Three-dimensional modeling of single stranded DNA hairpins for aptamer-based biosensors

Binding characterization of small protein-conjugated ssDNA aptamer to recombinant human ICAM-1

This study investigates the potential of a protein-DNA aptamer conjugate to enhance aptamer binding to recombinant human intercellular adhesion molecule 1 (rhICAM-1). Aptamers are single-stranded nucleic acids that bind target molecules through hydrogen bonding and hydrophobic interactions. Conjugating aptamers with antibodies or proteins has been shown to improve their binding affinity. Using Systematic Evolution of Ligands by Exponential Enrichment (SELEX), eight rounds of selection were performed with ICAM-1-coupled Dynabeads Protein A, identifying a DI05 as having the strongest binding affinity to rhICAM-1. An antibody inhibition assay showed a significant reduction in rhICAM-1 binding to immobilized aptamers (DI05, DI20, DI31, and DI33). Additionally, the binding affinity of eGFP-conjugated DI05 to rhICAM-1 was higher than that of unconjugated DI05. Docking simulations revealed close contact between DI05 and ICAM-1, with interactions primarily mediated by hydrogen bonds within three hairpin structures at ≤2.8 Å. These findings highlight the potential of aptamer-small protein conjugates as a promising strategy to enhance aptamer binding characteristics.

Introduce a novel, extremely sensitive aptamer against staphylococcal enterotoxin type D

BACKGROUND

Staphylococcus aureus (S. aureus) is a globally prevalent foodborne pathogen responsible for significant public health concerns. Staphylococcal food poisoning (SFP) results from staphylococcal enterotoxins (SEs) produced by specific strains of S. aureus. Rapid and effective detection of SEs remains a significant challenge for public health authorities. Aptamers, short single-stranded DNA(ssDNA), RNA, or synthetic xeno nucleic acid (XNA) molecules, exhibit high affinity for binding to their specific targets. Due to their unique properties, including low production costs, ease of chemical modification, high thermal stability, and reproducibility, aptamers present a viable alternative to antibodies for diagnostic and therapeutic applications.

OBJECTIVES

This research aimed to isolate high-affinity ssDNA aptamers with specificity for staphylococcal enterotoxin D (SED).

METHODS

The systematic evolution of ligands by the exponential enrichment (SELEX) method was utilized to identify specific aptamers. These aptamers were then validated using enzyme-linked apta-sorbent assay (ELASA) and surface plasmon resonance (SPR) to assess their binding characteristics and affinity.

RESULTS

SELEX successfully identified aptamers with strong binding affinity to SED. Among the identified candidates, one aptamer, Aptamer 1, exhibited the highest specificity for SED with a dissociation constant (KD) of 4.4 ± 2.26 nM. The limit of detection (LOD) for SED using this aptamer was determined to be 45 nM.

CONCLUSIONS

The findings indicate that the ELASA system designed using the aptamer developed in this study demonstrates higher specificity, sensitivity, and reproducibility in detecting enterotoxin D. This novel aptamer offers significant potential for applications in diagnostic platforms targeting S. aureus enterotoxins.

Refinement and Truncation of DNA Aptamers Based on Molecular Dynamics Simulations: Computational Protocol and Experimental Validation

Aptamers have proven useful for a wide variety of applications, such as drug delivery systems and analytical reagents for diagnosis or food safety control. Conventional aptamer selection methods typically produce sequences longer than necessary, which are optimized through a postselection trial and error process to obtain the shortest-length sequence that preserves binding affinity. Herein, we describe a general strategy to obtain the tridimensional structure of DNA aptamers using a semiautomated molecular dynamics protocol, which serves as a guide to rationally improve experimentally selected candidates. Based on this approach, we designed truncated aptamers from previously described ligands recognizing different peptides and proteins, which are 20-35% shorter than the original candidates and present similar or even improved binding affinities. Moreover, we also discriminate between energetically similar secondary structures in terms of the energetic scoring of the molecular dynamics trajectories and rationally explain the role of poly thymine spacers in the (de)stabilization of the structure. This work demonstrates how a protocol for generating the aptamers tridimensional structure can accelerate their optimization for obtaining better analytical reagents and therapeutic agents.

Engineering aptamer-directed phosphatase recruiting chimeras: a strategy for modulating receptor function and overcoming drug resistance

Receptor tyrosine kinases (RTKs) play a crucial role in the regulation of intracellular signal transduction, underscoring their significance as targets for drug therapy. Despite the widespread clinical use of kinase inhibitors, the increasing occurrence of off-target effects and drug resistance makes it urgent to explore alternative approaches to modulate RTKs functions. Here, we propose an approach for attenuating cell-surface receptor signaling, termed Aptamer-directed Phosphatase Recruiting Chimeras (Apt-PRCs). The Apt-PRC is composed of an aptamer to recruit phosphatases and a binder to target receptors. As a proof-of-concept, we design and construct Apt-PRCs intended for direct dephosphorylation of tyrosine residues on the receptor targets, i.e., epidermal growth factor receptor and mesenchymal-epithelial transition factor, respectively. The as-developed Apt-PRCs manage to inhibit specifically and efficiently the reception and transmission of phosphorylation signals both in vitro and in vivo. Furthermore, it is discovered that the induced dephosphorylation could enhance the susceptibility to gefitinib in drug-resistant cancer cells and a xenograft mouse model, indicating the potential of Apt-PRCs to overcome drug resistance in cancer. This work offers a versatile methodology to design molecular mediators to modulate receptor phosphorylation so as to regulate the downstream signal transduction and overcome drug resistance.

In silico selection against progesterone receptor DNA-binding domain

Progesterone receptor is one of the markers used in antibody-based immunohistochemistry for the diagnostics of breast cancer. The shortcomings of antibodies raise the need to focus on alternative molecular recognition. Aptamers are chosen due to their many advantages as compared to antibodies. However, the rigor of conventional SELEX intensifies the efforts to select DNA aptamers using in silico-docking approach. In this study, we performed in silico selection and experimental validation of DNA aptamers against the progesterone receptor DNA binding domain (PR DBD) using the ssDNA sequences derived from human progesterone response elements (PREs). Firstly, a library of sixty-four different ssDNA was subjected to secondary and tertiary structural determination prior to docking using PatchDock. PRDBDapt17 appeared to be the best candidate, with the highest docking scores of 11334. Molecular dynamic simulation also substantiates PRDBDapt17 as the most potent aptamer. This aptamer, PRDBDapt17 was validated by using direct ELASA. Direct ELASA demonstrated a limit of detection of 3.91 nM while the equilibrium dissociation constant was estimated at 366.6 nM. As PRDBDapt17 also interacts with estrogen receptor and androgen receptor, it can also be a potential universal binder of steroid hormone receptors. PRDBDapt17 can be used in the diagnostics of breast cancer.

Aptamer based immunotherapy: a potential solid tumor therapeutic

Aptamer-based immunotherapy can be a new hope for treating solid tumors with personalized and specific approaches toward cancer therapies. Aptamers are small synthetic single-stranded nucleic acids that may bring in a paradigm shift in treating solid tumors. These are highly selective drugs applied in cellular immunotherapy, cytokine modulation, and immune checkpoint suppression. This review provides an overview of the recent advances in aptamer-based technologies with specific key clinical trials involving AON-D21 and AM003. Aptamers are potently active in immune regulation and tumor targeting. However, aptamer stability and bioavailability are seriously compromised by the issues relating to renal clearance and rapid degradation through nucleases. The latter are reviewed here along with novel improvements, some of which involve chemical modifications that greatly enhance stability and prolong the circulation time; exemplary such modifications are PEGylation, cholesterol conjugation, and the synthesis of circular nucleic acids. The regulatory aspect is also crucial. For example, in addition to specific strategies to prevent drug-drug interactions (DDIs) in cancer remediation medications, this paper underscores the need of risk assessment, particularly because of immunogenicity and organ failure. The use of aptamers is expanded by the development of SOMAmers, X-aptamers, and bioinformatics. To make aptamer-based drugs a major part of cancer treatment, future research should concentrate more on resolving existing issues and expanding their beneficial uses.

Exploring precision therapeutics: computational design of antisense oligonucleotides targeting AXL gene transcripts in multiple sclerosis treatment management

Multiple sclerosis (MS) is a chronic autoimmune illness characterized by demyelination, neurodegeneration, and inflammation in the central nervous system. The AXL gene, which codes for a receptor tyrosine kinase, has emerged as a promising therapeutic target due to its involvement in neuroinflammation and oligodendrocyte dysfunction. In the current study, we employed in silico techniques to design Antisense Oligonucleotides (ASOs) that selectively target AXL gene transcripts to modulate AXL expression and mitigate MS pathology. Three ASOs, A1, A2, and A3, were designed to specifically target the 5' untranslated region (5'UTR) and coding region of the AXL gene transcripts. The ASOs were optimized with a focus on stability, binding affinity, and specificity towards AXL mRNA while minimizing off-target effects. To investigate ASO-mRNA interactions and gauge their ability to alter AXL expression, Molecular Docking was performed. Our analyses showed that A1, A2, and A3 had substantial interactions with AXL mRNA, with binding affinities of -9.5 kcal/mol, -10.8 kcal/mol, and -10.6 kcal/mol, respectively. The targeting of AXL gene transcripts through ASOs shows promise in reducing MS symptoms. Precision ASO-based therapies could effectively manage MS by targeting the essential pathways involved in the disease. ASOs provide a highly targeted approach for treating MS and offer a precise therapeutic strategy for this debilitating condition. The study lays the groundwork for future in vitro and in vivo studies to confirm the therapeutic potential of these ASOs for the treatment of MS.

Plant-nanoparticles enhance anti-PD-L1 efficacy by shaping human commensal microbiota metabolites

Diet has emerged as a key impact factor for gut microbiota function. However, the complexity of dietary components makes it difficult to predict specific outcomes. Here we investigate the impact of plant-derived nanoparticles (PNP) on gut microbiota and metabolites in context of cancer immunotherapy with the humanized gnotobiotic mouse model. Specifically, we show that ginger-derived exosome-like nanoparticle (GELN) preferentially taken up by Lachnospiraceae and Lactobacillaceae mediated by digalactosyldiacylglycerol (DGDG) and glycine, respectively. We further demonstrate that GELN aly-miR159a-3p enhances anti-PD-L1 therapy in melanoma by inhibiting the expression of recipient bacterial phospholipase C (PLC) and increases the accumulation of docosahexaenoic acid (DHA). An increased level of circulating DHA inhibits PD-L1 expression in tumor cells by binding the PD-L1 promoter and subsequently prevents c-myc-initiated transcription of PD-L1. Colonization of germ-free male mice with gut bacteria from anti-PD-L1 non-responding patients supplemented with DHA enhances the efficacy of anti-PD-L1 therapy compared to controls. Our findings reveal a previously unknown mechanistic impact of PNP on human tumor immunotherapy by modulating gut bacterial metabolic pathways.

Antitumor Activity of a Bispecific Chimera Targeting EGFR and Met in Gefitinib-Resistant Non-Small Cell Lung Cancer

Non-small cell lung cancers (NSCLC) frequently acquire resistance to tyrosine kinase inhibitors (TKI) due to epidermal growth factor receptor (EGFR) mutation or activation of the bypass pathway involving mesenchymal-epithelial transition factor (Met). To address this challenge, a bispecific nanobody-aptamer chimera is designed to target mutated EGFR and Met simultaneously to block their cross-talk in NSCLC. The EGFR-Met chimera is cost-effectively engineered using microbial transglutaminase and click chemistry strategies. With enhanced binding affinity toward the target proteins, the as-developed chimera inhibits efficiently the cross-talk between signaling pathways associated with EGFR and Met. This inhibition leads to the suppression of downstream pathways, such as Erk and Akt, and induces upregulation of cell cycle arrest-related proteins, including Rb, p21, and p27. Additionally, the chimera activates the caspase-dependent apoptotic signaling pathway. Consequently, it inhibits cell migration, induces cell death, and causes cell cycle arrest in vitro. Moreover, the chimera exhibits significant antitumor efficacy in drug-resistant xenograft mouse models, showcasing improved tissue penetration and low toxicity. This study accentuates the potential of the bispecific EGFR-Met chimera as a promising therapeutic option for NSCLC resistant to EGFR TKIs.

Aptamer's Structure Optimization for Better Diagnosis and Treatment of Glial Tumors

Background: Oncological diseases are a major focus in medicine, with millions diagnosed each year, leading researchers to seek new diagnostic and treatment methods. One promising avenue is the development of targeted therapies and rapid diagnostic tests using recognition molecules. The pharmaceutical industry is increasingly exploring nucleic acid-based therapeutics. However, producing long oligonucleotides, especially aptamers, poses significant production challenges. Objectives: This study aims to demonstrate the efficacy of using molecular modeling, supported by experimental procedures, for altering aptamer nucleotide sequences while maintaining their binding capabilities. The focus is on reducing production costs and enhancing binding dynamics by removing nonfunctional regions and minimizing nonspecific binding. Methods: A molecular modeling approach was employed to elucidate the structure of a DNA aptamer, Gli-55, facilitating the truncation of nonessential regions in the Gli-55 aptamer, which selectively binds to glioblastoma (GBM). This process aimed to produce a truncated aptamer, Gli-35, capable of forming similar structural elements to the original sequence with reduced nonspecific binding. The efficiency of the truncation was proved by flow cytometry, fluorescence polarization (FP), and confocal microscopy. Results: The molecular design indicated that the new truncated Gli-35 aptamer retained the structural integrity of Gli-55. In vitro studies showed that Gli-35 had a binding affinity comparable to the initial long aptamer while the selectivity increased. Gli-35 internalized inside the cell faster than Gli-55 and crossed the blood-brain barrier (BBB), as demonstrated in an in vitro model. Conclusions: The success of this truncation approach suggests its potential applicability in scenarios where molecular target information is limited. The study highlights a strategic and resource-efficient methodology for aptamer development. By employing molecular modeling and truncation, researchers can reduce production costs and avoid trial and error in sequence selection. This approach is promising for enhancing the efficiency of therapeutic agent development, particularly in cases lacking detailed molecular target insights.

Active-reset protein sensors enable continuous in vivo monitoring of inflammation

Continuous measurement of proteins in vivo is important for real-time disease management and prevention. Implantable sensors for monitoring small molecules such as glucose have been available for more than a decade. However, analysis of proteins remains an unmet need because the lower physiological levels require that sensors have high affinities, which are linked to long complexation half-lives (t1/2 ~20 hours) and slow equilibration when concentrations decrease. We report active-reset sensors by use of high-frequency oscillations to accelerate dissociation, which enables regeneration of the unbound form of the sensor within 1 minute. When implemented within implanted devices, these sensors allow for real-time, in vivo monitoring of proteins within interstitial fluid. Active-reset protein sensors track biomarker levels on a physiological timescale for inflammation monitoring in living animals.

High-throughput and computational techniques for aptamer design

INTRODUCTION

Aptamers refer to short ssDNA/RNA sequences that target small molecules, proteins, or cells. Aptamers have significantly advanced diagnostic applications, including biosensors for detecting specific biomarkers, state-of-the-art imaging, and point-of-care technology. Molecular computation helps identify aptamers with high-binding affinity, enabling high-throughput screening, predicting 3D structures, optimizing aptamers for improved stability, specificity, and complex target interactions.

AREA COVERED

Aptamers are versatile in the development of specific and sensitive diagnostics. However, there needs to be more understanding of the precise workflow that integrates sequence, structure, and interaction with the target. In this review, the author discusses how significant progress has been made in aptamer discovery using bioinformatics for sequence analysis, docking to model interactions, and MD simulations to account for dynamicity and predict free-energy. Furthermore, the author discusses how quantum chemical calculations are critical for modelling electronic structures and assignin spectroscopic signals.

EXPERT OPINION

Incorporating machine learning into the aptamer discovery brings a transformative advancement. With NGS datasets, SELEX, and experimental structures, the implementation of newer workflows yields aptamers with improved binding affinity. Leveraging transfer learning to models using experimental structures and aptamer sequences expands the aptamer design space significantly. As ML continues to evolve, it is poised to become central in accelerating aptamer discovery for biomedical applications in the next 5 years.

Integration of secreted signaling molecule sensing on cell monitoring platforms: a critical review

Monitoring cell secretion in complex microenvironments is crucial for understanding cellular behavior and advancing physiological and pathological research. While traditional cell culture methods, including organoids and spheroids, provide valuable models, real-time monitoring of cell secretion of signaling molecules remains challenging. Integrating advanced monitoring technologies into these systems often disrupts the delicate balance of the microenvironment, making it difficult to achieve sensitivity and specificity. This review explored recent strategies for integrating the monitoring of cell secretion of signaling molecules, crucial for understanding and replicating cell microenvironments, within cell culture platforms, addressing challenges such as non-adherent cell models and the focus on single-cell methodologies. We highlight advancements in biosensors, microfluidics, and three-dimensional culture methods, and discuss their potential to enhance real-time, multiplexed cell monitoring. By examining the advantages, limitations, and future prospects of these technologies, we aim to contribute to the development of integrated systems that facilitate comprehensive cell monitoring, ultimately advancing biological research and pharmaceutical development.

Using Computer Modeling and Experimental Methods to Screen for Aptamers That Bind to the VV-GMCSF-LACT Virus

Oncolytic virotherapy is a promising approach for cancer treatment. However, when introduced into the body, the virus provokes the production of virus-neutralizing antibodies, which can reduce its antitumor effect. To shield viruses from the immune system, aptamers that can cover the membrane of the viral particle are used. Aptamers that specifically bind to the JX-594 strain of the vaccinia virus were developed earlier. However, the parameters for binding to the recombinant virus VV-GMCSF-Lact, developed based on the LIVP strain of the vaccinia virus, may differ due its different repertoire of antigenic determinants on its membrane compared to JX-594. In this work, the spatial atomic structures of aptamers to JX-594 and bifunctional aptamers were determined using molecular modeling. The efficiency of viral particles binding to the aptamers (EC50), as well as the cytotoxicity and stability of the aptamers were studied. The synergistic effect of the VV-GMCSF-Lact combination with the aptamers in the presence of serum was investigated using human glioblastoma cells. This proposed approach allowed us to conduct a preliminary screening of sequences using in silico modeling and experimental methods, and identified potential candidates that are capable of shielding VV-GMCSF-Lact from virus-neutralizing antibodies.

In silico analysis of aptamer-RNA conjugate interactions with human transferrin receptor

The human transmembrane protein Transferrin Receptor-1 is regarded as a promising target for the systemic delivery of therapeutic agents, particularly of nucleic acid therapeutics, such as short double stranded RNAs. This ubiquitous receptor is involved in cellular iron uptake, keeping intracellular homeostasis. It is overexpressed in multiple cancer cell types and is internalized via clathrin-mediated endocytosis. In previous studies, a human transferrin receptor-1 RNA aptamer, identified as TR14 ST1-3, was shown to be capable of effectively internalizing into cells in culture and to deliver small, double stranded RNAs in vitro and in vivo, via systemic administration. To understand, at the molecular level, the aptamer binding to the receptor and the impact of conjugation with the therapeutic RNA, a multi-level in silico protocol was employed, including protein-aptamer docking, molecular dynamics simulations and free energy calculations. The competition for the binding pocket, between the aptamer and the natural ligand human Transferrin, was also evaluated. The results show that the aptamer binds to the same region as Transferrin, with residues from the helical domain showing a critical role. Moreover, the conjugation to the therapeutic RNA, was shown not to affect aptamer binding. Overall, this study provides an atomic-level understanding of aptamer association to human Transferrin Receptor-1 and of its conjugation with a short model-therapeutic RNA, providing also important clues for futures studies aiming to deliver other oligonucleotide-based therapeutics via Transferrin Receptor.

Prediction of aptamer affinity using an artificial intelligence approach

Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesized. Even if the SELEX approach has been improved a lot, it is frequently challenging and time-consuming to identify aptamers experimentally. In particular, structure-based methods are the most used in computer-aided design and development of aptamers. For this purpose, numerous web-based platforms have been suggested for the purpose of forecasting the secondary structure and 3D configurations of RNAs and DNAs. Also, molecular docking and molecular dynamics (MD), which are commonly utilized in protein compound selection by structural information, are suitable for aptamer selection. On the other hand, from a large number of sequences, artificial intelligence (AI) may be able to quickly discover the possible aptamer candidates. Conversely, sophisticated machine and deep-learning (DL) models have demonstrated efficacy in forecasting the binding properties between ligands and targets during drug discovery; as such, they may provide a reliable and precise method for forecasting the binding of aptamers to targets. This research looks at advancements in AI pipelines and strategies for aptamer binding ability prediction, such as machine and deep learning, as well as structure-based approaches, molecular dynamics and molecular docking simulation methods.

Visualization of Brain Tumors with Infrared-Labeled Aptamers for Fluorescence-Guided Surgery

Gliomas remain challenging brain tumors to treat due to their infiltrative nature. Accurately identifying tumor boundaries during surgery is crucial for successful resection. This study introduces an innovative intraoperative visualization method utilizing surgical fluorescence microscopy to precisely locate tumor cell dissemination. Here, the focus is on the development of a novel contrasting agent (IR-Glint) for intraoperative visualization of human glial tumors comprising infrared-labeled Glint aptamers. The specificity of IR-Glint is assessed using flow cytometry and microscopy on primary cell cultures. In vivo effectiveness is studied on mouse and rabbit models, employing orthotopic xenotransplantation of human brain gliomas with various imaging techniques, including PET/CT, in vivo fluorescence visualization, confocal laser scanning, and surgical microscopy. The experiments validate the potential of IR-Glint for the intraoperative visualization of gliomas using infrared imaging. IR-Glint penetrates the blood-brain barrier and can be used for both intravenous and surface applications, allowing clear visualization of the tumor. The surface application directly to the brain reduces the dosage required and mitigates potential toxic effects on the patient. The research shows the potential of infrared dye-labeled aptamers for accurately visualizing glial tumors during brain surgery. This novel aptamer-assisted fluorescence-guided surgery (AptaFGS) may pave the way for future advancements in the field of neurosurgery.

3dDNAscoreA: A scoring function for evaluation of DNA 3D structures

DNA molecules are vital macromolecules that play a fundamental role in many cellular processes and have broad applications in medicine. For example, DNA aptamers have been rapidly developed for diagnosis, biosensors, and clinical therapy. Recently, we proposed a computational method of predicting DNA 3D structures, called 3dDNA. However, it lacks a scoring function to evaluate the predicted DNA 3D structures, and so they are not ranked for users. Here, we report a scoring function, 3dDNAscoreA, for evaluation of DNA 3D structures based on a deep learning model ARES for RNA 3D structure evaluation but using a new strategy for training. 3dDNAscoreA is benchmarked on two test sets to show its ability to rank DNA 3D structures and select the native and near-native structures.

In silico selection of aptamers against SARS-CoV-2

Aptamers are molecular recognition elements that have been extensively deployed in a wide array of applications ranging from diagnostics to therapeutics. Due to their unique properties as compared to antibodies, aptamers were also largely isolated during the COVID-19 pandemic for multiple purposes. Typically generated by conventional SELEX, the inherent drawbacks of the process including the time-consuming, cumbersome and resource-intensive nature catalysed the move to adopt in silico approaches to isolate aptamers. Impressive performances of these in silico-derived aptamers in their respective assays have been documented thus far, bearing testimony to the huge potential of the in silico approaches, akin to the traditional SELEX in isolating aptamers. In this study, we provide an overview of the in silico selection of aptamers against SARS-CoV-2 by providing insights into the basic steps involved, which comprise the selection of the initial single-stranded nucleic acids, determination of the secondary and tertiary structures and in silico approaches that include both rigid docking and molecular dynamics simulations. The different approaches involving aptamers against SARS-CoV-2 were illuminated and the need to verify these aptamers by experimental validation was also emphasized. Cognizant of the need to continuously improve aptamers, the strategies embraced thus far for post-in silico selection modifications were enumerated. Shedding light on the steps involved in the in silico selection can set the stage for further improvisation to augment the functionalities of the aptamers in the future.

Aptamer biosensor design for the detection of endocrine-disrupting chemicals small organic molecules using novel bioinformatics methods

Endocrine-disrupting chemicals (EDCs) are substances that can disrupt the normal functioning of hormones.Using aptamers, which are biological recognition elements, biosensors can quickly and accurately detect EDCs in environmental samples. However, the elucidation of aptamer structures by conventional methods is highly challenging due to their complexity. This has led to the development of three-dimensional aptamer structures based on different models and techniques. To do this, we developed a way to predict the 3D structures of the SS DNA needed for this sequence by starting with an aptamer sequence that has biosensor properties specific to bisphenol-A (BPA), one of the chemicals found in water samples that can interfere with hormones. In addition, we will elucidate the intermolecular mechanisms and binding affinity between aptamers and endocrine disruptors using bioinformatics techniques such as molecular docking, molecular dynamics simulation, and binding energies. The outcomes of our study are to compare modeling programs and force fields to see how reliable they are and how well they agree with results found in the existing literature, to understand the intermolecular mechanisms and affinity of aptamer-based biosensors, and to find a new way to make aptamers that takes less time and costs less.

Based on mutated aptamer-smartphone colorimetric detection of metronidazole in milk

Excessive residue of metronidazole (MNZ) in food is harmful to the human body. There is an urgent demand to develop a portable tool for MNZ detection on-site. In this study, fifteen aptamers were prepared through targeted base mutation. Apt1-3 with the highest enrichment was chosen for further study. Its affinity was characterized by molecular docking simulation, AuNPs colorimetric assay, graphene oxide (GO) fluorescence assay, and exonuclease assay. Kd was determined by GO fluorescence assay (Kd: 92.60 ± 25.59 nM). Its specificity was also characterized by an exonuclease assay. A novel aptasensor was constructed by using the newly identified aptamer combined with the smartphone dark box. The principle of color change is caused by the aggregation state of AuNPs. Smartphones act as reading instruments. The detection can be completed in just a few seconds without the aid of instruments, achieving a detection limit of 0.15 nmol/mL and a range of 6.7-44.4 nmol/mL (R 2 = 0.9810). Therefore, the constructed smartphone colorimetric sensor based on mutant aptamers has important applications in food detection.

DNA aptamer-functionalized PDA nanoparticles: from colloidal chemistry to biosensor applications

Polydopamine nanoparticles (PDA NPs) are widely utilized in the field of biomedical science for surface functionalization because of their unique characteristics, such as simple and low-cost preparation methods, good adhesive properties, and ability to incorporate amine and oxygen-rich chemical groups. However, challenges in the application of PDA NPs as surface coatings on electrode surfaces and in conjugation with biomolecules for electrochemical sensors still exist. In this work, we aimed to develop an electrochemical interface based on PDA NPs conjugated with a DNA aptamer for the detection of glycated albumin (GA) and to study DNA aptamers on the surfaces of PDA NPs to understand the aptamer-PDA surface interactions using molecular dynamics (MD) simulation. PDA NPs were synthesized by the oxidation of dopamine in Tris buffer at pH 10.5, conjugated with DNA aptamers specific to GA at different concentrations (0.05, 0.5, and 5 μM), and deposited on screen-printed carbon electrodes (SPCEs). The charge transfer resistance of the PDA NP-coated SPCEs decreased, indicating that the PDA NP composite is a conductive bioorganic material. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) confirmed that the PDA NPs were spherical, and dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy data indicated the successful conjugation of the aptamers on the PDA NPs. The as-prepared electrochemical interface was employed for the detection of GA. The detection limit was 0.17 μg/mL. For MD simulation, anti-GA aptamer through the 5'terminal end in a single-stranded DNA-aptamer structure and NH2 linker showed a stable structure with its axis perpendicular to the PDA surface. These findings provide insights into improved biosensor design and have demonstrated the potential for employing electrochemical PDA NP interfaces in point-of-care applications.

Oligomeric amyloid-β targeted contrast agent for MRI evaluation of Alzheimer's disease mouse models

Background

Oligomeric amyloid beta (oAβ) is a toxic factor that acts in the early stage of Alzheimer's disease (AD) and may initiate the pathologic cascade. Therefore, detecting oAβ has a crucial role in the early diagnosis, monitoring, and treatment of AD.

Purpose

The purpose of this study was to evaluate MRI signal changes in different mouse models and the time-dependent signal changes using our novel gadolinium (Gd)-dodecane tetraacetic acid (DOTA)- ob5 aptamer contrast agent.

Methods

We developed an MRI contrast agent by conjugating Gd-DOTA-DNA aptamer called ob5 to evaluate its ability to detect oAβ deposits in the brain using MRI. A total of 10 control mice, 9 3xTg AD mice, and 11 APP/PS/Tau AD mice were included in this study, with the age of each model being 16 or 36 weeks. A T1-weighted image was acquired at the time points before (0 min) and after injection of the contrast agent at 5, 10, 15, 20, and 25 min. The analyses were performed to compare MRI signal differences among the three groups and the time-dependent signal differences in different mouse models.

Results

Both 3xTg AD and APP/PS/Tau AD mouse models had higher signal enhancement than control mice at all scan-time points after injection of our contrast media, especially in bilateral hippocampal areas. In particular, all Tg AD mouse models aged 16 weeks showed a higher contrast enhancement than those aged 36 weeks. For 3xTg AD and APP/PS/Tau AD groups, the signal enhancement was significantly different among the five time points (0 min, 5 min, 10 min, 15 min, 20 min, and 25 min) in multiple ROI areas, typically in the bilateral hippocampus, left thalamus, and left amygdala.

Conclusion

The findings of this study suggest that the expression of the contrast agent in different AD models demonstrates its translational flexibility across different species. The signal enhancement peaked around 15-20 min after injection of the contrast agent. Therefore, our novel contrast agent targeting oAβ has the potential ability to diagnose early AD and monitor the progression of AD.

Bispecific Nanobody-Aptamer Conjugates for Enhanced Cancer Therapy in Solid Tumors

Bispecific antibodies possess exceptional potential as therapeutic agents due to their capacity to bind to two different antigens simultaneously. However, challenges pertain to unsatisfactory stability, manufacturing complexity, and limited tumor penetration hinder their broad applicability. In this study, a versatile technology is presented for the rapid generation of bispecific nanobody-aptamer conjugates with efficient tumor penetration. The approach utilizes microbial transglutaminase (MTGase) and click chemistry to achieve site-specific conjugation of nanobodies and aptamers, which are termed nanotamers. The nanotamers recognize and bind to two types of molecular targets expressed on cancer cells. As a prototype, a bispecific nanotamer is developed that binds both clusters of differentiation 47 (CD47) and mesenchymal epithelial transition receptor (Met) expressed on the tumor cell membrane. This CD47-Met nanotamer demonstrates high affinity and specificity toward tumor cells expressing both targets, exhibits improved receptor functional inhibition through a strong steric hindrance effect. Moreover, its capacity for deep tumor penetration greatly enhances the impact of conventional chemotherapy on antitumor efficacy. The as-developed nanotamer synthesis approach shows promise to customize bispecific molecular probes targeting different cancer types and different therapeutic goals.

Distinctive Nucleic Acid Recognition by Lysine-Embedded Phenanthridine Peptides

Three new phenanthridine peptide derivatives (19, 22, and 23) were synthesized to explore their potential as spectrophotometric probes for DNA and RNA. UV/Vis and circular dichroism (CD) spectra, mass spectroscopy, and computational analysis confirmed the presence of intramolecular interactions in all three compounds. Computational analysis revealed that compounds alternate between bent and open conformations, highlighting the latter's crucial influence on successful polynucleotide recognition. Substituting one glycine with lysine in two regioisomers (22, 23) resulted in stronger binding interactions with DNA and RNA than for a compound containing two glycines (19), thus emphasizing the importance of lysine. The regioisomer with lysine closer to the phenanthridine ring (23) exhibited a dual and selective fluorimetric response with non-alternating AT and ATT polynucleotides and induction of triplex formation from the AT duplex. The best binding constant (K) with a value of 2.5 × 107 M-1 was obtained for the interaction with AT and ATT polynucleotides. Furthermore, apart from distinguishing between different types of ds-DNA and ds-RNA, the same compound could recognize GC-rich DNA through distinct induced CD signals.

An Ensemble Docking Approach for Analyzing and Designing Aptamer Heterodimers Targeting VEGF165

Vascular endothelial growth factor 165 (VEGF165) is a prominent isoform of the VEGF-A protein that plays a crucial role in various angiogenesis-related diseases. It is homodimeric, and each of its monomers is composed of two domains connected by a flexible linker. DNA aptamers, which have emerged as potent therapeutic molecules for many proteins with high specificity and affinity, can also work for VEGF165. A DNA aptamer heterodimer composed of monomers of V7t1 and del5-1 connected by a flexible linker (V7t1:del5-1) exhibits a greater binding affinity with VEGF165 compared to either of the two monomers alone. Although the structure of the complex formed between the aptamer heterodimer and VEGF165 is unknown due to the highly flexible linkers, gaining structural information will still be valuable for future developments. Toward this end of accessing structural information, we adopt an ensemble docking approach here. We first obtain an ensemble of structures for both VEGF165 and the aptamer heterodimer by considering both small- and large-scale motions. We then proceed through an extraction process based on ensemble docking, molecular dynamics simulations, and binding free energy calculations to predict the structures of the VEGF165/V7t1:del5-1 complex. Through the same procedures, we reach a new aptamer heterodimer that bears a locked nucleic acid-modified counterpart of V7t1, namely RNV66:del5-1, which also binds well with VEGF165. We apply the same protocol to the monomeric units V7t1, RNV66, and del5-1 to target VEGF165. We observe that V7t1:del5-1 and RNV66:del5-1 show higher binding affinities with VEGF165 than any of the monomers, consistent with experiments that support the notion that aptamer heterodimers are more effective anti-VEGF165 aptamers than monomeric aptamers. Among the five different aptamers studied here, the newly designed RNV66:del5-1 shows the highest binding affinity with VEGF165. We expect that our ensemble docking approach can help in de novo designs of homo/heterodimeric anti-angiogenic drugs to target the homodimeric VEGF165.

Aptamer-Based Recognition of Breast Tumor Cells: A New Era for Breast Cancer Diagnosis

Breast cancer is one of the leading causes of death among women worldwide and can be classified into four major distinct molecular subtypes based on the expression of specific receptors. Despite significant advances, the lack of biomarkers for detailed diagnosis and prognosis remains a major challenge in the field of oncology. This study aimed to identify short single-stranded oligonucleotides known as aptamers to improve breast cancer diagnosis. The Cell-SELEX technique was used to select aptamers specific to the MDA-MB-231 tumor cell line. After selection, five aptamers demonstrated specific recognition for tumor breast cell lines and no binding to non-tumor breast cells. Validation of aptamer specificity revealed recognition of primary and metastatic tumors of all subtypes. In particular, AptaB4 and AptaB5 showed greater recognition of primary tumors and metastatic tissue, respectively. Finally, a computational biology approach was used to identify potential aptamer targets, which indicated that CSKP could interact with AptaB4. These results suggest that aptamers are promising in breast cancer diagnosis and treatment due to their specificity and selectivity.

Application and evaluation of molecular docking for aptamer and small molecular interaction - A case study with tetracycline antibiotics

Molecular docking (MD) analysis is currently the most commonly used theoretical simulation method to investigate the interaction of aptamers (receptors) and small molecules (ligands) and understand the recognition mechanism between them at a molecular level. Using the specific aptamers of tetracycline antibiotics (tetracycline (TET), oxytetracycline (OTC), doxycycline (DOC)) as the docking models, three steady-state aptamers of tertiary structures (SATS) were established for each aptamer with the UNAFold and RNAComposer tools. The binding free energy (BFE), docking score (DS), and binding site (base) of the specific ligands (TET, OTC, and DOC) with their respective SATS were obtained by molecular docking. The results revealed one or more binding sites in the established SATS of the aptamers. The BFE and DS of different binding sites of one specific SATS varied significantly. The results also revealed that the site with the highest BFE represented the most dominant binding site, even if it was not the SATS with minimum energy. The BFE values could also be used to evaluate the affinity and specificity of the aptamer to its target. For the first time, this study proposes a method for MD analysis of the aptamer and its target based on different SATS, clarification of the binding mode, and prediction of the binding sites (bases). This study provides a theoretical basis for tailoring; structural optimization; and base modification of aptamers; identifying aptamers with high affinity and specificity.

TBA for Sensing Toxic Cations: A Critical Analysis of Structural and Electrical Properties

Food and drinks can be contaminated with pollutants such as lead and strontium, which poses a serious danger to human health. For this reason, a number of effective sensors have been developed for the rapid and highly selective detection of such contaminants. TBA, a well-known aptamer developed to selectively target and thereby inhibit the protein of clinical interest α-thrombin, is receiving increasing attention for sensing applications, particularly for the sensing of different cations. Indeed, TBA, in the presence of these cations, folds into the stable G-quadruplex structure. Furthermore, different cations produce small but significant changes in this structure that result in changes in the electrical responses that TBA can produce. In this article, we give an overview of the expected data regarding the use of TBA in the detection of lead and strontium, calculating the expected electrical response using different measurement techniques. Finally, we conclude that TBA should be able to detect strontium with a sensitivity approximately double that achievable for lead.

Aptamer-Based Strategies to Address Challenges in COVID-19 Diagnosis and Treatments

Coronavirus disease (COVID-19), a highly contagious and rapidly spreading disease with significant fatality in the elderly population, has swept across the world since 2019. Since its first appearance, the causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has undergone multiple mutations, with Omicron as the predominant circulating variant of concern at the moment. The gold standard for diagnosis of COVID-19 by real-time polymerase chain reaction (RT-PCR) to detect the virus is laborious and requires well-trained personnel to perform sophisticated procedures. Also, the genetic variants of SARS-CoV-2 that arise regularly could result in false-negative detection. Meanwhile, the current COVID-19 treatments such as conventional medicine, complementary and alternative medicine, passive antibody therapy, and respiratory therapy are associated with adverse effects. Thus, there is an urgent need to discover novel diagnostic and therapeutic approaches against SARS-CoV-2 and its variants. Over the past 30 years, nucleic acid-based aptamers have gained increasing attention and serve as a promising alternative to the antibodies in the diagnostic and therapeutic fields with their uniqueness of being small, nonimmunogenicity, and thermally stable. Aptamer targeting the SARS-CoV-2 structural proteins or the host receptor proteins represent a powerful tool to control COVID-19 infection. In this review, challenges faced by currently available diagnostic and therapeutic tools for COVID-19 are underscored, along with how aptamers can shed a light on the current COVID-19 pandemic, focusing on the critical factors affecting the discovery of high-affinity aptamers and their potential applications to control COVID-19 infection.

In Silico Selection and Validation of High-Affinity ssDNA Aptamers Targeting Paromomycin

Glycans are promising for disease diagnosis since glycan biosynthesis is significantly affected by disease states, and glycosylation changes are probably more pronounced than protein expression during the transformation to the diseased condition. Glycan-specific aptamers can be developed for challenging applications such as cancer targeting; however, the high flexibility of glycosidic bonds and scarcity of studies on glycan-aptamer binding mechanisms increased the difficulty of screening. In this work, the model of interactions between glycans and ssDNA aptamers synthesized based on the sequence of rRNA genes was developed. Our simulation-based approach revealed that paromomycin as a representative example of glycans is preferred to bind base-restricted stem structures of aptamers because they are more critical in stabilizing the flexible structures of glycans. Combined experiments and simulations have identified two optimal mutant aptamers. Our work would provide a potential strategy that the glycan-binding rRNA genes could act as the initial aptamer pools to accelerate aptamer screening. In addition, this in silico workflow would be potentially applied in the more extensive in vitro development and application of RNA-templated ssDNA aptamers targeting glycans.

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding

DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences.

Monitoring of breast cancer progression via aptamer-based detection of circulating tumor cells in clinical blood samples

Introduction: Breast cancer (BC) diagnostics lack noninvasive methods and procedures for screening and monitoring disease dynamics. Admitted CellSearch^® is used for fluid biopsy and capture of circulating tumor cells of only epithelial origin. Here we describe an RNA aptamer (MDA231) for detecting BC cells in clinical samples, including blood. The MDA231 aptamer was originally selected against triple-negative breast cancer cell line MDA-MB-231 using cell-SELEX. Methods: The aptamer structure in solution was predicted using mFold program and molecular dynamic simulations. The affinity and specificity of the evolved aptamers were evaluated by flow cytometry and laser scanning microscopy on clinical tissues from breast cancer patients. CTCs were isolated form the patients' blood using the developed method of aptamer-based magnetic separation. Breast cancer origin of CTCs was confirmed by cytological, RT-qPCR and Immunocytochemical analyses. Results: MDA231 can specifically recognize breast cancer cells in surgically resected tissues from patients with different molecular subtypes: triple-negative, Luminal A, and Luminal B, but not in benign tumors, lung cancer, glial tumor and healthy epithelial from lungs and breast. This RNA aptamer can identify cancer cells in complex cellular environments, including tumor biopsies (e.g., tumor tissues vs. margins) and clinical blood samples (e.g., circulating tumor cells). Breast cancer origin of the aptamer-based magnetically separated CTCs has been proved by immunocytochemistry and mammaglobin mRNA expression. Discussion: We suggest a simple, minimally-invasive breast cancer diagnostic method based on non-epithelial MDA231 aptamer-specific magnetic isolation of circulating tumor cells. Isolated cells are intact and can be utilized for molecular diagnostics purposes.

Oligonucleotide nanoassemblies with allyl bromide scaffold-based small molecules

The development of oligonucleotide nanoassemblies with small molecules has shown great potential in bio-medical applications. However, the interaction of negatively charged oligonucleotides with halogenated small molecules represents a scientific challenge. Here, we introduced a distinct allyl bromide halogenated scaffold, which exhibits specific interaction with adenine nucleic bases of the oligonucleotides, thus leading to the formation of self-assembled nanostructures.

Next Generation of Ovarian Cancer Detection Using Aptamers

Ovarian cancer is among the seven most common types of cancer in women, being the most fatal gynecological tumor, due to the difficulty of detection in early stages. Aptamers are important tools to improve tumor diagnosis through the recognition of specific molecules produced by tumors. Here, aptamers and their potential targets in ovarian cancer cells were analyzed by in silico approaches. Specific aptamers were selected by the Cell-SELEX method using Caov-3 and OvCar-3 cells. The five most frequent aptamers obtained from the last round of selection were computationally modeled. The potential targets for those aptamers in cells were proposed by analyzing proteomic data available for the Caov-3 and OvCar-3 cell lines. Overexpressed proteins for each cell were characterized as to their three-dimensional model, cell location, and electrostatic potential. As a result, four specific aptamers for ovarian tumors were selected: AptaC2, AptaC4, AptaO1, and AptaO2. Potential targets were identified for each aptamer through Molecular Docking, and the best complexes were AptaC2-FXYD3, AptaC4-ALPP, AptaO1-TSPAN15, and AptaO2-TSPAN15. In addition, AptaC2 and AptaO1 could detect different stages and subtypes of ovarian cancer tissue samples. The application of this technology makes it possible to propose new molecular biomarkers for the differential diagnosis of epithelial ovarian cancer.

Discovery of DNA aptamers targeting SARS-CoV-2 nucleocapsid protein and protein-binding epitopes for label-free COVID-19 diagnostics

The spread of COVID-19 has affected billions of people across the globe, and the diagnosis of viral infection still needs improvement. Because of high immunogenicity and abundant expression during viral infection, SARS-CoV-2 nucleocapsid (N) protein could be an important diagnostic marker. This study aimed to develop a label-free optical aptasensor fabricated with a novel single-stranded DNA aptamer to detect the N protein. The N-binding aptamers selected using asymmetric-emulsion PCR-SELEX and their binding affinity and cross-reactivity were characterized by biolayer interferometry. The tNSP3 aptamer (44 nt) was identified to bind the N protein of wild type and Delta and Omicron variants with high affinity (K_D in the range of 0.6-3.5 nM). Utilizing tNSP3 to detect the N protein spiked in human saliva evinced the potential of this aptamer with a limit of detection of 4.5 nM. Mass spectrometry analysis was performed along with molecular dynamics simulation to obtain an insight into how tNSP3 binds to the N protein. The identified epitope peptides are localized within the RNA-binding domain and C terminus of the N protein. Hence, we confirmed the performance of this aptamer as an analytical tool for COVID-19 diagnosis.

In Silico Approaches for the Identification of Aptamer Binding Interactions to Leptospira spp. Cell Surface Proteins

Aptamers are nucleic acids that can bind with high affinity and specificity to a range of target molecules. However, their functionality relies on their secondary and tertiary structures such that the combination of nucleotides determines their three-dimensional conformation. In this study, the binding mechanisms of candidate aptamers and their interactions with selected target proteins found in the cell surface of Leptospira were predicted to select high-affinity aptamers. Four aptamers were evaluated through molecular modeling and docking using available software and web-based tools, following the workflow previously designed for in silico evaluation of DNA aptamers. The most predominant and highly conserved surface-exposed proteins among pathogenic Leptospira species were used as aptamer targets. The highest number of interactions was seen in aptamers AP5 and AP1. Hydrogen bonds, along with a few hydrophobic interactions, occur in most aptamer-protein complexes. Further analysis revealed serine, threonine, glutamine, and lysine as main protein residues. H-bond interactions occur mostly with polar amino acids, as reflected in the predicted interaction profiles of aptamer-protein complexes. In silico strategies allowed the identification of key residues crucial in aptamer-target interaction during aptamer screening. Such information can be used in aptamer modification for improved binding affinity and accuracy for diagnostics application.

Design and Prediction of Aptamers Assisted by In Silico Methods

An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding affinity. Aptamers are developed through the process of systematic evolution of ligands by exponential enrichment (SELEX), which is repeated to increase the binding power and specificity. However, the SELEX process is time-consuming, and the characterization of aptamer candidates selected through it requires additional effort. Here, we describe in silico methods in order to suggest the most efficient way to develop aptamers and minimize the laborious effort required to screen and optimise aptamers. We investigated several methods for the estimation of aptamer-target molecule binding through conformational structure prediction, molecular docking, and molecular dynamic simulation. In addition, examples of machine learning and deep learning technologies used to predict the binding of targets and ligands in the development of new drugs are introduced. This review will be helpful in the development and application of in silico aptamer screening and characterization.

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, and so on. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.g., SELEX) and then optimize performance by introducing only minor modifications to the empirically found candidates. Sequences that possess the desired properties but differ drastically in chemical composition will add diversity to the search space and facilitate the discovery of useful nucleic acid aptamers. Systematic diversification protocols are needed. Here we propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity. We start by training a Potts model using the maximum entropy principle on a small set of empirically identified sequences unified by a common feature. To generate new candidate sequences with a controllable degree of diversity, we take advantage of the model's spectral feature: an "energy" bandgap separating sequences that are similar to the training set from those that are distinct. By controlling the Potts energy range that is sampled, we generate sequences that are distinct from the training set yet still likely to have the encoded features. To demonstrate performance, we apply our approach to design diverse pools of sequences with specified secondary structure motifs in 30-mer RNA and DNA aptamers.

A Novel and Label-Free Chemiluminescence Detection of Zearalenone Based on a Truncated Aptamer Conjugated with a G-Quadruplex DNAzyme

Zearalenone (ZEN), one of the most frequently occurring mycotoxin contaminants in foods and feeds, poses considerable threat to human and animal health, owing to its acute and chronic toxicities. Thus, rapid and accurate detection of ZEN has attracted broad research interest. In this work, a novel and label-free chemiluminescence aptasensor based on a ZEN aptamer and a G-quadruplex DNAzyme was constructed. It was established on a competitive assay between ZEN and an auxiliary DNA for the aptamer, leading to activation of the G-quadruplex/hemin DNAzyme and subsequent signal amplification by chemiluminescence generation after substrate addition. To maximize the detection sensitivity, numerous key parameters including truncated aptamers were optimized with molecular docking analysis. Upon optimization, our aptasensor exhibited a perfect linear relationship (R² = 0.9996) for ZEN detection in a concentration range of 1-100 ng/mL (3.14-314.10 nM) within 40 min, achieving a detection limit of 2.85 ng/mL (8.95 nM), which was a 6.7-fold improvement over that before optimization. Most importantly, the aptasensor obtained a satisfactory recovery rate of 92.84-137.27% and 84.90-124.24% for ZEN-spiked wheat and maize samples, respectively. Overall, our label-free chemiluminescence aptasensor displayed simplicity, sensitivity, specificity and practicality in real samples, indicating high application prospects in the food supply chain for rapid detection of ZEN.

The SHERLOCK Platform: An Insight into Advances in Viral Disease Diagnosis

Persistence and prevalence of microbial diseases (pandemics, epidemics) is the most alarming threats to the human resulting in huge health and economic losses. Rapid detection and understanding of the disease dynamics by molecular biotechnology tools allow for robust reporting, treatment and control of diseases. As per WHO, the optimal diagnostic approach should be quick, specific, sensitive, without a stringed instrument, and low cost. The drawbacks of traditional detection techniques promote the use of CRISPR-mediated nucleic acid detection methods such as SHERLOCK as detection method. It takes advantage of the unexpected in vitro features of CRISPR-Cas system to develop field-deployable sensitive detection tools. Previously, CRISPR-mediated diagnostic methods have extensively been reviewed particularly for SARS-COV-2 detection, but it fails to provide the insight into advances of this technique. This study is the first attempt to review the advances of SHERLOCK approach as diagnostic tool for viral diseases detection. Variations of SHERLOCK mechanism for improved efficiency are discussed. Particularly integrated SHERLOCK approaches in terms of extraction-free assay and Bluetooth-enabled detection are reviewed to access their feasibility for the development of simpler and cost-effective diagnostic toolkits. Insight in to perks and limitations of diagnostic methods indicates its potential as ultimate diagnostic instrument for disease management.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Selection of an Aptamer against the Enzyme 1-deoxy-D-xylulose-5-phosphate Reductoisomerase from Plasmodium falciparum

The methyl erythritol phosphate (MEP) pathway of isoprenoid biosynthesis is essential for malaria parasites and also for several human pathogenic bacteria, thus representing an interesting target for future antimalarials and antibiotics and for diagnostic strategies. We have developed a DNA aptamer (D10) against Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR), the second enzyme of this metabolic route. D10 binds in vitro to recombinant DXR from P. falciparum and Escherichia coli, showing at 10 µM a ca. 50% inhibition of the bacterial enzyme. In silico docking analysis indicates that D10 associates with DXR in solvent-exposed regions outside the active center pocket. According to fluorescence confocal microscopy data, this aptamer specifically targets in P. falciparum in vitro cultures the apicoplast organelle where the MEP pathway is localized and is, therefore, a highly specific marker of red blood cells parasitized by Plasmodium vs. naïve erythrocytes. D10 is also selective for the detection of MEP+ bacteria (e.g., E. coli and Pseudomonas aeruginosa) vs. those lacking DXR (e.g., Enterococcus faecalis). Based on these results, we discuss the potential of DNA aptamers in the development of ligands that can outcompete the performance of the well-established antibody technology for future therapeutic and diagnostic approaches.

A Novel ssDNA Aptamer Targeting Carcinoembryonic Antigen: Selection and Characterization

One of the major causes of a drastically shorter life expectancy and one of the most prevalent diseases in the world today is cancer. Given the data on the rise in cancer cases throughout the world, it is obvious that, despite the diagnostic techniques currently being used, there is a pressing need to develop precise and sensitive techniques for early diagnosis of the disease. A high degree of affinity and specificity towards particular targets is maintained by the short nucleic acid molecules known as aptamers. Aptamers outperform antibodies due to their unique benefits, such as their simplicity in synthesis and modification, lack of toxicity, and long-term stability. Utilizing an accurate recognition element and a robust signal transduction mechanism, molecular diagnostics can be extremely sensitive and specific. In this study, development of new single-stranded DNA aptamers against CEA for use in cancer diagnostics was accomplished using SELEX and NGS methods. As a result of 12 iterative SELEX rounds, nine aptamer candidates against CEA were developed. NGS comparative analysis revealed that round twelve had an enriched number of aptamers that were specifically bound, as opposed to round eight. Among the selected nine sequences characterized by bioinformatics analysis and ELONA, an aptamer sequence with the highest specificity and affinity for the target protein was identified and further examined. Aptamer sequence (6) was screened in a concentration-dependent assay, specificity analysis was performed, and its potential secondary and tertiary structures were predicted, which enabled us to test one of the possible putative interactions with CEA. Finally, aptamer sequence (6) labelled with a Cy5 fluorescent tag was used in confocal microscopy to observe its binding towards the CEA expressed in HT-29 human colon adenocarcinoma cell line.

Ab initio predictions for 3D structure and stability of single- and double-stranded DNAs in ion solutions

The three-dimensional (3D) structure and stability of DNA are essential to understand/control their biological functions and aid the development of novel materials. In this work, we present a coarse-grained (CG) model for DNA based on the RNA CG model proposed by us, to predict 3D structures and stability for both dsDNA and ssDNA from the sequence. Combined with a Monte Carlo simulated annealing algorithm and CG force fields involving the sequence-dependent base-pairing/stacking interactions and an implicit electrostatic potential, the present model successfully folds 20 dsDNAs (≤52nt) and 20 ssDNAs (≤74nt) into the corresponding native-like structures just from their sequences, with an overall mean RMSD of 3.4Å from the experimental structures. For DNAs with various lengths and sequences, the present model can make reliable predictions on stability, e.g., for 27 dsDNAs with/without bulge/internal loops and 24 ssDNAs including pseudoknot, the mean deviation of predicted melting temperatures from the corresponding experimental data is only ~2.0°C. Furthermore, the model also quantificationally predicts the effects of monovalent or divalent ions on the structure stability of ssDNAs/dsDNAs.

To determine 3D structures and quantify stability of single- (ss) and double-stranded (ds) DNAs is essential to unveil the mechanisms of their functions and to further guide the production and development of novel materials. Although many DNA models have been proposed to reproduce the basic structural, mechanical, or thermodynamic properties of dsDNAs based on the secondary structure information or preset constraints, there are very few models can be used to investigate the ssDNA folding or dsDNA assembly from the sequence. Furthermore, due to the polyanionic nature of DNAs, metal ions (e.g., Na⁺ and Mg²⁺) in solutions can play an essential role in DNA folding and dynamics. Nevertheless, ab initio predictions for DNA folding in ion solutions are still an unresolved problem. In this work, we developed a novel coarse-grained model to predict 3D structures and thermodynamic stabilities for both ssDNAs and dsDNAs in monovalent/divalent ion solutions from their sequences. As compared with the extensive experimental data and available existing models, we showed that the present model can successfully fold simple DNAs into their native-like structures, and can also accurately reproduce the effects of sequence and monovalent/divalent ions on structure stability for ssDNAs including pseudoknot and dsDNAs with/without bulge/internal loops.

Computational Modeling of RNA Aptamers: Structure Prediction of the Apo State

RNA aptamers are single-stranded oligonucleotides that bind to specific molecular targets with high affinity and specificity. To design aptamers for new applications, it is critical to understand the ligand binding mechanism in terms of the structure and dynamics of the ligand-bound and apo states. The problem is that most of the NMR or X-ray crystal structures available for RNA aptamers are for ligand-bound states. Available apo state structures, mostly characterized by crystallization under nonphysiological conditions or probed by low resolution techniques, might fail to represent the diverse structural variations of the apo state in solution. Here, we develop an approach to obtain a representative ensemble of apo structures that are based on in silico RNA 3D structure prediction and in vitro experiments that characterize base stacking. Using the neomycin-B aptamer as a case study, an ensemble of structures for the aptamer in the apo (unbound) state are validated and then used to investigate the ligand-binding mechanism for the aptamer in complex with neomycin-B.