The affinity of peptides is a crucial factor in studying peptide–protein interactions. Despite the development of various techniques to evaluate peptide–receptor affinity, the results may not always reflect the actual affinity of the peptides accurately. The current study provides a free tool to assess the actual peptide affinity based on virtual docking data. This study employed a dataset that combined actual peptide affinity information (active and inactive) and virtual peptide–receptor docking data, and different machine learning algorithms were utilized. Compared with the other algorithms, the random forest (RF) algorithm showed the best performance and was used in building three RF models using different numbers of significant features (four, three, and two). Further analysis revealed that the four-feature RF model achieved the highest Accuracy of 0.714 in classifying an independent unknown peptide dataset designed with the PEDV spike protein, and it also revealed overfitting problems in the other models. This four-feature RF model was used to evaluate peptide affinity by constructing the relationship between the actual affinity and the virtual docking scores of peptides to their receptors.

Due to the rapid mutation of porcine epidemic diarrhea virus (PEDV), existing vaccines cannot provide sufficient immune protection for pigs. Therefore, it is urgent to design the affinity peptides for the prevention and control of this disease. In this study, we made use of a molecular docking technology for virtual screening of affinity peptides that specifically recognized the PEDV S1 C-terminal domain (CTD) protein for the first time. Experimentally, the affinity, cross-reactivity and sensitivity of the peptides were identified by an enzyme-linked immunosorbent assay (ELISA) and a surface plasmon resonance (SPR) test, separately. Subsequently, Cell Counting Kit-8 (CCK-8), quantitative real-time PCR (qRT-PCR), Western blot and indirect immunofluorescence were used to further study the antiviral effect of different concentrations of peptide 110766 in PEDV. Our results showed that the P/N value of peptide 110766 at 450 nm reached 167, with a K_D value of 216 nM. The cytotoxic test indicated that peptide 110766 was not toxic to vero cells. Results of the absolute quantitative PCR revealed that different concentrations (3.125 μM, 6.25 μM, 12.5 μM, 25 μM, 50 μM, 100 μM, 200 μM) of peptide 110766 could significantly reduce the viral load of PEDV compared with the virus group (p < 0.0001). Similarly, results of Western blot and indirect immunofluorescence also suggested that the antiviral effect of peptide 110766 at 3.125 is still significant. Based on the above research, high-affinity peptide 110766 binding to the PEDV S1-CTD protein was attained by a molecular docking technology. Therefore, designing, screening, and identifying affinity peptides can provide a new method for the development of antiviral drugs for PEDV.

Background: Viral attachment mediated by host cell surface receptors is the first step in viral infection. As a key cell surface receptor, heparan sulfate (HS) mediates the attachment and entry of numerous non-enveloped viruses in livestock, thereby serving as a crucial molecular target for studying virus–host interactions. Methods: Based on the structural scaffold of a nanobody (Nb; PDB: 7TJC), we rationally designed and constructed a mutant Nb targeting HS, designated HS-Mut-Nb1, using molecular docking, site-directed mutagenesis, molecular dynamics (MD) simulations, and experimental characterization. Results: Molecular docking indicated that the active site of wild-type Nb for HS binding was located within the cavity jointly formed by the complementarity-determining region 3 (CDR3) and the framework regions (FRs) of the wild-type Nb. A comprehensive analysis integrating virtual alanine scanning, site-directed mutagenesis, and MD simulations revealed that the combination of three point mutations (Phe47Arg, Asp99Tyr, and Tyr108Pro) significantly enhanced the binding affinity of Mut-Nb1 for HS, with a calculated binding free energy (ΔG) of −83.26 ± 3.06 kcal/mol. Enzyme-linked immunosorbent assay (ELISA) results further confirmed that Mut-Nb1 exhibited high affinity for HS (K_D = 65.87 nM) and specificity (positive/negative ratio, P/N = 3.84; cross-reactivity, CR < 6.60%). Conclusions: This study not only provides novel candidate molecules for elucidating the mechanism of HS–virus interactions and developing related inhibitors but also offers a reference for the rapid construction of mutant Nbs.