Signaling pathways are responsible for transmitting information between cells and regulating cell growth, differentiation, and death. Proteins in cells form complexes by interacting with each other through specific structural domains, playing a crucial role in various biological functions and cell signaling pathways. Protein-protein interactions (PPIs) within cell signaling pathways are essential for signal transmission and regulation. The spatiotemporal features of PPIs in signaling pathways are crucial for comprehending the regulatory mechanisms of signal transduction. Bimolecular fluorescence complementation (BiFC) is one kind of imaging tool for the direct visualization of PPIs in living cells and has been widely utilized to uncover novel PPIs in various organisms. BiFC demonstrates significant potential for application in various areas of biological research, drug development, disease diagnosis and treatment, and other related fields. This review systematically summarizes and analyzes the technical advancement of BiFC and its utilization in elucidating PPIs within established cell signaling pathways, including TOR, PI3K/Akt, Wnt/β-catenin, NF-κB, and MAPK. Additionally, it explores the application of this technology in revealing PPIs within the plant hormone signaling pathways of ethylene, auxin, Gibberellin, and abscisic acid. Using BiFC in conjunction with CRISPR-Cas9, live-cell imaging, and ultra-high-resolution microscopy will enhance our comprehension of PPIs in cell signaling pathways.

Fusarium oxysporum f. sp. cubense (FOC4) is a pathogen of banana fusarium wilt, which is a serious problem that has plagued the tropical banana industry for many years. The pathogenic mechanism is complex and unclear, so the prevention and control in agricultural production applications is ineffective. SNP-D4, an artificial peptide aptamer, was identified and specifically inhibited FOC4. To evaluate the efficacy of SNP-D4, FoC4 spores were treated with purified SNP-D4 to calculate the germination and fungicide rates. Damage of FOC4 spores was observed by staining with propidium iodide (PI). Eight proteins of FOC4 were identified to have high affinity for SNP-D4 by a pull-down method combined with Q-Exactive mass spectrometry. Of these eight proteins, A0A5C6SPC6, the aldehyde dehydrogenase of FOC4, was selected as an example to scrutinize the interaction sites with SNP-D4. Molecular docking revealed that Thr66 on the peptide loop of SNP-D4 bound with Tyr437 near the catalytic center of A0A5C6SPC6. Subsequently 42 spore proteins which exhibited associations with the eight proteins were retrieved for protein-protein interaction analysis, demonstrating that SNP-D4 interfered with pathways including \'translation\', \'folding, sorting and degradation\', \'transcription\', \'signal transduction\' and \'cell growth and death\', eventually causing the inhibition of growth of FOC4. This study not only investigated the possible pathogenic mechanism of FOC4, but also provided a potential antifungal agent SNP-D4 for use in the control of banana wilt disease.

Protein-protein interactions (PPIs) play a key role in signal transduction and pharmacogenomics, and hence, accurate PPI prediction is crucial. Graph structures have received increasing attention owing to their outstanding performance in machine learning. In practice, PPIs can be expressed as a signed network (i.e., graph structure), wherein the nodes in the network represent proteins, and edges represent the interactions (positive or negative effects) of protein nodes. PPI predictions can be realized by predicting the links of the signed network; therefore, the use of gated graph attention for signed networks (SN-GGAT) is proposed herein. First, the concept of graph attention network (GAT) is applied to signed networks, in which \"attention\" represents the weight of neighbor nodes, and GAT updates the node features through the weighted aggregation of neighbor nodes. Then, the gating mechanism is defined and combined with the balance theory to obtain the high-order relations of protein nodes to improve the attention effect, making the attention mechanism follow the principle of \"low-order high attention, high-order low attention, different signs opposite\". PPIs are subsequently predicted on the Saccharomyces cerevisiae core dataset and the Human dataset. The test results demonstrate that the proposed method exhibits strong competitiveness.

The human peroxiredoxin-5 (hPRDX5) is a member of the family of antioxidant enzymes, which could resist immunosuppression by promoting immune organs development, lymphocyte proliferation and up-regulation of the levels of serum cytokines. However, being a recombinant protein, the hPRDX5 exhibits some problems including the high production cost and bad tissue penetration. Compared to macromolecular therapeutic agents, synthetic peptides have several advantages as drug candidates, such as lower manufacturing costs, reduced immunogenicity, and better organ or tumor penetration. The purpose of this research was to design the novel peptides come from hPRDX5 that can block the interaction of PD-1 and PD-L1.
Herein in this work, we firstly confirmed the inhibitory activity of hPRDX5 on the binding of PD-L1 to PD-1 based on the previous observation, subsequently, in silico proteolysis and rational design (such as alanine scanning mutagenesis and truncation) were used to automate the design of new peptides derived from hPRDX5 with anti-tumour activity.
We found that the most potent peptide could block the PD-1/PD-L1 interaction effectively with an IC50 of 0.646 μM, and could restore the function of Jurkat T cells which had been suppressed by stimulated HCT116 cells. Moreover, experiments with tumor-bearing mice models showed that the peptide IMB-P6-10 could effectively inhibit tumor growth and showed extraordinary low acute toxicity in vivo.
The peptides described in this paper may provide novel low-molecular-weight drug candidates for cancer immunotherapy.

Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.