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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has created an urgent need for new technologies to treat COVID-19. Here we report a 2′-fluoro protected RNA aptamer that binds with high affinity to the receptor binding domain (RBD) of SARS-CoV-2 spike protein, thereby preventing its interaction with the host receptor ACE2. A trimerized version of the RNA aptamer matching the three RBDs in each spike complex enhances binding affinity down to the low picomolar range. Binding mode and specificity for the aptamer–spike interaction is supported by biolayer interferometry, single-molecule fluorescence microscopy, and flow-induced dispersion analysis in vitro. Cell culture experiments using virus-like particles and live SARS-CoV-2 show that the aptamer and, to a larger extent, the trimeric aptamer can efficiently block viral infection at low concentration. Finally, the aptamer maintains its high binding affinity to spike from other circulating SARS-CoV-2 strains, suggesting that it could find widespread use for the detection and treatment of SARS-CoV-2 and emerging variants.

Lyme borreliosis (LB) is the most prevalent tick-borne illness, with an estimated 700 000 cases annually in the United States and Europe. The LB diagnosis based on a two-tiered serology remains controversial due to its indirect nature and low sensitivity during the early stage of the disease. Aptamers are single-stranded DNA or RNA oligonucleotides that exhibit high selectivity and specificity for their target due to their unique three-dimensional structure. By applying cross-over-SELEX process, an enrichment of DNA oligonucleotide sequences against a surface protein of Borrelia , named CspZ, has been performed and monitored using absorbance at 260 nm, melting curves and NGS analyses. Beyond sequence enrichment, oligonucleotides binding to CspZ were observed during the selection rounds by Dot Blot and beads assays. Thirteen unique and highly redundant oligonucleotide sequences were further characterized using multiple approaches such as Dot Blot, BioLayer Interferometry and Surface Plasmon Resonance. The selected aptamers showed K D values from tens of nanomolar to the micromolar range by BLI and SPR. Two aptamers, Apta9 and Apta10, characterized by flow cytometry and epifluorescence microscopy, were able to specifically recognize Borrelia burgdorferi sensu stricto. This strategy holds promise for the development of an improved diagnostic assay. Lyme borreliosis is challenging to diagnose, particularly in its early stages. Aptamers targeting CspZ from Borrelia , selected through cross-over SELEX, bind both the recombinant protein and bacteria, demonstrating potential for enhanced diagnostics.

Aptamers are versatile molecular tools with broad applications including biosensing, bioimaging, drug delivery, therapeutics, and diagnostics.Chemical modification of aptamers is an important research area for enhancing their performance. Although recent progress has been made in modified aptamers, there are still obstacles to overcome.Next-generation sequencing enables the identification of numerous aptamer candidates as potential target binders. However, the throughput of the binding affinity and specificity assays, as well as the procedures for the validation and characterization of modified aptamer–target binding, remain time-consuming and labor-intensive.Modified aptamers can recognize different classes of molecular targets. Nevertheless, their application in vivo is currently limited and requires further exploration.Recent significant advances in the aforementioned key directions provide a solid foundation and a new paradigm for aptamer research in both fundamental and applied biosciences. Aptamers are single-stranded oligonucleotides that bind to their targets via specific structural interactions. To improve the properties and performance of aptamers, modified nucleotides are incorporated during or after a selection process such as systematic evolution of ligands by exponential enrichment (SELEX). We summarize the latest modified nucleotides and strategies used in modified (mod)-SELEX and post-SELEX to develop modified aptamers, highlight the methods used to characterize aptamer–target interactions, and present recent progress in modified aptamers that recognize different targets. We discuss the challenges and perspectives in further advancing the methodologies and toolsets to accelerate the discovery of modified aptamers, improve the throughput of aptamer-target characterization, and expand the functional diversity and complexity of modified aptamers.

IntroductionMany bacteria are responsible for infections in humans and plants, being found in vegetables, water, and medical devices. Most bacterial detection methods are time-consuming and take days to give the result. Aptamers are a promising alternative for a quick and reliable measurement technique to detect bacteria present in food products. Selected aptamers are DNA or RNA oligonucleotides that can bind with bacteria or other molecules with affinity and specificity for the target cells by the SELEX or cell-SELEX technique. This method is based on some rounds to remove the non-ligand oligonucleotides, leaving the aptamers specific to bind to the selected bacteria. Compared with conventional methodologies, the detection approach using aptamers is a rapid, low-cost form of analysis. ObjectiveThis review summarizes obtention methods and applications of aptamers in the food industry and biotechnology. Besides, different techniques with aptamers are presented, which enable more effective target detection.ConclusionApplications of aptamers as biosensors, or the association of aptamers with nanomaterials, may be employed in analyses by colorimetric, fluorescence, or electrical devices. Additionally, more efficient ways of sample preparation are presented, which can support food safety to provide human health, with a low-cost method for contaminant detection.Key points• Aptamers are promising for detecting contaminants outbreaks.• Studies are needed to identify aptamers for different targets.

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

G-quadruplexes constitute a unique class of nucleic acid structures formed by G-rich oligonucleotides of DNA- or RNA-type. Depending on their chemical nature, loops length, and localization in the sequence or structure molecularity, G-quadruplexes are highly polymorphic structures showing various folding topologies. They may be formed in the human genome where they are believed to play a pivotal role in the regulation of multiple biological processes such as replication, transcription, and translation. Thus, natural G-quadruplex structures became prospective targets for disease treatment. The fast development of systematic evolution of ligands by exponential enrichment (SELEX) technologies provided a number of G-rich aptamers revealing the potential of G-quadruplex structures as a promising molecular tool targeted toward various biologically important ligands. Because of their high stability, increased cellular uptake, ease of chemical modification, minor production costs, and convenient storage, G-rich aptamers became interesting therapeutic and diagnostic alternatives to antibodies. In this review, we describe the recent advances in the development of G-quadruplex based aptamers by focusing on the therapeutic and diagnostic potential of this exceptional class of nucleic acid structures.

Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents. Current aptamer discovery approaches are unable to probe the complete space of possible sequences. Here, the authors use machine learning to facilitate the development of DNA aptamers with improved binding affinities, and truncate them without significantly compromising binding affinity.

Aptamers are short single stranded DNA or RNA oligonucleotides that can recognize analytes with extraordinary target selectivity and affinity. Despite their promising properties and diagnostic potential, the number of commercial applications remains scarce. In order to endow them with novel recognition motifs and enhanced properties, chemical modification of aptamers has been pursued. This review focuses on chemical modifications, aimed at increasing the binding affinity for the aptamer’s target either in a non-covalent or covalent fashion, hereby improving their application potential in a diagnostic context. An overview of current methodologies will be given, thereby distinguishing between pre- and post-SELEX (Systematic Evolution of Ligands by Exponential Enrichment) modifications.

Aptamers are short, single-stranded oligonucleotides that bind to specific target molecules. The shape-forming feature of single-stranded oligonucleotides provides high affinity and excellent specificity toward targets. Hence, aptamers can be used as analogs of antibodies. In December 2004, the US Food and Drug Administration approved the first aptamer-based therapeutic, pegaptanib (Macugen), targeting vascular endothelial growth factor, for the treatment of age-related macular degeneration. Since then, however, no aptamer medication for public health has appeared. During these relatively silent years, many trials and improvements of aptamer therapeutics have been performed, opening multiple novel directions for the therapeutic application of aptamers. This review summarizes the basic characteristics of aptamers and the chemical modifications available for aptamer therapeutics.

We describe the integration of microfluidic selection with highthroughput DNA sequencing technology for rapid and efficient discovery of nucleic acid aptamers. The Quantitative Selection of Aptamers through Sequencing method tracks the copy number and enrichment-fold of more than 10 million individual sequences through multiple selection rounds, enabling the identification of high-affinity aptamers without the need for the pool to fully converge to a small number of sequences. Importantly, this method allows the discrimination of sequences that arise from experimental biases rather than true high-affinity target binding. As a demonstration, we have identified aptamers that specifically bind to PDGF-BB protein with Kd < 3 nM within 3 rounds. Furthermore, we show that the aptamers identified by Quantitative Selection of Aptamers through Sequencing have ∼3–8-fold higher affinity and ∼2–4-fold higher specificity relative to those discovered through conventional cloning methods. Given that many biocombinatorial libraries are encoded with nucleic acids, we extrapolate that our method may be extended to other types of libraries for a range of molecular functions.