Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach

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.

Development of the 12-Base Short Dimeric Myogenetic Oligodeoxynucleotide That Induces Myogenic Differentiation

A myogenetic oligodeoxynucleotide (myoDN), iSN04 (5'-AGA TTA GGG TGA GGG TGA-3'), is a single-stranded 18-base telomeric DNA that serves as an anti-nucleolin aptamer and induces myogenic differentiation, which is expected to be a nucleic acid drug for the prevention of disease-associated muscle wasting. To improve the drug efficacy and synthesis cost of myoDN, shortening the sequence while maintaining its structure-based function is a major challenge. Here, we report the novel 12-base non-telomeric myoDN, iMyo01 (5'-TTG GGT GGG GAA-3'), which has comparable myogenic activity to iSN04. iMyo01 as well as iSN04 promoted myotube formation of primary-cultured human myoblasts with upregulation of myogenic gene expression. Both iMyo01 and iSN04 interacted with nucleolin, but iMyo01 did not bind to berberine, the isoquinoline alkaloid that stabilizes iSN04. Nuclear magnetic resonance revealed that iMyo01 forms a G-quadruplex structure despite its short sequence. Native polyacrylamide gel electrophoresis and a computational molecular dynamics simulation indicated that iMyo01 forms a homodimer to generate a G-quadruplex. These results provide new insights into the aptamer truncation technology that preserves aptamer conformation and bioactivity for the development of efficient nucleic acid drugs.

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 .

Aptamer-Protein Interactions: From Regulation to Biomolecular Detection

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

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

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.

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.

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.

Myogenetic Oligodeoxynucleotides as Anti-Nucleolin Aptamers Inhibit the Growth of Embryonal Rhabdomyosarcoma Cells

Embryonal rhabdomyosarcoma (ERMS) is the muscle-derived tumor retaining myogenic ability. iSN04 and AS1411, which are myogenetic oligodeoxynucleotides (myoDNs) serving as anti-nucleolin aptamers, have been reported to inhibit the proliferation and induce the differentiation of myoblasts. The present study investigated the effects of iSN04 and AS1411 in vitro on the growth of multiple patient-derived ERMS cell lines, ERMS1, KYM1, and RD. RT-PCR and immunostaining revealed that nucleolin was abundantly expressed and localized in nucleoplasm and nucleoli in all ERMS cell lines, similar to myoblasts. Both iSN04 and AS1411 at final concentrations of 10-30 μM significantly decreased the number of all ERMS cells; however, their optimal conditions were different among the cell lines. In all ERMS cell lines, iSN04 at a final concentration of 10 μM markedly reduced the ratio of EdU+ cells, indicating the inhibition of cell proliferation. Quantitative RT-PCR or immunostaining of phosphorylated histone H3 and myosin heavy chain demonstrated that iSN04 suppressed the cell cycle and partially promoted myogenesis but did not induce apoptosis in ERMS cells. Finally, both iSN04 and AS1411 at final concentrations of 10-30 μM disrupted the formation and outgrowth of RD tumorspheres in three-dimensional culture mimicking in vivo tumorigenesis. In conclusion, ERMS cells expressed nucleolin, and their growth was inhibited by the anti-nucleolin aptamers, iSN04 and AS1411, which modulates several cell cycle-related and myogenic gene expression. The present study provides evidence that anti-nucleolin aptamers can be used as nucleic acid drugs for chemotherapy against ERMS.

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.

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

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