In silicopost-SELEX screening and experimental characterizations for acquisition of high affinity DNA aptamers against carcinoembryonic antigen

A novel aptasensor platform for the detection of carcinoembryonic antigen using quartz crystal microbalance

In this study, a quartz crystal microbalance (QCM) aptasensor for carcinoembryonic antigen (CEA), a well-known biomarker for various cancer types, was reported, utilizing two different aptamers. To achieve this, a nanofilm of 4-mercaptophenyl was electrochemically attached to gold-coated QCM crystal surfaces via the reduction of 4-mercaptobenzenediazonium salt (4 MB-DAT) using cyclic voltammetry. Subsequently, gold nanoparticles (AuNP) were affixed to this structure, and then aptamers (antiCEA1 and antiCEA2) modified with SH-functional ends bound to AuNPs completed the modification. The analytical performance of the CEA sensor was evaluated through simultaneous QCM measurements employing CEA solutions ranging from 0.1 ng/mL to 25 ng/mL. The detection limit (LOD) for CEA was determined to be 102 pg/mL for antiCEA1 and 108 pg/mL for antiCEA2 aptamers. Interday and intraday precision and accuracy tests yielded maximum results of 4.3 and + 3.8, respectively, for both aptasensors, as measured by relative standard deviation (RSD%) and relative error (RE%). The kinetic data of the aptasensors resulted in affinity values (KD) of 0.43 ± 0.14 nM for antiCEA1 and 0.75 ± 0.42 nM for antiCEA2. These values were lower than the reported values of 3.9 nM and 37.8 nM for both aptamers, respectively. The selectivity of the aptasensor was evaluated by measuring the signal changes caused by alpha-fetoprotein (AFP), cancer antigen (CA-125), and vascular endothelial growth factor (VEGF-165) individually and together at a concentration of 500 ng/mL, resulting in a maximum 4.1 % change, which was comparable to precision and accuracy values reported in the literature. After confirming the selectivity of the aptamers, recovery experiments were conducted using spiked commercial serum samples to simulate real samples, and the lowest recovery value obtained was 95.4 %. It was determined that two different aptasensors could be successfully used for the QCM-based detection of CEA in this study.

Surface plasmon resonance aptasensing and computational analysis of Staphylococcus aureus IsdA surface protein

Staphylococcus aureus (S. aureus), a common foodborne pathogen, poses significant public health challenges due to its association with various infectious diseases. A key player in its pathogenicity, which is the IsdA protein, is an essential virulence factor in S. aureus infections. In this work, we present an integrated in-silico and experimental approach using MD simulations and surface plasmon resonance (SPR)-based aptasensing measurements to investigate S. aureus biorecognition via IsdA surface protein binding. SPR, a powerful real-time and label-free technique, was utilized to characterize interaction dynamics between the aptamer and IsdA protein, and MD simulations was used to characterize the stable and dynamic binding regions. By characterizing and optimizing pivotal parameters such as aptamer concentration and buffer conditions, we determined the aptamer's binding performance. Under optimal conditions of pH 7.4 and 150 mM NaCl concentration, the kinetic parameters were determined; ka = 3.789 × 104/Ms, kd = 1.798 × 103/s, and KD = 4.745 × 10-8 M. The simulations revealed regions of interest in the IsdA-aptamer complex. Region I, which includes interactions between amino acid residues H106 and R107 and nucleotide residues 9G, 10U, 11G and 12U of the aptamer, had the strongest interaction, based on ΔG and B-factor values, and hence contributed the most to the stability of the interaction. Region II, which covers residue 37A reflects the dynamic nature of the interaction due to frequent contacts. The approach presents a rigorous characterization of aptamer-IsdA binding behavior, supporting the potential application of the IsdA-binding aptamer system for S. aureus biosensing.

An efficient and universal In silico screening strategy for acquisition of high-affinity Aptamer and its application in analytical utility

Numerous aptamers against various targets have been identified through the technology of systematic evolution of ligands by exponential enrichment (SELEX), but the affinity of these aptamers are often insufficient due to the limitations of SELEX. Therefore, a more rational in silico screening strategy (ISS) was developed for efficient screening of high affinity aptamers, which took shape complementarity and thermodynamic stability into consideration. Neuron specific enolase (NSE), a tumor marker, was selected as the target molecule. In the screening process, three aptamer candidates with good shape complementarity, lower ΔG values, and higher ZDOCK scores were produced. The dissociation constant (Kd) of these candidates to NSE was determined to be 10.13 nM, 14.82 nM, and 2.76 nM, respectively. Each of them exhibited higher affinity to NSE than the parent aptamer (Kd = 23.83 nM). Finally, an antibody-free fluorescence aptasensor assay, based on the aptamer with the highest affinity, P-5C8G, was conducted, resulting in a limit of detection (LOD) value of 1.8 nM, which was much lower than the parental aptamer (P, LOD = 12.6 nM). The proposed ISS approach provided an efficient and universal strategy to improve the aptamer to have a high affinity and good analytical utility.

Application and evaluation of molecular docking for aptamer and small molecular interaction - A case study with tetracycline antibiotics

Molecular docking (MD) analysis is currently the most commonly used theoretical simulation method to investigate the interaction of aptamers (receptors) and small molecules (ligands) and understand the recognition mechanism between them at a molecular level. Using the specific aptamers of tetracycline antibiotics (tetracycline (TET), oxytetracycline (OTC), doxycycline (DOC)) as the docking models, three steady-state aptamers of tertiary structures (SATS) were established for each aptamer with the UNAFold and RNAComposer tools. The binding free energy (BFE), docking score (DS), and binding site (base) of the specific ligands (TET, OTC, and DOC) with their respective SATS were obtained by molecular docking. The results revealed one or more binding sites in the established SATS of the aptamers. The BFE and DS of different binding sites of one specific SATS varied significantly. The results also revealed that the site with the highest BFE represented the most dominant binding site, even if it was not the SATS with minimum energy. The BFE values could also be used to evaluate the affinity and specificity of the aptamer to its target. For the first time, this study proposes a method for MD analysis of the aptamer and its target based on different SATS, clarification of the binding mode, and prediction of the binding sites (bases). This study provides a theoretical basis for tailoring; structural optimization; and base modification of aptamers; identifying aptamers with high affinity and specificity.

Recent progress of SELEX methods for screening nucleic acid aptamers

Nucleic acid aptamers are oligonucleotide sequences screened by an in vitro methodology called Systematic Evolution of Ligands by Exponential Enrichment (SELEX). Known as "chemical antibodies", aptamers can achieve specific recognition towards the targets through conformational changes with high affinity, and possess multiple attractive features including, but not limited to, easy and inexpensive to prepare by chemical synthesis, relatively stable and low batch-to-batch variability, easy modification and signal amplification, and low immunogenicity. Now, aptamers are attracting researchers' attentions from more than 25 disciplines, and have showed great potential for application and economic benefits in disease diagnosis, environmental detection, food security, drug delivery and discovery. Although some aptamers exist naturally as the ligand-binding elements of riboswitches, SELEX is a recognized method for aptamers screening. After thirty-two years of development, a series of SELEX methods have been investigated and developed, as well as have shown unique advantages to improve sequence performances or to explore screening mechanisms. This review would mainly focus on the novel or improved SELEX methods that are available in the past five years. Firstly, we present a clear overview of the aptamer's history, features, and SELEX development. Then, we highlight the specific examples to emphasize the recent progress of SELEX methods in terms of carrier materials, technical improvements, real sample-improved screening, post-SELEX and other methods, as well as their respects of screening strategies, implementation features, screening parameters. Finally, we discuss the remaining challenges that have the potential to hinder the success of SELEX and aptamers in practical applications, and provide the suggestions and future directions for developing more convenient, efficient, and stable SELEX methods in the future.

Simultaneous subset tracing and miRNA profiling of tumor-derived exosomes via dual-surface-protein orthogonal barcoding

The clinical potential of miRNA-based liquid biopsy has been largely limited by the heterogeneous sources in plasma and tedious assay processes. Here, we develop a precise and robust one-pot assay called dual-surface-protein-guided orthogonal recognition of tumor-derived exosomes and in situ profiling of microRNAs (SORTER) to detect tumor-derived exosomal miRNAs and enhance the diagnostic accuracy of prostate cancer (PCa). The SORTER uses two allosteric aptamers against exosomal marker CD63 and tumor marker EpCAM to create an orthogonal labeling barcode and achieve selective sorting of tumor-specific exosome subtypes. Furthermore, the labeled barcode on tumor-derived exosomes initiated targeted membrane fusion with liposome probes to import miRNA detection reagents, enabling in situ sensitive profiling of tumor-derived exosomal miRNAs. With a signature of six miRNAs, SORTER differentiated PCa and benign prostatic hyperplasia with an accuracy of 100%. Notably, the diagnostic accuracy reached 90.6% in the classification of metastatic and nonmetastatic PCa. We envision that the SORTER will promote the clinical adaptability of miRNA-based liquid biopsy.

Bioaffinity Nanoprobes for Foodborne Pathogen Sensing

Bioaffinity nanoprobes are a type of biosensor that utilize the specific binding properties of biological molecules, such as antibodies, enzymes, and nucleic acids, for the detection of foodborne pathogens. These probes serve as nanosensors and can provide highly specific and sensitive detection of pathogens in food samples, making them an attractive option for food safety testing. The advantages of bioaffinity nanoprobes include their ability to detect low levels of pathogens, rapid analysis time, and cost-effectiveness. However, limitations include the need for specialized equipment and the potential for cross-reactivity with other biological molecules. Current research efforts focus on optimizing the performance of bioaffinity probes and expanding their application in the food industry. This article discusses relevant analytical methods, such as surface plasmon resonance (SPR) analysis, Fluorescence Resonance Energy Transfer (FRET) measurements, circular dichroism, and flow cytometry, that are used to evaluate the efficacy of bioaffinity nanoprobes. Additionally, it discusses advances in the development and application of biosensors in monitoring foodborne pathogens.

Analysis of aptamer-target binding and molecular mechanisms by thermofluorimetric analysis and molecular dynamics simulation

Introduction: Aptamers are valuable for bioassays, but aptamer-target binding is susceptible to reaction conditions. In this study, we combined thermofluorimetric analysis (TFA) and molecular dynamics (MD) simulations to optimize aptamer-target binding, explore underlying mechanisms and select preferred aptamer. Methods: Alpha-fetoprotein (AFP) aptamer AP273 (as the model) was incubated with AFP under various experimental conditions, and melting curves were measured in a real-time PCR system to select the optimal binding conditions. The intermolecular interactions of AP273-AFP were analysed by MD simulations with these conditions to reveal the underlying mechanisms. A comparative study between AP273 and control aptamer AP-L3-4 was performed to validate the value of combined TFA and MD simulation in selecting preferred aptamers. Results: The optimal aptamer concentration and buffer system were easily determined from the dF/dT peak characteristics and the melting temperature (Tm) values on the melting curves of related TFA experiments, respectively. A high Tm value was found in TFA experiments performed in buffer systems with low metal ion strength. The molecular docking and MD simulation analyses revealed the underlying mechanisms of the TFA results, i.e., the binding force and stability of AP273 to AFP were affected by the number of binding sites, frequency and distance of hydrogen bonds, and binding free energies; these factors varied in different buffer and metal ion conditions. The comparative study showed that AP273 was superior to the homologous aptamer AP-L3-4. Conclusion: Combining TFA and MD simulation is efficient for optimizing the reaction conditions, exploring underlying mechanisms, and selecting aptamers in aptamer-target bioassays.

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.

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.

Computationally Designed Anti-LuxP DNA Aptamer Suppressed Flagellar Assembly- and Quorum Sensing-Related Gene Expression in Vibrio parahaemolyticus

(1) Background: Quorum sensing (QS) is the chemical communication between bacteria that sense chemical signals in the bacterial population to control phenotypic changes through the regulation of gene expression. The inhibition of QS has various potential applications, particularly in the prevention of bacterial infection. QS can be inhibited by targeting the LuxP, a periplasmic receptor protein that is involved in the sensing of the QS signaling molecule known as the autoinducer 2 (AI-2). The sensing of AI-2 by LuxP transduces the chemical information through the inner membrane sensor kinase LuxQ protein and activates the QS cascade. (2) Methods: An in silico approach was applied to design DNA aptamers against LuxP in this study. A method combining molecular docking and molecular dynamics simulations was used to select the oligonucleotides that bind to LuxP, which were then further characterized using isothermal titration calorimetry. Subsequently, the bioactivity of the selected aptamer was examined through comparative transcriptome analysis. (3) Results: Two aptamer candidates were identified from the ITC, which have the lowest dissociation constants (Kd) of 0.2 and 0.5 micromolar. The aptamer with the lowest Kd demonstrated QS suppression and down-regulated the flagellar-assembly-related gene expression. (4) Conclusions: This study developed an in silico approach to design an aptamer that possesses anti-QS properties.

Evolution of Interferon-Gamma Aptamer with Good Affinity and Analytical Utility by a Rational In Silico Base Mutagenesis Post-SELEX Strategy

The Systematic Evolution of Ligands by EXponential enrichment (SELEX) is conventionally an effective method to identify aptamers, which are oligonucleotide sequences with desired properties to recognize targets specifically and sensitively. However, there are some inherent limitations, e.g., the loss of potential high-affinity sequences during biased iterative PCR enrichment processes and the limited structural diversity of the initial library, which seriously restrict their real-world applications. To overcome these limitations, the in silico base mutagenesis post-SELEX strategy based on the low Gibbs free energy (ΔG) and genetic algorithm was developed for the optimization of the interferon-gamma aptamer (B1-4). In the process of evolution, new sequences were created and the aptamer candidates with low ΔG values and advanced structures were produced. After five rounds of selection, systematic studies revealed that the affinity of the newly developed evolutionary aptamer (M5-5) was roughly 10-fold higher than that of the parent aptamer (B1-4), and an aptasensor detection system with a limit-of-detection (LOD) value of 3.17 nM was established based on the evolutionary aptamer. The proposed approach provided an efficient strategy to improve the aptamer with low energy and a high binding ability, and the good analytical utility thereof.

Recent advances in the selection of cancer-specific aptamers for the development of biosensors

An early diagnosis has the potential to greatly decrease cancer mortality. For that purpose, specific cancer biomarkers have been molecularly targeted by aptamer sequences to enable an accurate and rapid detection. Aptamer-based biosensors for cancer diagnostics are a promising alternative to those using antibodies, due to their high affinity and specificity to the target molecules and advantageous production. Synthetic nucleic acid aptamers are generated by in vitro Systematic Evolution of Ligands by Exponential enrichment (SELEX) methodologies that have been improved over the years to enhance the efficacy and to shorten the selection process. Aptamers have been successfully applied in electrochemical, optical, photoelectrochemical and piezoelectrical-based detection strategies. These aptasensors comprise a sensitive, accurate and inexpensive option for cancer detection being used as point-of-care devices. This review highlights the recent advances in cancer biomarkers, achievements and optimizations made in aptamer selection, as well as the different aptasensors developed for the detection of several cancer biomarkers.

A DNAzyme-catalyzed label-free aptasensor based on multifunctional dendrimer-like DNA assembly for sensitive detection of carcinoembryonic antigen

Carcinoembryonic antigen (CEA) is an important malign tumor marker. In this study, a simple, label-free and antibody-free aptasensor was fabricated based on a multifunctional dendrimer-like DNA nanoassembly. The DNA nanoassembly was embedded with multiple G-quadruplex DNAzyme motifs and a hanging CEA aptamer motif. It was prepared from short DNA sequences by autonomous-assembly. The aptasensor was prepared simply by self-assembly of a capture DNA (cpDNA) on a gold electrode, followed by hybridization with a CEA aptamer (AptGAC-P). CEA as a model target was detected through competitive binding of CEA with AptGAC-P, exposing cpDNA to bind with the DNA nanoassembly. The detection process only contains 2 incubation steps. The high load of G-quadruplex DNAzyme motifs and their catalytic activity resulted in an amplified and label-free differential pulse voltammetry (DPV) electrochemical signal. The peak current correlated linearly with the CEA concentration, with a linear range of 2-45 ng mL^-1, and an LOD value of 0.24 ng mL^-1. The aptasensor showed high specificity and reproducibility, and retained 96.5% of detection signal intensities after 31 days of storage. The recovery rates for spiked CEA in human serum were within 100 ± 5%, and the coincidence rates for clinical human serum samples with ELISA kits were 80.7-111%. Conceivably, possessing simplicity, sensitivity, reproducibility, storage stability, and accuracy, the aptasensor should be a very prominent and applicable tool for clinical CEA detection and cancer diagnosis, and is promisingly applicable as a platform for detecting other targets of interests.

Improving aptamer performance with nucleic acid mimics: de novo and post-SELEX approaches

Aptamers are structural single-stranded oligonucleotides generated in vitro to bind to a specific target molecule. Aptamers' versatility can be enhanced with nucleic acid mimics (NAMs) during or after a selection process, also known as systematic evolution of ligands by exponential enrichment (SELEX). We address advantages and limitations of the technologies used to generate NAM aptamers, especially the applicability of existing engineered polymerases to replicate NAMs and methodologies to improve aptamers after SELEX. We also discuss the limitations of existing methods for sequencing NAM sequences and bioinformatic tools to predict NAM aptamer structures. As a conclusion, we suggest that NAM aptamers might successfully compete with molecular tools based on proteins such as antibodies for future application.

Artificial Intelligence in Aptamer-Target Binding Prediction

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

Aptasensor for Quantification of Leptin Through PCR Amplification of Short DNA-Aptamers

Protein quantification is traditionally performed through enzyme-linked immunosorbent assay (ELISA), which involves long preparation times. To overcome this, new approaches use aptamers as an alternative to antibodies. In this paper, we present a new approach to quantify proteins with short DNA aptamers through polymerase chain reaction (PCR) resulting in shorter protocol times with comparatively improved limits of detection. The proposed method includes a novel way to quantify both the target protein and the corresponding short DNA-aptamers simultaneously, which also allows us to fully characterize the performance of aptasensors. Human leptin is used as a target protein to validate this technique, because it is considered an important biomarker for obesity-related studies. In our experiments, we achieved the lowest limit of detection of 100 pg/mL within less than 2 h, a limit affected by the dissociation constant of the leptin aptamer, which could be improved by selecting a more specific aptamer. Because of the simple and inexpensive approach, this technique can be employed for Lab-On-Chip implementations and for rapid "on-site" quantification of proteins.

In silico approach for Post-SELEX DNA aptamers: A mini-review

Aptamers are short oligonucleotides that possess high specificity and affinity against their target. Generated via Systematic Evolution of Ligands by Exponential Enrichment, (SELEX) in vitro, they were screened and enriched. This review covering the study utilizing bioinformatics tools to analyze primary sequence, secondary and tertiary structure prediction, as well as docking simulation for various aptamers and their ligand interaction. Literature was pooled from Web of Science (WoS) and Scopus databases until December 18, 2020 using specific search string related to DNA aptamers, in silico, structure prediction, and docking simulation. Out of 330 published articles, 38 articles were assessed in the analysis based on the predefined inclusion and exclusion criteria. It was found that Mfold and RNA Composer web server is the most popular tool in secondary and tertiary structure prediction of DNA aptamers, respectively. Meanwhile, in docking simulation, ZDOCK and AutoDock are preferred to analyze binding interaction in the aptamer-ligand complex. This review reports a brief framework of recent developments of in silico approaches that provide predictive structural information of ssDNA aptamer.

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.

Aptamers Which Target Proteins: What Proteotronics Suggests to Pharmaceutics

Aptamers represent a challenging field of research, relevant for diagnosis in macular degeneration, cancer, thrombosis and many inflammatory diseases, and promising in drug discovery and development. Their selection is currently performed by a stable in vitro technology, namely, SELEX. Furthermore, computationalstatistical tools have been developed to complement the SELEX selection; they work both in the preliminary stage of selection, by designing high affinity aptamers for the assigned target, and also in the final stage, analyzing the features of the best performers to implement the selection technique further. A massive use of the in silico approach is, at present, only restricted by the limited knowledge of the specific aptamer-target topology. Actually, only about fifty X-ray structures of aptamer-protein complexes have been experimentally resolved, highlighting how this knowledge has to be improved. The structure of biomolecules like aptamer-protein complexes can be represented by networks, from which several parameters can be extracted. This work briefly reviews the literature, discussing if and how general network parameters in the framework of Proteotronics and graph theory (such as electrical features, link number, free energy change, and assortativity), are important in characterizing the complexes, anticipating some features of the biomolecules. To better explain this topic, a case-study is proposed, constituted by a set of anti-angiopoietin (Ang2) aptamers, whose performances are known from the experiments, and for which two different types of conformers were predicted. A topological indicator is proposed, named Möbius (M), which combines local and global information, and seems able to discriminate between the two possible types of conformers, so that it can be considered as a useful complement to the in vitro screening for pharmaceutical aims.

In silico molecular docking in DNA aptamer development

Aptamers are single-stranded DNA or RNA oligonucleotides generated by SELEX that exhibit binding affinity and specificity against a wide variety of target molecules. Compared to RNA aptamers, DNA aptamers are much more stable and therefore are widely adopted in a number of applications especially in diagnostics. The tediousness and rigor associated with certain steps of the SELEX intensify the efforts to adopt in silico molecular docking approaches together with in vitro SELEX procedures in developing DNA aptamers. Inspired by these endeavors, we carry out an overview of the in silico molecular docking approaches in DNA aptamer generation, by detailing the stepwise procedures as well as shedding some light on the various softwares used. The in silico maturation strategy and the limitations of the in silico approaches are also underscored.

Polydopamine nanosphere@silver nanoclusters for fluorescence detection of multiplex tumor markers

There is an increasing demand to establish a convenient and stable analytical methodology for screening multiplex tumor markers in early diagnosis of cancers. In this work, an innovative fluorescence method is proposed for simultaneous detection of alpha-fetoprotein (AFP) and carcinoembryonic antigen (CEA). A polydopamine nanosphere@silver nanocluster (PDAN@AgNC) system is introduced for fluorescence quenching and recovery. The AgNCs with different emissions are synthesized using different DNA templates, which also contain aptamer sequences towards AFP and CEA, respectively. These single-stranded DNA sequences could be adsorbed on the surface of the PDAN through π-π stacking, which results in the quenching of AgNCs. However, in the presence of the corresponding tumor marker, the aptamer/target complex forms which releases AgNCs from the surface of the PDAN and the recovered fluorescence could be used to indicate the concentration of the tumor marker. This PDAN@AgNC system has been validated preliminarily to screen human serum samples with excellent results. Taking advantages of simplicity, enzyme/antibody-free nature, low cost and convenient operation, the proposed biosensor has great potential to be used in biomedical research studies and clinical diagnosis.