Dynamic interactions between transcription factors govern changes in gene expression that mediate changes in cell state accompanying injury response and regeneration. Transcription factors frequently function as obligate dimers whose activity is often modulated by post-translational modifications. These critical and often transient interactions are not easily detected by traditional methods to investigate protein-protein interactions. This chapter discusses the design and validation of a fusion protein involving a transcription factor tethered to a proximity labeling ligase, APEX2. In this technique, proteins are biotinylated within a small radius of the transcription factor of interest, regardless of time of interaction. Here we discuss the validations required to ensure proper functioning of the transcription factor proximity labeling tool and the sample preparation of biotinylated proteins for mass spectrometry analysis of putative protein interactors.

Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average.

Inhibition of CD95/Fas activation is currently under clinical investigation as a therapy for glioblastoma multiforme and preclinical studies suggest that disruption of the CD95-CD95L interaction could also be a strategy to treat inflammatory and neurodegenerative disorders. Besides neutralizing anti-CD95L/FasL antibodies, mainly CD95ed-Fc, a dimeric Fc fusion protein of the extracellular domain of CD95 (CD95ed), is used to prevent CD95 activation. In view of the fact that full CD95 activation requires CD95L-induced CD95 trimerization and clustering of the resulting liganded CD95 trimers, we investigated whether fusion proteins of the extracellular domain of CD95 with a higher valency than CD95ed-Fc have an improved CD95L-neutralization capacity. We evaluated an IgG1(N297A)-based tetravalent CD95ed fusion protein which was obtained by replacing the variable domains of IgG1(N297A) with CD95ed (CD95ed-IgG1(N297A)) and a hexavalent variant obtained by fusion of CD95ed with a TNC-Fc(DANA) scaffold (CD95ed-TNC-Fc(DANA)) promoting hexamerization. The established N297A and DANA mutations were used to minimize FcγR binding of the constructs under maintenance of neonatal Fc receptor (FcRn) binding. Size exclusion high-performance liquid chromatography indicated effective assembly of CD95ed-IgG1(N297A). More important, CD95ed-IgG1(N297A) was much more efficient than CD95ed-Fc in protecting cells from cell death induction by human and murine CD95L. Surprisingly, despite its hexavalent structure, CD95ed-TNC-Fc(DANA) displayed an at best minor improvement of the capacity to neutralize CD95L suggesting that besides valency, other factors, such as spatial organization and agility of the CD95ed domains, play also a role in neutralization of CD95L trimers by CD95ed fusion proteins. More studies are now required to evaluate the superior CD95L-neutralizing capacity of CD95ed-IgG1(N297A) in vivo.

The use of computational methods in drug discovery research has increased substantially in recent years. Computational chemistry techniques, such as quantum chemical calculations and molecular dynamics simulations, continue to be widely used. In this review, we focused on drug discovery-related studies that employ fragment molecular orbital methods. Furthermore, we focused on inhibitor discovery, protein-protein interaction analysis, including antigen-antibody interaction analysis, and integration with molecular dynamics simulations.

Autophagy is a catabolic process that was described to play a critical role in advanced stages of cancer, wherein it maintains tumor cell homeostasis and growth by supplying nutrients. Autophagy is also described to support alternative cellular trafficking pathways, providing a non-canonical autophagy-dependent inflammatory cytokine secretion mechanism. Therefore, autophagy inhibitors have high potential in the treatment of cancer and acute inflammation. In our study, we identified compound 1 as an inhibitor of the ATG12-ATG3 protein-protein interaction. We focused on the systematic modification of the original hit 1, a casein kinase 2 (CK2) inhibitor, to find potent disruptors of ATG12-ATG3 protein-protein interaction. A systematic modification of the hit structure led us to a wide plethora of compounds that maintain its ATG12-ATG3 inhibitory activity, which could act as a viable starting point to design new compounds with diverse therapeutic applications.

COVID-19 has caused millions of deaths and many times more infections worldwide, emphasizing the unpreparedness of the global health system in the face of new infections and the key role for vaccines and therapeutics, including virus-neutralizing antibodies, in prevention and containment of the disease. Continuous evolution of the SARS-CoV-2 coronavirus has been causing its new variants to evade the action of the immune system, which highlighted the importance of detailed knowledge of the epitopes of already selected potent virus-neutralizing antibodies. A single-chain antibody (\"nanobody\") targeting the SARS-CoV-2 receptor-binding domain (RBD), clone P2C5, had exhibited robust virus-neutralizing activity against all SARS-CoV-2 variants and, being a major component of the anti-COVID-19 formulation \"GamCoviMab\", had successfully passed Phase I of clinical trials. However, after the emergence of the Delta and XBB variants, a decrease in the neutralizing activity of this nanobody was observed. Here we report on the successful crystal structure determination of the RBD:P2C5 complex at 3.1 Å, which revealed the intricate protein-protein interface, sterically occluding full ACE2 receptor binding by the P2C5-neutralized RBD. Moreover, the structure revealed the developed RBD:P2C5 interface centered around residues Leu452 and Phe490, thereby explaining the evasion of the Delta or Omicron XBB, but not Omicron B.1.1.529 variant, as a result of the single L452R or F490S mutations, respectively, from the action of P2C5. The structure obtained is expected to foster nanobody engineering in order to rescue neutralization activity and will facilitate epitope mapping for other neutralizing nanobodies by competition assays.

The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in 2021 as a variant with heavy amino acid mutations in the spike protein, which is targeted by most vaccines, compared to previous variants. Amino acid substitutions in the spike proteins may alter their affinity for host viral receptors and the host interactome. Here, we found that the receptor-binding domain (RBD) of the omicron variant of SARS-CoV-2 exhibited an increased affinity for human angiotensin-converting enzyme 2, a viral cell receptor, compared to the prototype RBD. Moreover, we identified β- and γ-actin as omicron-specific binding partners of RBD. Protein complex predictions revealed that many omicron-specific amino acid substitutions affected the affinity between RBD of the omicron variant and actin. Our findings indicate that proteins localized to different cellular compartments exhibit strong binding to the omicron RBD.

Rice blast disease, caused by Magnaporthe oryzae, reigns as the top-most cereal killer, jeopardizing global food security. This necessitates the timely scouting of pathogen stress-responsive genes during the early infection stages. Thus, we integrated time-series microarray (GSE95394) and RNA-Seq (GSE131641) datasets to decipher rice transcriptome responses at 12- and 24-h post-infection (Hpi). Our analysis revealed 1580 differentially expressed genes (DEGs) overlapped between datasets. We constructed a protein-protein interaction (PPI) network for these DEGs and identified significant subnetworks using the MCODE plugin. Further analysis with CytoHubba highlighted eight plausible hub genes for pathogenesis: RPL8 (upregulated) and RPL27, OsPRPL3, RPL21, RPL9, RPS5, OsRPS9, and RPL17 (downregulated). We validated the expression levels of these hub genes in response to infection, finding that RPL8 exhibited significantly higher expression compared with other downregulated genes. Remarkably, RPL8 formed a distinct cluster in the co-expression network, whereas other hub genes were interconnected, with RPL9 playing a central role, indicating its pivotal role in coordinating gene expression during infection. Gene Ontology highlighted the enrichment of hub genes in the ribosome and protein translation processes. Prior studies suggested that plant immune defence activation diminishes the energy pool by suppressing ribosomes. Intriguingly, our study aligns with this phenomenon, as the identified ribosomal proteins (RPs) were suppressed, while RPL8 expression was activated. We anticipate that these RPs could be targeted to develop new stress-resistant rice varieties, beyond their housekeeping role. Overall, integrating transcriptomic data revealed more common DEGs, enhancing the reliability of our analysis and providing deeper insights into rice blast disease mechanisms.

Leaf size is a crucial agronomic trait directly affecting crop yield, which is mainly determined by coordinated cell proliferation, growth, and differentiation. Although endoreduplication is known to be correlated with the onset of cell differentiation and leaf size, the underlying molecular mechanisms are largely unclear. The DnaJ-like zinc finger domain-containing protein ORANGE (OR) was initially demonstrated to confer the massive accumulation of carotenoids in cauliflower curds. However, the cauliflower or mutant also possesses other phenotypes such as smaller curds, smaller leaves with elongated petioles, and delayed flowering. Here, we demonstrated that OR physically interacts with the transcription factor TCP7, which promotes endoreduplication by inducing the expression of the cell cycle gene CYCLIN D 1;1 (CYCD1;1). Overexpression of OR resulted in smaller rosette leaves, whereas the OR-silencing plants had larger rosette leaves than wild-type plants. Our microscopic observations and flow cytometry analysis revealed that the variation in leaf size was a result of different endoreduplication levels. Genetic analyses showed that OR functions antagonistically with TCP7 in regulating the endoreduplication levels in leaf cells. While the expression of OR is induced by TCP7, OR represses the transactivation activity of TCP7 by affecting its binding capability to the TCP-binding motif in the promoter region of CYCD1;1. Through this interaction, OR negatively regulates the expression of CYCD1;1 and reduces the nuclear ploidy level in rosette leaf cells. Our findings provide new insights into the regulatory network of leaf size and also reveal a regulatory circuit controlling endoreduplication in leaf cells.

Pregnancy is a complex process involving complex molecular interaction networks, such as between miRNA-protein, protein-protein, metabolite-metabolite, and protein-metabolite interactions. Advances in technology have led to the identification of many pregnancy-associated microRNA (miRNA), protein, and metabolite fingerprints in dairy cows. An array of miRNA, protein, and metabolite fingerprints produced during the early pregnancy of dairy cows were described. We have found the in silico interaction networks between miRNA-protein, protein-protein, metabolite-metabolite, and protein-metabolite. We have manually constructed miRNA-protein-metabolite interaction networks such as bta-miR-423-3p-IGFBP2-PGF2α interactomes. This interactome is obtained by manually combining the interaction network formed between bta-miR-423-3p-IGFBP2 and the interaction network between IGFBP2-PGF2α with IGFBP2 as a common interactor with bta-miR-423-3p and PGF2α with the provided sources of evidence. The interaction between bta-miR-423-3p and IGFBP2 has many sources of evidence including a high miRanda score of 169, minimum free energy (MFE) score of -25.14, binding probability (p) of 1, and energy of -25.5. The interaction between IGFBP2 and PGF2α occurs at high confidence scores (≥0.7 or 70%). Interestingly, PGF2α is also found to interact with different metabolites, such as PGF2α-PGD2, PGF2α-thromboxane B2, PGF2α-PGE2, and PGF2α-6-keto-PGF1α at high confidence scores (≥0.7 or 70%). Furthermore, the interactions between C3-PGE2, C3-PGD2, PGE2-PGD2, PGD2-thromboxane B2, PGE2-thromboxane B2, 6-keto-PGF1α-thromboxane B2, and PGE2-6-keto-PGF1α were also obtained at high confidence scores (≥0.7 or 70%). Therefore, we propose that miRNA-protein-metabolite interactomes involving miRNA, protein, and metabolite fingerprints of early pregnancy of dairy cows such as bta-miR-423-3p, IGFBP2, PGF2α, PGD2, C3, PGE2, 6-keto-PGF1 alpha, and thromboxane B2 may form the key regulatory networks and players of pregnancy regulation in dairy cows. This is the first study involving miRNA-protein-metabolite interactomes obtained in the early pregnancy stage of dairy cows.