1. INTRODUCTION
Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemia resulting from impaired insulin secretion, action, or both [1]. The disease often progresses asymptomatically until significant complications compromise vital organ function, underscoring the importance of early diagnosis [2]. However, many healthcare institutions face barriers such as limited resources and inadequate facilities [3]. Common diagnostic methods, including the oral glucose tolerance test and fasting blood glucose test, assess short-term plasma glucose levels but are susceptible to fluctuations from food intake, stress, or illness [4,5]. In contrast, glycated hemoglobin (HbA1c), formed by the binding of glucose to the N-terminal of the hemoglobin beta chain [6], reflects average blood glucose levels over the preceding 3 months, providing a stable and accurate measure of long-term glycemic control [7].
Despite its clinical advantages, conventional HbA1c detection methods often require centralized laboratory equipment, trained personnel, and complex procedures. These requirements can hinder accessibility and contribute to delayed diagnostic results, especially in resource-limited settings [8]. To address these challenges, biosensors have been explored as alternative diagnostic tools to simplify HbA1c detection. Their compatibility with miniaturized, portable formats makes them ideal for point-of-care use [9,10], particularly in underserved regions with limited laboratory infrastructure. By enabling rapid and user-friendly testing, these platforms have the capacity to reduce patient wait times, improve diagnostic coverage in remote areas, and alleviate pressures on centralized healthcare systems through earlier intervention and on-site disease monitoring [11].
These devices utilize bioreceptors, such as enzymes [12], antibodies [13], or aptamers [14], to specifically bind target molecules and generate measurable signals. Aptasensors, which incorporate aptamers, have been widely applied due to their specificity and stability [9,14,15]. Aptamers are short, single-stranded synthetic nucleic acids capable of folding into well-defined three-dimensional structures that enable selective binding to specific targets [16]. Compared to antibodies, aptamers offer superior thermal stability, lower production costs, and easier chemical modification, making them well-suited for integration into a point-of-care diagnostic platform [17–19]. Numerous studies have demonstrated the successful use of aptamers in detecting a range of disease biomarkers, such as thrombin [20], prostate-specific antigen (PSA) [21], C-reactive protein [22], and adenosine triphosphate (ATP) [23].
Specific to HbA1c detection, several studies have reported the development of HbA1c-targeting aptamers. Eissa and Zourob [24], Li et al. [25], and Devi et al. [26] demonstrated the feasibility of aptamer-based electrochemical, electrochemiluminescence, and colorimetric detection strategies for HbA1c, highlighting the applicability of aptamer technology in glycated hemoglobin diagnostics. Among the reported sequences, the aptamer developed by Duanghathaipornsuk et al. [27] was selected for this study. This aptamer was generated through an SELEX process incorporating counter-selection against nonglycated hemoglobin to improve target specificity. The resulting sequence (5′-CCA GGA TTA GTA CGA GCA GGA AAA GGA AAC TAT GAT CTT TAA GGT ACA TT-3′) features stem-loop structures and GC-rich domains, including CGA , GGA, and AAA motifs, that contribute to selective molecular recognition of HbA1c. The aptamer demonstrated strong binding affinity, with a dissociation constant (KD) of 56.1 ± 2.2 nM, as validated using a surface plasmon resonance system. This aptamer has also been previously evaluated in an electrochemical aptasensor platform [28], demonstrating effective HbA1c detection with good selectivity over hemoglobin and glucose. However, the molecular-level mechanisms underlying its selectivity and binding affinity remain unexplored.
In silico approaches offer substantial advantages in aptamer development by enabling rapid, cost-efficient, and high-throughput screening of aptamer-target interactions before experimental validation. These methods circumvent the laborious and time-consuming processes of aptamer synthesis, target purification, and iterative empirical testing. Computational techniques allow early identification of aptamer candidates with favorable binding characteristics under controlled, reproducible virtual conditions, thereby reducing reliance on exhaustive laboratory work [29,30]. In silico workflows also facilitate mechanistic understanding of molecular recognition, enabling precise evaluation of binding sites, structural flexibility, and interaction dynamics [31,32]. Such predictive capability enhances the design and rational selection of aptamers, ultimately streamlining the aptasensor development pipeline while minimizing experimental burden.
Bioinformatics is widely used in aptamer design to model molecular interactions, predict aptamer structures, and evaluate binding affinities [33,34]. This approach facilitates the identification of key residues and enhances binding selectivity, particularly for biomarkers like HbA1c in diabetes monitoring, to assist in improving diagnostic strategies. Advanced computational tools, including molecular dynamics (MD) simulations and docking studies, facilitate the exploration of molecular interactions [35,36]. Docking simulations predict aptamer orientation upon binding to its target, visualize binding interactions, and estimate binding affinities. MD simulations are used to validate the stability and dynamics of the aptamer-ligand complex over time, providing insights into molecular-level interactions, conformational changes, and binding energy fluctuations [37–40]. These simulations reveal how intermolecular interactions and aptamer characteristics are influenced by the target and experimental conditions.
By leveraging these computational methods, this study aims to optimize aptasensor designs for precise HbA1c detection by understanding aptamer behavior, thus enhancing the reliability of diabetes monitoring technologies. The in silico approach is employed to evaluate the stability, molecular interactions between the aptamer and HbA1c, and its selectivity against interfering molecules.
2. MATERIAL
The materials for this study are three-dimensional models of the aptamer (sequence: [5′-CCA GGA TTA GTA CGA GCA GGA AAA GGA AAC TAT GAT CTT TAA GGT ACA TT-3′] [27]), glycated hemoglobin (HbA1c), hemoglobin (Hb), glycated human serum albumin (GHSA), human serum albumin (HSA), and glucose. The equipment used in this study includes a computer with OS Ubuntu 20.04.5 LTS, an Intel Xeon® CPU 5E-2689 0 @2.60 GHz (16 cores), an NVIDIA Corporation 3080 GPU, and 2.3 TB of storage. Software tools utilized include 3dRNA/DNA v2.0 (http://biophy.hust.edu.cn/new/3dRNA), Amber20, Biovia Discovery Studio Visualizer v21.1.0.20298, HDOCK v1.1 (http://hdock.phys.hust.edu.cn/), mFold (http://www.unafold.org/), Modeller10.4, PROCHECK v6.1 (https://servicesn.mbi.ucla.edu/PROCHECK/), Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) (www.rcsb.org/), PubChem (https://pubchem.ncbi.nlm.nih.gov/), and Visual Molecular Dynamics (VMD) 1.9.3.
3. METHODS
3.1. Preparation of aptamer structures
The prediction of the HbA1c-binding aptamer structure was carried out in a sequential computational process. Secondary structure prediction was first performed using the mFold web server at 298?K, generating base-pair interactions in dot bracket notation [41]. The resulting structure was then submitted to the 3dRNA/DNA v2.0 web server for three-dimensional modeling [42]. This server utilizes a fragment assembly approach based on the smallest secondary elements (SSEs), such as stems, hairpin loops, internal loops, and bulges, and automatically selects between assembly and optimization strategies depending on the quality of template matching. The final three-dimensional structures were further refined through energy minimization using AMBER with the ff14SB force field in a generalized Born solvation model to remove steric clashes and improve structural stability. All other parameters were kept at default settings.
3.2. Preparation of protein structures
Structural data for HbA1c, Hb, GHSA, and HSA were acquired from the RCSB PDB database and further refined through homology modeling using Modeller10.4 [43]. The model with the lowest molecular probability density function (molpdf) score was analyzed using the PROCHECK program [44], which assesses stereochemical quality by analyzing φ (phi) and ψ (psi) torsion angles through Ramachandran plots. For the model to be of good structural quality, more than 90% of the residues must fall within the allowed regions. These steps of aptamer structure modeling and homology modeling of the protein are summarized in Figure 1.
 | Figure 1. Computational methodology workflow used in this study. [Click here to view] |
3.3. Docking simulation
The three-dimensional structures of the ssDNA aptamer and HbA1c were prepared in .pdb format and submitted to the HDOCK v1.1 web server for docking analysis [45]. The server produced predicted aptamer–HbA1c complexes along with corresponding docking scores, reported in kcal/mol. The same protocol was applied to Hb, GHSA, HSA, and glucose to generate comparative docking models. HDOCK was selected due to its compatibility with protein–nucleic acid docking and its hybrid algorithm that combines template-based and free docking strategies. Docking poses were ranked using the server’s scoring function and visually examined to ensure plausible binding orientation and interaction geometry [45]. The resulting models were used for subsequent structural comparison and MD simulation.
3.4. Molecular dynamics simulation
Molecular dynamics simulations were performed using Amber20 [46,47] to study the stability and dynamics of the aptamer-HbA1c complex. The system was parameterized in Leap, with the aptamer assigned to the leaprc.DNA.OL15 force field [48], HbA1c, and other proteins using leaprc.protein.ff14SB [49], and glucose parameterized with the GAFF2 [50]. The complex was solvated in an octahedral periodic box of TIP3P water molecules [51], with a 10 Å buffer distance from the solute, and Mg²? counterions were added to neutralize the system and emulate the ionic conditions typically found in binding buffers. Energy minimization was performed in three stages to gradually relieve steric clashes and optimize the initial structure. The system was then gradually heated from 0 K to 298 K over 50 ps under constant volume (NVT ensemble) using Langevin dynamics with a 1 fs time step and no SHAKE constraints. Equilibration was carried out in multiple stages, starting with a 100 ps equilibration under NVT conditions at 298 K, followed by a six-step NPT equilibration totaling 600 ps with gradually decreasing positional restraints. For all simulations, a nonbonded cut-off distance of 10 Å was applied for short-range interactions. Long-range electrostatics were calculated using the Particle Mesh Ewald method [52]. The production MD run was carried out for 400 ns at 298 K and 1 atm under NPT conditions using pmemd.cuda. The resulting trajectories were visualized using VMD 1.9.3 [53]. Analysis was performed using CPPTRAJ [54], including Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and hydrogen bonding between the aptamer and HbA1c. Water and ion molecules were stripped for RMSD and RMSF analysis but retained for hydrogen bond analysis. Binding free energy was estimated using the Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) py module from AmberTools [55]. Snapshots were extracted at regular intervals from the equilibrated trajectory, and ΔGtotal was calculated under the implicit solvation model. A comparative analysis was performed between the aptamer-HbA1c complex and models of Hb, GHSA, HSA, and glucose to evaluate the specificity and stability of aptamer binding across different targets. An overview of the MD simulation workflow and analysis process is shown in Figure 1.
4. RESULT AND DISCUSSION
4.1. Modeling of aptamer and protein structures
A 50-bp ssDNA aptamer sequence specific to HbA1c binding was utilized in this study. Since the tertiary structure of the aptamer was not available in any database, it was modeled. The optimal secondary structure was predicted using the mFold web server, which identified stable base-pair interactions for nucleic acid folding predictions [41] (Fig. S1). This predicted secondary structure served as the foundation for 3D modeling with 3dRNA/DNA v2.0 software, which facilitated the prediction of the aptamer 3D structure by breaking the sequence into SSEs and aligning them with available templates [56]. This approach leverages in silico methods for aptamer design, enabling the prediction of structures that preserve essential interactions, including Watson-Crick base pairing. Subsequent refinement minimizes free energy, producing stable configurations optimized for binding efficiency [30,57]. The final 3D structure exhibited folding consistent with the predicted secondary structure, supporting its capacity to interact with HbA1c as depicted in Figure 2.
 | Figure 2. 3D structure modeling result of the aptamer-HbA1c binding. [Click here to view] |
Hemoglobin (Hb) and glucose serve as nontarget interferences in HbA1c detection due to their involvement in HbA1c formation, while HSA and GHSA also pose interference, as they undergo similar glycation processes in the bloodstream. These proteins, although not directly related to HbA1c, can still affect the assay by mimicking the glycation of Hb, making it crucial to consider them during assay development. To explore these potential interferences, the 3D structures of the target and nontarget proteins—HbA1c (ID: 3B75), Hb (ID: 1A3N), HSA (ID: 4K2C), and GHSA (ID: 4IW2)—and glucose (CID: 5793) were obtained from the RCSB PDB and PubChem databases.
The target proteins obtained from the RCSB PDB were refined using homology modeling to ensure accurate sequence alignment and correct amino acid composition. This process refines protein structures from databases to accurately represent the target sequence, addressing potential discrepancies in experimentally determined structures [43]. Protein modeling was performed with Modeller 10.4, where a template structure based on atomic coordinates was input using a straightforward command script. Ten protein models were generated, and the one with the lowest molpdf score (Table S1), indicating the most stable structure, was selected. This selection aligns with the principle that native structures generally exhibit the lowest free energy.
Structural refinement was validated using a Ramachandran plot, with models considered satisfactory if more than 90% of residues are in favored regions. As shown in Figure S2, each model exhibited over 90% of residues in these regions, indicating a high degree of refinement and structural reliability (Fig. 3).
 | Figure 3. Homology models of the target proteins and the structure of glucose (a) HbA1c; (b) Hemoglobin; (c) GHSA; (d) HSA; (e) Glucose. [Click here to view] |
4.2. Molecular docking
The prepared aptamer and target molecule structures were initially refined by eliminating water molecules and incorporating hydrogen before being subjected to docking simulations using the HDOCK server. HDOCK employed a hybrid algorithm that combines template-based modeling with free docking, optimizing both global orientation and local interactions to identify the most favorable binding configurations [45]. Several docking poses were generated with varying ligand orientations, and the complex with the most negative docking score, representing the most stable conformation, was selected (Fig. S3).
The docking conformation between the aptamer and target molecules (Fig. 4) provides information on the binding affinity, as shown in Table 1, as well as details of the interactions involved. The binding affinity values indicate that the most stable complex is formed between the aptamer and HbA1c, with a binding affinity of −308.84 kcal/mol, compared to the aptamer complexes with Hb, HSA, GHSA, or glucose, which exhibit lower binding affinity values.
 | Figure 4. 3D visualization of aptamer docking results with targets (a) Aptamer-HbA1c complex; (b) Aptamer-Hb complex; (c) Aptamer-GHSA complex; (d) Aptamer-HSA complex; (e) Aptamer complex with glucose. [Click here to view] |
Table 1. The binding affinity and the interactions occurring between the aptamer and the target Molecule.
| Target | Binding affinity (kcal/mol) | Interaction (Hydrogen Bond) | Non-Bond | ||
|---|---|---|---|---|---|
| Favorable | Unfavorable | ||||
| Aptamer HbA1c-binding | HbA1c | −308.84 | C17, C37, G16, G43, G44, A15, A18, A35, A41, A42, T36 | 26 | 8 |
| Hb | −243.24 | C13, A15, A41, G14, T11 | 20 | 9 | |
| GHSA | −234,20 | G25, G44, A23, A24, A46, T40, T45 | 22 | 9 | |
| HSA | −224,87 | C47, A9, A42, A46, A48, G10, G43, T8, T49 | 21 | 12 | |
| Glucose | −132,52 | A12, C13, G14, A42 | 4 | 1 | |
The aptamer-HbA1c docking complex reveals that the nucleotide bases of the aptamer contribute significantly to the formation of hydrogen bonds with HbA1c. There are 26 favorable nonbonded interactions between the aptamer and HbA1c, including hydrogen bonds, electrostatic interactions, and Van der Waals forces. These interactions promote the formation of a stable complex with optimal conformational adjustments, enhancing the stability and affinity. Conversely, there are 8 unfavorable nonbonded interactions resulting from steric clashes or suboptimal positioning of certain aptamer regions relative to HbA1c, which may reduce the complex’s stability.
4.3. Molecular dynamics simulation and analysis
Molecular dynamics simulations were carried out for 400 ns to assess the stability of the aptamer-HbA1c complex and its selectivity for HbA1c. RMSD analysis showed that the aptamer-HbA1c complex had the lowest values with minimal deviations, indicating consistent binding stability (Fig. 5). As summarized in Table 2 and depicted in Figure 5, the RMSD graph of the aptamer-HbA1c complex, compared to other complexes, shows all values exceeding 2.0 Å. However, the RMSD values for the aptamer-HbA1c complex remain the lowest and most stable, ranging from 3 to 9 Å for the aptamer and 4–10 Å for HbA1c, indicating a tightly bound and stable conformation. On the other hand, complexes with nontarget molecules, such as Hb, GHSA, and HSA, reflect weaker interactions between the aptamer and these nontarget, leading to greater structural deviations. For the aptamer-Glucose complex, the very high RMSD values mainly reflect the movement of glucose itself, which, due to its small size and flexibility, causes increased conformational freedom.
 | Figure 5. Analysis and comparison of RMSD for 400 ns on aptamer-HbA1c (a); aptamer-Hb (b); aptamer-GHSA (c); aptamer-HSA (d); aptamer-glucose (e). [Click here to view] |
Table 2. Summary and comparison of RMSD values for aptamer-HbA1c and aptamer-non target complexes.
| Complex | RMSD of complexed aptamer (Å) | RMSD of complexed molecule (Å) |
|---|---|---|
| Aptamer-HbA1c | 3–9 | 4–10 |
| Aptamer-Hb | 20–45 | 10–35 |
| Aptamer-GHSA | 15–32 | 3–15 |
| Aptamer-HSA | 15–38 | 3–15 |
| Aptamer-Glucose | 14–18 | 60–100 |
Additionally, observations with VMD confirmed that the aptamer remained stably bound to HbA1c throughout the simulation, transitioning into a tighter conformation and forming specific folds that enable recognition and distinction from other ligands (Fig. 6). In contrast, in complexes with Hb, GHSA, and HSA, the aptamer remained mobile around these ligands, seeking potential binding sites without undergoing significant conformational changes. For the glucose complex, the molecule did not bind or move significantly, likely due to its small size. This lack of binding behavior, particularly with nontarget molecules, suggests that the aptamer effectively ‘locks’ onto HbA1c without engaging with other ligands, demonstrating its high specificity.
 | Figure 6. Snapshots of VMD representations after 400 ns of MD simulation for the complexes: aptamer-HbA1c (a); aptamer-hemoglobin (b); aptamer-GHSA (c); aptamer-HSA (d); complex of aptamer-glucose (e). [Click here to view] |
RMSF analysis is also performed using the same cpptraj features as RMSD. The RMSF analysis complements the RMSD data by assessing fluctuations across all residues throughout the simulation. While RMSD provides a measure of overall structural deviation, RMSF focuses on the flexibility of residues, highlighting conformational changes that may occur, particularly in the nitrogenous base residues responsible for the aptamer’s adaptability. Lower RMSD values correspond to greater stability, which is reflected by lower RMSF values.
The RMSF profile shows the fluctuations of the complexed aptamer residues (Fig. 7). In the aptamer-HbA1c complex, the aptamer residues exhibit limited movement, indicating strong binding to HbA1c. This stability enhances the structural integrity of the aptamer-HbA1c complex compared to complexes with nontarget ligands, where greater fluctuations are observed. In the aptamer-Hb complex, although fluctuations remain stable, the aptamer shows a wider range of movement. The aptamer appears less stable in the aptamer-HSA complex. In the GHSA complex, movement is lower, possibly due to similarities in glycan structures. For the aptamer-glucose complex, the movement remains low, as glucose does not bind directly but instead moves around the aptamer, occasionally affecting certain residues. Larger fluctuations at the terminal regions of the aptamer in all complexes are attributed to instability from unpaired base regions, leading to increased flexibility and movement.
 | Figure 7. Analysis and comparison of RMSF for 400 ns on aptamer-HbA1c (a); aptamer-Hb (b); aptamer-GHSA (c); aptamer-HSA (d); aptamer-glucose (e). [Click here to view] |
4.4. Binding analysis
Binding analysis shows that the interaction between the aptamer and HbA1c involves moderate hydrogen bonds and electrostatic interactions, with binding distances ranging from 2.8 to 3.5 Å. The bonds listed in Table 3 are hydrogen bonds with a presence of 10% or more, which are considered sufficiently significant in the simulation. Significant hydrogen bond interactions between the aptamer and HbA1c occur at residues A5, A8, A17, A26, A27, A41, G42, G43, and T37. At residues A41, A5, and A17, hydrogen bonds were observed between the nitrogen of adenine and arginine. Residues T37 and A26 interacted with histidine, while the oxygen of G42 formed a bond with serine. Additionally, A8 bonded with glutamine, A27 and G43 with lysine, and A5 with threonine. The negatively charged phosphodiester backbone of the aptamer plays a crucial role in promoting interactions with polar amino acids, facilitating electrostatic attractions with positively charged residues such as arginine, lysine, and histidine [58,59]. This interaction is vital for stabilizing the binding, as it enhances the overall affinity of the aptamer for HbA1c.
Table 3. Hydrogen bonding (≥10% occupancy) between HbA1c and the aptamer.
| Acceptor | Donor H | Donor | Frames | Fraction | Average distance | Average angle |
|---|---|---|---|---|---|---|
| DA_616@N6 | ARG_428@HE | ARG_428@NE | 19,226 | 0.9613 | 3.1356 | 152.8014 |
| DA_580@N1 | ARG_532@HH22 | ARG_532@NH2 | 9,851 | 0.4925 | 2.8727 | 151.3931 |
| DT_612@OP2 | HIE_376@H | HIE_376@N | 7,558 | 0.3779 | 2.8683 | 144.7135 |
| DA_592@N9 | ARG_532@HH12 | ARG_532@NH1 | 6,319 | 0.316 | 3.0137 | 149.7548 |
| DG_617@O4' | SER_437@H | SER_437@N | 5,370 | 0.2685 | 3.0173 | 146.2643 |
| HIE_143@N | DA_601@H61 | DA_601@N6 | 4,869 | 0.2434 | 2.9575 | 143.2815 |
| DA_602@N3 | LYS_560@H | LYS_560@N | 4,150 | 0.2075 | 3.5142 | 150.5771 |
| DG_618@OP1 | LYS_426@H | LYS_426@N | 3,653 | 0.1827 | 2.9579 | 151.2065 |
| DA_583@N7 | GLN_272@H | GLN_272@NE2 | 3,126 | 0.1563 | 3.0182 | 157.1136 |
| DA_580@N3 | THR_225@HG1 | THR_225@OG1 | 3,053 | 0.1527 | 2.8942 | 147.094 |
| ALA_315@O | DG_618@H1 | DG_618@N1 | 2,418 | 0.1209 | 3.3057 | 142.4752 |
In contrast, for nontarget molecules, such as Hb, GHSA, and HSA (Tables S2–S4), hydrogen bonds present in 10% or more of the simulation frames were less frequent than those observed with HbA1c, reflecting weaker and less specific binding interactions. Notably, no hydrogen bond interactions were detected between the aptamer and glucose, suggesting that free glucose in the bloodstream is unlikely to interact with the aptamer.
Compared to hemoglobin, HbA1c exhibited 335 bonds with the aptamer (Table S5), including 16 specific interactions with glucose molecules (Table S6), while hemoglobin showed 193 bonds (Table S7). These glucose-related interactions involved oxygen atoms engaging with residues T11, A12, A41, and T48, highlighting the aptamer’s increased affinity for HbA1c over hemoglobin due to the presence of glucose-specific interactions.
Quantitative binding free energy analysis was performed using the MMPBSA method to evaluate the thermodynamic stability of each aptamer-target complex. This approach estimates ΔGtotal by combining van der Waals and electrostatic interaction energies (gas phase) with polar and nonpolar solvation components, offering a reliable energy-based metric of molecular binding [60,61]. As summarized in Table 4, the aptamer–HbA1c complex exhibited the most favorable total binding free energy (ΔGtotal = –165.63 kcal/mol), while interactions with Hb (–4.09 kcal/mol), GHSA (–7.68 kcal/mol), and HSA (0.37 kcal/mol) were markedly weaker. These values quantitatively confirm the preferential and stable binding of the aptamer to HbA1c.
Table 4. Free Binding energy of molecular dynamics simulations for aptamer complexes.
| Aptamer-HbA1c Complex | Aptamer-Hb Complex | Aptamer-GHSA Complex | Aptamer-HSA Complex | |
|---|---|---|---|---|
| Total energy (kcal/mol) | ||||
| Complex | −16,354.89 | −16,394.38 | −20,396.02 | −20,420.92 |
| Receptor | −634.48 | −8,383.13 | −8,255.54 | −8,288.58 |
| Ligand | −15,554.77 | −4,275.81 | −12,132.81 | −12,132.72 |
| Binding energy | −165.63 | −4.09 | −7.68 | 0.3746 |
Bold values indicate the binding energy calculated as the difference between the total complex energy and the sum of receptor and ligand energies.
The enhanced interaction is supported by energy decomposition analysis (Table S8), which showed a more favorable electrostatic energy (EEL) in the HbA1c complex (–744.60 kcal/mol) compared to Hb (–680.76 kcal/mol). Electrostatic attraction and persistent hydrogen bonds play a central role in anchoring the aptamer near the glycation site. The glucose-modified region of HbA1c introduces additional polar groups that enhance charge complementarity and structural fit, thereby increasing binding specificity. This pattern was consistent throughout the simulation, as seen in the trajectory data. These molecular interactions are key to conformational stability and molecular recognition [62,63], and are reflected in prior experimental reports of stronger aptamer affinity for HbA1c (KD = 56.1 ± 2.2 nM) versus Hb (KD = 542.9 ± 14.2 nM) [27]. The aptamer’s selectivity was further confirmed in our electrochemical aptasensor study, which showed clear discrimination between HbA1c and Hb [28]. This experimental selectivity aligns with the present in silico results, where the aptamer–HbA1c complex exhibited 5- to 8-fold greater structural stability compared to Hb, as reflected by RMSD analysis. A summary of the differences in binding interactions, contact residues, and structural stability between HbA1c and Hb is provided in Table 5.
Table 5. Comparative analysis of aptamer interactions with HbA1c and Hb.
| Metrics | HbA1c | Hb |
|---|---|---|
| Docking score (HDOCK) | −308.84 kcal/mol | −243.24 kcal/mol |
| Predicted non-bonded contacts (HDOCK) | 26 favorable / 8 unfavorable | 20 favorable / 9 unfavorable |
| Total contact residues (MD, CPPTRAJ) | 335 residues (including 16 with glucose moiety) | 193 residues |
| Hydrogen bonds (≥10% occupancy) | 11 residues | 11 residues |
| Avg. RMSD aptamer | 3–9 Å | 20–45 Å |
| Avg. RMSD protein | 4–10 Å | 10–35 Å |
| Electrostatic energy (EEL) | –744.6028 | –680.7591 |
| Binding free energy (ΔGtotal) | –165.63 kcal/mol | –4.09 kca/mol |
The significantly lower binding affinities and reduced interaction stability observed for nontarget molecules compared to HbA1c highlight the favorable selectivity profile of the aptamer. This selectivity is critical for biosensor applications, as it reduces the risk of cross-reactivity and false-positive signals when analyzing complex clinical samples, such as whole blood, that contain multiple proteins with structural similarity to HbA1c.
By identifying the specific regions of the aptamer involved in both target and nontarget binding, rational modifications can be designed to enhance affinity toward HbA1c while further minimizing interactions with nontarget molecules. This structural insight provides a foundation for targeted sequence optimization, such as base substitutions or chemical modifications at critical binding sites, to strengthen interactions with HbA1c and reduce affinity for nontargets.
While a recent study using an insulin-binding aptamer employed molecular docking and long-timescale dynamics to evaluate selectivity against HbA1c and glucose, the aptamer was not designed for HbA1c. As a result, interactions with HbA1c were found to be less stable and energetically less favorable [37]. Although useful as a methodological reference, that study lacked structural insight into HbA1c-specific interactions, such as glycation-site recognition, which limits its diagnostic applicability for HbA1c. In contrast, the present work directly targets HbA1c and applies a comprehensive in silico framework, integrating molecular docking, energy decomposition, and extended MD simulations to evaluate its selectivity against multiple nontargets. While computational modeling has been widely applied to aptamers for biomarkers like thrombin [64], VEGF [65], PSA [66], and ATP [38], molecular-level evaluations of HbA1c aptamers remain limited. This study addresses that gap by providing residue-level insights into aptamer–HbA1c recognition, contributing to rational design strategies for biosensors with enhanced specificity and clinical applicability.
4.5. Practical implications for aptasensor development
To translate this aptamer design into a functional biosensor suitable for clinical or point-of-care use, several practical steps are required. First, the aptamer must be chemically modified at the 5′ or 3′ terminus with functional groups such as thiol, biotin, or amine to allow stable immobilization onto a sensor surface. Immobilization techniques can be tailored to the transducer substrate: for example, thiolated aptamers can form covalent bonds with gold electrodes via thiol–gold chemistry; biotinylated aptamers can bind to streptavidin-modified surfaces; and amine-terminated aptamers can be immobilized via carbodiimide coupling (EDC/NHS) on carboxyl-functionalized platforms [24,28,67,68]. Surface density and orientation of aptamers must be optimized to maintain accessibility of the binding site while ensuring signal responsiveness. Following immobilization, the electrode surface is typically treated with blocking agents such as bovine serum albumin or mercaptohexanol to minimize nonspecific adsorption and improve signal-to-noise ratio [68].
For signal transduction, electrochemical methods such as differential pulse voltammetry, square wave voltammetry, or electrochemical impedance spectroscopy are well-suited due to their high sensitivity, rapid response time, and compatibility with portable and battery-operated devices [69,70]. These methods detect changes in current or impedance that result from aptamer-target binding at the electrode interface.
In preparation for clinical application, the sensor platform should be compatible with minimally processed biological samples, particularly whole blood or plasma. Sample preparation can involve a simple dilution step, centrifugation, or membrane-based filtration to remove interfering particulates or excess proteins [71,72]. The prepared sample is applied to the aptamer-functionalized electrode, followed by a brief incubation period before signal measurement.
Analytical validation is necessary to ensure clinical reliability, covering key performance parameters including sensitivity, selectivity, reproducibility, and detection limits under physiologically relevant conditions [5,73]. Comparative testing with gold-standard clinical methods, such as HPLC or immunoassays, is essential to verify diagnostic accuracy [74,75].
For quantification, calibration curves based on known HbA1c concentrations can be integrated into the device firmware or associated mobile applications. Incorporating a user-friendly readout system, such as a digital display, smartphone interface, or colorimetric output, can enhance usability, particularly in decentralized settings. The complete system can be designed as a disposable strip or cartridge within a handheld reader, enabling rapid bedside or home-based testing [76].
Taken together, this study establishes an in silico framework for aptamer selectivity assessment and biosensor optimization, thereby reducing reliance on iterative wet-lab screening and accelerating development of clinically relevant devices. The computational predictions for this aptamer have already been supported by experimental validation in an electrochemical platform based on a gold-modified screen-printed carbon electrode, as reported in our recent study [28], where the same aptamer demonstrated sensitive HbA1c detection with clear discrimination against Hb and glucose. The consistency between computational and experimental findings strengthens confidence in the aptamer’s applicability and highlights its translational relevance for future diagnostic applications.
5. CONCLUSION
The HbA1c-binding aptamer was thoroughly evaluated using MD simulations and molecular docking, demonstrating the highest binding affinity with HbA1c compared to other targets such as Hb, HSA, GHSA, and glucose. The most favorable docking score for the aptamer-HbA1c complex was −308.84 kcal/mol, with 26 noncovalent interactions supporting stability. MD simulations over 400 ns showed that the aptamer-HbA1c complex had more stable RMSD and RMSF values compared to other fluctuating targets, indicating the aptamer’s instability with nontarget molecules. Hydrogen bond analysis identified significant interactions with residues A5, A8, A17, A26, A27, A41, G42, G43, and T37 of the aptamer, with a more negative free binding energy of −165.63 kcal/mol. These findings suggest that the aptamer is a promising candidate for HbA1c recognition and biosensor integration. Importantly, the computational results are consistent with previously reported experimental observations, reinforcing the aptamer’s applicability for clinical and point-of-care diagnostic platforms. Overall, the in silico approach provides detailed molecular insights into aptamer-target interactions that complement experimental observations and guide the rational design of aptamer-based diagnostics such as biosensors.
6. LIST OF ABBREVIATIONS
3D, Three-Dimensional; bp, Base Pair; DM, Diabetes Mellitus; DNA, Deoxyribonucleic Acid; Hb, Hemoglobin; HbA1c, Hemoglobin A1c or Glycated Hb; KD, Dissociation Constant; MD, Molecular Dynamics; MMPBSA, Molecular Mechanics Poisson-Boltzmann Surface Area; Molpdf, Molecular Probability Density Function; PDB, Protein Data Bank; RMSD, Root Mean Square Deviation; RMSF, Root Mean Square Fluctuation; RNA, Ribonucleic Acid; SELEX, Systematic Evolution of Ligands by Exponential Enrichment; ssDNA, Single-Strand Deoxyribonucleic Acid; VMD, Visual Molecular Dynamics.
7. AUTHOR CONTRIBUTIONS
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. All the authors are eligible to be author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.
8. FINANCIAL SUPPORT
There is no funding to report.
9. CONFLICTS OF INTEREST
The authors report no financial or any other conflicts of interest in this work.
10. ETHICAL APPROVALS
This study does not involve experiments on animals or human subjects.
11. DATA AVAILABILITY
All data generated and analyzed are included in this research article.
12. PUBLISHER’S NOTE
All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.
13. USE OF ARTIFICIAL INTELLIGENCE (AI)-ASSISTED TECHNOLOGY
The authors declare that they have not used artificial intelligence (AI)-tools for writing and editing of the manuscript, and no images were manipulated using AI.
14. CONTRIBUTION STATEMENT OF AUTHOR
All authors made substantial contributions to the conceptualization and writing of the article.
REFERENCES
1. International Diabetes Federation. International Diabetes Federation (IDF) Atlas. 10th edition. Vol. 102, Diabetes Research and Clinical Practice. Brussels, Belgium. 2021.
2. Papatheodorou K, Banach M, Bekiari E, Rizzo M, Edmonds M. Complications of Diabetes 2017. J Diabetes Res. 2018;2018. CrossRef
3. Kurniawati N, Suryawati C, Arso PS. Evaluasi program pengendalian diabetes mellitus pada usia produktif di Puskesmas Sapuran tahun 2019 [Internet]. J Kesehat Masy Internet. 2019;7(4):633–46. CrossRef
4. Seino Y, Nanjo K, Tajima N, Kadowaki T, Kashiwagi A, Araki E, et al. Report of the committee on the classification and diagnostic criteria of diabetes mellitus. J Diabetes Investig. 2010;1(5):212–28. CrossRef
5. Pohanka M. Glycated hemoglobin and methods for its point of care testing. Biosensors. 2021;11(3):70. CrossRef
6. Chehregosha H, Khamseh ME, Malek M, Hosseinpanah F, Ismail-Beigi F. A view beyond HbA1c: role of continuous glucose monitoring [Internet]. Diabetes Therapy. 2019;10(3):853–63. CrossRef
7. Lenters-Westra E, Schindhelm RK, Bilo HJ, Slingerland RJ. Haemoglobin A1c: historical overview and current concepts [Internet]. Diabetes Res Clin Pract. 2013;99(2):75–84. CrossRef
8. Salek-Maghsoudi A, Vakhshiteh F, Torabi R, Hassani S, Ganjali MR, Norouzi P, et al. Recent advances in biosensor technology in assessment of early diabetes biomarkers [Internet]. Biosens Bioelectron Internet. 2018;99:122–35. CrossRef
9. Zuliska S, Maksum IP, Einaga Y, Kadja GTM, Irkham I. Advances in electrochemical biosensors employing carbon-based electrodes for detection of biomarkers in diabetes mellitus. ADMET DMPK. 2024;12(3):487–527. CrossRef
10. Psoma SD, Kanthou C. Wearable insulin biosensors for diabetes management: advances and challenges. Biosensors. 2023;13(7):719. CrossRef
11. Heidt B, Siqueira W, Eersels K, Diliën H, Van Grinsven B, Fujiwara R, et al. Point of care diagnostics in resource-limited settings: a review of the present and future of PoC in its most needed environment. Biosensors. 2020;10(10):10. CrossRef
12. Fatoni A, Widanarto W, Anggraeni MD, Dwiasi DW. Glucose biosensor based on activated carbon – NiFe2O4 nanoparticles composite modified carbon paste electrode. Results Chem. 2022;4:100433. CrossRef
13. Syafira RS, Devi MJ, Gaffar S, Irkham, Kurnia I, Arnafia W, et al. Hydroxyapatite-gold modified screen-printed carbon electrode for selective SARS-CoV?2 antibody immunosensor. ACS Appl Bio Mater. 2024;7(2):950-60. CrossRef
14. Villalonga A, Pérez-Calabuig AM, Villalonga R. Electrochemical biosensors based on nucleic acid aptamers. Anal BioAnal Chem. 2020;412(1):55 -72. CrossRef
15. Haleem A, Javaid M, Singh RP, Suman R, Rab S. Biosensors applications in medical field: a brief review. Vol. 2, Sensors International. 2021.
16. Mulyani DE, Maksum IP. Detection of biomarker using aptasensors to determine the type of diabetes. Diagnostics. 2023;13(12):2035. CrossRef
17. Sriondee Y, Vijitvarasan P, Rattanachata A, Nakajima H, Oaew S, Cheunkar S. Real-time kinetic analysis and detection of glycated hemoglobin A1c using a quartz crystal microbalance-based aptasensor. Anal Methods. 2023;16(4):599-607. CrossRef
18. Bunyarataphan S, Dharakul T, Fucharoen S, Paiboonsukwong K, Japrung D. Glycated albumin measurement using an electrochemical aptasensor for screening and monitoring of diabetes mellitus. Electroanalysis. 2019;31(11):2254–61. CrossRef
19. Liu J, Zhu B, Dong H, Zhang Y, Xu M, Travas-Sejdic J, et al. A novel electrochemical insulin aptasensor: from glassy carbon electrodes to disposable, single-use laser-scribed graphene electrodes [Internet]. Bioelectrochemistry. 2022;143:107995. CrossRef
20. Sun J, Wang G, Cheng H, Han Y, Li Q, Jiang C. An antifouling electrochemical aptasensor based on hyaluronic acid functionalized polydopamine for thrombin detection in human serum. Bioelectrochemistry. 2022;145:108073. CrossRef
21. Yan R, Lu N, Han S, Lu Z, Xiao Y, Zhao Z, et al. Simultaneous detection of dual biomarkers using hierarchical MoS2 nanostructuring and nano-signal amplification-based electrochemical aptasensor toward accurate diagnosis of prostate cancer. Biosens Bioelectron. 2022;197:197:113797. CrossRef
22. Mahyari M, Hooshmand SE, Sepahvand H, Gholami S, Rezayan AH, Zarei MA. Gold nanoparticles anchored onto covalent poly deep eutectic solvent functionalized graphene: an electrochemical aptasensor for the detection of C-reactive protein. Mater Chem Phys. 2021;269:124730. CrossRef
23. Mulyani R, Yumna N, Maksum IP, Subroto T, Hartati YW. Optimization of aptamer-based electrochemical biosensor for ATP detection using screen-printed carbon electrode/gold nanoparticles (SPCE/AuNP). Indones J Chem. 2022;22(5):1256–68. CrossRef
24. Eissa, S., Zourob, M. Aptamer- Based Label-Free Electrochemical Biosensor Array for the Detection of Total and Glycated Hemoglobin in Human Whole Blood. Sci Rep [Internet]. 2017;7(1):1–8. CrossRef
25. Li D, Fang C, Li H, Tu Y. Fluorescence/electrochemiluminescence approach for instant detection of glycated hemoglobin index. Anal Biochem. 2022;659:114958. CrossRef
26. Devi R, Chauhan A, Yadav S, Kumar P, Pundir CS. Development of aptamer based colorimetric assay for whole blood glycated hemoglobin (HbA1c) estimation using gold nanoparticles. Biosens Bioelectron X. 2023;13:100304. CrossRef
27. Duanghathaipornsuk S, Reaver NGF, Cameron BD, Kim DS. Adsorption kinetics of glycated hemoglobin on aptamer microarrays with antifouling surface modification [Internet]. Langmuir. 2021;37(15):4647–57. CrossRef
28. Zuliska S, Irkham, Zakiyyah SN, Hartati YW, Einaga Y, Maksum IP. Electrochemical aptasensor for ultrasensitive detection of glycated hemoglobin (HbA1c) using gold-modified SPCE [Internet]. Sensors Bio-Sensors Res. 2025;47:100765. CrossRef
29. Lee SJ, Cho J, Lee BH, Hwang D, Park JW. Design and prediction of aptamers assisted by in silico methods. Biomedicines. 2023;11(2):356. CrossRef
30. Buglak AA, Samokhvalov AV, Zherdev AV, Dzantiev BB. Methods and applications of in silico aptamer design and modeling. Int J Mol Sci. 2020;21(22):8420. CrossRef
31. Puniya BL, Verma M, Damiani C, Bakr S, Dräger A. Perspectives on computational modeling of biological systems and the significance of the SysMod community. Bioinforma Adv. 2024;4(1):vbae090. CrossRef
32. Gayathiri E, Prakash P, Kumaravel P, Jayaprakash J, Ragunathan MG, Sankar S, et al. Computational approaches for modeling and structural design of biological systems: a comprehensive review. Prog Biophys Mol Biol. 2023;185:17-32. CrossRef
33. Makatita FA, Wardhani R, Nuraini. Riset in silico dalam pengembangan sains di bidang pendidikan, studi kasus: analisis potensi cendana sebagai agen anti-aging. J ABDI. 2020;2(1):59-67.
34. Ingólfsson HI, Arnarez C, Periole X, Marrink SJ. Computational ‘microscopy’ of cellular membranes. J Cell Sci. 2016;129(2):257 -268. CrossRef
35. Hollingsworth SA, Dror RO. Molecular dynamics simulation for all. Vol. 99, Neuron. 2018. CrossRef
36. Meng XY, Zhang HX, Mezei M, Cui M. Molecular Docking: a Powerful Approach for Structure-Based Drug Discovery. Curr Comput Aided-Drug Des. 2012;7(2):146 -157. CrossRef
37. Mulyani DE, Maksum IP, Yusuf M. In silico study of aptamer specificity for detection of insulin as development for diabetes mellitus diagnosis. Indones J Chem. 2024;24(3):855–64. CrossRef
38. Rustaman R, Rahmawan RR, Maksum IP. in silico study of aptamer specificity for detection of adenosine triphosphate (ATP) as biosensor development for mitochondria diabetes diagnosis. Turkish Comput Theor Chem. 2023;7(2):58–69. CrossRef
39. Nonaka Y, Yoshida W, Abe K, Ferri S, Schulze H, Bachmann TT, et al. Affinity improvement of a VEGF aptamer by in silico maturation for a sensitive VEGF-detection system. Anal Chem. 2013;85(2):1132–7. CrossRef
40. Rabal O, Pastor F, Villanueva H, Soldevilla MM, Hervas-Stubbs S, Oyarzabal J. In Silico Aptamer docking studies: from a retrospective validation to a prospective case study’ tim3 aptamers binding. Mol Ther Nucleic Acids. 2016;5:e376. CrossRef
41. Zuker M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003;31(13):3406–15. CrossRef
42. Zhang Y, Xiong Y, Xiao Y. 3dDNA: a computational method of building DNA 3D Structures. Molecules. 2022;27(18):5936. CrossRef
43. Webb B, Sali A. Comparative protein structure modeling using MODELLER. In Curr Protoc Bioinforma, 2016. Chapter 8. Hoboken, New Jersey, USA: John Wiley & Sons.
44. Laskowski RA, Macarthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr. 1993;26(2):283–91. CrossRef
45. Yan Y, Zhang D, Zhou P, Li B, Huang SY. HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res. 2017;45(W1):W365–73. CrossRef
46. Salomon-Ferrer R, Case DA, Walker RC. An overview of the Amber biomolecular simulation package. Wiley Interdiscip Rev Comput Mol Sci. 2013;3(2):198–210. CrossRef
47. Case DA, Aktulga HM, Belfon K, Cerutti DS, Cisneros GA, Cruzeiro VWD, et al. AmberTools. J Chem Inf Model. 2023;63(20):6183–91. CrossRef
48. Zgarbová M, Šponer J, Otyepka M, Cheatham TE, Galindo-Murillo R, Jure?ka P. Refinement of the sugar-phosphate backbone torsion beta for AMBER force fields improves the description of Z- and B-DNA. J Chem Theory Comput. 2015;11(12):5723–36. CrossRef
49. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. Ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput. 2015;11(8):3696–713. CrossRef
50. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA. Development and testing of a general Amber force field. J Comput Chem. 2004;25(9):1157–74. CrossRef
51. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys. 1983;79(2):926–35. CrossRef
52. Darden T, York D, Pedersen L. Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J Chem Phys. 1993;98(12):98(12):10089–92. CrossRef
53. Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph. 1996;14(1):33–8. CrossRef
54. Roe DR, Cheatham TE. PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput. 2013;9(7):3084–95. CrossRef
55. Miller BR, McGee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE. MMPBSA.py: an efficient program for end-state free energy calculations. J Chem Theory Comput. 2012;8(9):3314–21. CrossRef
56. Wang J, Wang J, Huang Y, Xiao Y. 3dRNA v2.0: an updated web server for RNA 3D structure prediction. Int J Mol Sci. 2019;20(17):4116. CrossRef
57. Afanasyeva A, Nagao C, Mizuguchi K. Prediction of the secondary structure of short DNA aptamers. Biophys Physicobiology. 2019;16:287–94. CrossRef
58. Zhang HL, Lv C, Li ZH, Jiang S, Cai D, Liu SS, et al. Analysis of aptamer-target binding and molecular mechanisms by thermofluorimetric analysis and molecular dynamics simulation. Front Chem. 2023;11:1144347. CrossRef
59. Sabri MZ, Abdul Hamid AA, Sayed Hitam SM, Abdul Rahim MZ. In silico screening of aptamers configuration against hepatitis B surface antigen. Adv Bioinformatics. 2019;2019.
60. Khan FI, Rehman MT, Sameena F, Hussain T, Alajmi MF, Lai D, et al. Investigating the binding mechanism of topiramate with bovine serum albumin using spectroscopic and computational methods. J Mol Recognit. 2022;35(7):e2958. CrossRef
61. Wang C, Greene D, Xiao L, Qi R, Luo R. Recent developments and applications of the MMPBSA method. Front Mol Biosciences. 2018;4:87. CrossRef
62. Cai S, Yan J, Xiong H, Liu Y, Peng D, Liu Z. Investigations on the interface of nucleic acid aptamers and binding targets. Analyst. 2018;143(22):5317–38. CrossRef
63. Troisi R, Balasco N, Autiero I, Vitagliano L, Sica F. Structural insights into protein–aptamer recognitions emerged from experimental and computational studies. Int J Mol Sci. 2023;24(22):16318. CrossRef
64. Tsvetkov VB, Varizhuk AM, Pozmogova GE, Smirnov IP, Kolganova NA, Timofeev EN. A universal base in a specific role: tuning up a thrombin aptamer with 5-nitroindole. Sci Rep. 2015;5:16337. CrossRef
65. Fukaya T, Abe K, Savory N, Tsukakoshi K, Yoshida W, Ferri S, et al. Improvement of the VEGF binding ability of DNA aptamers through in silico maturation and multimerization strategy. J Biotechnol. 2015;212:99–105. CrossRef
66. Rockey WM, Hernandez FJ, Huang SY, Cao S, Howell CA, Thomas GS, et al. Rational truncation of an RNA aptamer to prostate-specific membrane antigen using computational structural modeling. Nucleic Acid Ther. 2011;21(5):299–314. CrossRef
67. Jarczewska M, Górski ?, Malinowska E. Electrochemical aptamer-based biosensors as potential tools for clinical diagnostics. Anal Methods. 2016;8(19):3861–77. CrossRef
68. Oberhaus FV, Frense D, Beckmann D. Immobilization techniques for aptamers on gold electrodes for the electrochemical detection of proteins: a review. Biosensors. 2020;10(5):45. CrossRef
69. Hartati YW, Irkham I, Sumiati I, Wyantuti S, Gaffar S, Zakiyyah SN, et al. The optimization of a label-free electrochemical DNA biosensor for detection of Sus scrofa mtDNA as food adulterations. Biosensors. 2023;13(6):657. CrossRef
70. Chung S, Sicklick JK, Ray P, Hall DA. Development of a soluble KIT electrochemical aptasensor for cancer theranostics. ACS Sensors. 2021;6(5):1971–9. CrossRef
71. Chen HH, Wu CH, Tsai ML, Huang YJ, Chen SH. Detection of total and A1c-glycosylated hemoglobin in human whole blood using sandwich immunoassays on polydimethylsiloxane-based antibody microarrays. Anal Chem. 2012;84(20):8635–41. CrossRef
72. Kim DM, Shim YB. Disposable amperometric glycated hemoglobin sensor for the finger prick blood test. Anal Chem. 2013;85(13):6536–43. CrossRef
73. Mandali PK, Prabakaran A, Annadurai K, Krishnan UM. Trends in quantification of HbA1c using electrochemical and point-of-care analyzers. Sensors. 2023;23(4):1901. CrossRef
74. Rukmini MS, Ashritha, Nishmitha P, Yalla D, Christy A, Manjrekar P. Analytical calibre of high performance liquid chromatography and ion exchange chromatography resin methods in estimation of glycated hemoglobin: a comparitive study. Biomed Res. 2017;28(4):1765–9. CrossRef
75. Almusharraf AY, Eissa S, Zourob M. Truncated aptamers for total and glycated hemoglobin, and their integration into a graphene oxide-based fluorometric method for high-throughput screening for diabetes. Microchimica Acta. 2018;185(5):1–8. doi: https://doi.org/10.1007/s00604-018-2789-3
76. Wu J, Liu H, Chen W, Ma B, Ju H. Device integration of electrochemical biosensors. Nat Rev Bioeng. 2023;1(5):346–60. CrossRef