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Comparative immunology, microbiology and infectious diseases · 2026

Screening candidate intermediate hosts for porcine respiratory coronavirus using molecular docking.

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Sootichote Rochanawan, Chamkasem Aekkapot, Toniti Waraphan, Dato Micaella C, Chayapham Pailin, Laohasinnarong Dusit

Аннотация

Emerging coronavirus-associated acute respiratory diseases have posed major global health and economic disruption, highlighting the ongoing risk of zoonotic spillover. Cross-species transmission is largely determined by the interaction between viral receptor-binding domains (RBDs) and host cellular receptors. This study utilizes Porcine Respiratory Coronavirus (PRCoV) as a model to investigate host adaptation via the Aminopeptidase N (APN) receptor. Using molecular docking and structural analysis, we evaluated the docking-estimated binding affinity between the PRCoV RBD and APN proteins from 18 vertebrate species. Sequence analysis revealed that Camelidae, Bovidae, and Hipposiderid bats share the highest similarity (70%-85%) with porcine APN (pAPN). Notably, Hipposiderid bats showed up to 80% similarity with both pAPN and human APN (hAPN), suggesting a significant ecological role in viral evolution. Meanwhile, avian, reptile, and aquatic species showed greater divergence from mammals, with 45-60% sequence similarity. Molecular docking results highlight the high predicted receptor compatibility among pigs, cats, bats, horses, rabbits, and cattle. While wild-type PRCoV displayed a tenfold lower affinity for hAPN (Kd 2971 pM) compared to pAPN (Kd 261 pM), specific single amino acid substitutions in the RBD enhanced binding compatibility to hAPN, reducing Kd values to below 100 pM. These RBD variants, analogous to mutations seen in SARS-CoV-2, suggest that minimal genetic shifts could increase predicted hAPN binding in the in-silico prediction. These findings highlight the potential for receptor-binding adaptation candidates and emphasize the importance of monitoring viral evolution and host receptor diversity to support early detection and support receptor-based surveillance and future validation.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты