Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer.

The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.

Predicting TCR sequences for unseen antigen epitopes using structural and sequence features.

T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-ß regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-ß sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-ß sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.

Multi-epitope vaccine design using in silico analysis of glycoprotein and nucleocapsid of NIPAH virus.

According to the 2018 WHO R&D Blueprint, Nipah virus (NiV) is a priority disease, and the development of a vaccine against NiV is strongly encouraged. According to criteria used to categorize zoonotic diseases, NiV is a stage III disease that can spread to people and cause unpredictable outbreaks. Since 2001, the NiV virus has caused annual outbreaks in Bangladesh, while in India it has caused occasional outbreaks. According to estimates, the mortality rate for infected individuals ranges from 70 to 91%. Using immunoinformatic approaches to anticipate the epitopes of the MHC-I, MHC-II, and B-cells, they were predicted using the NiV glycoprotein and nucleocapsid protein. The selected epitopes were used to develop a multi-epitope vaccine construct connected with linkers and adjuvants in order to improve immune responses to the vaccine construct. The 3D structure of the engineered vaccine was anticipated, optimized, and confirmed using a variety of computer simulation techniques so that its stability could be assessed. According to the immunological simulation tests, it was found that the vaccination elicits a targeted immune response against the NiV. Docking with TLR-3, 7, and 8 revealed that vaccine candidates had high binding affinities and low binding energies. Finally, molecular dynamic analysis confirms the stability of the new vaccine. Codon optimization and in silico cloning showed that the proposed vaccine was expressed to a high degree in Escherichia coli. The study will help in identifying a potential epitope for a vaccine candidate against NiV. The developed multi-epitope vaccine construct has a lot of potential, but they still need to be verified by in vitro & in vivo studies.

Assessing nanobody interaction with SARS-CoV-2 Nsp9.

The interaction between SARS-CoV-2 non-structural protein Nsp9 and the nanobody 2NSP90 was investigated by NMR spectroscopy using the paramagnetic perturbation methodology PENELOP (Paramagnetic Equilibrium vs Nonequilibrium magnetization Enhancement or LOss Perturbation). The Nsp9 monomer is an essential component of the replication and transcription complex (RTC) that reproduces the viral gRNA for subsequent propagation. Therefore preventing Nsp9 recruitment in RTC would represent an efficient antiviral strategy that could be applied to different coronaviruses, given the Nsp9 relative invariance. The NMR results were consistent with a previous characterization suggesting a 4:4 Nsp9-to-nanobody stoichiometry with the occurrence of two epitope pairs on each of the Nsp9 units that establish the inter-dimer contacts of Nsp9 tetramer. The oligomerization state of Nsp9 was also analyzed by molecular dynamics simulations and both dimers and tetramers resulted plausible. A different distribution of the mapped epitopes on the tetramer surface with respect to the former 4:4 complex could also be possible, as well as different stoichiometries of the Nsp9-nanobody assemblies such as the 2:2 stoichiometry suggested by the recent crystal structure of the Nsp9 complex with 2NSP23 (PDB ID: 8dqu), a nanobody exhibiting essentially the same affinity as 2NSP90. The experimental NMR evidence, however, ruled out the occurrence in liquid state of the relevant Nsp9 conformational change observed in the same crystal structure.

Characterization of a novel format scFv×VHH single-chain biparatopic antibody against metal binding protein MtsA.

Biparatopic antibodies (bpAbs) are engineered antibodies that bind to multiple different epitopes within the same antigens. bpAbs comprise diverse formats, including fragment-based formats, and choosing the appropriate molecular format for a desired function against a target molecule is a challenging task. Moreover, optimizing the design of constructs requires selecting appropriate antibody modalities and adjusting linker length for individual bpAbs. Therefore, it is crucial to understand the characteristics of bpAbs at the molecular level. In this study, we first obtained single-chain variable fragments and camelid heavy-chain variable domains targeting distinct epitopes of the metal binding protein MtsA and then developed a novel format single-chain bpAb connecting these fragment antibodies with various linkers. The physicochemical properties, binding activities, complex formation states with antigen, and functions of the bpAb were analyzed using multiple approaches. Notably, we found that the assembly state of the complexes was controlled by a linker and that longer linkers tended to form more compact complexes. These observations provide detailed molecular information that should be considered in the design of bpAbs.

Animal-derived food allergen: A review on the available crystal structure and new insights into structural epitope.

Immunoglobulin E (IgE)-mediated food allergy is a rapidly growing public health problem. The interaction between allergens and IgE is at the core of the allergic response. One of the best ways to understand this interaction is through structural characterization. This review focuses on animal-derived food allergens, overviews allergen structures determined by X-ray crystallography, presents an update on IgE conformational epitopes, and explores the structural features of these epitopes. The structural determinants of allergenicity and cross-reactivity are also discussed. Animal-derived food allergens are classified into limited protein families according to structural features, with the calcium-binding protein and actin-binding protein families dominating. Progress in epitope characterization has provided useful information on the structural properties of the IgE recognition region. The data reveals that epitopes are located in relatively protruding areas with negative surface electrostatic potential. Ligand binding and disulfide bonds are two intrinsic characteristics that influence protein structure and impact allergenicity. Shared structures, local motifs, and shared epitopes are factors that lead to cross-reactivity. The structural properties of epitope regions and structural determinants of allergenicity and cross-reactivity may provide directions for the prevention, diagnosis, and treatment of food allergies. Experimentally determined structure, especially that of antigen-antibody complexes, remains limited, and the identification of epitopes continues to be a bottleneck in the study of animal-derived food allergens. A combination of traditional immunological techniques and emerging bioinformatics technology will revolutionize how protein interactions are characterized.

Development of multi epitope subunit vaccines against emerging carp viruses Cyprinid herpesvirus 1 and 3 using immunoinformatics approach.

Cyprinid herpesvirus is a causative agent of a destructive disease in common and koi carp (Cyprinus carpio), which leads to substantial global financial losses in aquaculture industries. Among the strains of C. herpesvirus, C. herpesvirus 1 (CyHV-1) and C. herpesvirus 3 (CyHV-3) are known as highly pathogenic to carp fishes in Europe, Asia, and Africa. To date, no effective vaccine has been developed to combat these viruses. This study aimed to develop unique multi-epitope subunit vaccines targeting the CyHV-1 and CyHV-3 using a reverse vaccinology approach. The study began with a comprehensive literature review to identify the most critical proteins, which were then subjected to in silico analyses to predict highly antigenic epitopes. These analyses involved assessing antigenicity, transmembrane topology screening, allergenecity, toxicity, and molecular docking approaches. We constructed two multi-epitope-based vaccines incorporating a suitable adjuvant and appropriate linkers. It revealed that both the vaccines are non-toxic and immunogenic. The tertiary structures of the vaccine proteins were generated, refined, and validated to ensure their suitability. The binding affinity between the vaccine constructs and TLR3 and TLR5 receptors were assessed by molecular docking studies. Molecular dynamics simulations indicated that vaccine construct V1 exhibited greater stability with both TLR3 and TLR5 based on RMSD analysis. Hydrogen bond analysis revealed a stronger binding affinity between the vaccine constructs and TLR5 compared to TLR3. Furthermore, MM-PBSA analysis suggested that both vaccine constructs exhibited a better affinity for TLR5. Considering all aspects, the results suggest that in silico development of CyHV vaccines incorporating multiple epitopes holds promise for management of diseases caused by CyHV-1 and CyHV-3. However, further in vivo trials are highly recommended to validate the efficacies of these vaccines.

Enhanced potency of an IgM-like nanobody targeting conserved epitope in SARS-CoV-2 spike N-terminal domain.

Almost all the neutralizing antibodies targeting the receptor-binding domain (RBD) of spike (S) protein show weakened or lost efficacy against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged or emerging variants, such as Omicron and its sub-variants. This suggests that highly conserved epitopes are crucial for the development of neutralizing antibodies. Here, we present one nanobody, N235, displaying broad neutralization against the SARS-CoV-2 prototype and multiple variants, including the newly emerged Omicron and its sub-variants. Cryo-electron microscopy demonstrates N235 binds a novel, conserved, cryptic epitope in the N-terminal domain (NTD) of the S protein, which interferes with the RBD in the neighboring S protein. The neutralization mechanism interpreted via flow cytometry and Western blot shows that N235 appears to induce the S1 subunit shedding from the trimeric S complex. Furthermore, a nano-IgM construct (MN235), engineered by fusing N235 with the human IgM Fc region, displays prevention via inducing S1 shedding and cross-linking virus particles. Compared to N235, MN235 exhibits varied enhancement in neutralization against pseudotyped and authentic viruses in vitro. The intranasal administration of MN235 in low doses can effectively prevent the infection of Omicron sub-variant BA.1 and XBB in vivo, suggesting that it can be developed as a promising prophylactic antibody to cope with the ongoing and future infection.

Design of multi-epitope chimeric vaccine against Monkeypox virus and SARS-CoV-2: A vaccinomics perspective.

The current era we experience is full with pandemic infectious agents that no longer threatens the major local source but the whole globe. Almost the most emerging infectious agents are severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), followed by monkeypox virus (MPXV). Since no approved antiviral drugs nor licensed active vaccines are yet available, we aimed to utilize immunoinformatics approach to design chimeric vaccine against the two mentioned viruses. This is the first study to deal with design divalent vaccine against SARS-CoV-2 and MPXV. ORF8, E and M proteins from Omicron SARS-CoV-2 and gp182 from MPXV were used as the protein precursor from which multi-epitopes (inducing B-cell, helper T cells, cytotoxic T cells and interferon-É£) chimeric vaccine was contrived. The structure of the vaccine construct was predicted, validated, and docked to toll-like receptor-2 (TLR-2). Moreover, its sequence was also used to examine the immune simulation profile and was then inserted into the pET-28a plasmid for in silico cloning. The vaccine construct was probable antigen (0.543) and safe (non-allergen) with strong binding energy to TLR-2 (-1169.8 kcal/mol) and found to have significant immune simulation profile. In conclusion, the designed chimeric vaccine was potent and safe against SARS-CoV-2 and MPXV, which deserves further consideration.

A native mass spectrometry approach to qualitatively elucidate interfacial epitopes of transient protein-protein interactions.

Native mass spectrometric analysis of TPR2A and GrpE with unpurified peptides derived from limited proteolysis of their respective PPI partners (HSP90 C-terminus and DnaK) facilitated efficient, qualitative identification of interfacial epitopes involved in transient PPI formation. Application of this approach can assist in elucidating interfaces of currently uncharacterised transient PPIs.

Bilateral efforts to improve SERS detection efficiency of exosomes by Au/Na7PMo11O39 Combined with Phospholipid Epitope Imprinting.

Detection of cancer-related exosomes in body fluids has become a revolutionary strategy for early cancer diagnosis and prognosis prediction. We have developed a two-step targeting detection method, termed PS-MIPs-NELISA SERS, for rapid and highly sensitive exosomes detection. In the first step, a phospholipid polar site imprinting strategy was employed using magnetic PS-MIPs (phospholipids-molecularly imprinted polymers) to selectively isolate and enrich all exosomes from urine samples. In the second step, a nanozyme-linked immunosorbent assay (NELISA) technique was utilized. We constructed Au/Na7PMo11O39 nanoparticles (NPs) with both surface-enhanced Raman scattering (SERS) property and peroxidase catalytic activity, followed by the immobilization of CD9 antibodies on the surface of Au/Na7PMo11O39 NPs. The Au/Na7PMo11O39-CD9 antibody complexes were then used to recognize CD9 proteins on the surface of exosomes enriched by magnetic PS-MIPs. Lastly, the high sensitivity detection of exosomes was achieved indirectly via the SERS activity and peroxidase-like activity of Au/Na7PMo11O39 NPs. The quantity of exosomes in urine samples from pancreatic cancer patients obtained by the PS-MIPs-NELISA SERS technique showed a linear relationship with the SERS intensity in the range of 6.21 × 107-2.81 × 108 particles/mL, with a limit of detection (LOD) of 5.82 × 107 particles/mL. The SERS signal intensity of exosomes in urine samples from pancreatic cancer patients was higher than that of healthy volunteers. This bidirectional MIPs-NELISA-SERS approach enables noninvasive, highly sensitive, and rapid detection of cancer, facilitating the monitoring of disease progression during treatment and opening up a new avenue for rapid early cancer screening.

Short-time ozone treatment promotes protease-mediated destruction of B cell allergen epitopes by altering the structural characteristics of whey protein.

A novel processing method combining short-time ozone pretreatment with hydrolysis has been developed to reduce whey protein allergenicity. The results showed that ozone treatment altered the whey protein spatial structure, initially increasing the surface hydrophobicity index, and then decreasing due to polymer formation as the time increased. Under the optimized conditions of alkaline protease-mediated hydrolysis, a 10-second pre-exposure to ozone significantly promoted the reduction in the IgE binding capacity of whey protein without compromising the hydrolysis efficiency. Compared with whey protein, the degranulation of KU812 cells stimulated by this hydrolysate decreased by 20.54%, 17.99%, and 22.80% for IL-6, ß-hexosaminidase, and histamine, respectively. In vitro simulated gastrointestinal digestion confirmed increased digestibility and reduced allergenicity. Peptidomics identification revealed that short-time ozonation exposed allergen epitopes, allowing alkaline protease to target these epitopes more effectively, particularly those associated with α-lactalbumin. These findings suggest the promising application of this processing method in mitigating the allergenicity of whey protein.

Multimodal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O.

An important element of antibody-guided vaccine design is the use of neutralizing or opsonic monoclonal antibodies to define protective epitopes in their native three-dimensional conformation. Here, we demonstrate a multimodal mass spectrometry-based strategy for in-depth characterization of antigen-antibody complexes to enable the identification of protective epitopes using the cytolytic exotoxin Streptolysin O (SLO) from Streptococcus pyogenes as a showcase. We first discovered a monoclonal antibody with an undisclosed sequence capable of neutralizing SLO-mediated cytolysis. The amino acid sequence of both the antibody light and the heavy chain was determined using mass-spectrometry-based de novo sequencing, followed by chemical cross-linking mass spectrometry to generate distance constraints between the antibody fragment antigen-binding region and SLO. Subsequent integrative computational modeling revealed a discontinuous epitope located in domain 3 of SLO that was experimentally validated by hydrogen-deuterium exchange mass spectrometry and reverse engineering of the targeted epitope. The results show that the antibody inhibits SLO-mediated cytolysis by binding to a discontinuous epitope in domain 3, likely preventing oligomerization and subsequent secondary structure transitions critical for pore-formation. The epitope is highly conserved across >98% of the characterized S. pyogenes isolates, making it an attractive target for antibody-based therapy and vaccine design against severe streptococcal infections.

Reinvestigation of the internal glycan rearrangement of Lewis a and blood group type H1 epitopes.

Protonated ions of fucose-containing oligosaccharides are prone to undergo internal glycan rearrangement which results in chimeric fragments that obfuscate mass-spectrometric analysis. Lack of accessible tools that would facilitate systematic analysis of glycans in the gas phase limits our understanding of this phenomenon. In this work, we use density functional theory modeling to interpret cryogenic IR spectra of Lewis a and blood group type H1 trisaccharides and to establish whether these trisaccharides undergo the rearrangement during gas-phase analysis. Structurally unconstrained search reveals that none of the parent ions constitute a thermodynamic global minimum. In contrast, predicted collision cross sections and anharmonic IR spectra provide a good match to available experimental data which allowed us to conclude that fucose migration does not occur in these antigens. By comparing the predicted structures with those obtained for Lewis x and blood group type H2 epitopes, we demonstrate that the availability of the mobile proton and a large difference in the relative stability of the parent ions and rearrangement products constitute the prerequisites for the rearrangement reaction.

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.

Localization of G1A1a Allergenic Domain Destroyed by Thermal Processing.

Glycinin is an important allergenic protein. A1a is the acidic chain of the G1 subunit in glycinin (G1A1a), and it has strong allergenicity. In this study, we used phage display technology to express the protein of G1A1a and its overlapping fragments and an indirect enzyme-linked immunosorbent assay (iELISA) to determine the antigenicity and allergenicity of the expressed protein. After three rounds of screening, it was determined that fragment A1a-2-B-I (151SLENQLDQMPRRFYLAGNQEQEFLKYQQEQG181) is the allergenic domain of G1A1a destroyed by thermal processing. In addition, three overlapping peptides were synthesized from fragments A1a-2-B-I, and a linear epitope was found in this domain through methods including dot blot and iELISA. Peptide 2 (157DQMPRRFYLANGNQE170) showed allergenicity, and after replacing it with alanine, it was found that amino acids D157, Q158, M159, and Y164 were the key amino acids that affected its antigenicity, while Q158, M159, R162, and N168 affected allergenicity.

CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses.

Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60 000 experimentally validated epitopes and over 12 000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70 000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/.

Facile generation of biepitopic antibodies with intrinsic agonism for activating tumor necrosis factor receptors.

Agonist antibodies are being pursued for therapeutic applications ranging from neurodegenerative diseases to cancer. For the tumor necrosis factor (TNF) receptor superfamily, higher-order clustering of three or more receptors is key to their activation, which can be achieved using antibodies that recognize two unique epitopes. However, the generation of biepitopic (i.e., biparatopic) antibodies typically requires animal immunization and is laborious and unpredictable. Here, we report a simple method for identifying biepitopic antibodies that potently activate TNF receptors without the need for additional animal immunization. Our approach uses existing, receptor-specific IgGs, which lack intrinsic agonist activity, to block their corresponding epitopes, then selects single-chain antibodies that bind accessible epitopes. The selected antibodies are fused to the light chains of IgGs to generate human tetravalent antibodies. We highlight the broad utility of this approach by converting several clinical-stage antibodies against OX40 and CD137 (4-1BB) into biepitopic antibodies with potent agonist activity.

Generation of novel anti-apoE monoclonal antibodies that selectively recognize apoE isoforms.

Apolipoprotein E (apoE) is a regulator of lipid metabolism, cholesterol transport, and the clearance and aggregation of amyloid ß in the brain. The three human apoE isoforms apoE2, apoE3, and apoE4 only differ in one or two residues. Nevertheless, the functions highly depend on the isoform types and lipidated states. Here, we generated novel anti-apoE monoclonal antibodies (mAbs) and obtained an apoE4-selective mAb whose epitope is within residues 110-117. ELISA and bio-layer interferometry measurements demonstrated that the dissociation constants of mAbs are within the nanomolar range. Using the generated antibodies, we successfully constructed sandwich ELISA systems, which can detect all apoE isoforms or selectively detect apoE4. These results suggest the usability of the generated anti-apoE mAbs for selective detection of apoE isoforms.

Computationally Designed Epitope-Mediated Imprinted Polymers versus Conventional Epitope Imprints for the Detection of Human Adenovirus in Water and Human Serum Samples.

Detection of pathogenic viruses for point-of-care applications has attracted great attention since the COVID-19 pandemic. Current virus diagnostic tools are laborious and expensive, while requiring medically trained staff. Although user-friendly and cost-effective biosensors are utilized for virus detection, many of them rely on recognition elements that suffer major drawbacks. Herein, computationally designed epitope-imprinted polymers (eIPs) are conjugated with a portable piezoelectric sensing platform to establish a sensitive and robust biosensor for the human pathogenic adenovirus (HAdV). The template epitope is selected from the knob part of the HAdV capsid, ensuring surface accessibility. Computational simulations are performed to evaluate the conformational stability of the selected epitope. Further, molecular dynamics simulations are executed to investigate the interactions between the epitope and the different functional monomers for the smart design of eIPs. The HAdV epitope is imprinted via the solid-phase synthesis method to produce eIPs using in silico-selected ingredients. The synthetic receptors show a remarkable detection sensitivity (LOD: 102 pfu mL-1) and affinity (dissociation constant (Kd): 6.48 × 10-12 M) for HAdV. Moreover, the computational eIPs lead to around twofold improved binding behavior than the eIPs synthesized with a well-established conventional recipe. The proposed computational strategy holds enormous potential for the intelligent design of ultrasensitive imprinted polymer binders.