Artificial Intelligence in Aptamer-Target Binding Prediction

Nucleic acid-responsive smart systems for controlled cargo delivery

Stimulus-responsive delivery systems allow controlled, highly regulated, and efficient delivery of various cargos while minimizing side effects. Owing to the unique properties of nucleic acids, including the ability to adopt complex structures by base pairing, their easy synthesis, high specificity, shape memory, and configurability, they have been employed in autonomous molecular motors, logic circuits, reconfigurable nanoplatforms, and catalytic amplifiers. Moreover, the development of nucleic acid (NA)-responsive intelligent delivery vehicles is a rapidly growing field. These vehicles have attracted much attention in recent years due to their programmable, controllable, and reversible properties. In this work, we review several types of NA-responsive controlled delivery vehicles based on locks and keys, including DNA/RNA-responsive, aptamer-responsive, and CRISPR-responsive, and summarize their advantages and limitations.

Prediction of aptamer affinity using an artificial intelligence approach

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

Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review

In the rapidly evolving landscape of nanomedicine, aptamers have emerged as powerful molecular tools, demonstrating immense potential in targeted therapeutics, diagnostics, and drug delivery systems. This paper explores the computational features of aptamers in nanomedicine, highlighting their advantages over antibodies, including selectivity, low immunogenicity, and a simple production process. A comprehensive overview of the aptamer development process, specifically the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process, sheds light on the intricate methodologies behind aptamer selection. The historical evolution of aptamers and their diverse applications in nanomedicine are discussed, emphasizing their pivotal role in targeted drug delivery, precision medicine and therapeutics. Furthermore, we explore the integration of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), Internet of Medical Things (IoMT), and nanotechnology in aptameric development, illustrating how these cutting-edge technologies are revolutionizing the selection and optimization of aptamers for tailored biomedical applications. This paper also discusses challenges in computational methods for advancing aptamers, including reliable prediction models, extensive data analysis, and multiomics data incorporation. It also addresses ethical concerns and restrictions related to AI and IoT use in aptamer research. The paper examines progress in computer simulations for nanomedicine. By elucidating the importance of aptamers, understanding their superiority over antibodies, and exploring the historical context and challenges, this review serves as a valuable resource for researchers and practitioners aiming to harness the full potential of aptamers in the rapidly evolving field of nanomedicine.

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.

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.

AptaDB: a comprehensive database integrating aptamer-target interactions

Aptamers have emerged as research hotspots of the next generation due to excellent performance benefits and application potentials in pharmacology, medicine, and analytical chemistry. Despite the numerous aptamer investigations, the lack of comprehensive data integration has hindered the development of computational methods for aptamers and the reuse of aptamers. A public access database named AptaDB, derived from experimentally validated data manually collected from the literature, was hence developed, integrating comprehensive aptamer-related data, which include six key components: (i) experimentally validated aptamer-target interaction information, (ii) aptamer property information, (iii) structure information of aptamer, (iv) target information, (v) experimental activity information, and (vi) algorithmically calculated similar aptamers. AptaDB currently contains 1350 experimentally validated aptamer-target interactions, 1230 binding affinity constants, 1293 aptamer sequences, and more. Compared to other aptamer databases, it contains twice the number of entries found in available databases. The collection and integration of the above information categories is unique among available aptamer databases and provides a user-friendly interface. AptaDB will also be continuously updated as aptamer research evolves. We expect that AptaDB will become a powerful source for aptamer rational design and a valuable tool for aptamer screening in the future. For access to AptaDB, please visit http://lmmd.ecust.edu.cn/aptadb/.

Non-immobilized GO-SELEX screening of aptamers against cyclosporine A and its application in a AuNPs colorimetric aptasensor

Cyclosporine A (CsA) is an immunosuppressive drug that is widely used in clinical practice. Due to its narrow therapeutic window and the significant differences between individuals, the therapeutic drug monitoring (TDM) of CsA is required to ensure patient safety. In this study, we screened a novel aptamer, named CsA7, which could specifically recognize CsA, and developed a AuNPs colorimetric aptasensor for the rapid detection of CsA. In the SELEX process, after eight rounds of screening, four aptamer candidate sequences were obtained and subjected to binding affinity and specificity tests. Finally, the CsA7 aptamer (Kd = 41.21 ng mL-1) showed the highest affinity for CsA. Based on CsA7, we also developed a AuNPs colorimetric aptasensor, which had a detection limit of 0.1 ng mL-1 and a quantitative range of 0.1-500 ng mL-1 and showed good selectivity among CsA and its analogs. According to the results, the CsA7 aptamer provides an alternative recognition molecule to the antibody in biosensor applications and shows great potential for the rapid and convenient detection of CsA.

Nano-composite system of traditional Chinese medicine for ocular applications: molecular docking and three-dimensional modeling insight for intelligent drug evaluation

The absorption of drugs was impeded in the posterior part of the eye due to the special structure. In addition, it was crucial to comprehend transport laws of molecules in ocular drug delivery for designing effective strategies. However, the current quality evaluation methods of the eye were backward and lack of dynamic monitoring of drug processes in vivo. Herein, nano-drug delivery system and three-dimensional (3D) model were combined to overcome the problems of low bioavailability and diffusion law. The model drugs were screened by molecular docking. The flexible nano-liposome (FNL) and temperature-sensitive gel (TSG) composite formulation was characterized through comprehensive evaluation. COMSOL software was utilized to build 3D eyeball to predict the bioavailability of drugs. The size of the preparation was about 98.34 nm which is relatively optimal for the enhanced permeability of the eyes. The formulation showed a stronger safety and non-irritant. The pharmacokinetics results of aqueous humor showed that the AUC of two drugs in this system increased by 3.79 and 3.94 times, respectively. The results of 3D calculation model proved that the concentrations of drugs reaching the retina were 1.90×10-5 mol/m3 and 6.37×10-6 mol/m3. In conclusion, the FNL-TSG markedly improved the bioavailability of multiple components in the eye. More importantly, a simplified 3D model was developed to preliminarily forecast the bioavailability of the retina after drug infusion, providing technical support for the accurate evaluation of ocular drug delivery. It provided new pattern for the development of intelligent versatile ophthalmic preparations.

Nucleic Acid Aptamer-Based Biosensors: A Review

Aptamers, short strands of either DNA, RNA, or peptides, known for their exceptional specificity and high binding affinity to target molecules, are providing significant advancements in the field of health. When seamlessly integrated into biosensor platforms, aptamers give rise to aptasensors, unlocking a new dimension in point-of-care diagnostics with rapid response times and remarkable versatility. As such, this review aims to present an overview of the distinct advantages conferred by aptamers over traditional antibodies as the molecular recognition element in biosensors. Additionally, it delves into the realm of specific aptamers made for the detection of biomarkers associated with infectious diseases, cancer, cardiovascular diseases, and metabolomic and neurological disorders. The review further elucidates the varying binding assays and transducer techniques that support the development of aptasensors. Ultimately, this review discusses the current state of point-of-care diagnostics facilitated by aptasensors and underscores the immense potential of these technologies in advancing the landscape of healthcare delivery.

Structural Insights into Protein-Aptamer Recognitions Emerged from Experimental and Computational Studies

Aptamers are synthetic nucleic acids that are developed to target with high affinity and specificity chemical entities ranging from single ions to macromolecules and present a wide range of chemical and physical properties. Their ability to selectively bind proteins has made these compounds very attractive and versatile tools, in both basic and applied sciences, to such an extent that they are considered an appealing alternative to antibodies. Here, by exhaustively surveying the content of the Protein Data Bank (PDB), we review the structural aspects of the protein-aptamer recognition process. As a result of three decades of structural studies, we identified 144 PDB entries containing atomic-level information on protein-aptamer complexes. Interestingly, we found a remarkable increase in the number of determined structures in the last two years as a consequence of the effective application of the cryo-electron microscopy technique to these systems. In the present paper, particular attention is devoted to the articulated architectures that protein-aptamer complexes may exhibit. Moreover, the molecular mechanism of the binding process was analyzed by collecting all available information on the structural transitions that aptamers undergo, from their protein-unbound to the protein-bound state. The contribution of computational approaches in this area is also highlighted.

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

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

DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design

Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities.

RNA aptamer-mediated gene therapy of prostate cancer: lessons from the past and future directions

INTRODUCTION

Prostate cancer (PCa) is one of the most prevalent cancers in the world, and the fifth cause of death from cancer in men. Among the non-surgical treatments for PCa, gene therapy strategies are in the early stages of development and recent clinical trials have provided new insights suggesting promising future.

AREAS COVERED

Recently, the creation of targeted gene delivery systems, based on specific PCa cell surface markers, has been viewed as a viable therapeutic approach. Prostate-specific membrane antigen (PSMA) is vastly expressed in nearly all prostate malignancies, and the intensity of expression increases with tumor aggressiveness, androgen independence, and metastasis. RNA aptamers are short and single-stranded oligonucleotides, which selectively bind to a specific ligand on the surface of the cells, which makes them fascinating small molecules for target delivery of therapeutics. PSMA-selective RNA aptamers represent great potential for developing targeted-gene delivery tools for PCa.

EXPERT OPINION

This review provides a thorough horizon for the researchers interested in developing targeted gene delivery systems for PCa via PSMA RNA aptamers. In addition, we provided general information about different prospects of RNA aptamers including discovery approaches, stability, safety, and pharmacokinetics.

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.

Aptamer-based expansion microscopy platform enables signal-amplified imaging of dendritic spines

Super-resolution imaging of dendritic spines (DS) can provide valuable information for mechanistic studies related to synaptic physiology and neural plasticity, but challenged by their small dimension (50-200 nm) below the spatial resolution of conventional optical microscopes. In this work, by combining the molecular recognition specificity of aptamer with high programmability of DNA nanotechnology, we developed an expansion microscopy (ExM) platform for imaging DS with enhanced spatial resolution and amplified signal output. Our results demonstrated that the aptamer probe could specifically bind to DS of primary hippocampal neurons. With physical expansion, the DS structure could be effectively enlarged by 4-5 folds, leading to the generation of more structural information. Meantime, the aptamer binding signal could be readily amplified by the introduction of DNA signal amplification strategy, overcoming the drawback of fluorescence dilution during the ExM treatment. This platform enabled evaluation of ischemia-induced early stroke based on the morphological change of DS, highlighting a promising avenue for studying nanoscale structures in biological systems.

Aptamers targeting SARS-COV-2: a promising tool to fight against COVID-19

SARS-CoV-2, the causative agent of COVID-19, remains among the main causes of global mortality. Although antigen/antibody-based immunoassays and neutralizing antibodies targeting SARS-CoV-2 have been successfully developed over the past 2 years, they are often inefficient and unreliable for emerging SARS-CoV-2 variants. Novel approaches against SARS-CoV-2 and its variants are therefore urgently needed. Aptamers have been developed for the detection and inhibition of several different viruses such as HIV, influenza viruses, Middle East respiratory syndrome coronavirus (MERS-CoV), and SARS-CoV. Aptamers targeting SARS-CoV-2 represent a promising tool in the fight against COVID-19, which is of paramount importance for the current and any future pandemics. This review presents recent advances and future trends in the development of aptamer-based approaches for SARS-CoV-2 diagnosis and treatment.

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.

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.

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.

Capture-SELEX: Selection Strategy, Aptamer Identification, and Biosensing Application

Small-molecule contaminants, such as antibiotics, pesticides, and plasticizers, have emerged as one of the substances most detrimental to human health and the environment. Therefore, it is crucial to develop low-cost, user-friendly, and portable biosensors capable of rapidly detecting these contaminants. Antibodies have traditionally been used as biorecognition elements. However, aptamers have recently been applied as biorecognition elements in aptamer-based biosensors, also known as aptasensors. The systematic evolution of ligands by exponential enrichment (SELEX) is an in vitro technique used to generate aptamers that bind their targets with high affinity and specificity. Over the past decade, a modified SELEX method known as Capture-SELEX has been widely used to generate DNA or RNA aptamers that bind small molecules. In this review, we summarize the recent strategies used for Capture-SELEX, describe the methods commonly used for detecting and characterizing small-molecule-aptamer interactions, and discuss the development of aptamer-based biosensors for various applications. We also discuss the challenges of the Capture-SELEX platform and biosensor development and the possibilities for their future application.

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.

Aptamers, a New Therapeutic Opportunity for the Treatment of Multiple Myeloma

Multiple Myeloma (MM) remains an incurable disease due to high relapse rates and fast development of drug resistances. The introduction of monoclonal antibodies (mAb) has caused a paradigm shift in MM treatment, paving the way for targeted approaches with increased efficacy and reduced toxicities. Nevertheless, antibody-based therapies face several difficulties such as high immunogenicity, high production costs and limited conjugation capacity, which we believe could be overcome by the introduction of nucleic acid aptamers. Similar to antibodies, aptamers can bind to their targets with great affinity and specificity. However, their chemical nature reduces their immunogenicity and production costs, while it enables their conjugation to a wide variety of cargoes for their use as delivery agents. In this review, we summarize several aptamers that have been tested against MM specific targets with promising results, establishing the rationale for the further development of aptamer-based strategies against MM. In this direction, we believe that the study of novel plasma cell surface markers, the development of intracellular aptamers and further research on aptamers as building blocks for complex nanomedicines will lead to the generation of next-generation targeted approaches that will undoubtedly contribute to improve the management and life quality of MM patients.

A Novel ssDNA Aptamer Targeting Carcinoembryonic Antigen: Selection and Characterization

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

Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This “smart” SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor’s response. This can be explained by considering the aptamers’ conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule.

Nucleic Acid Aptamers Emerging as Modulators of G-Protein-Coupled Receptors: Challenge to Difficult Cell Surface Proteins

G-protein-coupled receptors (GPCRs), among various cell surface proteins, are essential targets in the fields of basic science and drug discovery. The discovery and development of modulators for the receptors have provided deep insights into the mechanism of action of receptors and have led to a new therapeutic option for human diseases. Although various modulators against GPCRs have been developed to date, the identification of new modulators for GPCRs remains a challenge due to several technical problems and limitations. To overcome this situation, a variety of strategies have been developed by several modalities, including nucleic acid aptamers, which are emerging as unique molecules isolated by a repetitive selection process against various types of targets from an enormous combinatorial library. This review summarized the achievements in the development of aptamers targeting GPCRs, and discussed their isolation methods and the diverse functional features of aptamers against GPCRs.

Drug Discovery of DKK1 Inhibitors

Dickkopf-1 (DKK1) is a well-characterized Wnt inhibitor and component of the Wnt/β-catenin signaling pathway, whose dysregulation is associated with multiple abnormal pathologies including osteoporosis, Alzheimer's disease, diabetes, and various cancers. The Wnt signaling pathway has fundamental roles in cell fate determination, cell proliferation, and survival; thus, its mis-regulation can lead to disease. Although DKK1 is involved in other signaling pathways, including the β-catenin-independent Wnt pathway and the DKK1/CKAP4 pathway, the inhibition of DKK1 to propagate Wnt/β-catenin signals has been validated as an effective way to treat related diseases. In fact, strategies for developing DKK1 inhibitors have produced encouraging clinical results in different pathological models, and many publications provide detailed information about these inhibitors, which include small molecules, antibodies, and nucleic acids, and may function at the protein or mRNA level. However, no systematic review has yet provided an overview of the various aspects of their development and prospects. Therefore, we review the DKK1 inhibitors currently available or under study and provide an outlook on future studies involving DKK1 and drug discovery.

Three-dimensional modeling of streptomycin binding single-stranded DNA for aptamer-based biosensors, a molecular dynamics simulation approach

Streptomycin (STR) an aminoglycoside antibiotic which is used against bacteria in human and animal infection, have serious side effects on different parts of human body. Therefore, there is a crucial need to detect trace amount of it in serum and food products. Aptamers are oligonucleotides or peptides, which bind their targets with high affinity and specificity. These properties make aptamers as suitable candidates for biosensing applications. A 79-mer ss-DNA aptamer was applied for the detection of small amount of STR in various aptasensors. But there is no structural information on the STR-binding aptamer and molecular details underlying the aptamer-STR binding remain unexplored. In this study we provided a 3D-structural model for 79-mer ss-DNA aptamer from the sequence. Using docking program and molecular dynamics (MD) simulation we predicted the binding pocket of ss-DNA aptamer. Our results show STR streptose ring is buried within the groove of DNA model and capped by non Watson-Crick bases. STR interacts with aptamer through forming stable hydrogen bonds. Our computational findings are in fair agreement with experimental results. With the atomic structural details, we gained new insight into the Apt-STR binding interaction that can help to further optimize aptamer efficiency in biosensing applications.Communicated by Ramaswamy H. Sarma.

Aptamers functionalized hybrid nanomaterials for algal toxins detection and decontamination in aquatic system: Current progress, opportunities, and challenges

Purification and detection of algal toxins is the most effective technique to ensure that people have clean and safe drinking water. To achieve these objectives, various state-of-the-art technologies were designed and fabricated to decontaminate and detect algal toxins in aquatic environments. Amongst these technologies, aptamer-functionalized hybrid nanomaterials conjugates have received significant consideration as a result of their several benefits over other methods, such as good controllable selectivity, low immunogenicity, and biocompatibility. Because of their excellent properties, aptamer-functionalized hybrid nanomaterials conjugates are one of several remarkable agents. Several isolated aptamer sequences for algal toxins are addressed in this review, as well as aptasensor and decontamination aptamer functionalized metal nanoparticle-derived hybrid nanocomposites applications. In addition, we present diverse aptamer-functionalized hybrid nanomaterial conjugates designs and their applications for sensing and decontamination.

Aptamers targeting amyloidogenic proteins and their emerging role in neurodegenerative diseases

Aptamers are oligonucleotides selected from large pools of random sequences based on their affinity for bioactive molecules and are used in similar ways to antibodies. Aptamers provide several advantages over antibodies, including their small size, facile, large-scale chemical synthesis, high stability, and low immunogenicity. Amyloidogenic proteins, whose aggregation is relevant to neurodegenerative diseases, such as Alzheimer's, Parkinson's, and prion diseases, are among the most challenging targets for aptamer development due to their conformational instability and heterogeneity, the same characteristics that make drug development against amyloidogenic proteins difficult. Recently, chemical tethering of aptagens (equivalent to antigens) and advances in high-throughput sequencing-based analysis have been used to overcome some of these challenges. In addition, internalization technologies using fusion to cellular receptors and extracellular vesicles have facilitated central nervous system (CNS) aptamer delivery. In view of the development of these techniques and resources, here we review antiamyloid aptamers, highlighting preclinical application to CNS therapy.

Aptamer-Based Antibacterial and Antiviral Therapy against Infectious Diseases

Nucleic acid aptamers are single-stranded DNA or RNA molecules selected in vitro that can bind to a broad range of targets with high affinity and specificity. As promising alternatives to conventional anti-infective agents, aptamers have gradually revealed their potential in the combat against infectious diseases. This article provides an overview on the state-of-art of aptamer-based antibacterial and antiviral therapeutic strategies. Diverse aptamers targeting pathogen-related components or whole pathogenic cells are summarized according to the species of microorganisms. These aptamers exhibited remarkable in vitro and/or in vivo inhibitory effect for pathogenic invasion, enzymatic activities, or viral replication, even for some highly drug-resistant strains and biofilms. Aptamer-mediated drug delivery and controlled drug release strategies are also included herein. Critical technical barriers of therapeutic aptamers are briefly discussed, followed by some future perspectives for their implementation into clinical utility.

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.

Structural Biology for the Molecular Insight between Aptamers and Target Proteins

Aptamers are promising therapeutic and diagnostic agents for various diseases due to their high affinity and specificity against target proteins. Structural determination in combination with multiple biochemical and biophysical methods could help to explore the interacting mechanism between aptamers and their targets. Regrettably, structural studies for aptamer–target interactions are still the bottleneck in this field, which are facing various difficulties. In this review, we first reviewed the methods for resolving structures of aptamer–protein complexes and for analyzing the interactions between aptamers and target proteins. We summarized the general features of the interacting nucleotides and residues involved in the interactions between aptamers and proteins. Challenges and perspectives in current methodologies were discussed. Approaches for determining the binding affinity between aptamers and target proteins as well as modification strategies for stabilizing the binding affinity of aptamers to target proteins were also reviewed. The review could help to understand how aptamers interact with their targets and how alterations such as chemical modifications in the structures affect the affinity and function of aptamers, which could facilitate the optimization and translation of aptamers-based theranostics.