Jun Xia | PubMed Health

PubMed2026

The Q653R substitution in the spike protein is associated with attenuation of a GVI-1 infectious bronchitis virus strain.

The GVI-1 genotype of infectious bronchitis virus (IBV) has become increasingly prevalent in Asia. In this study, a highly pathogenic GVI-1 strain (GVI-1-WT) was attenuated by 110 serial passages in embryonated chicken eggs, yielding an attenuated strain (GVI-1-E110). Comparative genomic analysis identified two amino acid substitutions, S523I, Q653R and a nine-amino-acid truncation in the spike (S) protein. To evaluate the contribution of the two point mutations to virulence attenuation, recombinant viruses carrying Q653R and S523I substitutions were generated using a reverse genetics system based on the GVI-1-WT strain as the backbone. Their replication and pathogenicity were assessed in embryonated eggs and specific pathogen-free chickens. The Q653R substitution was associated with reduced viral replication in embryonated chicken eggs and pathogenicity in specific pathogen-free chickens, whereas the S523I mutation alone showed a limited effect but enhanced attenuation when combined with Q653R. However, the attenuation phenotype of the recombinant viruses did not fully recapitulate that of the passaged strain GVI-1-E110, suggesting that additional mutations, including the identified truncation and mutations in replicase-associated genes outside the S protein, may also contribute to virulence attenuation. This study indicates that spike protein mutations are involved in the attenuation of GVI-1 IBV strains, and provides insights into the molecular basis of IBV attenuation during serial passage. Further studies are required to elucidate the underlying mechanisms and to evaluate their potential relevance for vaccine development.

GVI-1 genotype↗

PubMed2025

An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.

MOTIVATION: Accurate prediction of protein-ligand binding (PLB) relationships plays a crucial role in drug discovery, which helps identify drugs that modulate the activity of specific targets. Traditional biological assays for measuring PLB relationships are time consuming and costly. In addition, models for predicting PLB relationships have been developed and widely used in drug discovery tasks. However, learning more accurate PLB representations is essential to meet the stringent standards required for drug discovery. RESULTS: We propose an image-based PLB representation learning framework, called ImagePLB, which equips ligand representation learner (LRL) and protein representation learner (PRL) to accept 3D multi-view ligand images and protein graphs as input, respectively, and learns rich interaction information between ligand and protein through a binding representation learner (BRL). Considering the scarcity of protein-ligand pairs, we further propose a multi-level next trajectory prediction (MLNTP) task to pre-train ImagePLB on the 4D flexible dynamics trajectory of 16 972 complexes, including ligand level, protein level, and complex level, to learn information related to trajectories. Besides, by introducing trajectory regularization (TR), we effectively alleviate the problem of high (even almost identical) feature similarity caused by adjacent trajectories. Compared with the current state-of-the-art methods, ImagePLB has achieved competitive improvements on PLB-related prediction tasks, including protein-ligand affinity and efficacy prediction tasks. This study opens the door to the image-based PLB learning paradigm. AVAILABILITY AND IMPLEMENTATION: All data and implementation details of code can be obtained from https://github.com/HongxinXiang/ImagePLB.

Ligands↗