IntroductionAs the most common type of dementia, Alzheimer’s disease causes 70% of the cases1affecting more than 40 million people worldwide2. Symptoms of this progressive neurodegenerative disease range from mild memory loss in the early stages3 to severe symptoms, including disorientation, development of depression4,5a loss of impulse control, difficulties in reading, writing, and speaking3 and life-threatening complications, such as difficulties in swallowing6. The pathology of Alzheimer’s disease is characterized by amyloid-β (Aβ) plaques and tau neurofibrillary tangles (NFT), leading to neuronal cell death and cognitive dysfunction7.Moreover, it is hypothesized that Aβ plaques and tau NFTs play a crucial role in the generation and progression of Alzheimer’s disease. By enzymatic, proteolytic cleavage of the amyloid precursor protein (APP), β-secretase and γ-secretase, beta-amyloid protein (Aβ) fragments are generated8. The resulting fragments, Aβ40 and Aβ42, can self-aggregate due to their hydrophobicity, forming soluble oligomers, fibrils, and amyloid plaques, which cause damage to axons, dendrites, and result in the loss of synapses9,10. While accumulations of Aβ fragments can also be observed in healthy brains, it is suggested that decreased degradation of Aβ drives neurotoxicity and tau pathology induction in Alzheimer’s disease, ultimately leading to neuronal cell death and neurodegeneration11. In addition to Aβ aggregates, hyperphosphorylation of the microtubule-associated tau protein leads to dissociation and instability of the microtubules and the formation of NFTs12. It is proposed that hyperphosphorylation of the tau protein is highly influenced by Aβ deposits and hyperactivation of mTOR, promoting the formation of paired helical filaments (PHFs) and NFTs13 causing instability-related disorders referred to as tauopathies14 (Fig. 1a).Current therapies for Alzheimer’s disease aim to improve symptoms, but disease-modifying therapies (DMTs) are highly needed. Aptamers are a promising approach for treating neurodegenerative diseases as clinical diagnostic tools, either in the form of biomarkers15 or as therapeutic agents in the form of aptamer drug conjugates (ApDCs)16,17 or inhibitors18,19. Targeting the pathophysiological mechanisms, immunotherapies, and small-molecule inhibitors are promising approaches to prevent the development and limit the progression of Alzheimer’s disease3. Previously, it was shown that inhibition of the secretases or aggregation of Aβ and tau has promising effects in different stages of Alzheimer’s disease13.The single-stranded oligomers, composed of DNA, RNA, or peptides, can bind to their targets with high affinity and specificity by folding into tertiary structures20,21. The interaction is based on non-covalent interactions, hydrogen bonding, electrostatic interactions, and van der Waals forces22enabling aptamers to recognize and bind targets like monoclonal antibodies23showing superior thermal stability, modifiability, and causing lower immunogenicity24. Due to their smaller size compared to antibodies, aptamers can bind to targets that are inaccessible to antibodies25. Using systematic evolution of ligands by exponential enrichment (SELEX)-based approaches, aptamers are positively selected against the target molecules, including β-secretase, Aβ fragments, and tau protein, identifying aptamers with high affinity from random libraries21 (Fig. 1a).While many aptamers have been selected and tested experimentally, little research has been conducted to compare different aptamers and classes based on their biochemical properties, molecular structures, and binding characteristics. In contrast to conventional methods, in silico approaches, including molecular dynamics simulation and molecular docking, can be used to predict aptamer conformers and their binding to target proteins. In recent years, it has been shown that molecular dynamics simulations can be used to predict protein-protein and protein-nucleic acid structures and interactions with increasing accuracy and correlation to experimentally determined values, especially for small nucleotide-based molecules26.Based on the primary sequence and experimentally determined dissociation constant, computational prediction of structures, docking, and molecular dynamics simulations allowed a comparison of structural features, changes in free energy, and the interacting amino acids as crucial factors for improving the binding affinities of aptamers27 and enable a more targeted selection of aptamers with high efficiency in the future.ResultsSecondary structure prediction revealed an increased loop formation and lower structure ΔG values, indicating a higher stability of RNA-based aptamersAptamer primary sequences were obtained from the literature, targeting key players in AD, including Aβ40 and Aβ42, tau, and β-secretase (Fig. 1a), along with dissociation constant (Kd) values (Supplementary Table S1, S3, S5) when available. To compare the different classes of RNA, DNA, and peptide aptamers, we predicted their secondary structures using Mfold, selecting the most stable structure for further processing based on the predicted changes in free energy (ΔG) values related to the formation of secondary structure. Additionally, we converted the nucleic acid sequences by thymine or uracil replacement to analyze changes in the free energy of secondary structure. For better visualization of the bioinformatic pipeline, a schematic overview was generated to guide the processes of aptamer preparation, molecular dynamics simulation, molecular docking, and binding analysis (Fig. 1b).Fig. 1Aptamers targeting key components in the pathology of Alzheimer’s disease and bioinformatic pipeline predicting and analyzing aptamer-protein binding modes. (a) Aptamers targeting key components in the pathology of Alzheimer’s disease were obtained from the literature targeting molecules in the Aβ- or Tau-mediated Alzheimer’s development. For comparison, aptamers were grouped into RNA (red), DNA (yellow), or peptide (blue) structures. (b) The schematic overview of the bioinformatic pipeline for predicting aptamer structures, performing molecular dynamics, and analyzing molecular docking.Full size imageWhen comparing the different classes of nucleic acid aptamers, experimental Kd values of RNA aptamers showed a high variance and significantly lower binding affinity towards their target proteins than DNA aptamers (Supplementary Table S1 and S3, Fig. 3a). However, predicted secondary structures revealed a higher formation of loop and stem structures, resulting in significantly lowered change in free energy (ΔG) of RNA aptamer structures (Supplementary Table S1 to S4, Fig. 3b), which was also observed for converted sequences. While the change in free energy decreases after thymine to uracil conversion of DNA aptamers, the stability of RNA aptamers was reduced in the corresponding DNA form (Supplementary Table S1 and S3, Fig. 3b), making the RNA aptamers more than twice as energetically favorable. These data suggest that RNA aptamers are more thermally stable due to the increased formation of secondary structures, but exhibit a reduced binding affinity compared to DNA aptamers.3D structure prediction, molecular dynamics simulations, and molecular Docking reveal the best binding modes for aptamer-target complexes and binding free energiesTo compare RNA, DNA, and peptide aptamer classes and characterize the aptamer-target interactions, we predicted aptamer 3d structures using 3dRNA/3dDNA for nucleic acid or PEP-Fold4 for peptide aptamers (Supplementary Table S2, S4, S5), with the most stable conformer selected for further processing, based on the webserver’s change in free energy scoring system. Molecular dynamics (MD) simulations were performed to account for the conformational flexibility of the biological molecules. The simulation trajectories were clustered using a root mean square deviation (RMSD)-based approach to identify structurally stable conformations. The average structures of the three most populated clusters were selected as representative conformations.RMSD analysis of the simulation trajectories revealed high inter-aptamer differences in structural flexibility among nucleic acid aptamers, with DNA and RNA aptamers exhibiting higher overall flexibility (Supplementary Fig. S1a, S1b) than peptide aptamers and target proteins (Supplementary Fig. S1c, S1d). The variability is likely influenced by differences in aptamer length, sequence composition, and presence of secondary structure elements such as loops or stems. Despite these differences, the overall RMSD values ranged between 0.5 and 1.5 nm, indicating that even the more flexible aptamers reached structural stability throughout the simulation. However, peptide aptamers showed reduced RMSD values below 0.4 nm, with minimal fluctuations over time (Supplementary Fig. S1c), which most likely results from the short length of the molecules. Similarly, target proteins exhibited lower RMSD values than nucleic acid aptamers, indicating reduced conformational flexibility consistent with their more compact folding and extensive intramolecular interactions such as hydrogen bonding and hydrophobic packing. Notably, Tau441 displayed a particularly low RMSD, potentially due to its oligomeric nature and intramolecular stabilization, in contrast to the monomeric forms of Aβ40, Aβ42, and β-secretase (Supplementary Fig. S1d). Regarding the pronounced RMSD spike observed in one of the DNA aptamer trajectories during the final nanosecond (Supplementary Fig. S1b), the behavior is likely an artifact caused by periodic boundary conditions (PBC) during the simulation and does not reflect a structural instability of the aptamer itself.To further evaluate the structural flexibility of the aptamers and targets during molecular dynamics simulations, we compared the initial models to the representative average structures derived from RMSD-based clustering. While most aptamers retained their overall fold, with only minor deviations observed primarily in single-stranded regions, aptamers β19, β55, BI2, and RNV95 displayed localized flexibility in unpaired regions, while their core secondary structures remained intact. For Tau441, one cluster conformation exhibited a noticeable shift, likely due to an altered global orientation rather than a true structural rearrangement, and BACE1 showed no significant deviations between the initial and clustered conformations. In contrast, the inherently more flexible targets Aβ40 and Aβ42 exhibited a higher degree of conformational variation (Supplementary Fig. 2), suggesting a more dynamic nature.Next, the identified average structures were used in an all-to-all molecular docking approach via the HDOCK webserver. Based on docking and confidence scores, the ten highest-ranked binding modes were evaluated, with the top three binding modes for each aptamer–target pair selected for further analysis (Table 1). Given the absence of well-defined or experimentally validated binding sites for Aβ40 and Aβ42, blind docking was employed for all aptamer–target pairs to ensure an unbiased evaluation of potential interaction sites and consistency across docking simulations. The docking results revealed a predominantly targeted binding of BACE1 near its known active site across most aptamer types, although aptamer orientation and contact interface varied among aptamers. In contrast, a less consistent binding was identified for Aβ40 and Aβ42 when docked with nucleic acid aptamers, likely reflecting these peptides’ structural heterogeneity and flexibility. Interestingly, peptide aptamers showed more localized binding preferences. Notably, the preferred binding site appeared to be more strongly influenced by the conformational state of the target protein than by the aptamer structure. This was particularly evident for Aβ species, where variations in protein folding and surface accessibility led to different binding interfaces, highlighting the importance of target flexibility in docking-based binding predictions (Fig. 2).Fig. 2Top-Ranked Binding Modes of Aptamer–Target Complexes. Molecular docking-derived binding poses of the three best-ranked docking modes for each aptamer–target pair were selected based on docking and confidence scores. Structures represent DNA (yellow), RNA (red), or peptide (blue) bound to the target proteins Aβ40 (PDB-ID 6TI5), Aβ42 (PDB-ID 6SZF), BACE1 (PDB-ID 6EJ3) and Tau441 (PDB-ID 5O3L) (grey), illustrating the spatial diversity of predicted binding orientations derived from blind docking using representative conformations obtained from RMSD-based clustering of MD simulations.Full size imageMoreover, molecular docking revealed significant differences in the binding between the classes of aptamers. While confidence scores of the best binding modes for nucleic acid aptamers were above 0.96, peptide aptamers showed low confidence scores ranging from 0.66 to 0.76, indicating a less favorable binding to the target proteins. Moreover, peptide aptamers presented a lower docking score of about − 180 to -200 (Table 1). In contrast, docking scores for nucleic acid aptamers ranged between − 300 and − 400, making the binding of nucleic acid aptamers more likely and with increased affinity. When comparing RNA and DNA aptamers, RNA aptamers exhibited a lower docking score, suggesting an even higher binding affinity (Table 1; Fig. 3c).Next, we performed a molecular dynamics simulation of the aptamer-protein complexes (Supplementary Fig. S3, S4, S5) and calculated the Molecular Mechanics/Poisson − Boltzmann Surface Area (MM/PBSA) ΔG prediction to quantify the aptamer-target binding further. After the molecular dynamics simulation of the aptamer-target complexes, ΔG values were obtained to compare the binding of the different aptamer classes. RNA aptamers showed the highest ΔG values, while no significant difference was observed between the classes of DNA and peptide aptamers (Fig. 3d). It is to be noted that for nucleic acid aptamers, a high variance between single aptamers within one class was noticed in the RMSD plots as well as binding free energies, suggesting highly different interactions between the aptamers.Table 1 Molecular Docking of aptamers and target proteins.Full size tableCorrelation analysis of RNA and DNA aptamers identifies predictive values for simulation-aided aptamer selectionTo explore potential predictors of aptamer affinity, we performed a Pearson correlation analysis comparing experimental dissociation constants (Kd) with computational parameters, including changes in free energy of secondary structure, docking scores, and MM/PBSA-derived binding free energies, to assess whether bioinformatically derived metrics can aid in selecting high-affinity aptamers and compare the classes of nucleic acid aptamers, with experimental Kd values obtained from the literature used as the reference for the correlation.For RNA aptamers, no significant correlation with changes in free energy of secondary structures was observed (r = − 0.119, p = 0.638), while DNA aptamers exhibited a statistically significant moderate negative correlation (r = − 0.657, p 0.12), * (p