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.

Structural information on protein-protein interactions (PPIs) is essential for improved understanding of regulatory interactome networks that confer various physiological and pathological responses. Additionally, maladaptive PPIs constitute desirable therapeutic targets due to inherently high disease state specificity. Recent advances in chemical cross-linking strategies coupled with mass spectrometry (XL-MS) have positioned XL-MS as a promising technology to not only elucidate the molecular architecture of individual protein assemblies, but also to characterize proteome-wide PPI networks. Moreover, quantitative in vivo XL-MS provides a new capability for the visualization of cellular interactome dynamics elicited by drug treatments, disease states, or aging effects. The emerging field of XL-MS based complexomics enables unique insights on protein moonlighting and protein complex remodeling. These techniques provide complimentary information necessary for in-depth structural interactome studies to better comprehend how PPIs mediate function in living systems.

Regulated induced proximity targeting chimeras (RIPTACs), a new class of heterobifunctional molecules, show promise in specifically targeting and eliminating cancer cells while leaving healthy cells unharmed. As a groundbreaking drug discovery approach, RIPTACs work by forming a stable complex with two proteins, one specifically found in cancer cells (target protein, TP) and the other pan-essential for cell survival (effector protein, EP), selectively disrupting the function of the EP in cancer cells and causing cell death. Interestingly, the TPs need not be linked to disease progression, broadening the spectrum of potential drug targets. This review summarizes the discovery and recent advances of the RIPTAC strategy. Additionally, it discusses the associated opportunities and challenges as well as future perspectives in this field.

Familial non-medullary thyroid cancer (FNMTC) is a well-differentiated thyroid cancer (DTC) of follicular cell origin in two or more first-degree relatives. Patients typically demonstrate an autosomal dominant inheritance pattern with incomplete penetrance. While known genes and chromosomal loci account for some FNMTC, the molecular basis for most FNMTC remains elusive. To identify the variation(s) causing FNMTC in an extended consanguineous family consisting of 16 papillary thyroid carcinoma (PTC) cases, we performed whole exome sequence (WES) analysis of six family patients. We demonstrated an association of ARHGEF28, FBXW10, and SLC47A1 genes with FNMTC. The variations in these genes may affect the structures of their encoded proteins and, thus, their function. The most promising causative gene is ARHGEF28, which has high expression in the thyroid, and its protein-protein interactions (PPIs) suggest predisposition of PTC through ARHGEF28-SQSTM1-TP53 or ARHGEF28-PTCSC2-FOXE1-TP53 associations. Using DNA from a patient\'s thyroid malignant tissue, we analyzed the possible cooperation of somatic variations with these genes. We revealed two somatic heterozygote variations in XRCC1 and HRAS genes known to implicate thyroid cancer. Thus, the predisposition by the germline variations and a second hit by somatic variations could lead to the progression to PTC.

Noncovalent interactions between biomolecules such as proteins and nucleic acids coordinate all cellular processes through changes in proximity. Tools that perturb these interactions are and will continue to be highly valuable for basic and translational scientific endeavors. By taking cues from natural systems, such as the adaptive immune system, we can design directed evolution platforms that can generate proteins that bind to biomolecules of interest. In recent years, the platforms used to direct the evolution of biomolecular binders have greatly expanded the range of types of interactions one can evolve. Herein, we review recent advances in methods to evolve protein-protein, protein-RNA, and protein-DNA interactions.

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.

The major classes of molecular chaperones have highly variable sequences, sizes, and shapes, yet they all bind to unfolded proteins, limit their aggregation, and assist in their folding. Despite the central importance of this process to protein homeostasis, it has not been clear exactly how chaperones guide this process or whether the diverse families of chaperones use similar mechanisms. For the first time, recent advances in NMR spectroscopy have enabled detailed studies of how unfolded, \"client\" proteins interact with both ATP-dependent and ATP-independent classes of chaperones. Here, we review examples from four distinct chaperones, Spy, Trigger Factor, DnaK, and HscA-HscB, highlighting the similarities and differences between their mechanisms. One striking similarity is that the chaperones all bind weakly to their clients, such that the chaperone-client interactions are readily outcompeted by stronger, intra- and intermolecular contacts in the folded state. Thus, the relatively weak affinity of these interactions seems to provide directionality to the folding process. However, there are also key differences, especially in the details of how the chaperones release clients and how ATP cycling impacts that process. For example, Spy releases clients in a largely folded state, while clients seem to be unfolded upon release from Trigger Factor or DnaK. Together, these studies are beginning to uncover the similarities and differences in how chaperones use weak interactions to guide protein folding.

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.

Dengue is emerging as one of the most prevalent mosquito-borne viral diseases of humans. The 11kb RNA genome of the dengue virus encodes three structural proteins (envelope, pre-membrane, capsid) and seven non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5), all of which are translated as a single polyprotein that is subsequently cleaved by viral and host cellular proteases at specific sites. Non-structural protein 5 (NS5) is the largest of the non-structural proteins, functioning as both an RNA-dependent RNA polymerase (RdRp) that replicates the viral RNA and an RNA methyltransferase enzyme (MTase) that protects the viral genome by RNA capping, facilitating polyprotein translation. Within the human host, NS5 interacts with several proteins such as those in the JAK-STAT pathway, thereby interfering with anti-viral interferon signalling. This mini-review presents annotated, consolidated lists of known and potential NS5 interactors in the human host as determined by experimental and computational approaches respectively. The most significant protein interactors and the biological pathways they participate in are also highlighted and their implications discussed, along with the specific serotype of dengue virus as appropriate. This information can potentially stimulate and inform further research efforts towards providing an integrative understanding of the mechanisms by which NS5 manipulates the human-virus interface in general and the innate and adaptive immune responses in particular.

BACKGROUND: Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals.
RESULTS: The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction.
BACKGROUND: https://smartgraph.ncats.io/.