Hybridization efficiency of molecular beacons bound to gold nanowires: effect of surface coverage and target length

Surface-bound nucleic acid probes designed to adopt specific secondary structures are becoming increasingly important in a range of biosensing applications but remain less well characterized than traditional single-stranded probes, which are typically designed to avoid secondary structure. We report the hybridization efficiency for surface-immobilized hairpin DNA probes. Our probes are molecular beacons, carrying a 3' dye moiety and a 5' thiol for attachment to gold nanowires, which serve as both scaffolds for probe attachment and quenchers. Hybridization efficiency was dependent on probe surface coverage, reaching a maximum of ～90% at intermediate coverages of (1-2) × 10(12) probes/cm(2) and dropping to ≤20% at higher or lower coverages. Fluorescence intensity did not track with the number of target molecules bound, and was highest for high probe coverage despite the lower bound targets per square centimeter. Backfilling with short thiolated oligoethylene glycol spacers increased hybridization efficiency at low hairpin probe coverages (～(3-4) × 10(11) probes/cm(2)), but not at higher probe coverages (1 × 10(12)/cm(2)). We also evaluated the effect of target length by adding up to 50 nonhybridizing nucleotides to the 3' or 5' end of the complementary target sequence. Additional nucleotides on the 3' end of the complementary target sequence (i.e., the end near the nanowire surface) had a much greater impact on hybridization efficiency as compared to nucleotides added to the 5' end. This work provides guidance in designing sensors in which surface-bound probes designed to adopt secondary structures are used to detect target sequences from solution.     

Effects of template sequence and secondary structure on DNA-templated reactivity

DNA-templated organic synthesis enables the translation, selection, and amplification of DNA sequences encoding synthetic small-molecule libraries. As the size of DNA-templated libraries increases, the possibility of forming intramolecularly base-paired structures within templates that impede templated reactions increases as well. To achieve uniform reactivity across many template sequences and to computationally predict and remove any problematic sequences from DNA-templated libraries, we have systematically examined the effects of template sequence and secondary structure on DNA-templated reactivity. By testing a series of template sequences computationally designed to contain different degrees of internal secondary structure, we observed that high levels of predicted secondary structure involving the reagent binding site within a DNA template interfere with reagent hybridization and impair reactivity, as expected. Unexpectedly, we also discovered that templates containing virtually no predicted internal secondary structure also exhibit poor reaction efficiencies. Further studies revealed that a modest degree of internal secondary structure is required to maximize effective molarities between reactants, possibly by compacting intervening template nucleotides that separate the hybridized reactants. Therefore, ideal sequences for DNA-templated synthesis lie between two undesirable extremes of too much or too little internal secondary structure. The relationship between effective molarity and intervening nucleic acid secondary structure described in this work may also apply to nucleic acid sequences in living systems that separate interacting biological molecules.     

Determining the folding and unfolding rate constants of nucleic acids by biosensor. Application to telomere G-quadruplex

Nucleic acid molecules may fold into secondary structures, and the formation of such structures is involved in many biological processes and technical applications. The folding and unfolding rate constants define the kinetics of conformation interconversion and the stability of these structures and is important in realizing their functions. We developed a method to determine these kinetic parameters using an optical biosensor based on surface plasmon resonance. The folding and unfolding of a nucleic acid is coupled with a hybridization reaction by immobilization of the target nucleic acid on a sensor chip surface and injection of a complementary probe nucleic acid over the sensor chip surface. By monitoring the time course of duplex formation, both the folding and unfolding rate constants for the target nucleic acid and the association and dissociation rate constants for the target-probe duplex can all be derived from the same measurement. We applied this method to determine the folding and unfolding rate constants of the G-quadruplex of human telomere sequence (TTAGGG)(4) and its association and dissociation rate constants with the complementary strand (CCCTAA)(4). The results show that both the folding and unfolding occur on the time scale of minutes at physiological concentration of K(+). We speculate that this property might be important for telomere elongation. A complete set of the kinetic parameters for both of the structures allows us to study the competition between the formation of the quadruplex and the duplex. Calculations indicate that the formation of both the quadruplex and the duplex is strand concentration-dependent, and the quadruplex can be efficiently formed at low strand concentration. This property may provide the basis for the formation of the quadruplex in vivo in the presence of a complementary strand.     

Machine learning algorithms for predicting protein folding rates and stability of mutant proteins: comparison with statistical methods

Machine learning algorithms have wide range of applications in bioinformatics and computational biology such as prediction of protein secondary structures, solvent accessibility, binding site residues in protein complexes, protein folding rates, stability of mutant proteins, and discrimination of proteins based on their structure and function. In this work, we focus on two aspects of predictions: (i) protein folding rates and (ii) stability of proteins upon mutations. We briefly introduce the concepts of protein folding rates and stability along with available databases, features for prediction methods and measures for prediction performance. Subsequently, the development of structure based parameters and their relationship with protein folding rates will be outlined. The structure based parameters are helpful to understand the physical basis for protein folding and stability. Further, basic principles of major machine learning techniques will be mentioned and their applications for predicting protein folding rates and stability of mutant proteins will be illustrated. The machine learning techniques could achieve the highest accuracy of predicting protein folding rates and stability. In essence, statistical methods and machine learning algorithms are complimenting each other for understanding and predicting protein folding rates and the stability of protein mutants. The available online resources on protein folding rates and stability will be listed.     

Learning the Fastest RNA Folding Path Based on Reinforcement Learning and Monte Carlo Tree Search

RNA molecules participate in many important biological processes, and they need to fold into well-defined secondary and tertiary structures to realize their functions. Like the well-known protein folding problem, there is also an RNA folding problem. The folding problem includes two aspects: structure prediction and folding mechanism. Although the former has been widely studied, the latter is still not well understood. Here we present a deep reinforcement learning algorithms 2dRNA-Fold to study the fastest folding paths of RNA secondary structure. 2dRNA-Fold uses a neural network combined with Monte Carlo tree search to select residue pairing step by step according to a given RNA sequence until the final secondary structure is formed. We apply 2dRNA-Fold to several short RNA molecules and one longer RNA 1Y26 and find that their fastest folding paths show some interesting features. 2dRNA-Fold is further trained using a set of RNA molecules from the dataset bpRNA and is used to predict RNA secondary structure. Since in 2dRNA-Fold the scoring to determine next step is based on possible base pairings, the learned or predicted fastest folding path may not agree with the actual folding paths determined by free energy according to physical laws.     

Effect of secondary structure on the thermodynamics and kinetics of PNA hybridization to DNA hairpins.

The binding of a series of PNA and DNA probes to a group of unusually stable DNA hairpins of the tetraloop motif has been observed using absorbance hypochromicity (ABS), circular dichroism (CD), and a colorimetric assay for PNA/DNA duplex detection. These results indicate that both stable PNA-DNA and DNA-DNA duplexes can be formed with these target hairpins, even when the melting temperatures for the resulting duplexes are up to 50 degrees C lower than that of the hairpin target. Both hairpin/single-stranded and hairpin/hairpin interactions are considered in the scope of these studies. Secondary structures in both target and probe molecules are shown to depress the melting temperatures and free energies of the probe-target duplexes. Kinetic analysis of hybridization yields reaction rates that are up to 160-fold slower than hybridization between two unstructured strands. The thermodynamic and kinetic obstacles to hybridization imposed by both target and probe secondary structure are significant concerns for the continued development of antisense agents and especially diagnostic probes.     

MVF Sensor Enables Analysis of Nucleic Acids with Stable Secondary Structures

One major challenge in nucleic acids analysis by hybridization probes is a compromise between the probe's tight binding and sequence‐selective recognition of nucleic acid targets folded into stable secondary structures. We have been developing a four‐way junction (4WJ)‐based sensor that consists of a universal stem‐loop (USL) probe immobilized on an electrode surface and two adaptor strands (M and F). The sensor was shown to be highly selective towards single base mismatches at room temperature, able to detect multiple targets using the same USL probe, and have improved ability to detect folded nucleic acids. However, some nucleic acid targets, including natural RNA, are folded into very stable secondary and tertiary structures, which may represent a challenge even for the 4WJ sensors. This work describes a new sensor, named MVF since it uses three probe stands M, V and F, which further improves the performance of 4WJ sensors with folded targets. The MVF sensor interrogating a 16S rRNA NASBA amplicon with calculated folding energy of ?32.82 kcal/mol has demonstrated 2.5‐fold improvement in a signal‐to‐background ratio in comparison with a 4WJ sensor lacking strand V. The proposed design can be used as a general strategy in the analysis of folded nucleic acids including natural RNA.     

Towards a fluorescent molecular switch for nucleic acid biosensing

A novel fluorescent molecular switch for the detection of nucleic acid hybridization has been explored in relation to the development of a structure that would be amenable for operation when immobilized for solid-phase analyses. The structure was prepared by self-assembly, and used Neutravidin as the central multivalent docking molecule, a newly synthesized biotinylated long-chain linker for intercalating dye that was modified with thiazole orange (TO) at one end, and a biotinylated probe oligonucleotide. Self-assembly of the biotinylated components on adjacent Neutravidin binding sites allowed for physical placement of an oligonucleotide probe molecule next to tethered TO. The TO located at the end of the flexible linker chain was available to intercalate, and could report if a duplex structure was formed by a probe–target interaction by means of fluorescence intensity. Subsequently, regeneration of the single-stranded probe was possible without loss of the intercalator to solution. The switch constructs were assembled in solution and subsequently immobilized onto biotin functionalized optical fibers to complete the sensor design. Solution-phase fluorescence lifetime data showed a biexponential behavior for switch constructs, suggesting intercalation as well as a significant secondary binding mode for the immobilized TO. It was found that the secondary binding mechanism for the dye to DNA could be decreased, thus shifting the dye to intercalative binding modes, by adjusting the solution conditions to a pH below the pI of Neutravidin, and by increasing the ionic strength of the buffer. Preliminary work demonstrated that it was possible to achieve up to a fivefold increase in fluorescence intensity on hybridization to the target.     

Ant colony optimization for predicting RNA folding pathways

RNA folding dynamics plays important roles in various functions of RNAs. To date, coarse-grained modeling has been successfully employed to simulate RNA folding dynamics on the energy landscape composed of secondary structures. In such a modeling, the energy barrier height between metastable structures is a key parameter that crucially affects the simulation results. Although a number of approaches ranging from the exact method to heuristic ones are available to predict the barrier heights, developing an efficient heuristic for this purpose is still an algorithmic challenge.We developed a novel RNA folding pathway prediction method, ACOfoldpath, based on Ant Colony Optimization (ACO). ACO is a widely used powerful combinatorial optimization algorithm inspired from the food-seeking behavior of ants. In ACOfoldpath, to accelerate the folding pathway prediction, we reduce the search space by utilizing originally devised structure generation rules. To evaluate the performance of the proposed method, we benchmarked ACOfoldpath on the known nineteen conformational RNA switches. As a result, ACOfoldpath successfully predicted folding pathways better than or comparable to the previous heuristics. The results of RNA folding dynamics simulations and pseudoknotted pathway predictions are also presented.     

Analysis of DNA hybridization regarding the conformation of molecular layer with piezoelectric microcantilevers

Lead Zirconate Titanate (PZT)-embedded microcantilevers were fabricated with dimensions of 30 × 90 × 3 μm(3) (width × length × thickness). A thicker PZT layer improved the actuation and enabled long-term data acquisition in common aqueous buffers with a frequency resolution of 20 Hz. A quantitative assay was conducted in the range of 1-20 μM and the resonant frequency was found to increase with the concentration of target DNAs and the probe DNAs were almost saturated at 20 μM. Back-filling with ethyleneglycol-modified alkanethiol was shown to facilitate the hybridization efficiency and stabilize the surface reaction, resulting in a signal enhancement of 40%. We report for the first time how secondary structures in oligonucleotide monolayer change the surface property of a dynamic mode microcantilever and subsequently affect its oscillating behavior. Using fabricated microcantilevers, the real time changes in resonant frequency upon hybridization were measured by utilizing different probe and target sets. The results revealed that the microcantilevers experienced a resonant frequency upshift during the hybridization with complementary DNAs if a dimer structure was present between DNA probes. A resonant frequency downshift was observed for DNA probes that did not contain any complex secondary structures. In addition, the results demonstrate the potential of using these microcantilevers to extract structural information of oligonucleotides.     

Coupling molecular beacons to barcoded metal nanowires for multiplexed, sealed chamber DNA bioassays

We have combined molecular beacon (MB) probes with barcoded metal nanowires to enable no-wash, sealed chamber, multiplexed detection of nucleic acids. Probe design and experimental parameters important in nanowire-based MB assays are discussed. Loop regions of 24 bases and 5 base pair stem regions in the beacon probes gave optimal performance. Our results suggest that thermodynamic predictions for secondary structure stability of solution-phase MB can guide probe design for nanowire-based assays. Dengue virus-specific probes with predicted solution-phase DeltaG of folding in 500 mM buffered NaCl of approximately -4 kcal/mol performed better than those with DeltaG > -2 or < -6 kcal/mol. Buffered 300-500 mM NaCl was selected after comparison of several buffers previously reported for similar types of assays, and 200-500 mM NaCl was found to be the optimal ionic strength for the hybridization temperatures (25 and 50 degrees C) and probe designs used here. Target binding to the surface as a function of solution concentration fit a Sips isotherm with Kd = 1.7 +/- 0.3 nM. The detection limit was approximately 100 pM, limited by incomplete quenching. Single base mismatches could be discriminated from fully complementary targets. Oligonucleotide target sequences specific for human immunodeficiency, hepatitis C, and severe acute respiratory viruses were assayed simultaneously in a no-wash, sealed chamber, multiplexed experiment in which each of three probe sequences was attached to a different pattern of encoded nanowires. Finally, we demonstrated that probe-coated nanowires retain their selectivity and sensitivity in a triplexed assay after storage for over 3 months.     

Sequence-specific, self-reporting hairpin inversion probes

Sequence-specific probes for detecting target nucleic acids are the cornerstone of the genomics revolution (e.g., microarrays) and of molecular diagnostics. Molecular beacons are self-reporting, nucleic acid probes whose structure includes complementary terminal arm sequences and a loop that is complementary to a target sequence; fluorescence detection is by changes in proximity of fluorophore and quencher pairs attached on opposite arms. However, molecular beacon design is not as simple as attaching arbitrary arm sequences onto previously designed linear probes. The stem arms can also interact with flanking target sequences, changing the hybridization specificity; constantly adapting the arms to avoid such interactions, if not desired, increases design complexity. Herein, I report the use of inversion linkages in probe backbones leading to stem arms of sequence polarity opposite to that of the target-binding region, thereby eliminating potential hybridization of the arms with the target. Using two microbial sequence categories, thermal denaturation and target titration analyses demonstrate that these new hairpin inversion probes retain closed-state stability comparable to that of molecular beacons, contain easily designed arm sequences that do not interact with targets, and, therefore, can be used universally with optimized linear probe sequences.     

Tracking fiber formation in human islet amyloid polypeptide with automated 2D-IR spectroscopy

Amyloid forming proteins have been implicated in many human diseases. The kinetics of amyloid fiber formation are of particular interest because evidence points to intermediate folding structures as potential cytotoxic species. The standard methods for monitoring the kinetics are to use fluorescence or circular dichroism spectroscopy, which do not uniquely resolve secondary structures. In this work, we use a new technology for rapidly scanning 2D-IR spectra that allows us to follow the fiber formation kinetics of the human islet amyloid polypeptide (hIAPP) that is involved in type II diabetes. Spectroscopic markers are identified that uniquely monitor random coil versus beta-sheet secondary structures as well as probe beta-sheet elongation and stacking. Our measurements provide more rigorous kinetics for the secondary structure evolution of amyloid formation than is available with other techniques.     

Elucidating Peptide and Protein Structure and Dynamics: UV Resonance Raman Spectroscopy

UV resonance Raman spectroscopy (UVRR) is a powerful method that has the requisite selectivity and sensitivity to incisively monitor biomolecular structure and dynamics in solution. In this perspective, we highlight applications of UVRR for studying peptide and protein structure and the dynamics of protein and peptide folding. UVRR spectral monitors of protein secondary structure, such as the Amide III(3) band and the C(α)-H band frequencies and intensities can be used to determine Ramachandran Ψ angle distributions for peptide bonds. These incisive, quantitative glimpses into conformation can be combined with kinetic T-jump methodologies to monitor the dynamics of biomolecular conformational transitions. The resulting UVRR structural insight is impressive in that it allows differentiation of, for example, different α-helix-like states that enable differentiating π- and 3(10)- states from pure α-helices. These approaches can be used to determine the Gibbs free energy landscape of individual peptide bonds along the most important protein (un)folding coordinate. Future work will find spectral monitors that probe peptide bond activation barriers that control protein (un)folding mechanisms. In addition, UVRR studies of sidechain vibrations will probe the role of side chains in determining protein secondary, tertiary and quaternary structures.     

The elastic net algorithm and protein structure prediction

Predicting protein structures from their amino acid sequences is a problem of global optimization. Global optima (native structures) are often sought using stochastic sampling methods such as Monte Carlo or molecular dynamics, but these methods are slow. In contrast, there are fast deterministic methods that find near-optimal solutions of well-known global optimization problems such as the traveling salesman problem (TSP). But fast TSP strategies have yet to be applied to protein folding, because of fundamental differences in the two types of problems. Here, we show how protein folding can be framed in terms of the TSP, to which we apply a variation of the Durbin-Willshaw elastic net optimization strategy. We illustrate using a simple model of proteins with database-derived statistical potentials and predicted secondary structure restraints. This optimization strategy can be applied to many different models and potential functions, and can readily incorporate experimental restraint information. It is also fast; with the simple model used here, the method finds structures that are within 5-6 A all-Calpha-atom RMSD of the known native structures for 40-mers in about 8 s on a PC; 100-mers take about 20 s. The computer time tau scales as tau approximately n, where n is the number of amino acids. This method may prove to be useful for structure refinement and prediction.     

Hybridization of PNA to structured DNA targets: quadruplex invasion and the overhang effect

Peptide nucleic acid (PNA) probes have been synthesized and targeted to quadruplex DNA. UV-vis and CD spectroscopy reveal that the quadruplex structure of the thrombin binding aptamer (TBA) is disrupted at 37 degrees C by a short PNA probe. The corresponding DNA probe fails to bind to the stable secondary structure at this temperature. Thermal denaturation experiments indicate surprisingly high thermal and thermodynamic stabilities for the PNA-TBA hybrid. Our results point to the nonbonded nucleobase overhangs on the DNA as being responsible for this stability. This "overhang effect" is found for two different PNA-DNA sequences and a variety of different overhang lengths and sequences. The stabilization offered by the overhangs assists the PNA in overcoming the stable secondary structure of the DNA target, an effect which may be significant in the targeting of biological nucleic acids, which will always be much longer than the PNA probe. The ability of PNA to invade a structured DNA target expands its potential utility as an antigene agent or hybridization probe.     

Relationship between catalytic activity and secondary structure of a hammerhead ribozyme: A study using thermodynamic parameters for RNA structure prediction

Abstract

The stabilization energy for the secondary structures of wild-type hammerhead and mutant ribozymes has been calculated at different salt conditions and temperatures by using the thermodynamic parameters for RNA structure prediction. The most stable structure at each condition has been searched and the obtained secondary structure is compared with the structure suggested phylogenetically or experimentally. The results indicate that the hammerhead-type secondary structure of the ribozyme and its reactivity correlate with each other. The multibranched loop containing the self-cleavage site of the ribozyme particularly should be a key structure in the hammerhead ribozyme reaction. The predicted secondary structures also suggest that the reactivity of the hammerhead ribozyme should be very much lower at 10°C than that at 37°C.     

A semi-supervised learning approach for RNA secondary structure prediction

RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited.