimise them into more specific and affordable detection probes for the development of potential PoC TB diagnostic tools

    Availability: A Metric for Nucleic Acid Strand Displacement Systems

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    DNA strand displacement systems have transformative potential in synthetic biology. While powerful examples have been reported in DNA nanotechnology, such systems are plagued by leakage, which limits network stability, sensitivity, and scalability. An approach to mitigate leakage in DNA nanotechnology, which is applicable to synthetic biology, is to introduce mismatches to complementary fuel sequences at key locations. However, this method overlooks nuances in the secondary structure of the fuel and substrate that impact the leakage reaction kinetics in strand displacement systems. In an effort to quantify the impact of secondary structure on leakage, we introduce the concepts of availability and mutual availability and demonstrate their utility for network analysis. Our approach exposes vulnerable locations on the substrate and quantifies the secondary structure of fuel strands. Using these concepts, a 4-fold reduction in leakage has been achieved. The result is a rational design process that efficiently suppresses leakage and provides new insight into dynamic nucleic acid networks

    Ab initio RNA folding

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    RNA molecules are essential cellular machines performing a wide variety of functions for which a specific three-dimensional structure is required. Over the last several years, experimental determination of RNA structures through X-ray crystallography and NMR seems to have reached a plateau in the number of structures resolved each year, but as more and more RNA sequences are being discovered, need for structure prediction tools to complement experimental data is strong. Theoretical approaches to RNA folding have been developed since the late nineties when the first algorithms for secondary structure prediction appeared. Over the last 10 years a number of prediction methods for 3D structures have been developed, first based on bioinformatics and data-mining, and more recently based on a coarse-grained physical representation of the systems. In this review we are going to present the challenges of RNA structure prediction and the main ideas behind bioinformatic approaches and physics-based approaches. We will focus on the description of the more recent physics-based phenomenological models and on how they are built to include the specificity of the interactions of RNA bases, whose role is critical in folding. Through examples from different models, we will point out the strengths of physics-based approaches, which are able not only to predict equilibrium structures, but also to investigate dynamical and thermodynamical behavior, and the open challenges to include more key interactions ruling RNA folding.Comment: 28 pages, 18 figure

    The dynamic equilibrium of human telomeric G-quadruplexes.

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    G-quadruplexes are thought to have biological importance, with studies based on small molecule interactions and quadruplex-interactive antibodies demonstrating their potential for formation in vivo. One potential biological function of quadruplex structures is the regulation and maintenance of telomeres. Telomeres are nucleoprotein complexes involved in chromosome stability. Human telomeres are composed of the repeated DNA sequence, 5\u27- d(TIAGGG), that terminates in a 3\u27 single-stranded overhang. DNA sequences with homology to the human telomere are capable of quadruplex formation in vitro. Specifically, sequences containing four-guanine stretches (e.g. 5\u27-d(AGGG(TIAGGGb)) are capable of forming at least five distinct unimolecular structures. Which structure is favored is believed to be linked to solvent composition and the addition of 3\u27- and 5\u27-flanking residues. This dissertation provides an essential biophysical investigation of the polymorphic equilibrium displayed by human telomeric quadruplexes. Multiple biophysical techniques are utilized to assemble a thermodynamic description of the influences of hydration and molecular crowding on conformational selection and elucidate complex unfolding mechanisms with unique intermediate states. This dissertation provides the first application of phasor diagrams in the study of quadruplexes. Phasor diagrams are shown to be sensitive to alterations in quadruplex structure (i.e. folding and unfolding) by monitoring changes in the complex lifetime distribution of 2-aminopurine. This dissertation contains the first multi-faceted biophysical investigation of the underlying mechanism of co-solvent driven conformational changes of human telomeric quadruplexes. The thermodynamic study illustrates that quadruplexes are stabilized by dehydration, a behavior opposite that of canonical duplex structures. Additionally, the ability of PEGs to drive the conformational selection of a parallel quadruplex through differential binding is clarified, addressing unsubstantiated claims that the propeller form is the most biologically relevant conformation. Finally, an in-depth thermodynamic investigation of the thermal unfolding of human telomeric quadruplexes is conducted. Multiple spectroscopic techniques are used to evaluate the thermal unfolding process and characterize potential intermediates states. This dissertation work is the first to apply spectroscopic deconvolutions to demonstrate that human telomeric quadruplexes unfold through sequential mechanisms requiring intermediate species. These results are highlighted by the recovery of an intermediate species whose biophysical description is best characterized by an ensemble of triple-helical conformations

    Improved Computational Prediction of Function and Structural Representation of Self-Cleaving Ribozymes with Enhanced Parameter Selection and Library Design

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    Biomolecules could be engineered to solve many societal challenges, including disease diagnosis and treatment, environmental sustainability, and food security. However, our limited understanding of how mutational variants alter molecular structures and functional performance has constrained the potential of important technological advances, such as high-throughput sequencing and gene editing. Ribonuleic Acid (RNA) sequences are thought to play a central role within many of these challenges. Their continual discovery throughout all domains of life is evidence of their significant biological importance (Weinreb et al., 2016). The self-cleaving ribozyme is a class of noncoding Ribonuleic Acid (ncRNA) that has been useful for relating sequence variants to structural features and their associated catalytic activities. Self-cleaving ribozymes possess tractable sequence spaces, perform easily identifiable catalytic functions, and have well documented structures. The determination of a self-cleaving ribozyme’s structure and catalytic activity within the laboratory is typically a slow and expensive process. Most current explorations of structure and function come from these empirical processes. Computational approaches to the prediction of catalytic activity and structure are fast and inexpensive, but have failed both to achieve atomic accuracy or to correctly identify all base-pair interactions (Watkins et al., 2018). One prominent impediment to computational approaches is the lack of existing structural and functional data typically required by predictive models (Jumper et al., 2021). Using data from deep-mutational scanning experiments and high-throughput sequencing technology, it is possible to computationally map mutational variants to their observed catalytic activity for a range of self-cleaving ribozymes. The resulting map reveals important base-pairing relationships that, in turn, facilitate accurate predictions of higher-order variants. Using sequence data from three experimental replicates of five model self-cleaving ribozymes, I will identify and map all single and double mutation variants to their observed cleavage activity. These mappings will be used to identify structural features within each ribozyme. Next, I will show within a training tool how observed cleavage for multiple reaction times can be used to identify the catalytic rates of our model ribozymes. Finally, I will predict the functional activity for model ribozyme variants of various mutational orders using machine learning models trained only on functionally labeled sequence variants. Together, these three dissertation chapters represent the kind of analysis needed to further the implementation of more accurate structural and functional prediction algorithms

    Improving the Ribozyme Toolbox: From Structure-Function Insights to Synthetic Biology Applications

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    Self-cleaving ribozymes are a naturally occurring class of catalytically active RNA molecules which cleave their own phosphate backbone. In nature, self-cleaving ribozymes are best known for their role in processing concatamers of viral genomes into monomers during viral replication in some RNA viruses, but to a lesser degree have also been implicated in mRNA regulation and processing in bacteria and eukaryotes. In addition to their biological relevance, these RNA enzymes have been harnessed as important biomolecular tools with a variety of applications in fields such as bioengineering. Self-cleaving ribozymes are relatively small and easy to generate in the lab using common molecular biology approaches, and have therefore been accessible and well exploited model systems used to interrogate RNA sequence-structure-function relationships. Furthermore, self-cleaving ribozymes are also being implemented as parts in the development of various biomolecular tools such as biosensors and gene regulatory elements. While much progress has been made in these areas, there are still challenges associated with the performance and implementation of such tools. The work contained in this dissertation aims to address several of these challenges and improve the ribozyme toolbox in several diverse areas. Chapter one provides an introduction to pertinent background information for this dissertation. Chapter two aims to improve the ribozyme toolbox by providing and analyzing new high-throughput sequence-structure-function data sets on five different self-cleaving ribozymes, and identifying how trends in epistasis relate to distinct structural elements. Chapter three uses such high-throughput data to train machine learning models that accurately predict the historically difficult to predict functional effects of higher order mutations in functional RNA’s. Finally, in chapter four, I developed a biologically relevant platform to study the real time performance and kinetics of self-cleaving ribozyme-based gene regulatory elements directly at the site of transcription in mammalian cells
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