The recent coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has exerted considerable impact on global health. To prepare for rapidly mutating viruses and for the forthcoming pandemic, effective therapies targeting the critical stages of the viral life cycle need to be developed. Viruses are dependent on the interaction between the receptor-binding domain (RBD) of the viral Spike (S) protein (S-RBD) and the angiotensin-converting enzyme 2 (ACE2) receptor to efficiently establish infection and the following replicate. Targeting this interaction provides a promising strategy to inhibit the entry process of the virus, which in turn has both preventive and therapeutic effects. In this study, we developed a robust and straightforward assay based on the Yeast-Two Hybrid system (Y2H) for identifying inhibitors targeting the S-RBD-ACE2 interaction of SARS-CoV-2. Through high-throughput screening, two compounds were identified as potential entry inhibitors. Among them, IMB-1C was superior in terms of pseudovirus entry inhibition and toxicity. It could bind to both ACE2 and S-RBD and induce conformational change in the S-RBD+ACE2 complex. This is the first study to verify the feasibility of utilizing the Y2H system to discover potent SARS-CoV-2 inhibitors targeting the receptor recognition stage. This approach may also be applied in the discovery of other virus receptor recognition inhibitors.

Rising drug resistance underscores the urgent need for novel anti-influenza strategies. Targeting the hemagglutinin (HA)–sialic acid receptor interaction offers a promising broad-spectrum approach, yet progress is impeded by the lack of screening models that report the receptor-recognition step in a subtype-independent manner. Herein, we established a robust cell-based ELISA platform for high-throughput screening (HTS) that operates on the subtype-independent receptor-recognition step. The model utilizes ST6GAL1-overexpressing MDCK cells to enrich human-type α2,6-sialic acid receptors and employs horseradish peroxidase (HRP)-conjugated Sambucus nigra lectin (SNA) as a functional, though not structural, surrogate for the receptor-recognition step, yielding a high Z’ factor of 0.78. Given that SNA is a lectin rather than HA, the assay cannot by itself distinguish HA-specific blockade from membrane-mediated or virucidal effects. Orthogonal validation is therefore required for every hit. Screening a library of 2600 compounds identified two hits, Hypericin and protoporphyrin IX (PPIX), which effectively blocked SNA–receptor recognition with IC₅₀ values of 18.39 ± 6.23 μM and 9.77 ± 3.05 μM, respectively. An orthogonal flow cytometry assay with FITC-conjugated SNA reproduced this inhibition on intact cells in a dose-dependent manner (IC₅₀ = 17.68 ± 9.54 μM for Hypericin and 25.31 ± 19.47 μM for PPIX), confirming that the inhibition is not an artifact of the colorimetric readout. Hemagglutination inhibition (HA) assays with distinct pre-incubation strategies indicated that both compounds act on the viral particle rather than on the host receptor. Surface plasmon resonance (SPR) and molecular docking studies further indicated direct, dose-dependent but low-affinity binding to HA proteins. Notably, cytopathic effect (CPE) assays demonstrated their potent antiviral efficacy across H1N1 and H3N2 subtypes, with PPIX exhibiting exceptionally potent activity against H3N2 (EC₅₀ = 0.50 ± 0.17 μM). Taken together, these data identify Hypericin and PPIX as viral-side attachment inhibitors with weak HA binding and multimodal antiviral activity. Because neither a virucidal nor a fusion-blocking control was included, the relative contribution of HA engagement to their overall antiviral effect remains to be established. In conclusion, this BSL-1-compatible ELISA platform, which probes the subtype-independent receptor-recognition step, provides an efficient tool for discovering broad-spectrum influenza entry inhibitors, and the identified hits offer novel pharmacological scaffolds for further structural optimization.