Nearest-neighbour parametrization of DNA single, double and triple mismatches at low sodium concentration.

DNA mismatches, that is, base pairs different from the canonical AT and CG, are involved in numerous biological processes and can be a problem for technological applications such as PCR amplification. The nearest-neighbour (NN) model is the standard approach for predicting melting temperatures and is used in methods of secondary structure predictions and modelling of hybridization kinetics. However, despite its biological and technological importance, existing NN parameters that include DNA mismatches are incomplete, and those available were obtained from a limited set of melting temperature at high sodium concentration. To our knowledge, there is currently no NN set of parameters for up to three mismatches covering all configurations at low sodium concentrations. Here, we are applying the NN model to a large set of 4096 published melting temperatures, covering all combinations of single, double and triple mismatches. Dealing with such a large set of temperature is challenging in several ways, bringing new methodological problems. Here, optimizing a large number of 252 independent parameters has required the development of a new method where we readjust the seed parameters using the definition of the Gibbs free energy. The new parameters predict the training set within 1.1 °C and the validation set to 2.7 °C.

Cation valence dependence of hydrogen bond and stacking potentials in DNA mesoscopic models.

Monovalent and divalent cations play a crucial role in living cells and for molecular techniques such as PCR. Here we evaluate DNA melting temperatures in magnesium (Mg2+) and magnesium­potassium (Mg2++ K+) buffers with a mesoscopic model that allows us to estimate hydrogen bonds and stacking interaction potentials. The Mg2+ and Mg2++ K+ results are compared to previous calculations for sodium ions (Na+), in terms of equivalent sodium concentration and ionic strength. Morse potentials, related to hydrogen bonding, were found to be essentially constant and unaffected by cation conditions. However, for stacking interactions we find a clear dependence with ionic strength and cation valence. The highest ionic strength variations, for both hydrogen bonds and stacking interactions, was found at the sequence terminals. This suggests that end-to-end interactions in DNA will be strongly dependent on cation valence and ionic strength.

Multi-Dimensional Regression Models for Predicting the Wall Thickness Distribution of Corrugated Pipes.

Corrugated pipes offer both higher stiffness and higher flexibility while simultaneously requiring less material than rigid pipes. Production rates of corrugated pipes have therefore increased significantly in recent years. Due to rising commodity prices, pipe manufacturers have been driven to produce corrugated pipes of high quality with reduced material input. To the best of our knowledge, corrugated pipe geometry and wall thickness distribution significantly influence pipe properties. Essential factors in optimizing wall thickness distribution include adaptation of the mold block geometry and structure optimization. To achieve these goals, a conventional approach would typically require numerous iterations over various pipe geometries, several mold block geometries, and then fabrication of pipes to be tested experimentally-an approach which is very time-consuming and costly. To address this issue, we developed multi-dimensional mathematical models that predict the wall thickness distribution in corrugated pipes as functions of the mold geometry by using symbolic regression based on genetic programming (GP). First, the blow molding problem was transformed into a dimensionless representation. Then, a screening study was performed to identify the most significant influencing parameters, which were subsequently varied within wide ranges as a basis for a comprehensive, numerically driven parametric design study. The data set obtained was used as input for data-driven modeling to derive novel regression models for predicting wall thickness distribution. Finally, model accuracy was confirmed by means of an error analysis that evaluated various statistical metrics. With our models, wall thickness distribution can now be predicted and subsequently used for structural analysis, thus enabling digital mold block design and optimizing the wall thickness distribution.

Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts.

Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.

Optimization and Clinical Validation of Colorimetric Reverse Transcription Loop-Mediated Isothermal Amplification, a Fast, Highly Sensitive and Specific COVID-19 Molecular Diagnostic Tool That Is Robust to Detect SARS-CoV-2 Variants of Concern.

The coronavirus disease 2019 (COVID-19) pandemic unfolded due to the widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission reinforced the urgent need for affordable molecular diagnostic alternative methods for massive testing screening. We present the clinical validation of a pH-dependent colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) for SARS-CoV-2 detection. The method revealed a limit of detection of 19.3 ± 2.7 viral genomic copies/µL when using RNA extracted samples obtained from nasopharyngeal swabs collected in guanidine-containing viral transport medium. Typical RT-LAMP reactions were performed at 65°C for 30 min. When compared to reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR), up to cycle-threshold (Ct) value 32, RT-LAMP presented 98% [95% confidence interval (CI) = 95.3-99.5%] sensitivity and 100% (95% CI = 94.5-100%) specificity for SARS-CoV-2 RNA detection targeting E and N genes. No cross-reactivity was detected when testing other non-SARS-CoV virus, confirming high specificity. The test is compatible with primary RNA extraction-free samples. We also demonstrated that colorimetric RT-LAMP can detect SARS-CoV-2 variants of concern and variants of interest, such as variants occurring in Brazil named gamma (P.1), zeta (P.2), delta (B.1.617.2), B.1.1.374, and B.1.1.371. The method meets point-of-care requirements and can be deployed in the field for high-throughput COVID-19 testing campaigns, especially in countries where COVID-19 testing efforts are far from ideal to tackle the pandemics. Although RT-qPCR is considered the gold standard for SARS-CoV-2 RNA detection, it requires expensive equipment, infrastructure, and highly trained personnel. In contrast, RT-LAMP emerges as an affordable, inexpensive, and simple alternative for SARS-CoV-2 molecular detection that can be applied to massive COVID-19 testing campaigns and save lives.

Complete Mesoscopic Parameterization of Single LNA Modifications in DNA Applied to Oncogene Probe Design.

The use of mesoscopic models to describe the thermodynamic properties of locked nucleic acid (LNA)-modified nucleotides can provide useful insights into their properties, such as hydrogen-bonding and stacking interactions. In addition, the mesoscopic parameters can be used to optimize LNA insertion in probes, to achieve accurate melting temperature predictions, and to obtain duplex opening profiles at the base-pair level. Here, we applied this type of model to parameterize a large set of melting temperatures for LNA-modified sequences, from published sources, covering all possible nearest-neighbor configurations. We have found a very large increase in Morse potentials, which indicates very strong hydrogen bonding as the main cause of improved LNA thermodynamic stability. LNA-modified adenine-thymine (AT) was found to have similar hydrogen bonding to unmodified cytosine-guanine (CG) base pairs, while for LNA CG, we found exceptionally large hydrogen bonding. In contrast, stacking interactions, which were thought to be behind the stability of LNA, were similar to unmodified DNA in most cases. We applied the new LNA parameters to the design of BRAF, KRAS, and EGFR oncogene variants by testing all possible LNA modifications. Selected sequences were then synthesized and had their hybridization temperatures measured, achieving a prediction accuracy within 1 °C. We performed a detailed base-pair opening analysis to discuss specific aspects of these probe hybridizations that may be relevant for probe design.

Salt dependent mesoscopic model for RNA at multiple strand concentrations.

Mesoscopic models can be used for the description of the thermodynamic properties of RNA duplexes. With the use of experimental melting temperatures, its parametrization can provide important insights into its hydrogen bonds and stacking interactions as has been done for high sodium concentrations. However, the RNA parametrization for lower salt concentrations is still missing due to the limited amount of published melting temperature data. While the Peyrard-Bishop (PB) parametrization was found to be largely independent of strand concentrations, it requires that all temperatures are provided at the same strand concentrations. Here we adapted the PB model to handle multiple strand concentrations and in this way we were able to make use of an experimental set of temperatures to model the hydrogen bond and stacking interactions at low and intermediate sodium concentrations. For the parametrizations we make a distinction between terminal and internal base pairs, and the resulting potentials were qualitatively similar as we obtained previously for DNA. The main difference from DNA parameters, was the Morse potentials at low sodium concentrations for terminal r(AU) which is stronger than d(AT), suggesting higher hydrogen bond strength.

Thermodynamic evaluation of the impact of DNA mismatches in PCR-type SARS-CoV-2 primers and probes.

BACKGROUND: DNA mismatches can affect the efficiency of PCR techniques if the intended target has mismatches in primer or probe regions. The accepted rule is that mismatches are detrimental as they reduce the hybridization temperatures, yet a more quantitative assessment is rarely performed. METHODS: We calculate the hybridization temperatures of primer/probe sets after aligning to SARS-CoV-2, SARS-CoV-1 and non-SARS genomes, considering all possible combinations of single, double and triple consecutive mismatches. We consider the mismatched hybridization temperature within a range of 5 ∘C to the fully matched reference temperature. RESULTS: We obtained the alignments of 19 PCR primers sets that were recently reported for the detection of SARS-CoV-2 and to 21665 SARS-CoV-2 genomes as well as 323 genomes of other viruses of the coronavirus family of which 10 are SARS-CoV-1. We find that many incompletely aligned primers become fully aligned to most of the SARS-CoV-2 when mismatches are considered. However, we also found that many cross-align to SARS-CoV-1 and non-SARS genomes. CONCLUSIONS: Some primer/probe sets only align substantially to most SARS-CoV-2 genomes if mismatches are taken into account. Unfortunately, by the same mechanism, almost 75% of these sets also align to some SARS-CoV-1 and non-SARS viruses. It is therefore recommended to consider mismatch hybridization for the design of primers whenever possible, especially to avoid undesired cross-reactivity.

Liquid-liquid phase separation and fibrillation of the prion protein modulated by a high-affinity DNA aptamer.

Structural conversion of cellular prion protein (PrPC) into scrapie PrP (PrPSc) and subsequent aggregation are key events associated with the onset of transmissible spongiform encephalopathies (TSEs). Experimental evidence supports the role of nucleic acids (NAs) in assisting this conversion. Here, we asked whether PrP undergoes liquid-liquid phase separation (LLPS) and if this process is modulated by NAs. To this end, two 25-mer DNA aptamers, A1 and A2, were selected against the globular domain of recombinant murine PrP (rPrP90-231) using SELEX methodology. Multiparametric structural analysis of these aptamers revealed that A1 adopts a hairpin conformation. Aptamer binding caused partial unfolding of rPrP90-231 and modulated its ability to undergo LLPS and fibrillate. In fact, although free rPrP90-231 phase separated into large droplets, aptamer binding increased the number of droplets but noticeably reduced their size. Strikingly, a modified A1 aptamer that does not adopt a hairpin structure induced formation of amyloid fibrils on the surface of the droplets. We show here that PrP undergoes LLPS, and that the PrP interaction with NAs modulates phase separation and promotes PrP fibrillation in a NA structure and concentration-dependent manner. These results shed new light on the roles of NAs in PrP misfolding and TSEs.

Optical and theoretical study of strand recognition by nucleic acid probes.

Detection of nucleic acids is crucial to the study of their basic properties and consequently to applying this knowledge to the determination of pathologies such as cancer. In this work, our goal is to determine new trends for creating diagnostic tools for cancer driver mutations. Herein, we study a library of natural and modified oligonucleotide duplexes by a combination of optical and theoretical methods. We report a profound effect of additives on the duplexes, including nucleic acids as an active crowder. Unpredictably and inconsistent with DNA+LNA/RNA duplexes, locked nucleic acids contribute poorly to mismatch discrimination in the DNA+LNA/DNA duplexes. We develop a theoretical framework that explains poor mismatch discrimination in KRAS oncogene. We implement our findings in a bead-bait genotyping assay to detect mutated human cancer RNA. The performance of rationally designed probes in this assay is superior to the LNA-primer polymerase chain reaction, and it agrees with sequencing data.

Melting temperature measurement and mesoscopic evaluation of single, double and triple DNA mismatches.

Unlike the canonical base pairs AT and GC, the molecular properties of mismatches such as hydrogen bonding and stacking interactions are strongly dependent on the identity of the neighbouring base pairs. As a result, due to the sheer number of possible combinations of mismatches and flanking base pairs, only a fraction of these have been studied in varying experiments or theoretical models. Here, we report on the melting temperature measurement and mesoscopic analysis of contiguous DNA mismatches in nearest-neighbours and next-nearest neighbour contexts. A total of 4032 different mismatch combinations, including single, double and triple mismatches were covered. These were compared with 64 sequences containing all combinations of canonical base pairs in the same location under the same conditions. For a substantial number of single mismatch configurations, 15%, the measured melting temperatures were higher than the least stable AT base pair. The mesoscopic calculation, using the Peyrard-Bishop model, was performed on the set of 4096 sequences, and resulted in estimates of on-site and nearest-neighbour interactions that can be correlated to hydrogen bonding and base stacking. Our results confirm many of the known properties of mismatches, including the peculiar sheared stacking of tandem GA mismatches. More intriguingly, it also reveals that a number of mismatches present strong hydrogen bonding when flanked on both sites by other mismatches. To highlight the applicability of our results, we discuss a number of practical situations such as enzyme binding affinities, thymine DNA glycosylase repair activity, and trinucleotide repeat expansions.

Replacing salt correction factors with optimized RNA nearest-neighbour enthalpy and entropy parameters.

We calculate the nearest-neighbour enthalpies and entropies at 5 salt concentrations of 18 RNA sequences, each for at least 9 different species concentrations, totalling 757 melting temperatures, using a melting temperature optimization method. These new parameters do not need to be salt-corrected and are shown to provide overall improved melting temperature predictions. They show a marked quadratic dependence with salt concentrations which are compensated to form linear Gibbs free energies. Two different parameter schemes were tested, with fixed or variable initial parameters. We have found that using variable initial parameters provides better predictive results than using salt correction factors and that the prediction uncertainty is considerably reduced for a validation set of independent sequences. An interpolation scheme is introduced to generate model parameters for arbitrary salt concentrations which performs better against a validation set than predictions using salt corrections.

Nearest-neighbour parameters optimized for melting temperature prediction of DNA/RNA hybrids at high and low salt concentrations.

Gene editing technologies sparked a renewed interest in the hybridization of DNA/RNA duplexes, yet little improvement on nearest-neighbour parameters was made over the past two decades. For low sodium concentration no parameter set was yet calculated. Here, we revised the existing experimental datasets and used an expanded set of sequences from which we recalculated the nearest-neighbour parameters, reducing the average temperature prediction uncertainty to 1.6 °C. Two experimental sets using temperatures extracted via different methods were used with similar results, with the curve-fitting method achieving a slight advantage in prediction quality over other methods. Additionally, we obtained new parameters for low salt with an average uncertainty of 0.98 °C. We also tested several types of salt correction factors and concluded that it is advisable to use those originally developed for RNA/RNA rather than for DNA/DNA.

An asymmetric mesoscopic model for single bulges in RNA.

Simple one-dimensional DNA or RNA mesoscopic models are of interest for their computational efficiency while retaining the key elements of the molecular interactions. However, they only deal with perfectly formed DNA or RNA double helices and consider the intra-strand interactions to be the same on both strands. This makes it difficult to describe highly asymmetric structures such as bulges and loops and, for instance, prevents the application of mesoscopic models to determine RNA secondary structures. Here we derived the conditions for the Peyrard-Bishop mesoscopic model to overcome these limitations and applied it to the calculation of single bulges, the smallest and simplest of these asymmetric structures. We found that these theoretical conditions can indeed be applied to any situation where stacking asymmetry needs to be considered. The full set of parameters for group I RNA bulges was determined from experimental melting temperatures using an optimization procedure, and we also calculated average opening profiles for several RNA sequences. We found that guanosine bulges show the strongest perturbation on their neighboring base pairs, considerably reducing the on-site interactions of their neighboring base pairs.

Mesoscopic modelling of Cy3 and Cy5 dyes attached to DNA duplexes.

Cy3 and Cy5 dyes linked to the 5' end of a double stranded DNA molecule are known to attach to both strands in a way that is very similar to an additional base pair and has a stabilizing effect on the oligonucleotide. Here we adapt the Peyrard-Bishop mesoscopic model to incorporate cyanine dyes and use the technique of thermal equivalence to obtain the appropriate parameters from existing melting temperatures. We have found that the stacking parameters are in the same range of ordinary AT and CG base pairs, in particular Cy3-A was found to be most rigidly stacked. While the cyanines stabilize the AT hydrogen bonds quite strongly the CG bonds are mostly unaffected.

Effect of 3'UTR RET Variants on RET mRNA Secondary Structure and Disease Presentation in Medullary Thyroid Carcinoma.

BACKGROUND: The RET S836S variant has been associated with early onset and increased risk for metastatic disease in medullary thyroid carcinoma (MTC). However, the mechanism by which this variant modulates MTC pathogenesis is still open to discuss. Of interest, strong linkage disequilibrium (LD) between RET S836S and 3'UTR variants has been reported in Hirschsprung's disease patients. OBJECTIVE: To evaluate the frequency of the RET 3'UTR variants (rs76759170 and rs3026785) in MTC patients and to determine whether these variants are in LD with S836S polymorphism. METHODS: Our sample comprised 152 patients with sporadic MTC. The RET S836S and 3'UTR (rs76759170 and rs3026785) variants were genotyped using Custom TaqMan Genotyping Assays. Haplotypes were inferred using the phase 2.1 program. RET mRNA structure was assessed by Vienna Package. RESULTS: The mean age of MTC diagnosis was 48.5±15.5 years and 57.9% were women. The minor allele frequencies of RET polymorphisms were as follows: S836S, 5.6%; rs76759170, 5.6%; rs3026785, 6.2%. We observed a strong LD among S836S and 3'UTR variants (|D'| = -1, r2 = 1 and |D'| = -1, r2 = 0,967). Patients harboring the S836S/3'UTR variants presented a higher percentage of lymph node and distant metastasis (P = 0.013 and P<0.001, respectively). Accordingly, RNA folding analyses demonstrated different RNA secondary structure predictions for WT(TCCGT), S836S(TTCGT) or 3'UTR(GTCAC) haplotypes. The S836S/3'UTR haplotype presented a greater number of double helices sections and lower levels of minimal free energy when compared to the wild-type haplotype, suggesting that these variants provides the most thermodynamically stable mRNA structure, which may have functional consequences on the rate of mRNA degradation. CONCLUSION: The RET S836S polymorphism is in LD with 3'UTR variants. In silico analysis indicate that the 3'UTR variants may affect the secondary structure of RET mRNA, suggesting that these variants might play a role in posttranscriptional control of the RET transcripts.

Evaluating Hydrogen Bonds and Base Stacking of Single, Tandem and Terminal GU Mismatches in RNA with a Mesoscopic Model.

Guanine-Uracil (GU) mismatches are crucial to the stability of the RNA double helix and need to be considered in RNA folding algorithms for numerous biotechnological applications. Yet despite its importance, many aspects of GU base pairs are still poorly understood. There is also a lack of parametrization which prevents it to be considered in mesoscopic models. Here, we adapted the mesoscopic Peyrard-Bishop model to deal with context-dependent hydrogen bonds of GU mismatches and calculated the model parameters related to hydrogen bonding and base stacking from available experimental melting temperatures. The context-dependence causes a proliferation of parameters which made the problem computationally very demanding. We were able to overcome this problem by systematically regrouping the parameters during the minimization procedure. Our results not only provide the much needed parametrization but also answer several questions about the general properties of GU base pairs, as they can be associated straightforwardly to hydrogen bonding and base stacking. In particular, we found a very small Morse potential for tandem 5'-GU-3', which confirms a single hydrogen bond for this configuration, answering a long-standing question over conflicting experimental findings. Terminal GU base pairs are known to increase the duplex stability, but it is not clear why. Our results suggest that the increased terminal stability is mostly due to stronger hydrogen bonding.

DNA terminal base pairs have weaker hydrogen bonds especially for AT under low salt concentration.

DNA base pairs are known to open more easily at the helix terminal, a process usually called end fraying, the details of which are still poorly understood. Here, we present a mesoscopic model calculation based on available experimental data where we consider separately the terminal base pairs of a DNA duplex. Our results show an important reduction of hydrogen bond strength for terminal cytosine-guanine (CG) base pairs which is uniform over the whole range of salt concentrations, while for AT base pairs, we obtain a nearly 1/3 reduction but only at low salt concentrations. At higher salt concentrations, terminal adenine-thymine (AT) pair has almost the same hydrogen bond strength than interior bases. The calculated terminal stacking interaction parameters display some peculiarly contrasting behavior. While there is mostly no perceptible difference to internal stacking, for some cases, we observe an unusually strong dependence with salt concentration which does not appear follow any pattern or trend.

Optimization method for obtaining nearest-neighbour DNA entropies and enthalpies directly from melting temperatures.

MOTIVATION: Free energy nearest-neighbour (NN) thermodynamics is widely used in DNA biochemistry, ranging from the calculation of melting temperatures to the prediction of secondary structures. Methods to calculate NN parameters require the knowledge of total sequence entropies and enthalpies, which are not always available. RESULTS: Here, we implement and test a new melting temperature optimization method where we obtain the NN parameters directly from the temperatures. In this way, we bypass the constraints imposed by total sequence entropies and enthalpies. This enabled us to calculate the missing NN entropies and enthalpies for some published datasets, including salt-dependent parameters. Also this allowed us to combine 281 sequences from different types of melting temperature data for which we derived a new set of NN parameters, which have a smaller uncertainty and an improved predictive power. AVAILABILITY AND IMPLEMENTATION: C++ source code and compiled binaries for several Linux distributions are available from https://sites.google.com/site/geraldweberufmg/vargibbs and from OpenSuse build service at https://build.opensuse.org/package/show/home:drgweber/VarGibbs. The software package contains scripts and data files to reproduce all results presented here.

TfReg: calculating DNA and RNA melting temperatures and opening profiles with mesoscopic models.

SUMMARY: The mesoscopic statistical physics models, known generically as Peyrard-Bishop (PB) models, have found many applications for the study of oligonucleotide properties. Unfortunately, PB models have not reached a wider non-specialized audience for the lack of freely available software implementations. Here we present an extensible C++ implementation of four variants of the PB model, which allows the user to calculate melting temperatures from tested model parameters. Even for a non-specialist, it should be straightforward to change these parameters to reflect different experimental environments or different types of oligonucleotides. For users with some proficiency in C++ programming, it should be feasible to extend the code to other PB models owing to the generic programming implementation adopted for TfReg. Pre-calculated parameters are included that allow the immediate calculation of melting temperatures and thermal equivalence indexes for DNA and RNA. AVAILABILITY: C++ source code and compiled binaries for several Linux distributions are available from https://sites.google.com/site/geraldweberufmg/tfreg and from OpenSuse build service at http://build.opensuse.org. CONTACT: gweberbh@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.