Methods and Applications of In Silico Aptamer Design and Modeling

Multiplexed In Vivo Screening Using Barcoded Aptamer Technology to Identify Oligonucleotide-Based Targeting Reagents

Recent FDA approvals of mRNA vaccines, short-interfering RNAs, and antisense oligonucleotides highlight the success of oligonucleotides as therapeutics. Aptamers are excellent affinity reagents that can selectively label protein biomarkers, but their clinical application has lagged. When formulating a given aptamer for in vivo use, molecular design details can determine biostability and biodistribution; therefore, extensive postselection manipulation is often required for each new design to identify clinically useful reagents harboring improved pharmacokinetic properties. Few methods are available to comprehensively screen such aptamers, especially in vivo, constituting a significant bottleneck in the field. In this study, we introduce barcoded aptamer technology (BApT) for multiplexed screening of predefined aptamer formulations in vitro and in vivo. We demonstrate this technology by simultaneously investigating 20 aptamer formulations, each harboring different molecular designs, for targeting Non-Small Cell Lung Cancer cells and tumors. Screening in vitro identified a 45 kDa bispecific formulation as the best cancer cell targeting reagent, whereas screening in vivo identified a 30 kDa monomeric formulation as the best tumor-specific targeting reagent. The multiplexed analysis pipeline also identified biodistribution phenotypes shared among formulations with similar molecular architectures. The BApT approach we describe here has the potential for broad application to fields where oligonucleotide-based targeting reagents are desired.

Designing molecules: directing stem cell differentiation

Stem cells have been widely applied in regenerative and therapeutic medicine for their unique regenerative properties. Although much research has shown their potential, it remains tricky in directing stem cell differentiation. The advancement of genetic and therapeutic technologies, however, has facilitated this issue through development of design molecules. These molecules are designed to overcome the drawbacks previously faced, such as unexpected differentiation outcomes and insufficient migration of endogenous or exogenous MSCs. Here, we introduced aptamer, bacteriophage, and biological vectors as design molecules and described their characteristics. The methods of designing/developing discussed include various Systematic Evolution of Ligands by Exponential Enrichment (SELEX) procedures, in silico approaches, and non-SELEX methods for aptamers, and genetic engineering methods such as homologous recombination, Bacteriophage Recombineering of Electroporated DNA (BRED), Bacteriophage Recombineering with Infectious Particles (BRIP), and genome rebooting for bacteriophage. For biological vectors, methods such as alternate splicing, multiple promoters, internal ribosomal entry site, CRISPR-Cas9 system and Cre recombinase mediated recombination were used to design viral vectors, while non-viral vectors like exosomes are generated through parental cell-based direct engineering. Besides that, we also discussed the pros and cons, and applications of each design molecule in directing stem cell differentiation to illustrate their great potential in stem cells research. Finally, we highlighted some safety and efficacy concerns to be considered for future studies.

Protocol for Testing the Effects of ssDNA Aptamer in HeLa and MCF-7

Conventional approaches for treating tumors encompass chemotherapy, radiotherapy, and surgery. However, these methods come with their limitations when applied in clinical practice. Aptamers are often referred to as "chemical antibodies" and consist of short DNA or RNA molecules, designed to bind to a wide range of targets, including proteins or nucleic acid structures. They exhibit strong affinities and remarkable specificity for their target molecules, making them capable of functioning as therapeutic agents to directly impede tumor cell proliferation. This approach helps minimize the harm to normal cells, thus reducing toxicity through decreased side effects. Here we report the procedure to develop ssDNA aptamer and investigate its ability to inhibit cancer cell proliferation in HeLa and MCF-7 cancer cell lines.

Aptamers as an approach to targeted cancer therapy

Conventional cancer treatments can cause serious side effects because they are not specific to cancer cells and can damage healthy cells. Aptamers often are single-stranded oligonucleotides arranged in a unique architecture, allowing them to bind specifically to target sites. This feature makes them an ideal choice for targeted therapeutics. They are typically produced through the systematic evolution of ligands by exponential enrichment (SELEX) and undergo extensive pharmacological revision to modify their affinity, specificity, and therapeutic half-life. Aptamers can act as drugs themselves, directly inhibiting tumor cells. Alternatively, they can be used in targeted drug delivery systems to transport drugs directly to tumor cells, minimizing toxicity to healthy cells. In this review, we will discuss the latest and most advanced approaches to using aptamers for cancer treatment, particularly targeted therapy overcoming resistance to conventional therapies.

Computational aptamer design for spike glycoprotein (S) (SARS CoV-2) detection with an electrochemical aptasensor

A new bioinformatic platform (APTERION) was used to design in a short time and with high specificity an aptamer for the detection of the spike protein, a structural protein of SARS-CoV-2 virus, responsible for the COVID-19 pandemic. The aptamer concentration on the carbon electrode surface was optimized using static contact angle and fluorescence method, while specificity was tested using differential pulse voltammetry (DPV) associated to carbon screen-printed electrodes. The data obtained demonstrated the good features of the aptamer which could be used to create a rapid method for the detection of SARS-CoV-2 virus. In fact, it is specific for spike also when tested against bovine serum albumin and lysozyme, competitor proteins if saliva is used as sample to test for the virus presence. Spectrofluorometric characterization allowed to measure the amount of aptamer present on the carbon electrode surface, while DPV measurements proved the affinity of the aptamer towards the spike protein and gave quantitative results. The acquired data allowed to conclude that the APTERION bioinformatic platform is a good method for aptamer design for rapidity and specificity. KEY POINTS: • Spike protein detection using an electrochemical biosensor • Aptamer characterization by contact angle and fluorescent measurements on electrode surface • Computational design of specific aptamers to speed up the aptameric sequence time.

Lable-free aptamer portable colorimetric smartphone for gliadin detection in food

For individuals with celiac disease (CD), the current clinical therapy option available is a lifelong gluten-free diet. Therefore, it is essential to swiftly and efficiently detect gluten in foods. A colorimetric sensor has been developed, which operates by regulating the aggregation and dispersion state of AuNPs induced by high concentration NaCl through the specific binding of gliadin and aptamer, thereby achieving rapid detection of gliadin in flour. It is found that the sensor exhibits good linearity in the concentration range of 0.67-10 μM and the LOD (3σ/S) is 12 nM. And it can accurately distinguish various types of free-gliadin samples, with a spiked recovery rate of 85%-122.3%. To make the detection process more convenient, the colorimetric results of the biosensor were translated into RGB color-gamut parameters by a smartphone color-picking program for further analysis. Gliadin can still be accurately quantified with the established smartphone platform, and a correlation coefficient of 0.988 was found. The proposed portable smartphone aptamer colorimetric sensing device has achieved satisfactory results in the rapid detection of gliadin in food.

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.

Multi-aptamer-mediated hairpin allosteric and aptamer-assisted CRISPR system for detection of S. pneumoniae and S. aureus

A novel nucleic acid aptamer nanoprobes-mediated hairpin allosteric and aptamer-assisted CRISPR system for detection of Streptococcus pneumoniae and Staphylococcus aureus is presented. In this fluorescence assay system, utilizing the hairpin allosteric effect caused by the aptamer binding to the target bacteria, the detection of S. pneumoniae is first achieved through changes in fluorescence due to FRET. Subsequently, a Cas12a protein mixture is added to detect S. aureus. The amplified output signal is triggered by two methods to ensure the sensitivity of the method: the synergistic FRET effect is achieved by the assembly of multi-aptamer through the conjugation of streptavidin-biotin, and the trans-cleavage function of CRISPR/Cas 12a. Under the optimized conditions, the proposed hairpin allosteric aptasensor could achieve high sensitivity (a detection limit of 135 cfu/mL) and broad-concentration quantification (dynamic range of 103-107 cfu/mL) of S. pneumoniae. The aptamer-assisted CRISPR system for S. aureus detection showed good linearity (R2 = 0.996) in the concentration range 102-108 cfu/mL, with a detection limit of 39 cfu/mL. No cross-reactivity with other foodborne pathogenic bacteria was observed in both systems. Taking only 55 min, this method of multiple pathogen detection proved to be promising.

In silico selection of aptamers for bacterial toxins detection

The most commonly used toxins in biological warfare are staphylococcal enterotoxin B (3SEB), cholera toxin (1XTC), and botulinum toxin (3BTA). Uncovering novel strategies for identifying these toxins is paramount; therefore, aptamers are used for this purpose. Aptamers are single-stranded DNA or RNA oligonucleotides selected via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) with high binding affinity and specificity against target molecules. However, SELEX in vitro is tedious; hence, adopting alternative in silico molecular docking approaches is necessary. We aimed to conduct molecular docking with accessible tools and obtain RNA aptamers. First, 4,820,095 sequences obtained from an initial library of 9.5 × 109 Python script sequences were used. The GraphClust program was used to create representative groups or clusters, and the DoGSiteScorer (https://proteins.plus/) was used to conduct binding site detection of the proteins: 5DO4 (thrombin), 3SEB, 1XTC, and 3BTA. rDock, HDock, and PatchDock were adopted, combining different docking program results (consensus scoring), to improve receptor-ligand prediction. An analysis of the poses and root mean square deviation (RMSD) was performed, and 468 structurally different aptamers were obtained. The DoGSiteScorer program predicted the binding site of each protein to direct the interaction with the aptamer. Candidate aptamers for 3SEB, 1XTC, and 3BTA were selected according to the pose value considering the closeness of the interaction with a lower mean of 45.923 Å, 45.854 Å, and 72.490 Å, respectively.Communicated by Ramaswamy H. Sarma.

Aptamer-Protein Interactions: From Regulation to Biomolecular Detection

Serving as the basis of cell life, interactions between nucleic acids and proteins play essential roles in fundamental cellular processes. Aptamers are unique single-stranded oligonucleotides generated by in vitro evolution methods, possessing the ability to interact with proteins specifically. Altering the structure of aptamers will largely modulate their interactions with proteins and further affect related cellular behaviors. Recently, with the in-depth research of aptamer-protein interactions, the analytical assays based on their interactions have been widely developed and become a powerful tool for biomolecular detection. There are some insightful reviews on aptamers applied in protein detection, while few systematic discussions are from the perspective of regulating aptamer-protein interactions. Herein, we comprehensively introduce the methods for regulating aptamer-protein interactions and elaborate on the detection techniques for analyzing aptamer-protein interactions. Additionally, this review provides a broad summary of analytical assays based on the regulation of aptamer-protein interactions for detecting biomolecules. Finally, we present our perspectives regarding the opportunities and challenges of analytical assays for biological analysis, aiming to provide guidance for disease mechanism research and drug discovery.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

Aptamer-Based Point-of-Care Devices: Emerging Technologies and Integration of Computational Methods

Recent innovations in point-of-care (POC) diagnostic technologies have paved a critical road for the improved application of biomedicine through the deployment of accurate and affordable programs into resource-scarce settings. The utilization of antibodies as a bio-recognition element in POC devices is currently limited due to obstacles associated with cost and production, impeding its widespread adoption. One promising alternative, on the other hand, is aptamer integration, i.e., short sequences of single-stranded DNA and RNA structures. The advantageous properties of these molecules are as follows: small molecular size, amenability to chemical modification, low- or nonimmunogenic characteristics, and their reproducibility within a short generation time. The utilization of these aforementioned features is critical in developing sensitive and portable POC systems. Furthermore, the deficiencies related to past experimental efforts to improve biosensor schematics, including the design of biorecognition elements, can be tackled with the integration of computational tools. These complementary tools enable the prediction of the reliability and functionality of the molecular structure of aptamers. In this review, we have overviewed the usage of aptamers in the development of novel and portable POC devices, in addition to highlighting the insights that simulations and other computational methods can provide into the use of aptamer modeling for POC integration.

Selecting antibacterial aptamers against the BamA protein in Pseudomonas aeruginosa by incorporating genetic algorithm to optimise computational screening method

Antibiotic resistance is one of the biggest threats to global health resulting in an increasing number of people suffering from severe illnesses or dying due to infections that were once easily curable with antibiotics. Pseudomonas aeruginosa is a major pathogen that has rapidly developed antibiotic resistance and WHO has categorised this pathogen under the critical list. DNA aptamers can act as a potential candidate for novel antimicrobial agents. In this study, we demonstrated that an existing aptamer is able to affect the growth of P. aeruginosa. A computational screen for aptamers that could bind to a well-conserved and essential outer membrane protein, BamA in Gram-negative bacteria was conducted. Molecular docking of about 100 functional DNA aptamers with BamA protein was performed via both local and global docking approaches. Additionally, genetic algorithm analysis was carried out to rank the aptamers based on their binding affinity. The top hits of aptamers with good binding to BamA protein were synthesised to investigate their in vitro antibacterial activity. Among all aptamers, Apt31, which is known to bind to an antitumor, Daunomycin, exhibited the highest HADDOCK score and resulted in a significant (p < 0.05) reduction in P. aeruginosa growth. Apt31 also induced membrane disruption that resulted in DNA leakage. Hence, computational screening may result in the identification of aptamers that bind to the desired active site with high affinity.

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.

Biomedical applications of aptamer-modified chitosan nanomaterials: An updated review

Among polysaccharides of environmental and economic interest, chitosan (CS) is receiving much attention, particularly in the food and biotechnology industries to encapsulate active food ingredients and immobilize enzymes. CS nanoparticles (CS NPs) combine the intrinsic beneficial properties of both natural polymers and nanoscale particles such as quantum size effect, biocompatibility, biodegradability, and ease of modification, and have great potential for bioimaging, drug delivery, and biosensing applications. Aptamers are single-stranded oligonucleotides that can fold into predetermined structures and bind to the corresponding biomolecules. They are mainly used as targeting ligands in biosensors, disease diagnostic kits and treatment strategies. They can deliver contrast agents and drugs into cancer cells and tissues, control microorganism growth and precisely target pathogens. Aptamer-conjugated CS NPs can significantly improve the efficacy of conventional therapies, minimize their side effects on normal tissues, and overcome the enhanced permeability retention (EPR) effect. Further, aptamer-conjugated carbohydrate-based nanobiopolymers have shown excellent antibacterial and antiviral properties and can be used to develop novel biosensors for the efficient detection of antibiotics, toxins, and other biomolecules. This updated review aims to provide a comprehensive overview of the bioapplications of aptamer-conjugated CS NPs used as innovative diagnostic and therapeutic platforms, their limitations, and potential future directions.

In silico selection of an aptamer for the design of aptamer-modified magnetic beads bearing ferrocene co-immobilized label for capacitive detection of acetamiprid

In silico evaluation of aptamer/target interactions can facilitate the development of efficient biosensor with high specificity and affinity. In this work, we present in silico, i.e. structural similarity, molecular docking and molecular dynamics selection of the aptamer with sufficient binding properties for acetamiprid (ACE), a nicotine-like pesticide, and its use to design aptamer-modified magnetic beads bearing ferrocene co-immobilized label for capacitive detection of ACE. Taking advantages of the aptamer higher stability and binding affinity, the specific properties of magnetic beads and the redox properties of ferrocene moiety, the developed aptasensor showed promising analytical performances for ACE detection, using electrochemical capacitance spectroscopy, with a linear response ranging from 1 fM to 100 pM and a limit of detection of 0.94 fM (S/N = 3). Furthermore, it was successfully applied to detect ACE in fortified tomatoes samples, proving a promising approach for routine detection of pesticide in real agricultural samples.

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.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

An Overview of Biosensors for the Detection of Patulin Focusing on Aptamer-Based Strategies

Patulin is a low molecular weight mycotoxin and poses a global problem, especially threatening food safety. It is also resistant to processing temperatures and is commonly found in fruits and vegetables. Studies have shown that it has toxic effects on animals and humans and the severity of patulin toxicity depends on the amount ingested. Therefore, the consumption of contaminated products, especially in infants and children, is important. The maximum daily intake of PAT that can be tolerated is found to be 0.4 µg/kg body weight to prevent chronic effects and the maximum residue limits in food samples were given as 50 ng/g (∼320 nM). Conventional methods for the detection of PAT have many disadvantages such as the use of expensive equipment, the need for trained personnel, and complicated sample preparation steps. To this extent, various numbers of research have been conducted on selective and sensitive detection of patulin using biosensor platforms in various media. This review presents an overview of the current literature dealing with the studies to develop patulin-specific aptamer-based biosensors and adapts various immobilization methods to increase the sensor response using different nanomaterials. Furthermore, a comparison of biosensors with conventional methods is presented using analytical performance parameters and their practicality for the detection of patulin.

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.

Machine Learning Directed Aptamer Search from Conserved Primary Sequences and Secondary Structures

Computer-aided prediction of aptamer sequences has been focused on primary sequence alignment and motif comparison. We observed that many aptamers have a conserved hairpin, yet the sequence of the hairpin can be highly variable. Taking such secondary structure information into consideration, a new algorithm combining conserved primary sequences and secondary structures is developed, which combines three scores based on sequence abundance, stability, and structure, respectively. This algorithm was used in the prediction of aptamers from the caffeine and theophylline selections. In the late rounds of the selections, when the libraries were converged, the predicted sequences matched well with the most abundant sequences. When the libraries were far from convergence and the sequences were deemed challenging for traditional analysis methods, this algorithm still predicted aptamer sequences that were experimentally verified by isothermal titration calorimetry. This algorithm paves a new way to look for patterns in aptamer selection libraries and mimics the sequence evolution process. It will help shorten the aptamer selection time and promote the biosensor and chemical biology applications of aptamers.

Screening and Identification of DNA Nanostructure Aptamer Using the SELEX Method for ‎Detection of Epsilon Toxin

Background

Epsilon toxin (ETX), produced by Clostridium perfringens, is one of the most potent toxins known, with a lethal potency approaching that of botulinum neurotoxins. Epsilon toxin is responsible for enteritis. Therefore, the development of rapid and simple methods to detect ETX is imperative. Aptamers are single-stranded oligonucleotides that can bind tightly to specific target molecules with an affinity comparable to that of monoclonal antibodies (mAbs). DNA aptamers can serve as tools for the molecular identification of organisms, such as pathogen subspecies.

Objectives

This study aimed to isolate high-affinity single-stranded DNA (ssDNA) aptamers against ETX.

Methods

This study identified aptamers using the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) method, enzyme-linked apta-sorbent assay (ELASA), and surface plasmon resonance (SPR) to determine the affinity and specificity of the newly obtained aptamers targeting ETX.

Results

Several aptamers obtained through the SELEX process were studied. Among them, 2 aptamers, ETX clone 3 (ETX3; dissociation constant (Kd = 8.4 ± 2.4E-9M) and ETX11 (Kd = 6.3 ± 1.3E-9M) had favorable specificity for ETX. The limits of detection were 0.21 and 0.08 μg/mL for ETX3 and ETX11, respectively.‎.

Conclusions

The discovered aptamers can be used in various aptamer-based rapid diagnostic tests for the detection of ETX.

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.

Successes and Failures of Static Aptamer-Target 3D Docking Models

While Molecular Dynamics simulation programs are probably superior for predicting the binding and affinity of aptamers and their cognate ligands, such molecular dynamics programs require more computing power and analysis time than static docking programs that are more widely accessible to the scientific community on the internet. Static docking programs can be used to investigate the geometric fit of rigid DNA or RNA aptamer 3D structures and their ligands to aid in predicting the relative affinities and cross-reactivity of various potential ligands. Herein, the author describes when such static 3D docking analysis has worked well to produce useful predictions or confirmation of high-affinity aptamer interactions or successful aptamer beacon behavior and when it has not worked well. The analysis of why failures may occur with static 3D computer models is also discussed.

Aptamer-Protein Structures Guide In Silico and Experimental Discovery of Aptamer-Short Peptide Recognition Complexes or Aptamer-Amino Acid Cluster Complexes

A method to computationally and experimentally identify aptamers against short peptides or amino acid clusters is introduced. The method involves the selection of a well-defined protein aptamer complex and the extraction of the peptide sequence participating in the binding of the protein to the aptamer. The subsequent fragmentation of the peptide sequence into short peptides and the in silico docking-guided identification of affinity complexes between the miniaturized peptides and the antiprotein aptamer, followed by experimental validation of the binding features of the short peptides with the antiprotein aptamers, leads to the identification of new short peptide-aptamer complexes. This is exemplified with the identification of the pentapeptide RYERN as the scaffold that binds thrombin to the DNA thrombin aptamer (DNA TA). In silico docking studies followed by microscale thermophoresis (MST) experiments demonstrate that the miniaturized tripeptides RYE, YER, and ERN reveal selective binding affinities toward the DNA TA. In addition, docking and MST experiments show that the ribonucleotide-translated RNA TA shows related binding affinities of YER to the DNA TA. Most importantly, we demonstrate that the separated amino acids Y/E/R assemble as a three amino acid cluster on the DNA TA and RNA TA aptamers in spatial configurations similar to the tripeptide YER on the respective aptamers. The clustering phenomenon is selective for the YER tripeptide system. The method to identify binding affinities of miniaturized peptides to known antiprotein aptamers and the specific clustering of single amino acids on the aptamers is further demonstrated by in silico and experimental identification of the binding of the tripeptide RET and the selective clustering of the separated amino acids R/E/T onto a derivative of the AS1411 aptamer against the nucleolin receptor protein.

Computationally Designed Anti-LuxP DNA Aptamer Suppressed Flagellar Assembly- and Quorum Sensing-Related Gene Expression in Vibrio parahaemolyticus

(1) Background: Quorum sensing (QS) is the chemical communication between bacteria that sense chemical signals in the bacterial population to control phenotypic changes through the regulation of gene expression. The inhibition of QS has various potential applications, particularly in the prevention of bacterial infection. QS can be inhibited by targeting the LuxP, a periplasmic receptor protein that is involved in the sensing of the QS signaling molecule known as the autoinducer 2 (AI-2). The sensing of AI-2 by LuxP transduces the chemical information through the inner membrane sensor kinase LuxQ protein and activates the QS cascade. (2) Methods: An in silico approach was applied to design DNA aptamers against LuxP in this study. A method combining molecular docking and molecular dynamics simulations was used to select the oligonucleotides that bind to LuxP, which were then further characterized using isothermal titration calorimetry. Subsequently, the bioactivity of the selected aptamer was examined through comparative transcriptome analysis. (3) Results: Two aptamer candidates were identified from the ITC, which have the lowest dissociation constants (Kd) of 0.2 and 0.5 micromolar. The aptamer with the lowest Kd demonstrated QS suppression and down-regulated the flagellar-assembly-related gene expression. (4) Conclusions: This study developed an in silico approach to design an aptamer that possesses anti-QS properties.

Novel Design of RNA Aptamers as Cancer Inhibitors and Diagnosis Targeting the Tyrosine Kinase Domain of the NT-3 Growth Factor Receptor Using a Computational Sequence-Based Approach

Aptamers, the nucleic acid analogs of antibodies, bind to their target molecules with remarkable specificity and sensitivity, making them promising diagnostic and therapeutic tools. The systematic evolution of ligands by exponential enrichment (SELEX) is time-consuming and expensive. However, regardless of those issues, it is the most used in vitro method for selecting aptamers. Therefore, recent studies have used computational approaches to reduce the time and cost associated with the synthesis and selection of aptamers. In an effort to present the potential of computational techniques in aptamer selection, a simple sequence-based method was used to design a 69-nucleotide long aptamer (mod_09) with a relatively stable structure (with a minimum free energy of −32.2 kcal/mol) and investigate its binding properties to the tyrosine kinase domain of the NT-3 growth factor receptor, for the first time, by employing computational modeling and docking tools.

In silico SELEX screening and statistical analysis of newly designed 5mer peptide-aptamers as Bcl-xl inhibitors using the Taguchi method

Drug development for cancer treatment is a complex process that requires special efforts. Targeting crucial proteins is the most essential purpose of drug design in cancers. Bcl-xl is an anti-apoptotic protein that binds to pro-apoptotic proteins and interrupts their signals. Pro-apoptotic Bcl-xl effectors are short BH3 sequences that form an alpha helix and bind to anti-apoptotic proteins to inhibit their activity. Computational systematic evolution of ligands by exponential enrichment (SELEX) is an exclusive approach for developing peptide aptamers as potential effectors. Here, the amino acids with a high tendency for constructing an alpha-helical structure were selected. Due to the enormous number of pentapeptides, Taguchi method was used to study a selected number of peptides. The binding affinity of the peptides to Bcl-xl was assessed using molecular docking, and after analysis of the obtained results, a final set of optimized peptides was arranged and constructed. For a better comparison, three chemical compounds with approved anti-Bcl-xl activity were selected for comparison with the top-ranked 5mer peptides. The optimized peptides showed considerable binding affinity to Bcl-xl. The molecular dynamics (MD) simulation indicated that the designed peptide (PO5) could create considerable interactions with the BH3 domain of Bcl-xl. The MM/GBSA calculations revealed that these interactions were even stronger than those created by chemical compounds. In silico SELEX is a novel approach to design and evaluate peptide-aptamers. The experimental design improves the SELEX process considerably. Finally, PO5 could be considered a potential inhibitor of Bcl-xl and a potential candidate for cancer treatment.

Probing TDP-43 condensation using an in silico designed aptamer

Aptamers are artificial oligonucleotides binding to specific molecular targets. They have a promising role in therapeutics and diagnostics but are often difficult to design. Here, we exploited the catRAPID algorithm to generate aptamers targeting TAR DNA-binding protein 43 (TDP-43), whose aggregation is associated with Amyotrophic Lateral Sclerosis. On the pathway to forming insoluble inclusions, TDP-43 adopts a heterogeneous population of assemblies, many smaller than the diffraction-limit of light. We demonstrated that our aptamers bind TDP-43 and used the tightest interactor, Apt-1, as a probe to visualize TDP-43 condensates with super-resolution microscopy. At a resolution of 10 nanometers, we tracked TDP-43 oligomers undetectable by standard approaches. In cells, Apt-1 interacts with both diffuse and condensed forms of TDP-43, indicating that Apt-1 can be exploited to follow TDP-43 phase transition. The de novo generation of aptamers and their use for microscopy opens a new page to study protein condensation.

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.

Electrochemical Aptasensors for Antibiotics Detection: Recent Achievements and Applications for Monitoring Food Safety

Antibiotics are often used in human and veterinary medicine for the treatment of bacterial diseases. However, extensive use of antibiotics in agriculture can result in the contamination of common food staples such as milk. Consumption of contaminated products can cause serious illness and a rise in antibiotic resistance. Conventional methods of antibiotics detection such are microbiological assays chromatographic and mass spectroscopy methods are sensitive; however, they require qualified personnel, expensive instruments, and sample pretreatment. Biosensor technology can overcome these drawbacks. This review is focused on the recent achievements in the electrochemical biosensors based on nucleic acid aptamers for antibiotic detection. A brief explanation of conventional methods of antibiotic detection is also provided. The methods of the aptamer selection are explained, together with the approach used for the improvement of aptamer affinity by post-SELEX modification and computer modeling. The substantial focus of this review is on the explanation of the principles of the electrochemical detection of antibiotics by aptasensors and on recent achievements in the development of electrochemical aptasensors. The current trends and problems in practical applications of aptasensors are also discussed.

Exploring molecular biology in sequence space: The road to next-generation single-molecule biophysics

Next-generation sequencing techniques have led to a new quantitative dimension in the biological sciences. In particular, integrating sequencing techniques with biophysical tools allows sequence-dependent mechanistic studies. Using the millions of DNA clusters that are generated during sequencing to perform high-throughput binding affinity and kinetics measurements enabled the construction of energy landscapes in sequence space, uncovering relationships between sequence, structure, and function. Here, we review the approaches to perform ensemble fluorescence experiments on next-generation sequencing chips for variations of DNA, RNA, and protein sequences. As the next step, we anticipate that these fluorescence experiments will be pushed to the single-molecule level, which can directly uncover kinetics and molecular heterogeneity in an unprecedented high-throughput fashion. Molecular biophysics in sequence space, both at the ensemble and single-molecule level, leads to new mechanistic insights. The wide spectrum of applications in biology and medicine ranges from the fundamental understanding of evolutionary pathways to the development of new therapeutics.

Modelling aptamers with nucleic acid mimics (NAM): From sequence to three-dimensional docking

Aptamers are single-stranded oligonucleotides, formerly evolved by Systematic Evolution of Ligands by EXponential enrichment (SELEX), that fold into functional three-dimensional structures. Such conformation is crucial for aptamers' ability to bind to a target with high affinity and specificity. Unnatural nucleotides have been used to develop nucleic acid mimic (NAM) aptamers with increased performance, such as biological stability. Prior knowledge of aptamer-target interactions is critical for applying post-SELEX modifications with unnatural nucleotides since it can affect aptamers' structure and performance. Here, we describe an easy-to-apply in silico workflow using free available software / web servers to predict the tertiary conformation of NAM, DNA and RNA aptamers, as well as the docking with the target molecule. Representative 2'-O-methyl (2'OMe), locked nucleic acid (LNA), DNA and RNA aptamers, with experimental data deposited in Protein Data Bank, were selected to validate the workflow. All aptamers' tertiary structure and docking models were successfully predicted with good structural similarity to the experimental data. Thus, this workflow will boost the development of aptamers, particularly NAM aptamers, by assisting in the rational modification of specific nucleotides and avoiding trial-and-error approaches.

Recent advances in the selection of cancer-specific aptamers for the development of biosensors

An early diagnosis has the potential to greatly decrease cancer mortality. For that purpose, specific cancer biomarkers have been molecularly targeted by aptamer sequences to enable an accurate and rapid detection. Aptamer-based biosensors for cancer diagnostics are a promising alternative to those using antibodies, due to their high affinity and specificity to the target molecules and advantageous production. Synthetic nucleic acid aptamers are generated by in vitro Systematic Evolution of Ligands by Exponential enrichment (SELEX) methodologies that have been improved over the years to enhance the efficacy and to shorten the selection process. Aptamers have been successfully applied in electrochemical, optical, photoelectrochemical and piezoelectrical-based detection strategies. These aptasensors comprise a sensitive, accurate and inexpensive option for cancer detection being used as point-of-care devices. This review highlights the recent advances in cancer biomarkers, achievements and optimizations made in aptamer selection, as well as the different aptasensors developed for the detection of several cancer biomarkers.

Structure- and Interaction-Based Design of Anti-SARS-CoV-2 Aptamers

Aptamer selection against novel infections is a complicated and time-consuming approach. Synergy can be achieved by using computational methods together with experimental procedures. This study aims to develop a reliable methodology for a rational aptamer in silico et vitro design. The new approach combines multiple steps: (1) Molecular design, based on screening in a DNA aptamer library and directed mutagenesis to fit the protein tertiary structure; (2) 3D molecular modeling of the target; (3) Molecular docking of an aptamer with the protein; (4) Molecular dynamics (MD) simulations of the complexes; (5) Quantum-mechanical (QM) evaluation of the interactions between aptamer and target with further analysis; (6) Experimental verification at each cycle for structure and binding affinity by using small-angle X-ray scattering, cytometry, and fluorescence polarization. By using a new iterative design procedure, structure- and interaction-based drug design (SIBDD), a highly specific aptamer to the receptor-binding domain of the SARS-CoV-2 spike protein, was developed and validated. The SIBDD approach enhances speed of the high-affinity aptamers development from scratch, using a target protein structure. The method could be used to improve existing aptamers for stronger binding. This approach brings to an advanced level the development of novel affinity probes, functional nucleic acids. It offers a blueprint for the straightforward design of targeting molecules for new pathogen agents and emerging variants.

Atomistic Simulations of Functionalized Nano-Materials for Biosensors Applications

Nanoscale biosensors, a highly promising technique in clinical analysis, can provide sensitive yet label-free detection of biomolecules. The spatial and chemical specificity of the surface coverage, the proper immobilization of the bioreceptor as well as the underlying interfacial phenomena are crucial elements for optimizing the performance of a biosensor. Due to experimental limitations at the microscopic level, integrated cross-disciplinary approaches that combine in silico design with experimental measurements have the potential to present a powerful new paradigm that tackles the issue of developing novel biosensors. In some cases, computational studies can be seen as alternative approaches to assess the microscopic working mechanisms of biosensors. Nonetheless, the complex architecture of a biosensor, associated with the collective contribution from "substrate-receptor-analyte" conjugate in a solvent, often requires extensive atomistic simulations and systems of prohibitive size which need to be addressed. In silico studies of functionalized surfaces also require ad hoc force field parameterization, as existing force fields for biomolecules are usually unable to correctly describe the biomolecule/surface interface. Thus, the computational studies in this field are limited to date. In this review, we aim to introduce fundamental principles that govern the absorption of biomolecules onto functionalized nanomaterials and to report state-of-the-art computational strategies to rationally design nanoscale biosensors. A detailed account of available in silico strategies used to drive and/or optimize the synthesis of functionalized nanomaterials for biosensing will be presented. The insights will not only stimulate the field to rationally design functionalized nanomaterials with improved biosensing performance but also foster research on the required functionalization to improve biomolecule-surface complex formation as a whole.

Investigating the anti-streptococcal biofilm effect of ssDNA aptamer-silver nanoparticles complex on a titanium-based substrate

Streptococcus mutans is a commensal and opportunistic pathogen that causes several diseases by forming a biofilm in humans and animals in many areas such as nasopharyngeal, cardiac valves, lungs, and oral cavity. Biofilms are very important in prosthetic infections associated with medical implants. The use of nanoparticles is one of the evolving fields in biofilm targeting. Silver nanoparticles can be used for biofilm targeting due to their inherent antimicrobial properties. Hybridization of nanoparticles with small molecules increases their biological properties and makes them multifunctional. The present investigation aimed to design an appropriate silver nanoparticles–aptamer complex that binds to the surface receptors of streptococcal strains. For this reason, silver nanoparticles with particle sizes in a range of 50 to 70 nm were synthesized and connected to a designed aptamer with a streptavidin–biotin linker. Then, the effect of the complex was investigated on the S. mutans biofilm formed on the surface of a medical-grade titanium substrate. The silver nanoparticles–aptamer complex at a concentration of 100 μg mL⁻¹ after 48 h inhibited 43% of the biofilm formation and degraded 63% of the formed biofilm. Also, the cell availability reached 96% and the complex was stable in cell medium culture for 360 min. It was concluded that this complex could be a good candidate for removing the formed biofilms on the surface of titanium implants.

Silver nanoparticles attached to the specific aptamer forStreptococcus mutans, can significantly remove the biofilm of this bacterium attached to the surface of the titanium implant.

Profiling Cancer Cells by Cell-SELEX: Use of Aptamers for Discovery of Actionable Biomarkers and Therapeutic Applications Thereof

The identification of tumor cell-specific surface markers is a key step towards personalized cancer medicine, allowing early assessment and accurate diagnosis, and development of efficacious targeted therapies. Despite significant efforts, currently the spectrum of cell membrane targets associated with approved treatments is still limited, causing an inability to treat a large number of cancers. What mainly limits the number of ideal clinical biomarkers is the high complexity and heterogeneity of several human cancers and still-limited methods for molecular profiling of specific cancer types. Thanks to the simplicity, versatility and effectiveness of its application, cell-SELEX (Systematic Evolution of Ligands by Exponential Enrichment) technology is a valid complement to the present strategies for biomarkers' discovery. We and other researchers worldwide are attempting to apply cell-SELEX to the generation of oligonucleotide aptamers as tools for both identifying new cancer biomarkers and targeting them by innovative therapeutic strategies. In this review, we discuss the potential of cell-SELEX for increasing the currently limited repertoire of actionable cancer cell-surface biomarkers and focus on the use of the selected aptamers as components of innovative conjugates and nano-formulations for cancer therapy.

Pheno-SELEX: Engineering Anti-Metastatic Aptamers through Targeting the Invasive Phenotype Using Systemic Evolution of Ligands by Exponential Enrichment

Multiple methods (e.g., small molecules and antibodies) have been engineered to target specific proteins and signaling pathways in cancer. However, many mediators of the cancer phenotype are unknown and the ability to target these phenotypes would help mitigate cancer. Aptamers are small DNA or RNA molecules that are designed for therapeutic use. The design of aptamers to target cancers can be challenging. Accordingly, to engineer functionally anti-metastatic aptamers we used a modification of systemic evolution of ligands by exponential enrichment (SELEX) we call Pheno-SELEX to target a known phenotype of cancer metastasis, i.e., invasion. A highly invasive prostate cancer (PCa) cell line was established and used to identify aptamers that bound to it with high affinity as opposed to a less invasive variant to the cell line. The anti-invasive aptamer (AIA1) was found to inhibit in vitro invasion of the original highly invasive PCa cell line, as well as an additional PCa cell line and an osteosarcoma cell line. AIA1 also inhibited in vivo development of metastasis in both a PCa and osteosarcoma model of metastasis. These results indicate that Pheno-SELEX can be successfully used to identify aptamers without knowledge of underlying molecular targets. This study establishes a new paradigm for the identification of functional aptamers.

Aptamer Applications in Neuroscience

Being the predominant cause of disability, neurological diseases have received much attention from the global health community. Over a billion people suffer from one of the following neurological disorders: dementia, epilepsy, stroke, migraine, meningitis, Alzheimer's disease, Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, Huntington's disease, prion disease, or brain tumors. The diagnosis and treatment options are limited for many of these diseases. Aptamers, being small and non-immunogenic nucleic acid molecules that are easy to chemically modify, offer potential diagnostic and theragnostic applications to meet these needs. This review covers pioneering studies in applying aptamers, which shows promise for future diagnostics and treatments of neurological disorders that pose increasingly dire worldwide health challenges.

COVID-19 challenges: From SARS-CoV-2 infection to effective point-of-care diagnosis by electrochemical biosensing platforms

In January 2020, the World Health Organization (WHO) identified a new zoonotic virus, SARS-CoV-2, responsible for causing the COVID-19 (coronavirus disease 2019). Since then, there has been a collaborative trend between the scientific community and industry. Multidisciplinary research networks try to understand the whole SARS-CoV-2 pathophysiology and its relationship with the different grades of severity presented by COVID-19. The scientific community has gathered all the data in the quickly developed vaccines that offer a protective effect for all variants of the virus and promote new diagnostic alternatives able to have a high standard of efficiency, added to shorter response analysis time and portability. The industry enters in the context of accelerating the path taken by science until obtaining the final product. In this review, we show the principal diagnostic methods developed during the COVID-19 pandemic. However, when we observe the diagnostic tools section of an efficient infection outbreak containment report and the features required for such tools, we could observe a highlight of electrochemical biosensing platforms. Such devices present a high standard of analytical performance, are low-cost tools, easy to handle and interpret, and can be used in the most remote and low-resource regions. Therefore, probably, they are the ideal point-of-care diagnostic tools for pandemic scenarios.

Key criteria for engineering mycotoxin binding aptamers via computational simulations: Aflatoxin B1 as a case study

Due to the difficulties in monoclonal antibody production specific to mycotoxins, aptameric probes have been considered as suitable alternatives. The low efficiency of the SELEX procedure in screening high affinity aptamers for binding mycotoxins as small molecules can be significantly improved through computational techniques. Previously, we designed five new aptamers to aflatoxin B₁ (AFB1) based on a known aptamer sequence (Patent: PCT/CA2010/001 292, Apt1) through a genetic algorithm-based in silico maturation strategy and experimentally measured their affinity to the target toxin. Here, integrated molecular dynamic simulation (MDs) studies with molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis to clarify the binding modes, critical interacting nucleic bases and energy component contributions in the six AFB1-binding aptamers. The aptamer F20, which was selected in the first work, showed the best free binding energy and complex stability compared to other aptamers. The trajectory analysis revealed that AFB1 recognized F20 through the groove binding mode along with precise shape complementarity. The MD simulation results revealed that dynamic water intermediate interactions also play a key role in promoting complex stability. According to the MM-PBSA calculations, van der Waals contacts were identified as dominant energy components in all complexes. Interestingly, a high consistency is observed between the experimentally obtained binding affinities of the six aptamers with their free energy solvation. The computational findings, confirmed via previous experiments, highlighted the binding modes, the dynamic hydration of complex components and the total free interacting energy as the crucial criteria in discovering high functional aptameric probes.

Improving aptamer performance with nucleic acid mimics: de novo and post-SELEX approaches

Aptamers are structural single-stranded oligonucleotides generated in vitro to bind to a specific target molecule. Aptamers' versatility can be enhanced with nucleic acid mimics (NAMs) during or after a selection process, also known as systematic evolution of ligands by exponential enrichment (SELEX). We address advantages and limitations of the technologies used to generate NAM aptamers, especially the applicability of existing engineered polymerases to replicate NAMs and methodologies to improve aptamers after SELEX. We also discuss the limitations of existing methods for sequencing NAM sequences and bioinformatic tools to predict NAM aptamer structures. As a conclusion, we suggest that NAM aptamers might successfully compete with molecular tools based on proteins such as antibodies for future application.

Molecular Dynamics Simulations of the Aptamer Domain of Guanidinium Ion Binding Riboswitch ykkC-III: Structural Insights into the Discrimination of Cognate and Alternate Ligands

Guanidinium ion is a toxic cellular metabolite. The ykkC-III riboswitch, an mRNA stretch, regulates the gene expression by undergoing a conformational change in response to the binding of a free guanidinium ion and thereby plays a potentially important role in alleviating guanidinium toxicity in cells. An experimental crystal structure of the guanidinium-bound aptamer domain of the riboswitch from Thermobifida Fusca revealed the overall RNA architecture and mapped the specific noncovalent interactions that stabilize the ligand within the binding pocket aptamer. However, details of how the aptamer domain discriminates the cognate ligand from its closest structurally analogous physiological metabolites (arginine and urea), and how the binding of cognate ligand arrays information from the aptamer domain to the expression platform for regulating the gene expression, are not well understood. To fill this void, we perform a cumulative of 2 μs all-atom explicit-solvent molecular dynamics (MD) simulations on the full aptamer domain, augmented with quantum-chemical calculations on the ligand-binding pocket, to compare the structural and dynamical details of the guanidinium-bound state with the arginine or urea bound states, as well as the unbound (open) state. Analysis of the ligand-binding pocket reveals that due to unfavorable interactions with the binding-pocket residues, urea cannot bind the aptamer domain and thereby cannot alter the gene expression. Although interaction of the guanidyl moiety of arginine within the binding pocket is either comparable or stronger than the guanidinium ion, additional non-native hydrogen-bonding networks, as well as differences in the dynamical details of the arginine-bound state, explain why arginine cannot transmit the information from the aptamer domain to the expression platform. Based on our simulations, we propose a mechanism of how the aptamer domain communicates with the expression platform. Overall, our work provides interesting insights into the ligand recognition by a specific class of riboswitches and may hopefully inspire future studies to further understand the gene regulation by riboswitches.

E2EDNA: Simulation Protocol for DNA Aptamers with Ligands

We present E2EDNA, a simulation protocol and accompanying code for the molecular biophysics and materials science communities. This protocol is both easy to use and sufficiently efficient to simulate single-stranded (ss)DNA and small analyte systems that are important to cellular processes and nanotechnologies such as DNA aptamer-based sensors. Existing computational tools used for aptamer design focus on cost-effective secondary structure prediction and motif analysis in the large data sets produced by SELEX experiments. As a rule, they do not offer flexibility with respect to the choice of the theoretical engine or direct access to the simulation platform. Practical aptamer optimization often requires higher accuracy predictions for only a small subset of sequences suggested, e.g., by SELEX experiments, but in the absence of a streamlined procedure, this task is extremely time and expertise intensive. We address this gap by introducing E2EDNA, a computational framework that accepts a DNA sequence in the FASTA format and the structures of the desired ligands and performs approximate folding followed by a refining step, analyte complexation, and molecular dynamics sampling at the desired level of accuracy. As a case study, we simulate a DNA-UTP (uridine triphosphate) complex in water using the state-of-the-art AMOEBA polarizable force field. The code is available at https://github.com/InfluenceFunctional/E2EDNA.

Oligonucleotide aptamers for pathogen detection and infectious disease control

During an epidemic or pandemic, the primary task is to rapidly develop precise diagnostic approaches and effective therapeutics. Oligonucleotide aptamer-based pathogen detection assays and control therapeutics are promising, as aptamers that specifically recognize and block pathogens can be quickly developed and produced through simple chemical synthesis. This work reviews common aptamer-based diagnostic techniques for communicable diseases and summarizes currently available aptamers that target various pathogens, including the SARS-CoV-2 virus. Moreover, this review discusses how oligonucleotide aptamers might be leveraged to control pathogen propagation and improve host immune system responses. This review offers a comprehensive data source to the further develop aptamer-based diagnostics and therapeutics specific for infectious diseases.