Prediction of the secondary structure of short DNA aptamers

Diagnostic and Therapeutic Aptamers: A Promising Pathway to Improved Cardiovascular Disease Management

Despite advances in care, cardiovascular diseases remain the leading cause of death worldwide. As a result, identifying suitable biomarkers for early diagnosis and improving therapeutic and diagnostic strategies is crucial. Because of their significant advantages over other therapeutic approaches, nucleic-based therapies, particularly aptamers, are gaining increased attention. Aptamers are innovative synthetic polymers or oligomers of single-stranded DNA (ssDNA) or RNA molecules that can form 3-dimensional structures and thus interact with their targets with high specificity and affinity. Furthermore, they outperform classical protein-based antibodies in terms of in vitro selection, production, ease of modification and conjugation, high stability, low immunogenicity, and suitability for nanoparticle functionalization for targeted drug delivery. This work aims to review the advances made in the aptamers' field in biomarker detection, diagnosis, imaging, and targeted therapy, which highlight their huge potential in the management of cardiovascular diseases.

Comparative Study of Single-stranded Oligonucleotides Secondary Structure Prediction Tools

BACKGROUND

Single-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i.e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure.

RESULTS

The minimum free energy-based tools, namely mfold and RNAfold, and some tools based on artificial intelligence, namely CONTRAfold and MXfold2, provided the best results, with [Formula: see text] of exact predictions, whilst MC-fold seemed to be the worst performing tool, with only [Formula: see text] of exact predictions. In addition, UFold and SPOT-RNA are the only options for pseudoknots prediction. Including in the analysis of mfold and RNAfold results 5-10 suboptimal solutions further improved the performances of these tools. Nevertheless, we could observe issues in predicting particular motifs, such as multiple-ways junctions and mini-dumbbells, or the ssNAs whose structure has been determined in complex with a protein. In addition, our benchmark shows that some effort has to be paid for ssDNA secondary structure predictions.

CONCLUSIONS

In general, Mfold, RNAfold, and MXfold2 seem to currently be the best choice for the ssNAs secondary structure prediction, although they still show some limits linked to specific structural motifs. Nevertheless, actual trends suggest that artificial intelligence has a high potential to overcome these remaining issues, for example the recently developed UFold and SPOT-RNA have a high success rate in predicting pseudoknots.

A potent anticoagulant hybrid of snake venom derived FIX-binding protein and anti-factor IX RNA aptamer: Assessed by in-silico and electrochemical analyses

Anticoagulant therapies are crucial in the management of surgical complications as well as the prophylaxis of thrombosis. Many studies are being conducted on the Habu snake-venom anticoagulant, FIX-binding protein (FIX-Bp), for its greater potency and strong affinity to FIX clotting factor. On the other hand, the capacity to promptly reverse such acute anticoagulation is equally important. Combining a reversible anticoagulant with FIX-Bp may be advantageous in maintaining the balance between adequate anticoagulation and repealing when necessary. In this study, authors integrated FIX-Bp and RNA aptamer-based anticoagulants into a single target, FIX clotting factor, in order to achieve a robust anticoagulant effect. An in-silico and electrochemical approach were used to investigate the combination of FIX-Bp and RNA aptamers as a bivalent anticoagulant and to verify the competing or predominant binding sites of each anticoagulant. The in-silico analysis discovered that both the venom- and aptamer-anticoagulant had a strong affinity for the FIX protein at the Gla-domain and EGF-1 domain by holding 9 conventional hydrogen bonds with the binding energy of -34.859 kcal/mol. The electrochemical technique verified that both anticoagulants had different binding sites. The impedance load upon RNA aptamer binding to FIX protein was 14 %, whereas the addition of FIX-Bp caused a significant impedance rise of 37 %. This indicates that the addition of aptamers prior to FIX-Bp is a promising strategy for the conception of a hybrid anticoagulant.

One Single Tube Reaction of Aptasensor-Based Magnetic Sensing System for Selective Fluorescent Detection of VEGF in Plasma

In this study, a simple, easy and convenient fluorescent sensing system for the detection of the vascular endothelial growth factor (VEGF) based on VEGF aptamers, aptamer-complementary fluorescence-labeled probe and streptavidin magnetic beads was developed in one single tube. The VEGF is the most important biomarker in cancer, and it is investigated that the serum VEGF level varied according to the different types and courses of cancers. Hence, efficient quantification of VEGF is able to improve the accuracy of cancer diagnoses and the precision of disease surveillance. In this research, the VEGF aptamer was designed to be able to bind with the VEGF by forming G-quadruplex secondary structures; then, the magnetic beads would capture the non-binding aptamers due to non-steric interference; and finally, the fluorescence-labeled probes were hybridized with the aptamers captured by the magnetic beads. Therefore, the fluorescent intensity in the supernatant would specifically reflect the present VEGF. After an overall optimization, the optimal conditions for the detection of VEGF were as followed, KCl, 50 μM; pH 7.0; aptamer, 0.1 μM; and magnetic beads, 10 μL (4 μg/μL). The VEGF could be well quantified within a range of 0.2-2.0 ng/mL in plasma, and the calibration curve possessed a good linearity (y = 1.0391x + 0.5471, r = 0.998). The detection limit (LOD) was calculated to be 0.0445 ng/mL according to the formula (LOD = 3.3 × σ/S). The specificity of this method was also investigated under the appearance of many other serum proteins, and the data showed good specificity in this aptasensor-based magnetic sensing system. This strategy provided a simple, sensitive and selective biosensing platform for the detection of serum VEGF. Finally, it was expected that this detection technique can be used to promote more clinical applications.

A massively parallel screening platform for converting aptamers into molecular switches

Aptamer-based molecular switches that undergo a binding-induced conformational change have proven valuable for a wide range of applications, such as imaging metabolites in cells, targeted drug delivery, and real-time detection of biomolecules. Since conventional aptamer selection methods do not typically produce aptamers with inherent structure-switching functionality, the aptamers must be converted to molecular switches in a post-selection process. Efforts to engineer such aptamer switches often use rational design approaches based on in silico secondary structure predictions. Unfortunately, existing software cannot accurately model three-dimensional oligonucleotide structures or non-canonical base-pairing, limiting the ability to identify appropriate sequence elements for targeted modification. Here, we describe a massively parallel screening-based strategy that enables the conversion of virtually any aptamer into a molecular switch without requiring any prior knowledge of aptamer structure. Using this approach, we generate multiple switches from a previously published ATP aptamer as well as a newly-selected boronic acid base-modified aptamer for glucose, which respectively undergo signal-on and signal-off switching upon binding their molecular targets with second-scale kinetics. Notably, our glucose-responsive switch achieves ~30-fold greater sensitivity than a previously-reported natural DNA-based switch. We believe our approach could offer a generalizable strategy for producing target-specific switches from a wide range of aptamers.

G4Boost: a machine learning-based tool for quadruplex identification and stability prediction

Background

G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies.

Results

G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R² of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans.

Conclusion

G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04782-z.

In-Silico Selection of Aptamer Targeting SARS-CoV-2 Spike Protein

Aptamers are single-stranded, short DNA or RNA oligonucleotides that can specifically bind to various target molecules. To diagnose the infected cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in time, numerous conventional methods are applied for viral detection via the amplification and quantification of DNA or antibodies specific to antigens on the virus. Herein, we generated a large number of mutated aptamer sequences, derived from a known sequence of receptor-binding domain (RBD)-1C aptamer, specific to the RBD of SARS-CoV-2 spike protein (S protein). Structural similarity, molecular docking, and molecular dynamics (MD) were utilized to screen aptamers and characterize the detailed interactions between the selected aptamers and the S protein. We identified two mutated aptamers, namely, RBD-1CM1 and RBD-1CM2, which presented better docking results against the S protein compared with the RBD-1C aptamer. Through the MD simulation, we further confirmed that the RBD-1CM1 aptamer can form the most stable complex with the S protein based on the number of hydrogen bonds formed between the two biomolecules. Based on the experimental data of quartz crystal microbalance (QCM), the RBD-1CM1 aptamer could produce larger signals in mass change and exhibit an improved binding affinity to the S protein. Therefore, the RBD-1CM1 aptamer, which was selected from 1431 mutants, was the best potential candidate for the detection of SARS-CoV-2. The RBD-1CM1 aptamer can be an alternative biological element for the development of SARS-CoV-2 diagnostic testing.

Optimization of Short RNA Aptamers for TNBC Cell Targeting

Triple-negative breast cancer (TNBC) is an aggressive cancer with limited targeted therapies. RNA aptamers, suitably chemically modified, work for therapeutic purposes in the same way as antibodies. We recently generated 2′Fluoro-pyrimidines RNA-aptamers that act as effective recognition elements for functional surface signatures of TNBC cells. Here, we optimized three of them by shortening and proved the truncated aptamers as optimal candidates to enable active targeting to TNBC. By using prediction of secondary structure to guide truncation, we identified structural regions that account for the binding motifs of the full-length aptamers. Their chemical synthesis led to short aptamers with superb nuclease resistance, which specifically bind to TNBC target cells and rapidly internalize into acidic compartments. They interfere with the growth of TNBC cells as mammospheres, thus confirming their potential as anti-tumor agents. We propose sTN145, sTN58 and sTN29 aptamers as valuable tools for selective TNBC targeting and promising candidates for effective treatments, including therapeutic agents and targeted delivery nanovectors.

Spectroscopic ellipsometry methods for brevetoxin detection

The spectroscopic ellipsometry (SE), and attenuated internal reflection spectroscopic ellipsometry (TIRE) are promising methods in label-free biosensing applications. An ellipsometer running under surface plasmon resonance (SPR) conditions has unique advantages over other SPR-based methods in terms of sensitivity and real-time/label-free measurement capability. In this study, both SE and TIRE-based brevetoxin B (BTX) sensors were developed using two anti-BTX aptamers reported before. A new aptamer sequence was also derived from these two antiBTX aptamers using predictive modeling tools and an exclusion method. All three antiBTX aptamers' analytical performances were quite competitive in terms of both detecting range and detection limits. However, the selectivity of the previously reported aptamers against analogs of BTX was poor at low detection ranges, especially for okadaic acid. Furthermore, the selectivity of the derived aptamer was lower than its predecessors. The sensors were capable of detecting BTX in the range of 0.05 nM-1600 nM in the TIRE and 0.5 nM-2000 nM in the SE configuration. The detection limits of the sensors were 1.48 nM (1.32 ng/mL) and 0.80 nM (0.72 ng/mL) for SE and TIRE configurations, respectively. Both configurations have been used successfully to detect BTX standards spiked into real fish and shrimp samples.

Study on the binding mode of aptamer to ampicillin and its electrochemical response behavior in two different reaction media

A study was carried out to investigate the binding mode of aptamer to ampicillin (AMP) and its electrochemical response behavior. The binding mode was confirmed using the molecular dynamics (MD) simulation method to obtain the corresponding binding dynamic change process. Following the confirmed binding mode, a qualitative elucidation was provided on the electrochemical response characteristics of a single-probe aptamer-based folding sensor. The results show that there exist two different binding modes in two different solution systems, Phys2 and H₂O (0.1 M NaCl). These two binding modes can respectively induce two different contraction changes, thereby driving the methylene blue (MB)-modified aptamer probe to show a "close-to-interface" convergence behavior with different degrees on the actual electrode surface, which validates two apparently different electrochemical response behavior characteristics of "signal-on" for the sensor. By contrast, H₂O (0.1 M NaCl) as the reaction medium is more conducive to the formation of a stable aptamer/AMP complex and the development of a high-sensitivity analytical method with a low detection limit of 0.033 μM. The simulation results effectively support the experimental results, which is helpful in gaining a deeper understanding of the relationship between the signaling mechanism and practical analytical performance for aptamer-based folding sensors at the molecular level.

A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers

The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucleotide-specific molecule. Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences. Thus, a novel machine learning-based approach was developed to support and simplify the early steps of the SELEX process, to help discriminate binding between DNA aptamers from those unspecified targets of DNA-binding sequences. An Artificial Intelligence (AI) approach to identify aptamers were implemented based on Natural Language Processing (NLP) and Machine Learning (ML). NLP method (CountVectorizer) was used to extract information from the nucleotide sequences. Four ML algorithms (Logistic Regression, Decision Tree, Gaussian Naïve Bayes, Support Vector Machines) were trained using data from the NLP method along with sequence information. The best performing model was Support Vector Machines because it had the best ability to discriminate between positive and negative classes. In our model, an Accuracy (A) of 0.995, the fraction of samples that the model correctly classified, and an Area Under the Receiving Operating Curve (AUROC) of 0.998, the degree by which a model is capable of distinguishing between classes, were observed. The developed AI approach is useful to identify potential DNA aptamers to reduce the amount of rounds in a SELEX selection. This new approach could be applied in the design of DNA libraries and result in a more efficient and faster process for DNA aptamers to be chosen during SELEX.

In this manuscript authors explain the development and validation of a novel artificial intelligence approach to support and simplify the early steps of the process from SELEX, to help discriminate binding between deoxynucleotide aptamers from those unspecified targets of DNA-binding sequences. The approach was implemented based on Natural Language Processing and Machine Learning. CountVectorizer, a Natural Language Processing method, was used to extract information from nucleotide sequences. Four Machine Learning algorithms (Logistic Regression, Decision Tree, Gaussian Naïve Bayes, and Support Vector Machines) were trained using data from the Natural Language Processing method along with sequence information. From these four trained machine learning algorithms, the best performance and selected model was Support Vectors Machines, because it had the best discriminatory metrics (i.e., Accuracy (A) = 0.995; AUROC (AU) = 0.998). In general, all models showed good metric results for predicting DNA aptamer sequences. The Machine Learning model complexity and difficult interpretation may hinder its application into the standard practice. For this reason, the development of a web-app is already taking place to facilitate the interpretation and application of the obtained results.

Annotation of miRNAs in COVID-19 coronavirus

The COVID-19 coronavirus is a new strain of coronavirus that had not been previously detected in humans. As its severe pathogenicity is concerned, it is important to study it thoroughly to aid in the discovery of a cure. In this study, the microRNAs (miRNAs) of COVID-19 were annotated to provide a powerful tool for the study of this novel coronavirus. We obtained 16 novel coronavirus genome sequences and the mature sequences of all viruses in the microRNA database (miRbase), and then used the miRNA mature sequences of the virus to perform the Basic Local Alignment Search Tool (BLAST) analysis in the coronavirus genome, extending the matched regions of approximately 20 bp to two segments by 200 bp. Six sequences were obtained after deleting redundant sequences. Then, the hairpin structures of the mature miRNAs were determined using RNAfold. The mature sequence on one hairpin arm was selected into a total of 4 sequences, and finally the relevant miRNA precursor prediction tools were used to verify whether the selected sequences are miRNA precursor sequences of the novel coronavirus. The miRNAs of the novel coronavirus were annotated by our newly developed method, which will lay the foundation for further study of this virus.

Porcine IGF-1R synonymous mutations in the intracellular domain affect cell proliferation and alter kinase activity

Miniature pigs are regarded as ideal organ donors for xenotransplantation into humans. Elucidating the formation mechanism of miniature pigs is important. The insulin-like growth factor 1 receptor (IGF-1R) is crucial in the regulation of cell proliferation and organismal growth. According to our previous research, the IGF-1R expression levels between large and miniature pigs showed different profiles in liver and muscle tissues. Here, five synonymous mutations of IGF-1R in the coding sequence (CDS) of intracellular domain (ICD) between large and miniature pigs were analysed by constructing expression vectors of two haplotypes and named pcDNA3.1-LP (with the CDS of IGF-1R ICD of Large White pigs, LP group) and pcDNA3.1-BM (with the CDS of IGF-1R ICD of Bama Xiang pigs, BM group). The IGF-1R of the BM group was expressed lower than that of the LP group in transcription, translation and autophosphorylation levels. The IGF-1R of the BM group also down-regulated the protein levels of p-AKT/p-ERK than that of the LP group. PK-15 and C2C12 cell proliferation were detected to further understand the function of the haplotype. Results showed that the proliferation viability of PK-15 and C2C12 cells weakened in the BM group. Moreover, the mRNA and protein stabilities of the BM group were higher than those of the LP group. Our data indicated that two haplotypes of IGF-1R CDS in ICD between large and miniature pigs altered IGF-1R expression and down-regulated AKT and ERK signalling pathways at translation levels, resulting in an inhibitory effect on PK-15 and C2C12 cell proliferation.