Protein-protein Interaction Interaction Research Articles

Identification and prognostic analysis of metabolic genes associated with TP53 mutation in multiple myeloma

ABSTRACT Background TP53 gene mutation is crucial in determining the prognosis of Multiple Myeloma (MM) patients. Understanding metabolic genes linked to TP53 mutation is vital for developing targeted therapies for these patients. Method We analyzed The Cancer Genome Atlas (TCGA) dataset to identify genes related to TP53 mutation and metabolism. Using univariate Cox regression and protein–protein interaction (PPI) analysis, we identified key genes. We categorized patients into high and low metabolism groups via non-negative matrix factorization (NMF) clustering, which led to the discovery of relevant differential genes. Integrating these with genes from the Gene Expression Omnibus (GEO) datasets and PPI interactions, we pinpointed crucial metabolic genes associated with TP53 mutation in MM. Additionally, we conducted prognostic analyses involving survival curves and receiver operating characteristic (ROC) charts. Results Our study reveals that the metabolic gene ribonucleotide reductase M2 (RRM2), linked to TP53 mutation, correlates positively with the International Staging System (ISS) stage in MM patients and is an independent prognostic risk factor. In the TCGA dataset, among the 767 patients, the 35 MM patients with TP53 mutation generally had poor survival outcomes. Specifically, the patients with both TP53 mutation and high RRM2 expression had a 2-year survival rate of only 38.87%, whereas those with normal TP53 function and low RRM2 expression had a 2-year survival rate of 86.31% (p < 0.001). Conclusion RRM2 significantly impacts MM prognosis and is associated with TP53 mutation, presenting itself as a potential therapeutic target and prognostic marker for MM.

Hydrophobic core evolution of major histocompatibility complex class I chain-related protein A for dramatic enhancing binding affinity

Interface residues at sites of protein–protein interaction (PPI) are the focus for affinity optimisation. However, protein hydrophobic cores (HCs) play critical roles and shape the protein surface. We hypothesise that manipulating protein HCs can enhance PPI interaction affinities. A cell stress molecule, major histocompatibility complex class I chain-related protein A (MICA), binds to the natural killer group 2D (NKG2D) homodimer to form three molecule interactions. MICA was used as a study subject to support our hypothesis. We redesigned MICA HCs by directed mutagenesis and isolated high-affinity variants through a newly designed partial-denature panning (PDP) method. A few mutations in MICA HCs increased the NKG2D–MICA interaction affinity by 325–5613-fold. Crystal structures of the NKG2D–MICA variant complexes indicated that mutagenesis of MICA HCs stabilised helical elements for decreasing intermolecular interactive free energy (ΔG) of the NKG2D–MICA heterotrimer. The repacking of MICA HC mutants maintained overall surface residues and the authentic binding specificity of MICA. In conclusion, this study provides a new method for MICA redesign and affinity optimisation through HC manipulation without mutating PPI interface residues. Our study introduces a novel approach to protein manipulation, potentially expanding the toolkit for protein affinity optimisation.

Abstract 6814: E2F1-induced autocrine IL-6 inflammatory loop mediates cancer-immune cell crosstalk that regulates metastatic properties in melanoma

Abstract Melanoma is a cancer that is metastatic, drug-refractory and fatal if managed late or inadequately. The transcription factor E2F1 is a key player in melanoma pathology, promoting metastatic traits like EMT, neoangiogenesis, chemoresistance and evasion of immune surveillance. Particularly, high levels of E2F1 trigger tumor progression by developing protein-protein interactions (PPI) with coregulators that enhance its potential to activate a prometastatic gene regulatory network (GRN). We aimed to investigate the extent to which E2F1-expressing melanoma cells influence the immune response. Methods: We combined transcriptomic approaches with bioinformatics and structural modeling to search for target genes involved in E2F1-activated immunomodulatory and prometastatic GRNs in aggressive melanoma. Immunoprecipitation and ChIP were performed to verify both PPI and protein-DNA interactions. Functional analyses, including qRT-PCR, immunoblotting and luciferase assays, were performed to verify expression and regulation of downstream targets. Additionally, we established a melanoma-immune cell coculture system with patient-derived CD4+ and CD8+ T cells. Cytokine arrays and ELISA were used for measurement of cytokine secretion to analyze the E2F1-induced tumor-immuno-crosstalk. Results: We identified an E2F1-dependent GRN with IL-6 as a central factor mediating EMT and immune regulation. Mechanistically, E2F1-induced activation and secretion of IL-6 leads to an autocrine inflammatory forward-feedback loop, which in turn further promotes invasiveness and EMT marker expression in melanoma cells. Interestingly, IL6 activated STAT3 associates with E2F1 in a structure-dependent manner, forming a complex that enhances IL-6 expression by binding to a composite E2F1:STAT3-responsive promoter element. Moreover, we observed that the E2F1-IL6 axis dispenses immunomodulatory effects on surrounding CD4+ and CD8+ T cells, further stimulating IL-6 release, but also leading to secretion of IL-10 into the TME. Although IL-10 has an immunosuppressive function, it also showed anti-metastatic properties in melanoma cells. Thus, in contrast to IL-6, IL-10 decreased cell motility and reduced the expression of Vim, Slug, and Snai1, while simultaneous knockdown of E2F1 increased these effects. The findings are supported by clinical data sets showing that melanoma patients who have high levels of E2F1 and IL-6 and low IL-10 exhibit poor survival. Conclusion: Our results unveil a novel gene regulatory program by which E2F1-associated IL-6 release induces the formation of an E2F1:STAT3 complex, resulting in a forward-feedback loop that promotes cancer invasion and regulates T cells response through intercellular cytokine signaling in the tumor microenvironment. Citation Format: Prabir Dhar, Krishna P. Singh, Shailendra K. Gupta, Dhanya Peringot, Alf Spitschak, Brigitte M. Pützer. E2F1-induced autocrine IL-6 inflammatory loop mediates cancer-immune cell crosstalk that regulates metastatic properties in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6814.

Screening of promising targets for the diagnosis and treatment of the aging pituitary gland by bioinformatics

Background: Pituitary senescence constitutes a multifaceted process characterized by numerous morphological alterations, functional disruptions, and metabolic impairments within the pituitary tissue. It stands as a pivotal risk factor contributing to the heightened prevalence of neurodegenerative diseases. But the underlying molecular network mechanism remains to be known. Objective: This study analyzed the gene targets of pituitary with aging by bioinformatics, hoping to screen out promising targets for the diagnosis and treatment of aging pituitary. Methods: The GeneCards database (https://www.genecards.org) was utilized to retrieve targets associated with aging and the pituitary. The dataset was filtered using a score threshold of "Relevance score ≥10". Intersection genes were obtained through Venny intersection analysis. Protein-protein interaction (PPI) network analysis, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the intersection genes were conducted on these intersection genes. The association between the obtained findings and the aging process of the pituitary gland was explored and compared with the existing literature. Results: Through bioinformatics analysis, we obtained 17 common genes between aging and pituitary-related genes. GO enrichment, KEGG pathway analysis, and PPI interaction showed that the genes IGF1, AKT1, RET, and POMC manifested down-regulation in aging process, whereas, LEP exhibited a marked up-regulation. Moreover, GO analysis reported activation of protein kinase activity, regulation of multicellular organism growth, and glucose metabolic processes within the realm of GO Biological Process (BP) enrichment. Likewise, the GO Cellular Component (CC) enrichment implicated the Wnt signalosome and catenin complex. In terms of Molecular Function (MF), results pointed to receptor ligand activity, insulin receptor binding, and estrogen receptor binding. Moreover, KEGG pathway enrichment analysis highlighted significant pathways associated with aging, such as Growth hormone synthesis, secretion, and action, Breast cancer, Rap1 signaling pathway, and JAK-STAT signaling pathway. Conclusions: We delved into the intricate link between aging and the pituitary gland, and identify several gene targets through the GeneCards database. By analyzing protein interactions, GO, and KEGG pathways, we found the 17 intersecting genes, which could be used to explain the molecular-level connections in the process of pituitary aging.

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions.

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

Deregulated Gene Expression Profiles and Regulatory Networks in Adult and Pediatric RUNX1/RUNX1T1-Positive AML Patients

Acute myeloid leukemia (AML) is a heterogeneous and complex disease concerning molecular aberrations and prognosis. RUNX1/RUNX1T1 is a fusion oncogene that results from the chromosomal translocation t(8;21) and plays a crucial role in AML. However, its impact on the transcriptomic profile of different age groups of AML patients is not completely understood. Here, we investigated the deregulated gene expression (DEG) profiles in adult and pediatric RUNX1/RUNX1T1-positive AML patients, and compared their functions and regulatory networks. We retrospectively analyzed gene expression data from two independent Gene Expression Omnibus (GEO) datasets (GSE37642 and GSE75461) and computed their differentially expressed genes and upstream regulators, using limma, GEO2Enrichr, and X2K. For validation purposes, we used the TCGA-LAML (adult) and TARGET-AML (pediatric) patient cohorts. We also analyzed the protein-protein interaction (PPI) networks, as well as those composed of transcription factors (TF), intermediate proteins, and kinases foreseen to regulate the top deregulated genes in each group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses were further performed for the DEGs in each dataset. We found that the top upregulated genes in (both adult and pediatric) RUNX1/RUNX1T1-positive AML patients are enriched in extracellular matrix organization, the cell projection membrane, filopodium membrane, and supramolecular fiber. Our data corroborate that RUNX1/RUNX1T1 reprograms a large transcriptional network to establish and maintain leukemia via intricate PPI interactions and kinase-driven phosphorylation events.

Periostin regulation and cartilage degradation early after anterior cruciate ligament reconstruction.

The purpose of this study was to explore pathological processes during the first 4weeks after anterior cruciate ligament reconstruction (ACLR). Sixteen ACL-injured patients (8 females/8 males, mean age = 19.1, mean BMI = 28.6). Arthrocentesis wasperformed 1 and 4weeks after ACLR. Proteins in the synovial fluid were identified using nanoLC-ESI-MS/MS. Differentially up- or down-regulated proteins were identified and quantified, and a pathway analysis was performed. All identified proteins were mapped into a protein-protein interaction (PPI) network, and networks of PPIs with a combined score > 0.9 were then visualized. Seven pathways were upregulated after ACLR:PI3K-AKT signaling pathway, extracellular matrix (ECM)-receptor interaction, focal adhesion, protein digestion and absorption, ameobiasis, and platelet activation. Network analyses identified 8 proteins that were differentially upregulated with strong PPI interactions (periostin and 7 collagen-related proteins). Increases in periostin moderately correlated with increases in a synovial fluid biomarker of type II cartilage degradation (ρ = 0.51, p = 0.06). Pro-inflammatory pathways and periostin were upregulated after ACLR. Periostin demonstrated strong network connections with markers of collagen breakdown, and future work is needed to determine whether periostin may offer a biomarker of early cartilage degradation after ACLR and/or play an active role in early post-traumatic osteoarthritis (PTOA) progression.

Identification of candidate biomarkers associated with gastric cancer prognosis based on an integrated bioinformatics analysis.

BackgroundThis study sought to identify candidate biomarkers associated with gastric cancer (GC) prognosis based on an integrated bioinformatics analysis.MethodsFirst, the GSE54129 and GSE79973 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) identified between the 2 data sets were screened using the limma software package in R, and the intersection DEGs were obtained by a Venn analysis. Subsequently, gene clustering and a functional analysis were performed to explore the roles of the DEGs. The protein-protein interaction (PPI) network of the genes in clusters was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. A survival analysis evaluated the associations between the candidate genes and the overall survival of GC patients. A drug-gene interaction analysis and an external data set analysis were conducted using The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) data set to validate the prognostic genes.ResultsWe extracted 421 intersection DEGs from the 2 GEO data sets. There were 5 gene clusters, and the functional analysis revealed that they were mainly associated with the extracellular matrix-receptor interaction pathway. The PPI interaction analysis identified the top 36 hub genes. The survival analysis revealed that 7 upregulated genes [i.e., platelet-derived growth factor receptor beta (PDGFRB), angiopoietin 2 (ANGPT2), vascular endothelial growth factor C (VEGFC), collagen type IV alpha 2 chain (COL4A2), collagen type IV alpha 1 chain (COL4A1), thrombospondin 1 (THBS1), and fibronectin 1 (FN1)] were associated with the survival prognosis of GC patients. The 20 drug-gene interaction pairs among the 4 genes and 18 drugs were obtained. Finally, TCGA-STAD data set was used to validate the expression levels of COL4A1, PDGFRB, and FN1.ConclusionsWe found that 7 upregulated genes (i.e., PDGFRB, ANGPT2, VEGFC, COL4A2, COL4A1, THBS1, and FN1) were promising markers of prognosis in GC patients.

Bioinformatics Analysis of Prognosis-Related Genes and Expression of CXCL8 in Colorectal Cancer

Background Colorectal cancer (CRC), one of the main causes of death, remains a leading cause of mortality in gastrointestinal cancer and tends to affect the younger generation. However, the pathological process of colorectal cancer is unclear. Exploring potential pathogenesis and therapeutic targets of CRC is significant as its high prevalence and high mortality. Nowadays, the rapid development of bioinformatics provides us an opportunity to explore potential molecular markers of CRC. Materials and Methods First, three CRC gene chips with paracancerous controls were downloaded from the Gene Expression Omnibus (GEO) database. Second, after combining and batch correcting the three chips using the R language and Perl language, the differentially expressed genes (DEGs) were selected to investigate how they affect the CRC occurrence and development by GO and KEGG enrichment analysis. Third, based on the STRING website and the Cytoscape software, the protein-protein interaction (PPI) network was constructed and the core genes were screened out. Finally, through polymerase chain reaction (PCR) and immunohistochemistry (IHC), the expression and function of the core gene CXCL8 in CRC were explored. Results GSE10950, GSE44076, and GSE75970, including 126 intestinal cancer samples and 126 paracancer samples, were screened as the datasets. 192 DEGs were screened, including 43 upregulated genes and 149 downregulated genes. Through the DEGs screened out, GO enrichment analysis, KEGG enrichment analysis, and the construction of PPI interaction network were carried out. Finally, according to the nodes and edges in the PPI network, the DEGs were sorted and the core genes were selected. Through basic experiments, the first ranked CXCL8 was further studied, and the results suggest that the expression of CXCL8 is related to the proliferation, migration, invasion, and even distant metastasis of CRC. Conclusion The present study showed that DEGs of CRC are associated with multiple tumor-related biological processes and signaling pathways. The core gene CXCL8 has the potential to be a new therapeutic target for CRC.

Protein-Protein Interactions Followed by in-Situ Synthesis of Gold Nanoparticles

Protein-protein interaction (PPI) plays an important role in various biochemical processes. Various binary mixtures of industrially important zein have been blended with rice and wheat proteins to expand their biological applicability. Zein-rice, zein-hard wheat, and zein-soft wheat mixtures show different behavior toward in vitro synthesis of Au NPs. This can also be explained by the TEM, FESEM, and DLS studies of PPI-Au NPs mixtures. Zein-rice-Au NPs mixture showed their mutual favorable interaction with high colloidal stability and high reduction ability towards nucleating Au NPs. This may be due to both hydrophobic and hydrophilic interaction of zein-rice PPI, which resulted in regular and shape-controlled Au NPs. In the case of zein-wheat PPI interaction, lesser hydrophobic interaction resulted in the incomplete unfolding of wheat protein and demixing of wheat protein with zein protein to anisotropic and irregular Au NPs. The study of these interactions will lead to the development of therapeutic centers toward different diseases and their environmental applications.

Uncovering the pharmacological mechanism of Wei-Tong-Xin against gastric ulcer based on network pharmacology combined with in vivo experiment validation

Ethnopharmacological relevanceThe prescription of Wei-Tong-Xin (WTX) is improved based on the prescription “Wanyingyuan”, a famous decoction documented in the book of Huatuozhongzangjing in the Han dynasty. Many years of clinical verification have demonstrated that WTX can be used to treat gastrointestinal diseases, especially gastric ulcer (GU). However, the potential pharmacological mechanism is undefined. Aim of the studyThis research was conducted to explore the pharmacological mechanisms under the consideration of the therapeutical effect of WTX against GU by combining the network pharmacology strategy and in-vivo verified experiments. Materials and methodsA prediction network describing the relationship between WTX and GU was established based on information collected from multiple databases. Then, the intersecting protein-protein interaction (PPI) network of the drug-disease overlapping gene targets was constructed, and several key targets related to both WTX and GU were obtained. Besides, the Gene Ontology (GO) biological enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to investigate the key target genes and pathways of WTX against GU. Then, the candidate targets and signaling pathways of network pharmacology were validated in a rat model of GU induced by indomethacin following the results and available proof. ResultsThere are 243 targets obtained from the 65 active ingredients in WTX, and 1362 disease targets related to GU were identified. Then, 6 key targets were determined with the PPI interaction network, which was structured from 126 overlapping gene targets. GO and KEGG analyses revealed that the phosphoinositide-3-kinase/protein kinase B (PI3K/AKT) signaling pathway might play a crucial role in the therapeutic mechanism of GU. In vivo verified experiments, WTX significantly reduced the ulcer area and improved the histopathological appearance of gastric tissues. Moreover, down-regulated the protein levels of IL6, TNF-α, and Caspase 3 in the gastric tissues while up-regulating the expression of p-PI3K, p-AKT, p-P53, and VEGFA compared to the model group. ConclusionWTX, an ancient traditional Chinese medicine (TCM) compound prescription, may affect the inflammatory response and apoptosis process by regulating PI3K/AKT signaling pathway and related gene targets. Therefore, it is an effective drug candidate for the modern treatment of GU.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma.

Background A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA). Methods From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan–Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built. Results Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability. Conclusions The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Network Pharmacology Integrated Molecular Docking Revealed the Mechanism of Jianpi Yiqi Taohua Decoction Against Ulcerative Colitis.

BackgroundJianpi Yiqi Taohua decoction (JYTD) has shown therapeutic effects in ulcerative colitis (UC). However, the pharmacological mechanism of JYTD against UC remains unclear.Material/MethodsCompounds and targets of JYTD and UC-related genes were screened from public databases. Integrated analysis was performed to identify therapeutic targets of UC, followed by functional enrichment analysis. Protein–protein interaction (PPI) and pharmacological networks were then established. Molecular docking was used to validate the affinity of compounds and their targets. Further, the efficacy of JYTD was evaluated by meta-analysis. Relevant studies were searched from 5 databases. Outcomes were complete response rate (CRR) and overall response rate (ORR), and pooled results were estimated by risk ratio (RR) with corresponding 95% confidence intervals (CIs).ResultsThe pharmacological network identified 13 herbal medicines, 28 compounds, 54 targets, and 20 pathways. Stigmasterol, liquiritigenin, and naringenin were potential active compounds, and PRKCA, NFKB1, ESR1, NTRK1, AKT1, PPARG, RXRA, and VDR were hub targets. Pathway analysis revealed that genes were mainly involved in the cellular response to lipids. Molecular docking indicated that AKT1, NFKB1, ESR1, NTRK1, PRKCA, and PPARG exhibited good affinity to 6 key compounds of JYTD. Then, meta-analysis revealed that Tao Hua decoction treatment significantly improved CRR (RR, 1.21; 95% CI, 1.06–1.37; P=0.004) and ORR (RR, 1.16; 95% CI, 1.08–1.24; P<0.001).ConclusionsJYTD was found to have preventive and therapeutic effects on UC through multiple compounds, targets, and pathways. These findings enhanced our understanding of the potential pharmacological mechanisms of JYTD against UC.

Mechanically transduced immunosorbent assay to measure protein-protein interactions.

Measuring protein-protein interaction (PPI) affinities is fundamental to biochemistry. Yet, conventional methods rely upon the law of mass action and cannot measure many PPIs due to a scarcity of reagents and limitations in the measurable affinity ranges. Here, we present a novel technique that leverages the fundamental concept of friction to produce a mechanical signal that correlates to binding potential. The mechanically transduced immunosorbent (METRIS) assay utilizes rolling magnetic probes to measure PPI interaction affinities. METRIS measures the translational displacement of protein-coated particles on a protein-functionalized substrate. The translational displacement scales with the effective friction induced by a PPI, thus producing a mechanical signal when a binding event occurs. The METRIS assay uses as little as 20 pmols of reagents to measure a wide range of affinities while exhibiting a high resolution and sensitivity. We use METRIS to measure several PPIs that were previously inaccessible using traditional methods, providing new insights into epigenetic recognition.

Identification of key miRNAs and targeted genes involved in the progression of oral squamous cell carcinoma

Background/purposeOral squamous cell carcinoma (OSCC) is one of the most common types of head and neck squamous cell carcinoma. Accurate biomarkers are needed for early diagnosis and prognosis of OSCC. MicroRNAs (miRNAs) have shown great values in different types of cancers including OSCC. However, most of the miRNAs involved in the development of OSCC remain uncovered. This study aimed to identify hub miRNAs and mRNAs in OSCC. Materials and methodsWe explored the roles of key miRNAs, target genes and their relationships in OSCC using an integrated bioinformatics approach. Initially, Two OSCC microarray datasets from the Gene Expression Omnibus database were obtained to analyze miRNA expression. MiRNA-targeted mRNAs were acquired, and gene ontology/kyoto encyclopedia of genes and genomes analyses were performed. Thereafter, we constructed a protein–protein interaction (PPI) network to identify hub genes and a miRNA-mRNA interaction network was used to identify key miRNAs. Furthermore, differential gene expression and Kaplan–Meier Plotter survival analysis was performed to evaluate their potential clinical application values. ResultsFour upregulated, two downregulated miRNAs and 608 target genes of the differentially expressed miRNAs were identified. The PPI and miRNA-mRNA interaction networks highlighted 10 hub genes and two key miRNAs, and pathway analyses showed their correlative involvement in tumorigenesis-related processes. Of these miRNAs and genes, miR-125b, β-actin, vinculin and histone deacetylase 1 were correlated with overall survival (P < 0.05). ConclusionThese findings indicate that miR-21 and miR-125b, associated with the 10 hub genes, jointly participate in OSCC tumorigenesis, offering insight into the molecular mechanisms underlying OSCC as potential targets for early diagnosis, treatment and prognosis.

One Atom Makes All the Difference: Getting a Foot in the Door between SOS1 and KRAS.

KRAS, the most common oncogenic driver in human cancers, is controlled and signals primarily through protein-protein interactions (PPIs). The interaction between KRAS and SOS1, crucial for the activation of KRAS, is a typical, challenging PPI with a large contact surface area and high affinity. Here, we report that the addition of only one atom placed between Y884SOS1 and A73KRAS is sufficient to convert SOS1 activators into SOS1 inhibitors. We also disclose the discovery of BI-3406. Combination with the upstream EGFR inhibitor afatinib shows in vivo efficacy against KRASG13D mutant colorectal tumor cells, demonstrating the utility of BI-3406 to probe SOS1 biology. These findings challenge the dogma that large molecules are required to disrupt challenging PPIs. Instead, a "foot in the door" approach, whereby single atoms or small functional groups placed between key PPI interactions, can lead to potent inhibitors even for challenging PPIs such as SOS1-KRAS.

In Silico Identification of Potential Druggable Binding Sites on CIN85 SH3 Domain.

Protein-protein interactions (PPIs) play a pivotal role in the regulation of many physiological processes. The dysfunction of some PPIs interactions led to the alteration of different biological pathways causing various diseases including cancer. In this context, the inhibition of PPIs represents an attractive strategy for the design of new antitumoral agents. In recent years, computational approaches were successfully used to study the interactions between proteins, providing useful hints for the design of small molecules able to modulate PPIs. Targeting PPIs presents several challenges mainly due to the large and flat binding surface that lack the typical binding pockets of traditional drug targets. Despite these hurdles, substantial progress has been made in the last decade resulting in the identification of PPI modulators where some of them even found clinical use. This study focuses on MUC1-CIN85 PPI which is involved in the migration and invasion of cancer cells. Particularly, we investigated the presence of druggable binding sites on the CIN85 surface which provided new insights for the structure-based design of novel MUC1-CIN85 PPI inhibitors as anti-metastatic agents.

Application of Centrality Measures for Potential Drug Targets: Review

Protein-Protein Interactions (PPI) have important role in drug binding with the Proteins called drug targets. For identifying the potential drug targets there are different techniques. In this paper we are presenting application of Centrality Measures for identifying the drug targets. Centrality measure indicates importance of node in the graph or network. Protein-Protein Interactions for proteins which are involved in a particular disease are identified and centrality measures will be calculated based on the graph built suing the PPI interactions. Further the nodes which are playing crucial role will be identified using the various centrality measures and these drug targets can be used for drug discovery of a particular disease through insilico docking studies.

Investigation of protein-protein interactions and hot spot region between PD-1 and PD-L1 by fragment molecular orbital method

Inhibitors to interfere protein-protein interactions (PPI) between programmed cell death 1 (PD-1) and programmed death ligand-1 (PD-L1) block evasion of cancers from immune surveillance. Analyzing hot spot residues in PPI is important for small-molecule drug development. In order to find out hot spots on PPI interface in PD-1/PD-L1 complex, we analyzed PPI in PD-1/PD-L1 with a new analysis method, 3-dimensional scattered pair interactions energies (3D-SPIEs), which assorts significant interactions with fragment molecular orbital (FMO) method. By additionally analyzing PPI in PD-1/antibody and PD-L1/antibody complexes, and small-ligand interactions in PD-L1/peptide and PD-L1/small-molecule complexes, we narrowed down the hot spot region with 3D-SPIEs-based interaction map, which integrates PPI and small-ligand interactions. Based on the map, there are two hot spot regions in PPI of PD-1/PD-L1 and the first hot spot region is important for inhibitors. In particular, LY56, LE58, and LN66 in the first hot spot of PD-L1 are important for PD-L1-antibodies and small-inhibitors in common, while LM115 is important for small-inhibitors. Therefore, the 3D-SPIEs-based map would provide valuable information for designing new small-molecule inhibitors to inhibit PPI of PD-1/PD-L1 and the FMO/3D-SPIEs method provides an effectual tool to understand PPI and integrate PPI and small-ligand interactions at a quantum mechanical level.

Exploring Energy Profiles of Protein-Protein Interactions (PPIs) Using DFT Method

Background: Large-scale energy landscape characterization of protein-protein interactions (PPIs) is important to understand the interaction mechanism and protein-protein docking methods. The experimental methods for detecting energy landscapes are tedious and the existing computational methods require longer simulation time. Objective: The objective of the present work is to ascertain the energy profiles at the interface regions in a rapid manner to analyze the energy landscape of protein-protein interactions. Methods: The atomic coordinates obtained from the X-ray and NMR spectroscopy data are considered as inputs to compute cumulative energy profiles for experimentally validated protein-protein complexes. The energies computed by the program were comparable to the standard molecular dynamics simulations. Results: The PPI Profiler not only enables rapid generation of energy profiles but also facilitates the detection of hot spot residue atoms involved therein. Conclusion: The hotspot residues and their computed energies matched with the experimentally determined hot spot residues and their energies which correlated well by employing the MM/GBSA method. The proposed method can be employed to scan entire proteomes across species at an atomic level to study the key PPI interactions.