Protein-protein interactions play an important role in life activities. The study of protein-protein interactions helps to better understand the mechanism of protein complex interaction, which is crucial for drug design, protein function annotation and three-dimensional structure prediction of protein complexes. In this paper, we study the tetramer protein complex interaction. The research has two parts: The first part is to predict the interaction between chains of the tetramer protein complex. In this part, we proposed a feature map to represent a sample generated by two chains of the tetramer protein complex, and constructed a Convolutional Neural Network (CNN) model to predict the interaction between chains of the tetramer protein complex. The AUC value of testing set is 0.6263, which indicates that our model can be used to predict the interaction between chains of the tetramer protein complex. The second part is to predict the tetramer protein complex interface residue pairs. In this part, we proposed a Support Vector Machine (SVM) ensemble method based on under-sampling and ensemble method to predict the tetramer protein complex interface residue pairs. In the top 10 predictions, when at least one protein-protein interaction interface is correctly predicted, the accuracy of our method is 82.14%. The result shows that our method is effective for the prediction of the tetramer protein complex interface residue pairs.

Primordium formation is extremely important for yield of Pleurotus tuoliensis. However, the molecular mechanism underlying primordium formation is largely unknown. This study investigated the transcriptional properties during primordium formation of P. tuoliensis by comparing transcriptome. Clean reads were assembled into 57,075 transcripts and 6874 unigenes. A total of 1397 differentially expressed genes were identified (26 DEGs altered in all stages). GO and KEGG enrichment analysis showed that these DEGs were involved in “oxidoreductase activity”, “glycolysis/gluconeogenesis”, “MAPK signaling pathways”, and “ribosomes”. Our results support further understanding of the transcriptional changes and molecular processes underlying primordium formation and differentiation of P. tuoliensis.

Pleurotus pulmonarius is a high-value, commercially cultivated edible fungus whose primordium formation is a critical phase for yield and commercial value. To better understand the developmental processes of P. pulmonarius, samples from four key developmental stages were collected and subjected to transcriptome analysis. A total of 6530 DEGs were identified, including 50 transcription factors from 10 families. Among these, the PpZCP11 gene, encoding a Zn₂Cys₆ transcription factor, was found to be specifically highly expressed during the primordium stage. We cloned PpZCP11 gene and confirmed its nuclear localization. The OE-PpZCP11 strains produced abundant primordia, while primordium formation in the RNAi-PpZCP11 strains was severely suppressed. Moreover, RNA-seq and yeast-one-hybrid analysis suggested that PpZCP11 may regulate cell wall synthesis. These findings indicate that the PpZCP11 transcription factor acts as a positive regulator of primordium formation by regulating the expression of cell wall-related genes. This study provides a theoretical reference for elucidating the molecular mechanism underlying primordium formation in P. pulmonarius.

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.