APTANI2: update of aptamer selection through sequence-structure analysis

RaptGen-Assisted Generation of an RNA/DNA Hybrid Aptamer against SARS-CoV-2 Spike Protein

Optimization of aptamers in length and chemistry is crucial for industrial applications. Here, we developed aptamers against the SARS-CoV-2 spike protein and achieved optimization with a deep-learning-based algorithm, RaptGen. We conducted a primer-less SELEX against the receptor binding domain (RBD) of the spike with an RNA/DNA hybrid library, and the resulting sequences were subjected to RaptGen analysis. Based on the sequence profiling by RaptGen, a short truncation aptamer of 26 nucleotides was obtained and further optimized by a chemical modification of relevant nucleotides. The resulting aptamer is bound to RBD not only of SARS-CoV-2 wildtype but also of its variants, SARS-CoV-1, and Middle East respiratory syndrome coronavirus (MERS-CoV). We concluded that the RaptGen-assisted discovery is efficient for developing optimized aptamers.

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.

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

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

AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers

Aptamers are short single-stranded DNA or RNA molecules with high affinity and specificity for targets and are generated using the iterative Systematic Evolution of Ligands by EXponential enrichment (SELEX) process. Next-generation sequencing (NGS) revolutionized aptamer selections by allowing a more comprehensive analysis of SELEX-enriched aptamers as compared to Sanger sequencing. The current challenge with aptamer NGS datasets is identifying a diverse cohort of candidate aptamers with the highest likelihood of successful experimental validation. Herein we present AptamerRunner, an aptamer clustering algorithm that generates visual networks of aptamers that are related by sequence and/or structure. These networks can then be overlayed with ranking data, such as fold enrichment or data from scoring algorithms. The ability to visually integrate data using AptamerRunner represents a significant advancement over existing clustering tools by providing a natural context to depict groups of aptamers from which ranked or scored candidates can be chosen for experimental validation. The inherent flexibility, user-friendly design, and prospects for future enhancements with AptamerRunner has broad-reaching implications for aptamer researchers across a wide range of disciplines.

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.

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.

Generative aptamer discovery using RaptGen

Nucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment (SELEX). Various candidates are limited by actual sequencing data from an experiment. Here we developed RaptGen, which is a variational autoencoder for in silico aptamer generation. RaptGen exploits a profile hidden Markov model decoder to represent motif sequences effectively. We showed that RaptGen embedded simulation sequence data into low-dimensional latent space on the basis of motif information. We also performed sequence embedding using two independent SELEX datasets. RaptGen successfully generated aptamers from the latent space even though they were not included in high-throughput sequencing. RaptGen could also generate a truncated aptamer with a short learning model. We demonstrated that RaptGen could be applied to activity-guided aptamer generation according to Bayesian optimization. We concluded that a generative method by RaptGen and latent representation are useful for aptamer discovery.

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.

Recent Advances in Aptamer-Based Liquid Biopsy

Liquid biopsy capable of noninvasive and real-time molecular profiling is considered as a breakthrough technology, endowing an opportunity for precise diagnosis of individual patients. Extracellular vesicles (EVs) and circulating tumor cells (CTCs) consisting of substantial disease-related molecular information play an important role in liquid biopsy. Therefore, it is critically significant to exploit high-performance recognition ligands for efficient isolation and analysis of EVs and CTCs from complex body fluids. Aptamers exhibit extraordinary merits of high specificity and affinity, which are considered as superior recognition ligands for liquid biopsy. In this review, we first summarize recent advanced strategies for the evolution of high-performance aptamers and the construction of various aptamer-based recognition elements. Subsequently, we mainly discuss the isolation and analysis of EVs and CTCs based on the aptamer functioned biomaterials/biointerface. Ultimately, we envision major challenges and future direction of aptamer-based liquid biopsy for clinical utilities.

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.

Aptamer-Based Detection of Circulating Targets for Precision Medicine

The past decade has witnessed ongoing progress in precision medicine to improve human health. As an emerging diagnostic technique, liquid biopsy can provide real-time, comprehensive, dynamic physiological and pathological information in a noninvasive manner, opening a new window for precision medicine. Liquid biopsy depends on the sensitive and reliable detection of circulating targets (e.g., cells, extracellular vesicles, proteins, microRNAs) from body fluids, the performance of which is largely governed by recognition ligands. Aptamers are single-stranded functional oligonucleotides, capable of folding into unique tertiary structures to bind to their targets with superior specificity and affinity. Their mature evolution procedure, facile modification, and affinity regulation, as well as versatile structural design and engineering, make aptamers ideal recognition ligands for liquid biopsy. In this review, we present a broad overview of aptamer-based liquid biopsy techniques for precision medicine. We begin with recent advances in aptamer selection, followed by a summary of state-of-the-art strategies for multivalent aptamer assembly and aptamer interface modification. We will further describe aptamer-based micro-/nanoisolation platforms, aptamer-enabled release methods, and aptamer-assisted signal amplification and detection strategies. Finally, we present our perspectives regarding the opportunities and challenges of aptamer-based liquid biopsy for precision medicine.

Implementation of High-Throughput Sequencing (HTS) in Aptamer Selection Technology

Aptamers are nucleic acid ligands that bind specifically to a target of interest. Aptamers have gained in popularity due to their high potential for different applications in analysis, diagnostics, and therapeutics. The procedure called systematic evolution of ligands by exponential enrichment (SELEX) is used for aptamer isolation from large nucleic acid combinatorial libraries. The huge number of unique sequences implemented in the in vitro evolution in the SELEX process imposes the necessity of performing extensive sequencing of the selected nucleic acid pools. High-throughput sequencing (HTS) meets this demand of SELEX. Analysis of the data obtained from sequencing of the libraries produced during and after aptamer isolation provides an informative basis for precise aptamer identification and for examining the structure and function of nucleic acid ligands. This review discusses the technical aspects and the potential of the integration of HTS with SELEX.

RaptRanker: in silico RNA aptamer selection from HT-SELEX experiment based on local sequence and structure information

Aptamers are short single-stranded RNA/DNA molecules that bind to specific target molecules. Aptamers with high binding-affinity and target specificity are identified using an in vitro procedure called high throughput systematic evolution of ligands by exponential enrichment (HT-SELEX). However, the development of aptamer affinity reagents takes a considerable amount of time and is costly because HT-SELEX produces a large dataset of candidate sequences, some of which have insufficient binding-affinity. Here, we present RNA aptamer Ranker (RaptRanker), a novel in silico method for identifying high binding-affinity aptamers from HT-SELEX data by scoring and ranking. RaptRanker analyzes HT-SELEX data by evaluating the nucleotide sequence and secondary structure simultaneously, and by ranking according to scores reflecting local structure and sequence frequencies. To evaluate the performance of RaptRanker, we performed two new HT-SELEX experiments, and evaluated binding affinities of a part of sequences that include aptamers with low binding-affinity. In both datasets, the performance of RaptRanker was superior to Frequency, Enrichment and MPBind. We also confirmed that the consideration of secondary structures is effective in HT-SELEX data analysis, and that RaptRanker successfully predicted the essential subsequence motifs in each identified sequence.