In silico design and validation of high-affinity RNA aptamers for SARS-CoV-2 comparable to neutralizing antibodies

In silico design and validation of high-affinity RNA aptamers for SARS-CoV-2 comparable to neutralizing antibodies
eLife Assessment
This valuable study introduces CAAMO, a computational framework that combines structure prediction, in silico mutagenesis, molecular simulations, and energy calculations to design RNA aptamers with improved binding affinity. The computational methodology is solid, demonstrating strong theoretical foundations and systematic integration of multiple prediction techniques. Many of the previously identified methodological weaknesses that limit the strength of support for the computational predictions have been addressed.
https://doi.org/10.7554/eLife.107785.4.sa0Valuable: Findings that have theoretical or practical implications for a subfield
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Solid: Methods, data and analyses broadly support the claims with only minor weaknesses
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Abstract
Nucleic acid aptamers hold promise for clinical applications, yet understanding their molecular binding mechanisms to target proteins, and efficiently optimizing their binding affinities, remain challenging. Here, we present CAAMO (Computer-Aided Aptamer Modeling and Optimization), which integrates in silico aptamer design with experimental validation to accelerate the development of aptamer-based RNA therapeutics. Starting from the sequence information of a reported RNA aptamer, Ta, for the SARS-CoV-2 spike protein, our CAAMO method first determines its binding mode with the spike protein’s receptor binding domain (RBD) through a multi-strategy computational approach. We then optimize its binding affinity via structure-based rational design. Among the six designed candidates, five were experimentally verified and exhibited enhanced binding affinities compared to the original Ta sequence. Furthermore, we directly compared the binding properties of the RNA aptamers to neutralizing antibodies and found that the designed aptamer TaG34C demonstrated a comparable binding affinity to the RBD compared to the representative neutralizing antibodies analyzed in this study. This highlights its potential as an alternative to existing COVID-19 antibodies. Our work provides a robust approach for the efficient design of a relatively large number of high-affinity aptamers with complicated topologies. This approach paves the way for the development of aptamer-based RNA diagnostics and therapeutics.
Introduction
Aptamers are short, single-stranded RNA or DNA oligonucleotides that bind tightly and specifically to target molecules (Ellington and Szostak, 1990; Tuerk and Gold, 1990), earning the designation of ‘chemical antibodies’ (Keefe et al., 2010; Zhou et al., 2016). They combine the advantageous properties of small organic compounds and antibodies, exhibiting strong affinity and specificity comparable to monoclonal antibodies, while remaining mostly non-immunogenic and highly capable of tissue penetration similar to those of small molecules (Sun and Zu, 2015). These versatile attributes have led to their widespread applications in biomedical fields, including drug discoveries (Guo et al., 2025), biomarker identifications (Dassie et al., 2009), therapeutics (Zhou and Rossi, 2017), diagnostics (Abatemarco et al., 2017), and biosensors (Ning et al., 2020). The U.S. Food and Drug Administration’s (FDA) approval of Macugen (pegaptanib sodium, Pfizer/Eyetech) in 2004, which targets vascular endothelial growth factor to treat wet age-related macular degeneration, was a landmark achievement for aptamer-based drugs (Eyetech Study Group, 2003). This success has driven further exploration of aptamer-based therapies. To date, a variety of aptamers have been proposed to neutralize a range of disease-related proteins, such as human epidermal growth factor receptor 2 (Zhu et al., 2017), epidermal growth factor receptor (Wang et al., 2014), and prostate-specific membrane antigen (Lupold et al., 2002). Recently, the significant contributions of mRNA vaccines (Fang et al., 2022), aptamers (Valero et al., 2021), and other RNA biotherapeutics (ElNabi et al., 2020; Yang et al., 2020) have highlighted the potential of RNA-based interventions during the COVID-19 pandemic. This global health crisis has underscored the importance of RNA therapeutics, including medical aptamers.
The systematic evolution of ligands by exponential enrichment (SELEX) technique, the most commonly used method for aptamer screening, still faces several challenges (Zhu et al., 2024). Firstly, due to its random selection, the candidate aptamers are identified regardless of any atomic mechanisms underlying the interactions between the nucleic acids and target proteins; and secondly, because of limited sizes of oligonucleotide libraries, the identified candidates may not be the best aptamers, which, instead, can be used as lead sequences for further optimization (Brown et al., 2024). Therefore, developing in silico methods that can complement SELEX screening and provide atomic structure-based rational aptamer design is highly desirable. For instance, we successfully developed two optimized RNA aptamers targeting epithelial cellular adhesion molecule (EpCAM) using a structure-based in silico method. We further confirmed their enhanced affinities compared to a previously patented nanomolar aptamer (Bell et al., 2020). These two EpCAM-targeted aptamers were relatively small (19 nucleotides) and structurally simple, with a 4 bp stem and a 10-nucleotide-long hairpin loop. Their simplicity allowed for precise determination of the aptamer-protein complex structure and enabled structure-based design through conventional computational modeling and simulations. In comparison, RNA aptamers screened through the SELEX technique are typically much larger, often comprising tens of nucleotides and featuring complex topologies, including tertiary structural motifs such as internal loops and G-quadruplexes. These features not only contribute to increased difficulties in accurate structure modeling, but also increase the possibilities of conformational changes upon binding to the target molecules (i.e. folding upon binding; Stelzer et al., 2011). Furthermore, the limited number of experimentally determined structures for aptamer-protein complexes in the Protein Data Bank (PDB) presents further challenges for accurate structure modeling. These limitations highlight the need for more advanced computational frameworks to accurately predict the binding modes of RNA aptamers of varying sizes and topologies, thus enabling efficient in silico aptamer engineering.
A direct comparison between aptamers (also known as ‘chemical antibodies’) and conventional biological antibodies in terms of binding mechanisms and affinities is of great interest. However, such comparative studies are currently limited. A prerequisite for such comparison is the availability of a target protein of functional importance for which both effective aptamers and antibodies exist. The receptor binding domain (RBD) of the spike (S) protein expressed by the SARS-CoV-2 is one such target (see Figure 1A). It binds to human angiotensin-converting enzyme 2 (ACE2) with high affinity to facilitate viral entry into host cells (Wang et al., 2020; Yan et al., 2020). Targeting RBD to block the interactions between the SARS-CoV-2 spike protein and human ACE2 emerged as a promising therapeutic strategy (Hoffmann et al., 2020) to fight the COVID-19 pandemic. For example, RBD-targeted antibodies have shown substantial therapeutic potential due to their potent neutralizing effects (Ju et al., 2020; Cao et al., 2020). Alternatively, a recent study reported an RNA aptamer generated through SELEX, termed RBD-PB6-Ta (hereafter referred to as ‘Ta’), that binds to the RBD of the SARS-CoV-2 spike protein with high affinity and efficiently blocks viral infection at low concentrations (see Figure 1B; Valero et al., 2021). This aptamer was originally identified through multiple rounds of positive and negative SELEX and subsequently validated using surface plasmon resonance (SPR) and biolayer interferometry (BLI), which confirmed its high affinity and high specificity toward the RBD. Therefore, Ta provides a well-characterized and biologically relevant starting point for structure-based optimization. Based on these observations, the RBD of the SARS-CoV-2 spike protein represents an ideal target to directly compare the efficacy of aptamers and antibodies. Such comparisons could enhance our understanding of the aptamer binding mechanism, guiding the rational design of aptamers that can possibly be comparable with or even superior to neutralizing antibodies, thereby complementing the existing means of treatment (see Figure 1C).
In this work, we present the CAAMO (Computer-Aided Aptamer Modeling and Optimization) framework, which combines computational techniques and experimental validation to design high-affinity aptamers. Rather than serving as a de novo aptamer discovery tool, CAAMO is designed as a post-SELEX optimization platform that rationally improves existing aptamer leads by integrating atomic-level modeling and free-energy-based affinity prediction. Starting with an RNA sequence, we first predicted the most probable binding mode of the RNA aptamer with the target protein using a combination of in silico methods, including RNA structure prediction, ensemble docking, molecular dynamics (MD) simulations, steered molecular dynamics (SMD) simulations, and binding free energy calculations. We then performed a comparative analysis between the aptamer Ta and several popular neutralizing antibodies, focusing on their binding sites (using computational methods) and binding capabilities (through both computational and experimental approaches). We identified potential mutation sites for affinity enhancement and developed six novel aptamers through in silico mutagenesis study with free energy perturbation (FEP) method. Among these, electrophoretic mobility shift assay (EMSA) experiments confirmed that five had improved binding affinities compared to the original aptamer Ta. Notably, the aptamer TaG34C exhibited the highest binding affinity to the RBD, outperforming the tested neutralizing antibodies in competitive binding assays. These findings demonstrate the effectiveness of the CAAMO framework in developing high-affinity RNA aptamers targeting the RBD of SARS-CoV-2 spike protein, providing new therapeutic strategies against COVID-19. Moreover, the CAAMO framework can be extended to aptamers that are larger and with more complex topologies.
Results
Overview of the CAAMO framework for high-affinity RNA aptamer design
For RNA aptamers composed of tens of nucleotides with complicated topological structures, accurately determining their binding modes with target proteins is challenging due to the huge conformational space available, which hinders efforts in structure-based design of high-affinity aptamers. To address these challenges, we propose a promising framework named CAAMO, which integrates computational techniques with experimental validation. This CAAMO framework is designed not only to provide the structural insights into key nucleic-acid-protein interactions but also to facilitate the efficient design of aptamers with enhanced affinity. The framework consists of four main phases (see Figure 1D, Figure 1—figure supplement 1): (i) constructing an aptamer conformational ensemble by employing several cutting-edge RNA three-dimensional (3D) structure prediction methods, (ii) identifying an appropriate aptamer binding mode through integrating conformational selection docking, induced-fit dynamic simulation (Boehr et al., 2009), and binding energy-guided filtration, (iii) optimizing the aptamer sequence through in silico mutagenesis study using FEP calculations, and (iv) final experimental validation of the designed aptamer candidates. Further details on the CAAMO framework are provided in the following subsections and in the ‘Materials and methods’ section.
An RNA aptamer sequence termed ‘Ta’ (containing 52 nucleotides; see Figure 1B), previously shown to bind the RBD of SARS-CoV-2 spike protein with high affinity (Valero et al., 2021), was chosen to illustrate the CAAMO framework. The initial input to the CAAMO is the nucleotide sequence of the aptamer Ta, and the output is several computationally designed and subsequently experimentally validated aptamers with improved binding affinities compared to the initial sequence. A representative output (the aptamer TaG34C), that exhibits an ~3.3-fold stronger binding affinity than the original aptamer Ta, is shown in Figure 1E. Notably, the success rate of the in silico design via the CAAMO framework is very promising, with five out of the six (~83%) computationally designed candidate aptamers experimentally confirmed to have improved binding affinities. Furthermore, competitive binding experiments with popular neutralizing antibodies revealed that the designed RNA aptamer TaG34C has a higher binding affinity to the RBD of SARS-CoV-2 spike protein than the neutralizing antibodies (see Figure 1F). Thus, the newly identified aptamer TaG34C shows great potential as a complement to existing antibody-based neutralizing treatments for COVID-19, especially when antibody escape occurs in emerging SARS-CoV-2 variants (Qu et al., 2024). Overall, these results indicate that the proposed binding conformation of the aptamer Ta to the RBD serves as a plausible working binding model for structure-guided aptamer optimization and demonstrate the great potential of our CAAMO framework in aptamer design and optimization.
Determination of the binding model of the aptamer Ta to the RBD
Accurately predicting the binding complex of the RNA aptamer with the target protein is a critical step in structure-based in silico aptamer design, especially with aptamers of multiple nucleotides and complex topologies. To address this challenge, we constructed a multi-strategy approach that includes RNA 3D structure prediction, ensemble docking/clustering, and binding capacity assessment to identify the most probable binding conformation of the aptamer Ta with the RBD of SARS-CoV-2 spike protein (see Figure 2A). The RBD conformation was extracted from the crystal structure of the RBD-ACE2 complex (PDB id: 6LZG) and then refined using MD simulation (see Figure 2—figure supplement 1). For the aptamer Ta, we first predicted its secondary structure using Mfold (Zuker, 2003), forming a stem-loop structure containing five stems, two internal loops, two bulges, and one hairpin loop (see Figure 1B). Then, we built a 3D conformational ensemble using the state-of-the-art RNA 3D structure prediction models because of the inherent flexibility of unpaired nucleotides in loop regions and potential conformational changes upon binding to the target protein. A total of 25 representative aptamer Ta structures with different degrees of bending (see Figure 2—figure supplement 2A) were selected for subsequent molecular docking. It should be noted that, since most popular structure prediction models, such as FARFAR2 (Watkins et al., 2020), IsRNA2 (Zhang et al., 2021a), and SimRNA (Boniecki et al., 2016), adopt a conformation clustering strategy to obtain the top predictions, each of these 25 predicted structures represents a collection of similar 3D conformations.
From the ensemble docking complex pool (containing 1000 aptamer-RBD binding poses; see Figure 2—figure supplement 2B), we generated six major clusters, each representing distinct binding conformations. For each cluster, we selected the structure with the lowest binding energy, estimated using the molecular mechanics generalized Born surface area (MM/GBSA) approach based on single-frame conformation, as the representative binding conformation (see Figure 2B and conformations 01–06 in Figure 2—figure supplement 2C). MD simulations of these six representative conformations confirmed that they maintained stable binding modes over the course of the long-time simulations (see Figure 2—figure supplement 3). We further analyzed the binding abilities between the aptamer Ta and RBD for these six MD refined conformations using MM/GBSA and SMD simulations to determine the most likely binding conformation. As shown in Figure 2C, the MM/GBSA binding energy of conformation 01 (ΔG=−142.93 ± 7.65 kcal/mol) is significantly lower than the other five candidates (ΔG>-120 kcal/mol). Interestingly, we found that the binding energy of each conformation correlated with the sizes of its corresponding clusters (see Figure 2C, Figure 2—figure supplement 2C), with a larger cluster associated with lower binding energy. Additionally, we computed the rupture works required to separate the bound aptamer from the RBD using SMD simulations. Among four tested cases (conformations 01–04), the conformation 01 rupture work is the largest (254.2±47.9 kcal/mol) and significantly different from that of the other conformations (see Figure 2D). Despite the limitations of MM/GBSA and SMD methods (more rigorous FEP method will be used later), both approaches consistently showed that conformation 01 had the strongest binding ability between the aptamer Ta and the RBD. Based on these results, we selected conformation 01 as the putative binding model for further structure-based rational design of high-affinity aptamers.
In the putative binding complex, the aptamer Ta adopts a saddle-like shape to bind the RBD (see Figure 2E). We divided the aptamer into three parts: the apical loop (contains two base pairs [BPs]; nucleotides C24–C33), the bulge region (nucleotides G11-C23 and G34-C41), and the end stem part (contains one mismatch BP; nucleotides G1-U10 and A42-A52). The apical loop and end stem parts bind to opposite sides of the RBD, respectively, while the bulge acts as a linker, adjusting the bending angle of the saddle-like shape. The binding between the aptamer Ta and RBD is stabilized by electrostatic, hydrogen bonding, and van der Waals interactions. In detail, some important interactions include the electrostatic interactions between the phosphate groups of nucleotides C29 and U30 and the basic amino acid ARG408, and between the phosphate groups of C3, A42, and G43 and the LYS444; the hydrogen bonds between GLN409@Nε2-Hε21…C29@O2’, LYS417@Nζ-Hζ3…U31@O2, U42@O2’-HO2’…ASN448@Oδ1, ASN450@N-H…U42@ O2’, A14@N6-H61…GLY482@O, A14@O2’-HO2’…GLU484@Oε2, ASN487@ Nδ2-Hδ22…C24@O2’ and TYR489@OH-HH…G25@O2’ and the van der Waals packing interactions between nucleotides of the apical loop part and some aromatic residues of the RBD (such as TYR421, TYR453, PHE456, TYR473, PHE486, and PHE489; see Figure 2—figure supplement 4). These detailed interactions provide important insights for future in silico aptamer design. We note that CAAMO is not intended to establish experimentally validated complex structures, but rather to provide preliminary binding models that enable rational affinity maturation of aptamers in scenarios where structural information is limited or unavailable.
In addition to the aptamer Ta, we also analyzed a weaker binding aptamer sequence, RBD-PB6-Tc (Tc), as a negative control (Valero et al., 2021). We constructed a binding model for the Tc sequence with the RBD using the same approach as for the aptamer Ta (see Figure 2—figure supplement 5). SMD simulations confirmed that the binding strength of Tc to the RBD was significantly weaker than that of the aptamer Ta (see Figure 2—figure supplement 5D), which supports the robustness of our approach in generating informative binding models for comparative analysis and affinity optimization of an RNA aptamer with a target protein. Additionally, our EMSA experiments also confirmed that the binding affinity of the Ta-RBD complex is stronger than that of the Tc-RBD complex (see Figure 2F and G, Figure 2—source data 1, and Figure 2—source data 2), in good agreement with the previous study (Valero et al., 2021). The measured dissociation constant for the Ta-RBD complex is Kd = 110.7 μM, while the sequence Tc is unable to bind to the RBD under all conditions tested. These results motivate the design of high-affinity RNA aptamers targeting the RBD of the SARS-CoV-2 spike protein to enhance the treatment of COVID-19.
Comparison of binding properties between the aptamer Ta and neutralizing antibodies
In recent years, researchers have developed numerous antibodies to neutralize SARS-CoV-2 infection by blocking the RBD of the spike protein from binding to ACE2 (Tortorici et al., 2020). Their binding properties with the RBD have been extensively studied. Since we determined the most probable binding model of the aptamer Ta to the RBD, comparing the binding properties of the aptamer Ta with those of representative neutralizing antibodies to the RBD is both feasible and meaningful. A preliminary comparison of the respective binding modes of ACE2, the aptamer Ta, and neutralizing antibodies to the RBD (see Figure 3—figure supplement 1A) indicates that the aptamer Ta can precisely occupy the binding site of ACE2, similar to many neutralizing antibodies, thereby blocking SARS-CoV-2 invasion. To further explore this, we analyzed the contact ratios of residues on the RBD bound to ACE2 (derived from MD simulations), to the aptamer Ta (derived from MD simulations), or to the neutralizing antibodies (derived from all available experimentally resolved SARS-CoV-2 RBD–antibody complex structures curated in the Coronavirus Antibody Database, CoV-AbDab Raybould et al., 2021). CoV-AbDab is a publicly available, curated database that aggregates all published coronavirus-binding antibodies with associated structural information, providing a comprehensive and unbiased structural ensemble for contact frequency analysis. The results indicate that the contact regions on the RBD bound by the aptamer Ta, ACE2, or the neutralizing antibodies were largely similar (Figure 3A). Additionally, we analyzed the electrostatic potential distribution on the RBD surface to assess how the highly negatively charged RNA aptamer interacts with the RBD (see Figure 3A). The positively charged regions on both sides of the RBD promote the aptamer binding. To quantitatively analyze the binding complexes, we defined a key residue as the one with a contact ratio greater than 0.5. We found that ACE2 binds to the RBD using 12 key residues, such as LYS417, LEU455, PHE456, ALA475 (see Figure 3B), while the neutralizing antibodies engage 19 key residues on the RBD, including all of ACE2’s key residues except for THR500. This higher number of key residues corresponds with the stronger binding ability of neutralizing antibodies compared to ACE2, enhancing their ability to block SARS-CoV-2 invasion. For the aptamer Ta, we identified 27 key residues, most of which overlap with those of ACE2 and neutralizing antibodies. For instance, nine key residues on the RBD are shared by ACE2, neutralizing antibodies, and the aptamer Ta (see Figure 3B), suggesting that the aptamer Ta may have comparable or even better binding ability to the RBD compared to the neutralizing antibodies.
We performed binding energy estimations using MM/GBSA calculation and competitive binding assays with EMSA experiments to further compare the binding abilities of the aptamer Ta and neutralizing antibodies to the RBD. We selected three potent antibodies that can effectively neutralize SARS-CoV-2 infection, including antibody P2C-1F11 (Ge et al., 2021; PDB id: 7CDI), 2H2 (Zhang et al., 2021b; PDB id: 7DK4), and S2E12 (Tortorici et al., 2020; PDB id: 7K4N), and calculated their binding energy to the RBD, along with that of ACE2. As shown in Figure 3C, antibodies P2C-1F11 (–119.24±7.86 kcal/mol) and 2H2 (–107.18±8.89 kcal/mol) have lower binding energies (indicating stronger binding abilities) to RBD than ACE2 (–94.94±11.95 kcal/mol), while antibody S2E12 (–85.77±8.55 kcal/mol) exhibits a similar binding energy to ACE2. These findings confirm the reliability of our binding energy estimations using MM/GBSA calculation. The aptamer Ta, with a binding energy of –142.93±7.65 kcal/mol, has a significantly lower binding energy than both ACE2 and the three antibodies, indicating a stronger binding ability of the aptamer Ta to the RBD. We further tested the binding ability of the aptamer Ta with a commercial neutralizing antibody (SinoBiological, Cat: 40592-R001, termed 40592-R001) that targets the RBD of the SARS-CoV-2 spike protein. The antibody 40592-R001 demonstrated high affinity for the RBD in our native gel protein-protein binding assays (Figure 3D, Figure 3—source data 1, and Figure 3—source data 2). We then added the antibody 40592-R001 into a mixture of the aptamer Ta and RBD, monitoring the changes in the formation of the aptamer-RBD complex via fluorescence intensity in the EMSA experiments. In principle, if the aptamer Ta binds more strongly to RBD than the antibody 40592-R001, the presence of the antibody should not disrupt the formation of the aptamer-RBD complex; otherwise, the amount of the aptamer-RBD complex would be reduced. Notably, the Ta-RBD complex formation remained unchanged after adding the antibody (Figure 3E, Figure 3—source data 1, and Figure 3—source data 2), suggesting that the aptamer Ta exhibits binding capability comparable to the tested monoclonal neutralizing antibody. These results also confirmed that the aptamer Ta binds to the same site on the RBD as the 40592-R001 antibody. In conclusion, the aptamer Ta presents a promising alternative, or at least a complementary approach, to conventional antibody-based neutralizing therapies against COVID-19. These findings prompt us to further optimize the aptamer Ta to enhance its binding ability to the RBD of the SARS-CoV-2 spike protein.
Structure-based rational design of the aptamer Ta to improve binding affinity
Since the SELEX screening process explored only a limited sequence space, we anticipated that the SELEX-derived aptamer Ta could be further optimized via rational mutation scanning to improve its binding affinity to the RBD of the SARS-CoV-2 spike protein. In principle, nucleotide mutations in the aptamers can affect the binding affinity, which can be characterized by changes in binding free energy (ΔΔG), and we accurately assessed these changes using FEP calculations. A negative change in binding free energy (ΔΔG<0) indicates improved binding affinity, while a positive change (ΔΔG>0) means a decrease in binding affinity. FEP is regarded as one of the most rigorous and reliable methods in estimating binding free energy changes, achieving high accuracy in identifying key residues and their mutational effects for many protein-protein, protein-ligand, and protein-RNA complexes, with results comparable with experiments (Bell et al., 2020; Ahmed et al., 2019). However, unlike protein and peptide drugs, the structure of RNA molecules is very sensitive to its nucleotide composition and even a single mutation may cause significant changes in its secondary (2D) structure, potentially affecting its binding modes. To address this, we employed secondary structure analysis (SSA) to examine the structural similarity before and after nucleotide mutation. Moreover, we used MFold (Zuker, 2003) to predict the 2D structure with the lowest free energy for each mutated sequence. Structural similarity between the wild-type (WT) Ta sequence and its mutants was quantified by BP similarity, defined as the ratio of shared BPs (Nshared) to the total base pairs in the WT sequence (NWT). Overall, as summarized in Figure 4A, we combined rational mutation scanning, SSA, and FEP to design Ta analogues with enhanced binding affinity.
Based on the binding complex between the aptamer Ta and RBD constructed above, we selected 16 vital nucleotides (A14, C23-G34, A40, and A42-G43) on the aptamer Ta that showed high contact frequency with RBD (see Figure 4B) for exhaustive single-mutation scanning. These selected nucleotides play a crucial role in Ta-RBD binding interactions, and their mutagenesis studies may aid in designing analogues with improved binding affinity. After SSA based on BP similarity, we found that most single mutations preserved a WT-like 2D structure (Figure 4C) with BP similarity greater than 0.9 (such as TaG34C in Figure 4—figure supplement 1), but several single mutations cause significant rearrangement of their 2D structure with a BP similarity less than 0.9 (for instance, TaU30G in Figure 4—figure supplement 1). Mutations with a BP similarity greater than 0.9 were subjected to FEP calculations to further evaluate their impact on binding free energy changes, with the results shown in Figure 4D and Supplementary file 1A.
For nucleotides in the apical loop of the aptamer Ta (see Figures 1B and 2E), mutation to any other base types resulted in an increased binding free energy and weakened binding affinity, such as ΔΔG=1.94 ± 0.43 kcal/mol for G25C, 0.66±0.16 kcal/mol for U27G, and 4.66±0.76 kcal/mol for C29G. This suggests that G25, U27, and C29 are already optimal choices for RBD binding. The previous study (Valero et al., 2021) highlighted the importance of the apical loop in directing the aptamer Ta’s binding to the RBD, and truncation of this apical loop was proven to significantly reduce its binding ability. Our FEP calculations align with this observation. Next, although the end stem part of the aptamer Ta also tightly binds to the RBD (see Figures 2E and 4B), base mutations in this region had minimal effects on binding affinity, likely due to dominant electrostatic interactions between the negatively charged phosphate groups and the positively charged residues of RBD. For instance, A42G showed a ΔΔG of –0.22±0.56 kcal/mol and G43U a ΔΔG of 0.37±0.30 kcal/mol. Furthermore, since the bulge part of the aptamer Ta serves as a linker to regulate the saddle-like binding shape with RBD (see Figures 2E and 4B), nucleotide mutations in this region may also affect binding ability despite their low contact frequency with the RBD. Indeed, disruption of the C23-G34 BP improved the binding affinity of the aptamer to RBD, with C23A, C23G, and C23U yielding ΔΔG values of –2.15±0.43,–2.49±0.49, and –1.96±0.58 kcal/mol, respectively, and G34A, G34C, and G34U showing ΔΔG values of –2.50±0.28,–3.05±0.26, and –2.65±0.33 kcal/mol, respectively (see Figure 4D and Supplementary file 1A). Ultimately, we generated six candidate sequences (TaC23A, TaC23G, TaC23U, TaG34A, TaG34C, and TaG34U) predicted to have stronger binding affinity to RBD than WT aptamer Ta.
We conducted EMSA experiments to validate the binding ability of these designed aptamers. Preliminary binding results of the RBD-RNA complex bands showed that five of the six designed sequences (TaG34C, TaG34U, TaG34A, TaC23A, and TaC23U) exhibited higher fluorescence intensity than WT aptamer Ta (see Figure 4E, Figure 4—source data 1, and Figure 4—source data 2). Further, the binding curves with different RBD concentrations and the resulting dissociation constant (Kd) measurements confirmed the superior binding capabilities of these five sequences compared to WT aptamer Ta (see Figure 4—figure supplement 2, Supplementary file 1B, Figure 4—figure supplement 2—source data 1 and 2). As shown in Figure 4F, except for TaC23G, the binding free energy changes derived from EMSA experiments (ΔΔGexp) were consistent with FEP calculations (ΔΔG) for the remaining five designed aptamers. Here, the experimental binding free energy change is , where and are dissociation constants for WT aptamer Ta and the designed sequence, respectively, and is the Boltzmann constant and . The magnitude of the binding free energy changes generated from FEP calculations tend to be greater, likely due to limitations in the force field parameters. Nonetheless, the high success rate (0.83, 5/6) achieved in this structure-based rational design process underscores the reliability of the RBD-aptamer Ta complex model proposed in Figure 2E. These results highlight the potential of our CAAMO framework as an effective tool for optimizing aptamer binding affinity.
Designed aptamer TaG34C shows excellent binding ability to RBD
Among the five designed candidate sequences, both the FEP calculation and EMSA experiment confirmed that aptamer TaG34C has the highest binding affinity to the RBD of SARS-CoV-2 spike protein. As shown in Figure 5A, the dissociation constant derived from EMSA experiments for aptamer TaG34C is Kd = 33.5 ± 1.6 µM, which is approximately 3.3-fold higher compared to WT Ta (Kd = 110.7 µM). The remaining four designed aptamers exhibited approximately a twofold increase in binding affinity relative to WT Ta, with Kd = 55.5 ± 4.1 µM for TaG34A and Kd = 55.7 ± 9.8 µM for TaC23A (see Figure 4—figure supplement 2 and Supplementary file 1B).
We performed MD simulations on the TaG34C-RBD binding complex to explore the molecular mechanism underlying the improved binding affinity of aptamer TaG34C. As shown in Figure 5B, the C23-G34 BP is located in the bulge region of WT Ta, which plays a critical role in regulating its saddle-like binding shape. We speculated that disrupting the C23-G34 BP through mutation (e.g. G34C) could reduce the strain during aptamer binding to the RBD. The G34C substitution can alter the binding environment of adjacent nucleotides, such as U35, allowing them to form tighter contacts with the RBD loop region (from GLN474 to TYR489). For instance, our simulations showed that the distance between nucleotide U35 and PHE486 is shorter in the TaG34C-RBD complex than that in the WT Ta-RBD complex (see Figure 5C). This reduced distance remains stable throughout the MD simulations, indicating that U35 and PHE486 form a stable π–π stacking interaction after the G34C substitution. Additionally, the distance between C34 and PHE486 in TaG34C-RBD is also closer compared to G34 and PHE486 in WT Ta-RBD. These findings support our hypothesis regarding the improved binding affinity in aptamer TaG34C and provide a basis for further in silico design of new aptamers.
We also conducted competitive binding experiments to compare the binding capacities of the designed aptamer TaG34C and a commercial monoclonal SARS-CoV-2 neutralizing antibody (Nguyen et al., 2022) against the RBD. As shown in Figure 5D; Figure 5—source data 1 and 2 and Figure 1F, when the monoclonal SARS-CoV-2 neutralizing antibody 40592-R001 was added to the WT Ta-RBD complex, the fluorescence intensity of the complex band gradually decreased, indicating that the antibody at high concentrations can partially replace the aptamer Ta in the Ta-RBD binding complex and has comparable, though weaker, binding ability. However, when the same antibody was added to the designed TaG34C-RBD complex, the fluorescence intensity of the complex bands remained nearly unchanged at all tested antibody concentrations, indicating that the binding affinity of TaG34C is significantly stronger than that of the monoclonal SARS-CoV-2 neutralizing antibody or WT aptamer Ta.
To further exclude non-specific aptamer-protein interactions, we performed parallel EMSA assays using BSA as a non-target protein control for Ta, Tc, and the optimized TaG34C (see Figure 5—figure supplement 1, Figure 5—figure supplement 1—source data 1 and 2). Only weak, comparable background signals were observed for all three aptamers with BSA. Such minor non-specific binding may originate from BSA itself or trace contaminating proteins in the BSA samples. In contrast, markedly stronger binding was detected between RBD and Ta or TaG34C, whereas no detectable binding was observed with the negative control Tc (Figure 4E, Figure 5—figure supplement 1). Such distinct binding profiles of aptamers with RBD and BSA confirm that the aptamer-RBD interactions characterized in this study are target-specific.
In summary, the combined results from FEP calculated and EMSA measured binding affinities, binding molecular mechanism analysis, and antibody competitive binding assays clearly demonstrate that the designed aptamer TaG34C exhibits excellent binding ability to RBD. These findings highlight the importance of optimizing SELEX-derived aptamers through structure-based rational design to enhance their binding affinity.
Binding performance of Ta and TaG34C against SARS-CoV-2 RBD variants
To further evaluate the binding performance and specificity of the designed aptamer TaG34C toward different SARS-CoV-2 variants (Hodcroft, 2021), we conducted extensive free energy perturbation combined with Hamiltonian replica-exchange MD (FEP/HREX) (Sugita et al., 2000; Woods et al., 2003; Jiang and Roux, 2010) for both the wild-type aptamer Ta and the optimized TaG34C against a series of RBD mutants. The representative variants include the early Alpha (B.1.1.7) and Beta (B.1.351) lineages, as well as a panel of Omicron sublineages (BA.1-BA.5, BA.2.75, BQ.1, XBB, XBB.1.5, EG.5.1, HK.3, JN.1, and KP.3) carrying multiple mutations within the RBD region (residues 333–527). For each variant, mutations within 5 Å of the bound aptamer were included in the FEP to accurately estimate the relative binding free energy change (ΔΔG).
For the WT Ta aptamer, the FEP-predicted binding affinities toward the Alpha and Beta RBD variants were consistent with the experimental trends, validating the reliability of our model (see Supplementary file 1C). Specifically, Ta maintained comparable or slightly enhanced binding to the Alpha variant and showed only marginally reduced affinity for the Beta variant.
In contrast, the optimized aptamer TaG34C exhibited markedly improved and broad-spectrum binding toward most tested variants (see Supplementary file 1D). For early variants such as Alpha, Beta, and Gamma, TaG34C maintained enhanced affinities (ΔΔG<0). Notably, for multiple Omicron sublineages – including BA.1, BA.2, BA.2.12.1, BA.2.75, XBB, XBB.1.5, XBB.1.16, XBB.1.9, XBB.2.3, EG.5.1, XBB.1.5.70, HK.3, BA.2.86, JN.1, and JN.1.11.1 – the calculated binding free energy changes ranged from –1.89 to –7.58 kcal/mol relative to the wild-type RBD, indicating substantially stronger interactions despite the accumulation of multiple mutations at the aptamer-RBD interface. Only in a few other Omicron sublineages, such as BA.4, BA.5, and KP.3, a slight reduction in binding affinity was observed (ΔΔG>0).
These computational findings demonstrate that the TaG34C aptamer not only preserves high affinity and specificity for the RBD but also exhibits improved tolerance to the extensive mutational landscape of SARS-CoV-2. Collectively, our results suggest that TaG34C holds promise as a high-affinity and potentially cross-variant aptamer candidate for targeting diverse SARS-CoV-2 spike protein variants.
Discussion
Determining the 3D structure of RNA aptamer-target protein complexes is crucial for understanding the binding mechanism and optimizing binding efficacy for various applications. Despite the discovery of aptamers for over 1100 target proteins (Ali et al., 2019), only a limited number of aptamer-protein complex 3D structures are available (only 119 deposited in the PDB as of May, 2025). The scarcity of experimentally determined aptamer-protein structures is primarily due to the inherent flexibility of RNA molecules and the high cost of experimental procedures. However, with recent advancements in protein and RNA 3D structure prediction (Zhang et al., 2021a; Jumper et al., 2021; Abramson et al., 2024) and improvements in atomic force field parameters (Maier et al., 2015; Zgarbová et al., 2015), as well as the increased availability of high-performance computing resources, computational modeling of RNA aptamer-protein complexes has become increasingly promising. In this work, we developed a multi-strategy computational approach to determine the most probable RNA aptamer-protein binding conformation. Our approach integrates RNA 3D structure prediction, ensemble docking, clustering, and binding capacity assessment, with a critical focus on identifying the lowest-energy binding conformation using various energy assessment methods. We believe that the predicted binding conformation represents a plausible member of the predicted ensemble that is functionally informative for guiding structure-based aptamer optimization, although it may not correspond to the exact native structure.
We found that the aptamer Ta binds to the RBD of SARS-CoV-2 spike protein in a saddle-like shape, where the apical loop and end stem parts bind to two opposite sides of the RBD, while the bulge serves as a connector and regulates the bending angle. Theoretical analysis and experimental validation confirmed that this binding model is reliable. With this 3D structure, we were able to conduct structure-based rational optimization of the aptamer sequence to improve its binding affinity and then to perform a head-to-head comparison between the RNA aptamer and neutralizing antibodies.
The development of aptamer-based RNA therapeutics involves iterative rounds of design, testing, and optimization. A key step in this process is optimizing the original aptamer sequence to design a series of analogues with comparable or even improved binding affinities to the target protein. In silico structure-based aptamer design, which involves nucleotide mutation, insertion, deletion, and their binding affinity assessment, is gaining prominence (Gao et al., 2016). Our proposed framework, CAAMO, enables efficient aptamer lead optimization requiring only the aptamer’s nucleotide sequence as the input information. Unlike peptide and protein counterparts, RNA structures are highly sensitive to nucleotide composition, and even a single mutation may cause rearrangement in its secondary structure. To address this issue, we employed secondary structure analysis to evaluate the effect of mutations on RNA folding and only submitted those maintaining a similar folding structure to subsequent FEP-guided binding free energy evaluations.
For the Ta-RBD binding complex, mutation scanning revealed that mutations of nucleotides in direct contact with the RBD showed negligible or increased binding free energy changes, whereas mutations in the middle portion, which has fewer contacts with RBD but regulates the bending angle of the RNA aptamer, resulted in reduced binding free energy and improved binding affinity. These findings suggest that during structure-based optimization, attention should be paid not only to the nucleotides at the binding interface but also to the regulatory nucleotides that affect the binding shape. Our CAAMO framework generated six optimized candidate sequences, and EMSA experiments confirmed that five of them exhibited significantly stronger binding affinity to the RBD than the wild-type Ta (about 2- to 3.3-fold improvement). This high success rate (~83%) validates the reliability of the putative 3D binding model and demonstrates the effectiveness of our computational framework. Although the absolute Kd values determined by EMSA cannot be directly compared with surface-based methods such as SPR or BLI, the relative affinity trends remain highly consistent. While EMSA provides semi-quantitative affinity estimates, the close agreement between experimental EMSA trends and FEP-calculated ΔΔG values supports the robustness of the relative affinity changes reported here. In future studies, additional orthogonal biophysical techniques (e.g. filter-binding, SPR, or BLI) will be employed to further validate and refine the protein-aptamer interaction models. Furthermore, since the current study only considered single-nucleotide mutations, other design strategies such as double mutations, multiple mutations, nucleotide insertions, and deletions should be explored in future studies. Beyond the Ta-RBD system, the CAAMO framework itself is inherently generalizable. Our ongoing work applying CAAMO to optimize aptamers targeting other therapeutically relevant proteins, such as the epidermal growth factor receptor (EGFR) (Mahajan et al., 2021), has yielded promising preliminary results, further underscoring the potential of this computational-experimental integrated approach for broader aptamer engineering. While the present study primarily focused on affinity enhancement, we acknowledge that other key developability traits-such as nuclease resistance, structural and thermodynamic stability, and in vivo persistence-are equally critical for advancing aptamers toward therapeutic applications. These properties were not evaluated here but will be systematically addressed in future iterations of the CAAMO framework to enable comprehensive optimization of aptamer candidates.
Aptamers, often referred to as ‘chemical antibody’, offer a valuable comparison to real antibodies in terms of binding properties, yet such comparative analyses are currently limited. The RBD of SARS-CoV-2 spike protein is an ideal target for these comparisons, as binding modes for both the aptamer Ta and neutralizing antibody-RBD complexes are available. Our computational and experimental studies showed that the aptamer Ta has comparable binding abilities to the RBD compared to representative neutralizing antibodies analyzed in this study. Analysis of the underlying binding details revealed that more critical RBD residues are involved in aptamer Ta-RBD interaction, while MM/GBSA calculations and competitive binding assays confirmed the stronger binding capacity of the aptamer Ta. Specifically, a newly designed aptamer (TaG34C) exhibited excellent RBD binding ability, surpassing both WT Ta and commercial antibodies. Given the concerns surrounding antibody-dependent enhancement (ADE), which could worsen COVID-19 outcomes by increasing virus infectivity and virulence (Lee et al., 2020), there is an urgent need to develop smaller and safer molecules to neutralize the virus and complement existing treatment modalities. Furthermore, the previous study (Valero et al., 2021) has claimed that WT Ta aptamer can efficiently block viral infection at low concentration and provide a promising lead for the detection and treatment of SARS-CoV-2 and emerging variants. Therefore, the designed aptamer TaG34C, as well as four other successful candidates, provides a promising alternative to antibodies for fighting COVID-19.
Materials and methods
Generation of aptamer 3D conformation ensemble
Request a detailed protocolTo predict the 3D structures of the aptamers Ta (5′-GGCGACAUUU GUAAUUCCUG GACCGAUACU UCCGUCAGGA CAGAGGUUGC CA-3′) and Tc (5′-GGUCCUGGAC CGAUACUUCC GUCAGGACCA-3′’), five state-of-the-art RNA 3D structure prediction tools were employed: IsRNA2 (Zhang et al., 2021a), FARFAR2 (Watkins et al., 2020), SimRNA (Boniecki et al., 2016), iFoldRNA (Krokhotin et al., 2015), and RNAComposer (Sarzynska et al., 2023). The predicted secondary structure from Mfold (Zuker, 2003), along with the aptamer sequence, was used as input for 3D structure prediction. IsRNA2 was downloaded and ran locally following the identical procedure as in the previous studies (Zhang et al., 2023); FARFAR2 was executed in Rosetta 3.12 using the rna_denovo application with default parameters; the web-based version of SimRNA, iFoldRNA, and RNAComposer were assessed, and their default parameters were used. The top 5 predictions generated by each program (25 structures in total) were collected to construct a 3D conformation ensemble of a given RNA aptamer.
Generation of aptamer-RBD binding complex pool
Request a detailed protocolThe binding complex pool for aptamer Ta-RBD was generated using multiple popular docking tools, including HADDOCK (van Zundert et al., 2016), HDOCK (Yan et al., 2017), ZDOCK (Pierce et al., 2014), and RosettaDock (Lyskov et al., 2013). RosettaDock was performed locally using Rosetta software, while the other three docking tasks were completed on their respective webservers. The RBD structure (Wang et al., 2020; PDB id: 6LZG) refined by MD simulation, and 25 RNA 3D conformations predicted in the previous step were used as the receptor and ligands for docking, respectively. The top 10 binding poses from each docking were recorded, yielding a total of 1000 (4 × 25 × 10) aptamer Ta-RBD binding complexes in the pool. Of these, 879 conformations shared the same binding interface of the ACE2-RBD complex. Then, these 879 conformations were clustered into 6 major groups, each representing a significantly distinct binding conformation. The binding complex pool for Tc-RBD interaction (used as a negative control) was constructed following the same procedure.
Molecular dynamics simulations
Request a detailed protocolAll-atom MD simulations were performed using Gromacs (Páll et al., 2020; version 2021.5). For each system, a water box with at least 1.5 nm distance from the surface of the complex was used to solvate the systems, and NaCl ions were added to achieve a physiological concentration of 150 mM after neutralizing the system. The Amberff14SB force field was used for protein and RNA. Water molecules were described by the TIP3P model (Jorgensen et al., 1983), and Li and Merz ion parameters (Li et al., 2015) were used. The periodic boundary conditions were applied in all three dimensions. The particle mesh Ewald (PME) method was used to compute the long-range electrostatic interactions while the vdW interactions were truncated at 1.5 nm. The LINCS algorithm was adopted to constrain the H-bonds to allow an integration timestep of 2 fs. Before MD productions, an energy minimization, 100 ps NVT, and 10 ns NPT simulations with a temperature of T=310.15 K and pressure of 1 atm were executed sequentially to equilibrate the simulation box. To prevent unexpected structural deviations in the beginning, position restraints on backbone atoms of RNA and protein were performed in the NVT and NPT simulations. After that, a series of MD simulations were conducted in the NPT ensemble with a velocity-rescaled Berendsen thermostat: (1) 500 ns MD simulations were performed for the RBD. Snapshots extracted from the last 300 ns MD trajectories (600 snapshots recorded in the duration of 500 ps) were clustered based on the linkage method with a 0.2 nm RMSD cutoff to obtain the most probable receptor conformation for docking. (2) 100 ns simulations were performed on the representative Ta/Tc-RBD complex conformations obtained from the docking. 5 mM MgCl2 was added to the simulation system. These trajectories were used to calculate RMSD and MM/GBSA. (3) 100 ns simulations were performed on the ACE2-RBD complex (PDBID: 6LZG) and three antibody-RBD complexes (PDBID: 7CDI, 7DK4, and 7K4N). These also were used to perform MM/GBSA. (4) 500 ns simulations were conducted for the TaG34C-RBD and Ta-RBD complexes. 5 mM MgCl2 was added to the simulation system. A summary of MD simulations performed in this study was given in Supplementary file 1E. The 3D structure models were rendered using UCSF ChimeraX (version 1.6.1) programs.
MM/GBSA calculations
Request a detailed protocolIn this study, binding energies (ΔG) of ACE2, aptamers (Ta and Tc), and several neutralizing antibodies to RBD were assessed using the end-point Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method, implemented through the gmx_MMPBSA software (Valdés-Tresanco et al., 2021). Briefly, ΔG was calculated by summing up the changes in electrostatic energies (ΔEele), the van der Waals energies (ΔEvdW), the electrostatic solvation energy (ΔGGB, polar contribution), the nonpolar contribution (ΔGSA) between the solute and the continuum solvent, and conformational entropy (–TΔS) upon ligand binding. The dielectric constants for the solute and solvent were set to 10 and 78.5, respectively. The OBC solvation model (igb = 8) was used. The interaction entropy method was used to calculate the conformational entropy (–TΔS). Additionally, the lowest energy conformations from the complexes pool clustering were selected based on single-frame conformational MM/GBSA. The static complex structure was employed to calculate the enthalpy using MM/GBSA.
SMD and rupture works
Request a detailed protocolConstant velocity SMD was performed to calculate the rupture work required to separate the bound aptamer from the RBD. A steered velocity of 0.1 nm/ns was applied to the center of mass (COM) of the aptamer, while keeping the COM of RBD fixed, using the COM distance between aptamer (or antibody) and RBD as the collective variable. Four replicate SMD simulations (about 50 ns simulation time each) were performed for each binding complex. All simulation parameters remain the same as those used in the MD simulations. The force spectra were recorded during the SMD simulations at 0.1 ps intervals. The rupture works were obtained through integration of the force spectra over the COM distance.
FEP calculations
Request a detailed protocolThe binding free energy changes resulting from point mutations of key bases at the interface between the aptamer Ta and RBD were calculated using the free energy perturbation (FEP) method. We estimated the binding free energy changes for single nucleotide mutations in both the bound state (Ta and RBD complex) ΔGbound and the free state (the aptamer Ta only) ΔGfree using Gromacs 2021.5. Thus, the binding free energy change caused by nucleotide mutation was estimated as ΔΔGcalc=ΔGbound-ΔGfree. For each single nucleotide mutation, the dual-topology file was prepared in a pmx-like manner based on the Amberff14SB force field and 18 λ windows (0.0, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.8, 0.85, 0.9, 0.95, 0.99, and 1.0) with 1 ns/window were used. The vdW and electrostatic interactions were transferred simultaneously during simulations, and the soft-core potentials (α=0.3) were used. For each mutation, five independent replicas starting from different conditions were performed for sufficient sampling, and at least 180 ns (1 ns ×18 windows ×5 runs ×2 states) simulation time was generated, which resulted in reasonable convergence in the free energy calculations. The Gromacs bar analysis tool was used to estimate the binding free energy changes.
FEP/HREX
Request a detailed protocolTo evaluate the binding sensitivity of the optimized aptamer TaG34C toward SARS-CoV-2 RBD variants, we employed free energy perturbation combined with Hamiltonian replica-exchange MD (FEP/HREX) simulations for enhanced sampling efficiency and improved convergence. The relative binding free energy changes (ΔΔG) upon RBD mutations were estimated as:
where ΔGbound and ΔGfree represent the RBD mutations-induced free energy changes in the complexed and unbound states, respectively. All simulations were performed using GROMACS 2021.5 with the Amber ff14SB force field. For each mutation, dual-topology structures were generated in a pmx-like manner, and 32 λ-windows (0.0, 0.01, 0.02, 0.03, 0.06, 0.09, 0.12, 0.16, 0.20, 0.24, 0.28, 0.32, 0.36, 0.40, 0.44, 0.48, 0.52, 0.56, 0.60, 0.64, 0.68, 0.72, 0.76, 0.80, 0.84, 0.88, 0.91, 0.94, 0.97, 0.98, 0.99, 1.0) were distributed uniformly between 0.0 and 1.0. To ensure sufficient sampling, each window was simulated for 5 ns, with five independent replicas initiated from distinct velocity seeds. Replica exchange between adjacent λ states was attempted every 1 ps to enhance phase-space overlap and sampling convergence. The van der Waals and electrostatic transformations were performed simultaneously, employing a soft-core potential (α=0.3) to avoid singularities. For each RBD variant system, this setup resulted in an accumulated simulation time of approximately 1600 ns (5 ns ×32 windows ×5 replicas ×2 states). The Gromacs bar analysis tool was used to estimate the binding free energy changes.
EMSA experiments
Request a detailed protocolThe sequences of Ta, Tc, TaG34C, TaG34U, TaG34A, TaC23G, TaC23A, and TaC23U aptamers are shown in Supplementary file 1B. In these sequences, the uppercase letters A, U, G, and C indicated the ribonucleotide. The EMSA was performed according to a previous protocol (Zhang et al., 2023) with a minor modification. Synthesized 5′ end Cy3-labeled RNAs were resuspended with the RNase-free H2O to a concentration of 100 µM. 5 μl of Cy3-labeled RNAs were annealed with the 5 μl 2×annealing buffer (20 mM Tris-HCl pH 7.5, 200 mM KCl) under a predefined procedure: 68 °C for 5 min, then annealing at –0.1 °C/s to 25 °C, and finally at 25 °C for 5 min, then diluted to the final concentration of 5 µM. The SARS-CoV-2 Spike RBD protein was purchased from HuaBio (Cat: HA210064). Mouse monoclonal SARS-CoV-2 neutralizing antibody was purchased from Sino Biological (Cat: 40592-R001). 40 μM of RBD protein and 0.5 μM of annealed Cy3-labeled aptamers were mixed in the EMSA buffer (10 mM Sodium phosphate buffer pH 7.5, 1 U/μl SUPERase-In RNase Inhibitor [Thermo Fisher]). For competitive binding experiments, Cy3-labeled RNAs, RBD protein, and neutralizing antibody 40592-R001 were added simultaneously to the EMSA buffer and incubated at room temperature for 20 min.
The integrity and purity of the RBD protein were confirmed by denaturing SDS-PAGE (Figure 5—figure supplement 2, Figure 5—figure supplement 2—source data 1 and 2), showing a single intact band without degradation. The multiple bands observed in native PAGE (e.g. Figure 3D) are due to conformational and glycosylation heterogeneity (Cai et al., 2020; Casalino et al., 2020; Ives et al., 2024; Wrapp et al., 2020) rather than protein degradation. To rule out non-specific aptamer-protein interactions, BSA was additionally included as a non-target protein control in EMSA assays; the WT Ta, the negative control Tc, and the optimized TaG34C all showed only weak, comparable background signals with BSA but distinct target-specific binding to RBD (Figure 5—figure supplement 1). Uncropped EMSA gel images and consistent results from three biological replicates (Figure 2F, Figure 3—source data 1 and 2, Figure 4—figure supplement 2; Figure 4—figure supplement 2—source data 1 and 2) confirm the absence of protein aggregation and ensure data reliability.
For competitive binding experiments, 40 μM of RBD protein, 0.5 μM of annealed Cy3-labeled RNAs, and increasing concentrations of SARS-CoV-2 neutralizing antibody 40592-R001 (0–1.67 μM) were mixed in the EMSA buffer and incubated at room temperature for 20 min. Next, the mixtures were added 6×loading buffer (15% Ficoll 400, 0.25% Bromophenol Blue, 0.25% Xylene cyanol, 1×TBE), then resolved on a native 0.8% agarose gel and imaged with iBright1500 (Thermo Fisher). The images were quantified with Image J software. The dissociation constant Kd for RBD with aptamers was calculated using Prism 8 (GraphPad) software. All uncropped raw gel images corresponding to these EMSA experiments are provided as source data.
Data availability
All data supporting the findings of this study are available within the article and its supplementary information files. The original, uncropped gel images underlying the EMSA and SDS-PAGE experiments are provided as Source data files associated with the corresponding figures and figure supplements. The core computational code of the CAAMO framework (the free-energy perturbation toolchain) is publicly available at https://github.com/yqyang733/CAAMO (copy archived at Yang, 2026). The published protein structures used in this work were obtained from the RCSB Protein Data Bank under accession codes 6LZG, 7CDI, 7DK4, and 7K4N.
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Worldwide Protein Data BankSARS-CoV-2 spike in complex with the S2E12 neutralizing antibody Fab fragment.https://doi.org/10.2210/pdb7K4N/pdb
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Worldwide Protein Data BankS-2H2-F3a structure, two RBDs are up and one RBD is down, each RBD binds with a 2H2 Fab.https://doi.org/10.2210/pdb7DK4/pdb
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Worldwide Protein Data BankCrystal structure of SARS-CoV-2 antibody P2C-1F11 with RBD.https://doi.org/10.2210/pdb7CDI/pdb
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Worldwide Protein Data BankStructure of novel coronavirus spike receptor-binding domain complexed with its receptor ACE2.https://doi.org/10.2210/pdb6LZG/pdb
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Article and author information
Author details
Funding
National Key Research and Development Program of China (2021YFF1200404)
- Ruhong Zhou
National Key Research and Development Program of China (2024YFA1307502)
- Dong Zhang
National Natural Science Foundation of China (12474203)
- Dong Zhang
National Natural Science Foundation of China (U1967217)
- Ruhong Zhou
Zhejiang Provincial Natural Science Foundation of China (LZ25A040001)
- Dong Zhang
National Independent Innovation Demonstration Zone Shanghai Zhangjiang Major Projects (ZJZX2020014)
- Ruhong Zhou
Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University (SN-ZJU-SIAS-003)
- Ruhong Zhou
Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University (SN-ZJU-SIAS-006)
- Liquan Huang
Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University (SN-ZJU-SIAS-009)
- Dong Zhang
National Center of Technology Innovation for Biopharmaceuticals (NCTIB2022HS02010)
- Ruhong Zhou
Shanghai Artificial Intelligence Lab (P22KN00272)
- Ruhong Zhou
Zhejiang University Global Partnership Fund (188170+194452409/004)
- Ruhong Zhou
National Key Research and Development Program of China (2021YFA1201200)
- Ruhong Zhou
Aoming Biomedical Research (AO-ZJU-SIAS-001)
- Ruhong Zhou
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements
We thank Zaixing Yang and Teng Xie for their helpful discussions. This work was supported by funds from the National Key R&D Program of China (2021YFF1200404 to RZ, 2021YFA1201200 to RZ), National Natural Science Foundation of China (Nos. 12474203 to DZ, U1967217 to RZ), National Independent Innovation Demonstration Zone Shanghai Zhangjiang Major Projects (ZJZX2020014 to RZ), the Zhejiang Provincial Natural Science Foundation of China (No. LZ25A040001), Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University (SN-ZJU-SIAS-003 to RZ, SN-ZJU-SIAS-006 to LH), National Center of Technology Innovation for Biopharmaceuticals (NCTIB2022HS02010 to RZ), Shanghai Artificial Intelligence Lab (P22KN00272 to RZ), Aoming Biomedical Research (AO-ZJU-SIAS-001 to RZ), and Zhejiang University Global Partnership Fund (188170+194452409/004 to RZ).
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