The study of protein–protein interactions (PPIs) is of significant importance for elucidating biological processes, clarifying pathological mechanisms, and promoting drug development. In this study, we proposed a method to predict PPIs based on protein sequence and gene sequence information, combined with convolutional neural networks (CNNs). First, we extracted three types of features from protein sequence: global physicochemical properties features of the protein sequence, local same type of amino acid position variation features, and protein evolutionary conservation features; simultaneously, we extracted single nucleotide frequency and positional features, dinucleotide frequency features, and trinucleotide frequency features from the corresponding gene sequence. During the feature extraction process, we employed the amphiphilic pseudo amino acid composition (APAAC) method to extract the global hydrophobicity and hydrophilicity features of the protein sequence; we defined a new mathematical descriptor—$\mathsf{\theta }$ interval deviation product factor—to extract protein evolutionary conservation features from Position Specific Scoring Matrix (PSSM); we also defined a mapping function to map all nucleotides in the gene sequence onto a unit circle, and then extracted nucleotide positional features from the mapped points. Second, based on extracted features, we constructed a 36 × 32 sample feature grayscale map to represent a protein pair sample. Finally, we developed a CNN model to predict PPIs. Our method achieved superior results on four species test sets: an accuracy of 99.28% on the Saccharomyces cerevisiae dataset, 98.15% on the Drosophila melanogaster dataset, 98.62% on the Homo sapiens dataset, and 96.84% on the Mus musculus dataset, outperforming existing computational methods. Furthermore, we extended the application of this method to the prediction of protein–protein interaction networks and non-interaction networks, and also achieved promising results.

Infectious bronchitis virus (IBV) causes a highly contagious disease in chickens. The prevalence of GVI-1 is increasing; however, the genomic characteristics and antigenic properties of this genotype strain remain insufficiently characterized. In this study, the genome characteristics and antigenic properties of a naturally attenuated CK/CH/SC/YC_GVI-1-DK/LMB20210104 (abbreviated as YC_GVI-1) strain were systematically analyzed. YC_GVI-1 occupies a distinct phylogenetic lineage and shares a similarity of 98.2%, the highest nucleotide sequence homology, with the reference strain CK/CH/FJ/202005 (accession number: MW791835.1). This strain was likely originated through a genetic recombination event between two major parental strains, CK/CH/FJ/202005 and CK/CH/GX/HX (accession number: PP817796.1). However, its S protein harbors ten unique amino acid substitutions, compared to the same protein in the other two virulent strains in the same genotype. AlphaFold3-based structural prediction reveals that one of these substitutions, methionine 485 to valine substitution, may induce a conformational change in the adjacent phenylalanine residue at position 431, resulting in a shift in the local secondary structure from β-sheet to random coil. Characterization of its antigenicity showed that this strain induces a strong humoral immune response, with neutralizing antibody titers of 2^6.40 against homologous strain YC_GVI-1 and 2^4.00 against heterologous strain JS96_GI-19. Furthermore, vaccination of chickens with this strain conferred complete protection (100%) against JS96_GI-19. The findings provide novel insights into the molecular evolution and antigenicity of YC_GVI-1, offering key information for improving IBV surveillance and vaccine development.

Most life activities of organisms are realized through protein–protein interactions, and these interactions are mainly achieved through residue–residue contact between monomer proteins. Consequently, studying residue–residue contact at the protein interaction interface can contribute to a deeper understanding of the protein–protein interaction mechanism. In this paper, we focus on the research of the trimer protein interface residue pair. Firstly, we utilize the amino acid k-interval product factor descriptor ($\mathrm{A}\mathrm{A}\mathrm{I}\mathrm{P}\mathrm{F}\left(\mathrm{k}\right)$) to integrate the positional information and physicochemical properties of amino acids, combined with the electric properties and geometric shape features of residues, to construct an 8 × 16 multi-feature map. This multi-feature map represents a sample composed of two residues on a trimer protein. Secondly, we construct a CNN-GRU deep learning framework to predict the trimer protein interface residue pair. The results show that when each dimer protein provides 10 prediction results and two protein–protein interaction interfaces of a trimer protein needed to be accurately predicted, the accuracy of our proposed method is 60%. When each dimer protein provides 10 prediction results and one protein–protein interaction interface of a trimer protein needs to be accurately predicted, the accuracy of our proposed method is 93%. Our results can provide experimental researchers with a limited yet precise dataset containing correct trimer protein interface residue pairs, which is of great significance in guiding the experimental resolution of the trimer protein three-dimensional structure. Furthermore, compared to other computational methods, our proposed approach exhibits superior performance in predicting residue–residue contact at the trimer protein interface.