Computational predictive approaches for interaction and structure of aptamers

High-throughput and computational techniques for aptamer design

INTRODUCTION

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

AREA COVERED

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

EXPERT OPINION

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

Breaking Barriers: Nucleic Acid Aptamers in Gastrointestinal (GI) Cancers Therapy

Conventional cancer therapies can have significant adverse effects as they are not targeted to cancer cells and may damage healthy cells. Single-stranded oligonucleotides assembled in a particular architecture, known as aptamers, enable them to attach selectively to target areas. Usually, they are created by Systematic Evolution of Ligand by Exponential enrichment (SELEX), and they go through a rigorous pharmacological revision process to change their therapeutic half-life, affinity, and specificity. They could thus offer a viable substitute for antibodies in the targeted cancer treatment market. Although aptamers can be a better choice in some situations, antibodies are still appropriate for many other uses. The technique of delivering aptamers is simple and reasonable, and the time needed to manufacture them is relatively brief. Aptamers do not require animals or an immune response to be produced, in contrast to antibodies. When used as a medication, aptamers can directly suppress tumor cells. As an alternative, they can be included in systems for targeted drug delivery that administer medications specifically to tumor cells while reducing toxicity to healthy cells. The most recent and cutting-edge methods for treating gastrointestinal (GI) tract cancer with aptamers will be covered in this review, with a focus on targeted therapy as a means of conquering resistance to traditional medicines.

New Insights into Aptamers: An Alternative to Antibodies in the Detection of Molecular Biomarkers

Aptamers are short oligonucleotides with single-stranded regions or peptides that recently started to transform the field of diagnostics. Their unique ability to bind to specific target molecules with high affinity and specificity is at least comparable to many traditional biorecognition elements. Aptamers are synthetically produced, with a compact size that facilitates deeper tissue penetration and improved cellular targeting. Furthermore, they can be easily modified with various labels or functional groups, tailoring them for diverse applications. Even more uniquely, aptamers can be regenerated after use, making aptasensors a cost-effective and sustainable alternative compared to disposable biosensors. This review delves into the inherent properties of aptamers that make them advantageous in established diagnostic methods. Furthermore, we will examine some of the limitations of aptamers, such as the need to engage in bioinformatics procedures in order to understand the relationship between the structure of the aptamer and its binding abilities. The objective is to develop a targeted design for specific targets. We analyse the process of aptamer selection and design by exploring the current landscape of aptamer utilisation across various industries. Here, we illuminate the potential advantages and applications of aptamers in a range of diagnostic techniques, with a specific focus on quartz crystal microbalance (QCM) aptasensors and their integration into the well-established ELISA method. This review serves as a comprehensive resource, summarising the latest knowledge and applications of aptamers, particularly highlighting their potential to revolutionise diagnostic approaches.

Molecular modeling and simulation studies of SELEX-derived high-affinity DNA aptamers to the Ebola virus nucleoprotein

Ebola viral disease (EVD) is a highly infectious and potentially fatal illness with a case fatality rate ranging from 25% to 90%. To effectively control its spread, there is a need for rapid, reliable and lowcost point-of-care (P OC) diagnostic tests. While various EVD diagnostic tests exist, few are P OC tests, and many are not cost-effective. The use of antibodies in these tests has limitations, prompting the exploration of aptamers as potential alternatives. Various proteins from the Ebola virus (EBOV) proteome, including EBOV nucleoprotein (NP), are considered viable targets for diagnostic assays. A previous study identified three aptamers (Apt1. Apt2 and Apt3) with high affinity for EBOV NP using systemic evolution of ligands by exponential enrichment (SELEX). This study aimed to employ in silico methods, such as Phyre2, RNAfold, RNAComposer, HADDOCK and GROMACS, to model the structures of EBOV NP and the aptamers, and to investigate their binding. The in silico analysis revealed successful binding of all the three aptamers to EBOV NP, with a suggested ranking of Apt1 > Apt2 > Apt3 based on binding affinity. Microscale thermophoresis (MST) analysis confirmed the binding, providing dissociation constants of 25 ± 2.84, 56 ± 2.76 and 140 ±3.69 nM for Apt1, Apt2 and Apt3, respectively. The study shows that the findings of the in silico analysis was in agreement with the MST analysis. Inclusion of these in silico approaches in diagnostic assay development can expedite the selection of candidate aptamers, potentially overcoming challenges associated with aptamer application in diagnostics.Communicated by Ramaswamy H. Sarma.

AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders

BACKGROUND

Aptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critical in vitro technique employed to identify aptamers that bind specifically to target proteins. While advanced SELEX-based methods such as Cell- and HT-SELEX are available, they often encounter issues such as extended time consumption and suboptimal accuracy. Several In silico aptamer discovery methods have been proposed to address these challenges. These methods are specifically designed to predict aptamer-protein interaction (API) using benchmark datasets. However, these methods often fail to consider the physicochemical interactions between aptamers and proteins within tertiary structures.

RESULTS

In this study, we propose AptaTrans, a pipeline for predicting API using deep learning techniques. AptaTrans uses transformer-based encoders to handle aptamer and protein sequences at the monomer level. Furthermore, pretrained encoders are utilized for the structural representation. After validation with a benchmark dataset, AptaTrans has been integrated into a comprehensive toolset. This pipeline synergistically combines with Apta-MCTS, a generative algorithm for recommending aptamer candidates.

CONCLUSION

The results show that AptaTrans outperforms existing models for predicting API, and the efficacy of the AptaTrans pipeline has been confirmed through various experimental tools. We expect AptaTrans will enhance the cost-effectiveness and efficiency of SELEX in drug discovery. The source code and benchmark dataset for AptaTrans are available at https://github.com/pnumlb/AptaTrans .

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.

Electrochemical microfluidic paper-based analytical devices for cancer biomarker detection: From 2D to 3D sensing systems

Microfluidic paper-based analytical devices (μPADs) offer a unique possibility for a cost-effective portable and rapid detection of a wide range of small molecules and macromolecules and even microorganisms. In this line, electrochemical detection methods are key techniques for the qualitative analysis of different types of ligands. The electrochemical sensing μPADs have been devised for the rapid, accurate, and quantitative detection of oncomarkers through two-/three-dimensional (2D/3D) approaches. The 2D μPADs were first developed and then transformed into 3D systems via folding and/or twisting of paper. The microfluidic channels and connections were created within the layers of paper. Based on the fabrication methods, 3D μPADs can be classified into origami and stacking devices. Various fabrication methods and materials have been used to create hydrophilic channels in μPADs, among which the wax printing technique is the most common method in fabricating μPADs. In this review, we discuss the fabrication and design strategies of μPADs, elaborate on their detection modes, and highlight their applications in affinity-based electrochemical μPADs methods for the detection of oncomarkers.

Aptamers as promising nanotheranostic tools in the COVID-19 pandemic era

The emergence of SARS-COV-2, the causative agent of new coronavirus disease (COVID-19) has become a pandemic threat. Early and precise detection of the virus is vital for effective diagnosis and treatment. Various testing kits and assays, including nucleic acid detection methods, antigen tests, serological tests, and enzyme-linked immunosorbent assay (ELISA), have been implemented or are being explored to detect the virus and/or characterize cellular and antibody responses to the infection. However, these approaches have inherent drawbacks such as nonspecificity, high cost, are characterized by long turnaround times for test results, and can be labor-intensive. Also, the circulating SARS-COV-2 variant of concerns, reduced antibody sensitivity and/or neutralization, and possible antibody-dependent enhancement (ADE) have warranted the search for alternative potent therapeutics. Aptamers, which are single-stranded oligonucleotides, generated artificially by SELEX (Evolution of Ligands by Exponential Enrichment) may offer the capacity to generate high-affinity neutralizers and/or bioprobes for monitoring relevant SARS-COV-2 and COVID-19 biomarkers. This article reviews and discusses the prospects of implementing aptamers for rapid point-of-care detection and treatment of SARS-COV-2. We highlight other SARS-COV-2 targets (N protein, spike protein stem-helix), SELEX augmented with competition assays and in silico technologies for rapid discovery and isolation of theranostic aptamers against COVID-19 and future pandemics. It further provides an overview on site-specific bioconjugation approaches, customizable molecular scaffolding strategies, and nanotechnology platforms to engineer these aptamers into ultrapotent blockers, multivalent therapeutics, and vaccines to boost both humoral and cellular immunity against the virus. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > Biosensing Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Therapeutic Approaches and Drug Discovery > Nanomedicine for Respiratory Disease.

A review of machine learning approaches for drug synergy prediction in cancer

Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein-protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug-target interactions.

Emerging roles of the aptasensors as superior bioaffinity sensors for monitoring shellfish toxins in marine food chain

Shellfish toxins are derived from harmful algae and are easily accumulated in environment and marine food through the food chain, exposing high risks on human health. Preliminary rapid screening is one of the most effective monitoring ways to reduce the potential risks; however, the traditional methods encounter with many limitations, such as complicated procedures, low sensitivity and specificity, and ethical problems. Alternatively, bioaffinity sensors are proposed and draw particular attention. Among them, the aptasensors are springing up and emerging as superior alternatives in recent years, exhibiting high practicability to analyze shellfish toxins in real samples in the marine food chain. Herein, the latest research progresses of aptasensors towards shellfish toxins in the marine food chain in the past five years was reviewed for the first time, in terms of the aptamers applied in these aptasensors, construction principles, signal transduction techniques, response types, individual performance properties, practical applications, and advantages/disadvantages of these aptasensors. Synchronously, critical discussions were given and future perspectives were prospected. We hope this review can serve as a powerful reference to promote further development and application of aptasensors to monitor shellfish toxins, as well as other analytes with similar demands.

AptaNet as a deep learning approach for aptamer-protein interaction prediction

Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet-a new deep neural network-to predict the aptamer-protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer-protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .

A computational model for GPCR-ligand interaction prediction

G protein-coupled receptors (GPCRs) play an essential role in critical human activities, and they are considered targets for a wide range of drugs. Accordingly, based on these crucial roles, GPCRs are mainly considered and focused on pharmaceutical research. Hence, there are a lot of investigations on GPCRs. Experimental laboratory research is very costly in terms of time and expenses, and accordingly, there is a marked tendency to use computational methods as an alternative method. In this study, a prediction model based on machine learning (ML) approaches was developed to predict GPCRs and ligand interactions. Decision tree (DT), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), and Naive Bayes (NB) were the algorithms that were investigated in this study. After several optimization steps, receiver operating characteristic (ROC) for DT, RF, MLP, SVM, and NB algorithm were 95.2, 98.1, 96.3, 95.5, and 97.3, respectively. Accordingly final model was made base on the RF algorithm. The current computational study compared with others focused on specific and important types of proteins (GPCR) interaction and employed/examined different types of sequence-based features to obtain more accurate results. Drug science researchers could widely use the developed prediction model in this study. The developed predictor was applied over 16,132 GPCR-ligand pairs and about 6778 potential interactions predicted.

Information technology in emergency management of COVID-19 outbreak

Emergency management of the emerging infectious disease outbreak is critical for public health threats. Currently, control of the COVID-19 outbreak is an international concern and has become a crucial challenge in many countries. This article reviews significant information technologyIT) applications in emergency management of COVID-19 by considering the prevention/mitigation, preparedness, response, and recovery phases of the crisis. This review was conducted using MEDLINE PubMed), Embase, IEEE, and Google Scholar. Expert opinions were collected to show existence gaps, useful technologies for each phase of emergency management, and future direction. Results indicated that various IT-based systems such as surveillance systems, artificial intelligence, computational methods, Internet of things, remote sensing sensor, online service, and GIS geographic information system) could have different outbreak management applications, especially in response phases. Information technology was applied in several aspects, such as increasing the accuracy of diagnosis, early detection, ensuring healthcare providers' safety, decreasing workload, saving time and cost, and drug discovery. We categorized these applications into four core topics, including diagnosis and prediction, treatment, protection, and management goals, which were confirmed by five experts. Without applying IT, the control and management of the crisis could be difficult on a large scale. For reducing and improving the hazard effect of disaster situations, the role of IT is inevitable. In addition to the response phase, communities should be considered to use IT capabilities in prevention, preparedness, and recovery phases. It is expected that IT will have an influential role in the recovery phase of COVID-19. Providing IT infrastructure and financial support by the governments should be more considered in facilitating IT capabilities.

Methods and Applications of In Silico Aptamer Design and Modeling

Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.