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Protein–protein interactions orchestrate plant development and serve as crucial elements for cellular and environmental communication. Understanding these interactions offers a gateway to unravel complex protein networks that will allow a better understanding of nature. Methods for the characterization of protein–protein interactions have been around over 30 years, yet the complexity of some of these interactions has fueled the development of new techniques that provide a better understanding of the underlying dynamics. In many cases, the application of these techniques is limited by the nature of the available sample. While some methods require an in vivo set-up, others solely depend on protein sequences to study protein–protein interactions via an in silico set-up. The vast number of techniques available to date calls for a way to select the appropriate tools for the study of specific interactions. Here, we classify widely spread tools and new emerging techniques for the characterization of protein–protein interactions based on sample requirements while providing insights into the information that they can potentially deliver. We provide a comprehensive overview of commonly used techniques and elaborate on the most recent developments, showcasing their implementation in plant research. [ABSTRACT FROM AUTHOR]

Background: We aimed to identify hub genes in chronic obstructive pulmonary disease (COPD) plasma through the exploration of a putative miRNA–mRNA regulatory network. Methods: Three datasets (GSE24709, GSE102915, GSE136390) were utilized to discern differentially expressed miRNAs (DEMs) between COPD and normal plasma. miRNET was employed to predict the potential targets of DEMs. Subsequent GO and KEGG analyses were conducted using DAVID. For the construction of the protein-protein interaction (PPI) network and screening of hub genes, STRING and Cytoscape were employed. The expression validation was assessed through GSE56768. Results: The results revealed 395 genes targeted by up-regulated DEMs and 234 genes targeted by down-regulated DEMs. The target genes exhibited significant enrichment in the PI3K-Akt signaling pathway and the p53 signaling pathway. Through the validation of hub genes' expression, we proposed two potential miRNA-mRNA interactions: miR-126-5p/miR-495-3p/miR-193b-3p - YWHAZ and miR-937-5p/miR-183-5p/miR-34c-5p/miR-98-5p/miR-525-3p/miR-215-5p - ACTB. Conclusions: In conclusion, our study posits potential miRNA-mRNA interactions in COPD by analyzing datasets from public databases, contributing valuable insights into the understanding of COPD pathogenesis and potential therapeutic avenues. [ABSTRACT FROM AUTHOR]

Ion channels are integralmembrane proteinsmediating ion flow in response to changes in their environment. Among the different types of ion channels reported to date, the super-family of TRP channels stands out since its members have been linked to many pathophysiological processes. The family comprises 6 subfamilies and 28 members in mammals, which are widely distributed throughout most tissues and organs and have an important role in several aspects of cellular physiology. It has been evidenced that abnormal expression, post-translational modifications, and channel trafficking are associated with several pathologies, such as cancer, cardiovascular disease, diabetes, and brain disorders, among others. In this review, we present an updated summary of the mechanisms involved in the subcellular trafficking of TRP channels, with a special emphasis on whether different post-translational modifications and naturally occurring mutagenesis affect both expression and trafficking. Additionally, we describe how such changes have been associated with the development and progress of diverse pathologies associated with the gain or loss of functional phenotypes. The study of these processes will not only contribute to a better understanding the role of TRP channels in the different tissues but will also present novel possible therapeutic targets in diseases where their activity is dysregulated. [ABSTRACT FROM AUTHOR]

The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coinfection may result in complications that make treatment more difficult. However, the molecular mechanisms underpinning the interaction between TB and COVID-19 are unclear. Accordingly, transcriptome analysis was used to detect the shared pathways and molecular biomarkers in TB and COVID-19, allowing us to determine the complex relationship between COVID-19 and TB. Two RNA-seq datasets (GSE114192 and GSE163151) from the Gene Expression Omnibus were used to find concerted differentially expressed genes (DEGs) between TB and COVID-19 to identify the common pathogenic mechanisms. A total of 124 common DEGs were detected and used to find shared pathways and drug targets. Several enterprising bioinformatics tools were applied to perform pathway analysis, enrichment analysis and networks analysis. Protein–protein interaction analysis and machine learning was used to identify hub genes (GAS6, OAS3 and PDCD1LG2) and datasets GSE171110, GSE54992 and GSE79362 were used for verification. The mechanism of protein-drug interactions may have reference value in the treatment of coinfection of COVID-19 and TB. [ABSTRACT FROM AUTHOR]

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein–protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug–disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors. [ABSTRACT FROM AUTHOR]

Genomic surveillance based on sequencing the entire genetic code of SARS-CoV-2 involves monitoring and studying genetic changes and variations in disease-causing organisms such as viruses and bacteria. By tracing the virus, it is possible to prevent epidemic spread in the community, ensuring a 'precision public health' strategy. A peptide-based design was applied to provide an efficacious strategy that is able to counteract any emerging viral variant of concern dynamically and promptly to affect the outcomes of a pandemic at an early stage while waiting for the production of the anti-variant-specific vaccine, which require longer times. The inhibition of the interaction between the receptor-binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and one of the cellular receptors (DPP4) that its receptors routinely bind to infect human cells is an intriguing therapeutic approach to prevent the virus from entering human cells. Among the other modalities developed for this purpose, peptides surely offer unique advantages, including ease of synthesis, serum stability, low immunogenicity and toxicity, and small production and distribution chain costs. Here, we obtained a potent new inhibitor based on the rearrangement of a previously identified peptide that has been rationally designed on a cell dipeptidyl peptidase 4 (DPP4) sequence, a ubiquitous membrane protein known to bind the RBD-SPIKE domain of the virus. This novel peptide (named DPP4-derived), conceived as an endogenous "drug", is capable of targeting the latest tested variants with a high affinity, reducing the VSV* DG-Fluc pseudovirus Omicron's infection capacity by up to 14%, as revealed by in vitro testing in human Calu-3 cells. Surface plasmon resonance (SPR) confirmed the binding affinity of the new DPP4-derived peptide with Omicron variant RBD. [ABSTRACT FROM AUTHOR]

Progesterone receptor membrane component 1 (PGRMC1) is one of few proteins that have been recently described as direct modulators of the activity of human cytochrome P450 enzymes (CYP)s. These enzymes form a superfamily of membrane-bound hemoproteins that metabolize a wide variety of physiological, dietary, environmental, and pharmacological compounds. Modulation of CYP activity impacts the detoxification of xenobiotics as well as endogenous pathways such as steroid and fatty acid metabolism, thus playing a central role in homeostasis. This review is focused on nine main topics that include the most relevant aspects of past and current PGRMC1 research, focusing on its role in CYP-mediated drug metabolism. Firstly, a general overview of the main aspects of xenobiotic metabolism is presented (I), followed by an overview of the role of the CYP enzymatic complex (IIa), a section on human disorders associated with defects in CYP enzyme complex activity (IIb), and a brief account of cytochrome b5 (cyt b5)’s effect on CYP activity (IIc). Subsequently, we present a background overview of the history of the molecular characterization of PGRMC1 (III), regarding its structure, expression, and intracellular location (IIIa), and its heme-binding capability and dimerization (IIIb). The next section reflects the different effects PGRMC1 may have on CYP activity (IV), presenting a description of studies on the direct effects on CYP activity (IVa), and a summary of pathways in which PGRMC1’s involvement may indirectly affect CYP activity (IVb). The last section of the review is focused on the current challenges of research on the effect of PGRMC1 on CYP activity (V), presenting some future perspectives of research in the field (VI).

Progesterone receptor membrane component 1 (PGRMC1) is one of few proteins that have been recently described as direct modulators of the activity of human cytochrome P450 enzymes (CYP)s. These enzymes form a superfamily of membrane-bound hemoproteins that metabolize a wide variety of physiological, dietary, environmental, and pharmacological compounds. Modulation of CYP activity impacts the detoxification of xenobiotics as well as endogenous pathways such as steroid and fatty acid metabolism, thus playing a central role in homeostasis. This review is focused on nine main topics that include the most relevant aspects of past and current PGRMC1 research, focusing on its role in CYP-mediated drug metabolism. Firstly, a general overview of the main aspects of xenobiotic metabolism is presented (I), followed by an overview of the role of the CYP enzymatic complex (IIa), a section on human disorders associated with defects in CYP enzyme complex activity (IIb), and a brief account of cytochrome b5 (cyt b5)'s effect on CYP activity (IIc). Subsequently, we present a background overview of the history of the molecular characterization of PGRMC1 (III), regarding its structure, expression, and intracellular location (IIIa), and its heme-binding capability and dimerization (IIIb). The next section reflects the different effects PGRMC1 may have on CYP activity (IV), presenting a description of studies on the direct effects on CYP activity (IVa), and a summary of pathways in which PGRMC1's involvement may indirectly affect CYP activity (IVb). The last section of the review is focused on the current challenges of research on the effect of PGRMC1 on CYP activity (V), presenting some future perspectives of research in the field (VI). [ABSTRACT FROM AUTHOR]

Background: The occurrence of immunity and inflammation outside the central nervous system frequently results in acute cognitive impairment among elderly patients. However, there is currently a lack of standardized methods for diagnosing acute cognitive impairment. The objective of our study was to identify potential mRNA biomarkers and investigate the pathogenesis of acute cognitive impairment in mice brains. Methods: To analyze changes in hub genes associated with acute cognitive impairment, bioinformatics analysis was performed on the mouse brain injury data of sterile saline control group and lipopolysaccharide (LPS) induced experimental group in Gene Expression Omnibus (GEO). Functional analysis was conducted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), which facilitated to identify some potential mRNA biomarkers for hub gene expression in mice brains. Additionally, the ''CIBERSORT X″ R kit was employed to examine immune cell infiltrations of mice brains in LPS group and saline group. Results: In the LPS and saline group, 102 significantly upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were identified. The pathway enrichment analysis using GO and KEGG revealed that these DEGs were mainly related to the regulation of cytokine, cytokine-cytokine receptor interaction, as well as protein interaction with cytokine and cytokine receptor. Immune cell infiltration analysis indicated potential involvement of M1 macrophages, NK cells resting, T cells CD4 memory, and T cells CD8 naive in the process of cognitive impairment. By constructing a protein-protein interaction (PPI) network, five hub genes (Cxcl10, Cxcl12, Cxcr3, Gbp2, and Ifih1) showed significant associations with immune cell types by using a threshold Spearman's rank correlation coefficient of R > 0.50 and p

Ion channels are integral membrane proteins mediating ion flow in response to changes in their environment. Among the different types of ion channels reported to date, the super-family of TRP channels stands out since its members have been linked to many pathophysiological processes. The family comprises 6 subfamilies and 28 members in mammals, which are widely distributed throughout most tissues and organs and have an important role in several aspects of cellular physiology. It has been evidenced that abnormal expression, post-translational modifications, and channel trafficking are associated with several pathologies, such as cancer, cardiovascular disease, diabetes, and brain disorders, among others. In this review, we present an updated summary of the mechanisms involved in the subcellular trafficking of TRP channels, with a special emphasis on whether different post-translational modifications and naturally occurring mutagenesis affect both expression and trafficking. Additionally, we describe how such changes have been associated with the development and progress of diverse pathologies associated with the gain or loss of functional phenotypes. The study of these processes will not only contribute to a better understanding the role of TRP channels in the different tissues but will also present novel possible therapeutic targets in diseases where their activity is dysregulated.

Protein-protein interaction (PPI) play a pivotal role in cellular signal transduction. The bimolecular fluorescence complementation (BiFC) assay offers a rapid and intuitive means to ascertain the localization and interactions of target proteins within living cells. BiFC is based on fluorescence complementation by reconstitution of a functional fluorescent protein by co-expression of N- and C-terminal fragments of this protein. When fusion proteins interact, the N- and C-terminal fragments come into close proximity, leading to the reconstitution of the fluorescent protein. In the conventional approach, the N-terminal and C-terminal fragments of the fluorescent protein are typically expressed using two separate vectors, which largely relies on the efficiency of the transformation of the two vectors in the same cells. Furthermore, issues of vector incompatibility can often result in loss of one plasmid. To address these challenges, we have developed novel dual-transgenic BiFC vectors, designed as pDTQs, derived from the previously published pDT1 vector. This set of BiFC vectors offers the following advantages: 1) Both fluorescent fusion proteins are expressed sequentially within a single vector, enhancing expression efficiency; 2) Independent promoters and terminators regulate the expression of the two proteins potentially mitigating vector compatibility issues; 3) A long linker is inserted between the fluorescent protein fragment and the gene of interest, facilitating the recombination of the fused fluorescent protein into an active form; 4) Four distinct types of fluorescent proteins, namely, EYFP, mVenus, mRFP1Q66T and mCherry are available for BiFC analysis. We assessed the efficiency of the pDTQs system by investigating the oligomerization of Arabidopsis CRY2 and CRY2-BIC2 interactions in N. benthamiana. Notably, the pDTQs were found to be applicable in rice, underscoring their potential utility across various plant species. [ABSTRACT FROM AUTHOR]

Protein-protein interaction (PPI) participates in almost all important biological processes in the organism and plays a vital role in the basic life process of cells. It is of great biological significance to develop a high-throughput PPI detection method. At present, the next-generation sequencing (NGS) technology has developed rapidly and can determine more than 1 billion template DNA sequences in a few days. Due to the unique sensitivity, specificity, high throughput, and multiplexing advantages of parallel DNA sequencing technology, it has been used as a broad-spectrum molecular counter and applied to genome sequencing, transcriptome sequencing and other fields. The nucleic acid barcode technology realizes the high-throughput detection of PPI by connecting the oligonucleotide tag with the target proteins to mark the encoded protein, and then using the high-throughput sequencing method to detect the interacting protein. This technology promotes the rapid development of PPI detection methods, improves the throughput of single experiment detection, and provides a strong technical support for building PPI network. In this paper, we describe in detail the design, generation and reading of nucleic acid barcodes in the PPI detection method. By analyzing the application examples of the nucleic acid barcode technology in PPI research, this paper discusses their advantages and disadvantages, evaluates the reliability of data, and discusses the future development trend of PPI detection methods based on the nucleic acid barcode technology. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, includingGOand KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = -0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP. [ABSTRACT FROM AUTHOR]

Ras Proteins, play a pivotal role in the proliferation pathways, and Ras mutant forms are well-established cancer drivers. Ras mutations are found in about 30% of all human cancers widely known as challenging diseases to control and treat. The direct inhibition of Ras is challenging and makes Ras an “undruggable” target for many years. The important reason is, that Ras protein has a unique smooth surface and shows different dynamicity upon binding GDP and GTP. As a result, interfering peptides (IPs) targeting the Ras family protein-protein interactions (PPIs) are considered more likely to bind Ras effectively and inhibit the downstream signaling. In this review, we aimed to cover the recent approaches to design the peptides that target Ras family proteins, focusing on in silico methods. In this regard, the anti-cancer peptide development approaches including design and delivery strategies are discussed. Later, more specific methods regarding Ras-specific peptide design are presented. In conclusion, IPs are a promising group of cancer therapeutics to combat Ras mutant cancers. For future perspectives to have these peptides in clinical use, co-inhibition of other cancer targets as well as improving the pharmacokinetic features of peptides are suggested. [ABSTRACT FROM AUTHOR]

Medicinal chemistry is constantly searching for new approaches to develop more effective and targeted therapeutic molecules. The design of peptidomimetics is a promising emerging strategy that is aimed at developing peptides that mimic or modulate the biological activity of proteins. Among these, stapled peptides stand out for their unique ability to stabilize highly frequent helical motifs, but they have failed to be systematically reported. Here, we exploit chemically diverse helix-inducing i, i + 4 constraints—lactam, hydrocarbon, triazole, double triazole and thioether—on two distinct short sequences derived from the N-terminal peptidase domain of hACE2 upon structural characterization and in silico alanine scan. Our overall objective was to provide a sequence-independent comparison of α-helix-inducing staples using circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy. We identified a 9-mer lactam stapled peptide derived from the hACE2 sequence (His34-Gln42) capable of reaching its maximal helicity of 55% with antiviral activity in bioreporter- and pseudovirus-based inhibition assays. To the best of our knowledge, this study is the first comprehensive investigation comparing several cyclization methods with the goal of generating stapled peptides and correlating their secondary structures with PPI inhibitions using a highly topical model system (i.e., the interaction of SARS-CoV-2 Spike RBD with hACE2). [ABSTRACT FROM AUTHOR]

Objective To screen key autophagy-related genes in alcoholic hepatitis (AH) and investigate potential biomarkers and therapeutic targets for AH. Methods Two AH gene chips in Gene Expression Omnibus (GEO) and autophagy-related data sets obtained from MSigDB and GeneCards databases were used, and the key genes were verified and obtained by weighted gene co-expression network analysis (WGCNA). The screened key genes were subject to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) and immune infiltration analyses. Messenger RNA (mRNA)- microRNA (miRNA) network was constructed to analyze the expression differences of key autophagy-related genes during different stages of AH, which were further validated by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) in the liver tissues of AH patients and mice. Results Eleven autophagy-related genes were screened in AH (EEF1A2, CFTR, SOX4, TREM2, CTHRC1, HSPB8, TUBB3, PRKAA2, RNASE1, MTCL1 and HGF), all of which were up-regulated. In the liver tissues of AH patients and mice, the relative expression levels of SOX4, TREM2, HSPB8 and PRKAA2 in the AH group were higher than those in the control group. Conclusions SOX4, TREM2, HSPB8 and PRKAA2 may be potential biomarkers and therapeutic targets for AH. [ABSTRACT FROM AUTHOR]

Background: Most patients who had coronavirus disease 2019 (COVID-19) fully recovered, but many others experienced acute sequelae or persistent symptoms. It is possible that acute COVID-19 recovery is just the beginning of a chronic condition. Even after COVID-19 recovery, it may lead to the exacerbation of hyperglycemia process or a new onset of diabetes mellitus (DM). In this study, we used a combination of bioinformatics and machine learning algorithms to investigate shared pathways and biomarkers in DM and COVID-19 convalescence. Methods: Gene transcriptome datasets of COVID-19 convalescence and diabetes mellitus from Gene Expression Omnibus (GEO) were integrated using bioinformatics methods and differentially expressed genes (DEGs) were found using the R programme. These genes were also subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to find potential pathways. The hub DEGs genes were then identified by combining protein-protein interaction (PPI) networks and machine learning algorithms. And transcription factors (TFs) and miRNAs were predicted for DM after COVID-19 convalescence. In addition, the inflammatory and immune status of diabetes after COVID-19 convalescence was assessed by singlesample gene set enrichment analysis (ssGSEA). Results: In this study, we developed genetic diagnostic models for 6 core DEGs beteen type 1 DM (T1DM) and COVID-19 convalescence and 2 core DEGs between type 2 DM (T2DM) and COVID-19 convalescence and demonstrated statistically significant differences (p<0.05) and diagnostic validity in the validation set. Analysis of immune cell infiltration suggests that a variety of immune cells may be involved in the development of DM after COVID-19 convalescence. Conclusion: We identified a genetic diagnostic model for COVID-19 convalescence and DM containing 8 core DEGs and constructed a nomogram for the diagnosis of COVID-19 convalescence DM. [ABSTRACT FROM AUTHOR]

Genomic surveillance based on sequencing the entire genetic code of SARS-CoV-2 involves monitoring and studying genetic changes and variations in disease-causing organisms such as viruses and bacteria. By tracing the virus, it is possible to prevent epidemic spread in the community, ensuring a ‘precision public health’ strategy. A peptide-based design was applied to provide an efficacious strategy that is able to counteract any emerging viral variant of concern dynamically and promptly to affect the outcomes of a pandemic at an early stage while waiting for the production of the anti-variant-specific vaccine, which require longer times. The inhibition of the interaction between the receptor-binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and one of the cellular receptors (DPP4) that its receptors routinely bind to infect human cells is an intriguing therapeutic approach to prevent the virus from entering human cells. Among the other modalities developed for this purpose, peptides surely offer unique advantages, including ease of synthesis, serum stability, low immunogenicity and toxicity, and small production and distribution chain costs. Here, we obtained a potent new inhibitor based on the rearrangement of a previously identified peptide that has been rationally designed on a cell dipeptidyl peptidase 4 (DPP4) sequence, a ubiquitous membrane protein known to bind the RBD-SPIKE domain of the virus. This novel peptide (named DPP4-derived), conceived as an endogenous “drug”, is capable of targeting the latest tested variants with a high affinity, reducing the VSV* DG-Fluc pseudovirus Omicron’s infection capacity by up to 14%, as revealed by in vitro testing in human Calu-3 cells. Surface plasmon resonance (SPR) confirmed the binding affinity of the new DPP4-derived peptide with Omicron variant RBD.

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein–protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug–disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Synthetic binding proteins have emerged as modulators of protein functions through protein–protein interactions (PPIs). Because PPIs are influenced by the structural dynamics of targeted proteins, investigating whether the synthetic‐binders‐based strategy is applicable for proteins with large conformational changes is important. This study demonstrates the applicability of monobodies (fibronectin type‐III domain‐based synthetic binding proteins) in regulating the functions of proteins that undergo tens‐of‐angstroms‐scale conformational changes, using an example of the A55C/C77S/V169C triple mutant (Adktm; a phosphoryl transfer‐catalyzing enzyme with a conformational change between OPEN/CLOSED forms). Phage display successfully developed monobodies that recognize the OPEN form (substrate‐unbound form), but not the CLOSED form of Adktm. Two OPEN form‐specific clones (OP‐2 and OP‐4) inhibited Adktm kinase activity. Epitope mapping with a yeast‐surface display/flow cytometry indicated that OP‐2 binds to the substrate‐entry side of Adktm, whereas OP‐4 binding occurs at another site. Small angle X‐ray scattering coupled with size‐exclusion chromatography (SEC‐SAXS) indicated that OP‐4 binds to the hinge side opposite to the substrate‐binding site of Adktm, retaining the whole OPEN‐form structure of Adktm. Titration of the OP‐4–Adktm complex with Ap5A, a transition‐state analog of Adktm, showed that the conformational shift to the CLOSED form was suppressed although Adktm retained the OPEN‐form (i.e., substrate‐binding ready form). These results show that OP‐4 captures and stabilizes the OPEN‐form state, thereby affecting the hinge motion. These experimental results indicate that monobody‐based modulators can regulate the functions of proteins that show tens‐of‐angstroms‐scale conformational changes, by trapping specific conformational states generated during large conformational change process that is essential for function exertion. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the most common non-communicable diseases globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Mitochondria play critical roles in cellular metabolic homeostasis, cell survival, and cell death, as well as producing most of the cell’s energy. Protein–protein interactions (PPIs) have a significant role in physiological and pathological processes, and aberrant PPIs are associated with various diseases, therefore they are potential drug targets for a broad range of therapeutic areas. Due to their ability to mimic natural interaction motifs and cover relatively larger interaction region, peptides are very promising as PPI inhibitors. To expedite drug discovery, computational approaches are widely used for screening potential lead compounds. Here, we developed peptides that inhibit mitochondrial fission 1 (Fis1)/mitochondrial dynamics 51 kDa (Mid51) PPI to reduce the cellular damage that can lead to various human pathologies, such as CVDs. Based on a rational design approach we developed peptide inhibitors of the Fis1/Mid51 PPI. In silico and in vitro studies were done to evaluate the biological activity and molecular interactions of the peptides. Two peptides, CVP-241 and CVP-242 were identified based on low binding energy and molecular dynamics simulations. These peptides inhibit Fis1/Mid51 PPI (-1324.9 kcal mol−1) in docking calculations (CVP-241, -741.3 kcal mol−1, and CVP-242, -747.4 kcal mol−1), as well as in vitro experimental studies Fis1/Mid51 PPI (KD 0.054 µM) Fis1/Mid51 PPI + CVP-241 (KD 3.43 µM), and Fis1/Mid51 PPI + CVP-242 (KD 44.58 µM). Finally, these peptides have no toxicity to H9c2 cells, and they increase cell viability in cardiomyocytes (H9c2 cells). Consequently, the identified inhibitor peptides could serve as potent molecules in basic research and as leads for therapeutic development.

BackgroundMost patients who had coronavirus disease 2019 (COVID-19) fully recovered, but many others experienced acute sequelae or persistent symptoms. It is possible that acute COVID-19 recovery is just the beginning of a chronic condition. Even after COVID-19 recovery, it may lead to the exacerbation of hyperglycemia process or a new onset of diabetes mellitus (DM). In this study, we used a combination of bioinformatics and machine learning algorithms to investigate shared pathways and biomarkers in DM and COVID-19 convalescence.MethodsGene transcriptome datasets of COVID-19 convalescence and diabetes mellitus from Gene Expression Omnibus (GEO) were integrated using bioinformatics methods and differentially expressed genes (DEGs) were found using the R programme. These genes were also subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to find potential pathways. The hub DEGs genes were then identified by combining protein-protein interaction (PPI) networks and machine learning algorithms. And transcription factors (TFs) and miRNAs were predicted for DM after COVID-19 convalescence. In addition, the inflammatory and immune status of diabetes after COVID-19 convalescence was assessed by single-sample gene set enrichment analysis (ssGSEA).ResultsIn this study, we developed genetic diagnostic models for 6 core DEGs beteen type 1 DM (T1DM) and COVID-19 convalescence and 2 core DEGs between type 2 DM (T2DM) and COVID-19 convalescence and demonstrated statistically significant differences (p

IntroductionThe coronavirus disease 2019 (COVID-19) pandemic, stemming from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has persistently threatened the global health system. Meanwhile, tuberculosis (TB) caused by Mycobacterium tuberculosis (M. tuberculosis) still continues to be endemic in various regions of the world. There is a certain degree of similarity between the clinical features of COVID-19 and TB, but the underlying common pathogenetic processes between COVID-19 and TB are not well understood.MethodsTo elucidate the common pathogenetic processes between COVID-19 and TB, we implemented bioinformatics and systematic research to obtain shared pathways and molecular biomarkers. Here, the RNA-seq datasets (GSE196822 and GSE126614) are used to extract shared differentially expressed genes (DEGs) of COVID-19 and TB. The common DEGs were used to identify common pathways, hub genes, transcriptional regulatory networks, and potential drugs.ResultsA total of 96 common DEGs were selected for subsequent analyses. Functional enrichment analyses showed that viral genome replication and immune-related pathways collectively contributed to the development and progression of TB and COVID-19. Based on the protein-protein interaction (PPI) network analysis, we identified 10 hub genes, including IFI44L, ISG15, MX1, IFI44, OASL, RSAD2, GBP1, OAS1, IFI6, and HERC5. Subsequently, the transcription factor (TF)–gene interaction and microRNA (miRNA)–gene coregulatory network identified 61 TFs and 29 miRNAs. Notably, we identified 10 potential drugs to treat TB and COVID-19, namely suloctidil, prenylamine, acetohexamide, terfenadine, prochlorperazine, 3′-azido-3′-deoxythymidine, chlorophyllin, etoposide, clioquinol, and propofol.ConclusionThis research provides novel strategies and valuable references for the treatment of tuberculosis and COVID-19.

Dipeptidyl peptidase 3 (DPP3) is a zinc-dependent exopeptidase with broad specificity for four to eight amino acid residue substrates. It has a role in the regulation of oxidative stress response NRF2–KEAP1 pathway through the interaction with KEAP1. We have conducted stable isotope labeling by amino acids in a cell culture coupled to mass spectrometry (SILAC-MS) interactome analysis of TRex HEK293T cells using DPP3 as bait and identified SH2 Domain-Containing Protein 3C (SH2D3C) as prey. SH2D3C is one of three members of a family of proteins that contain both the SH2 domain and a domain similar to guanine nucleotide exchange factor domains of Ras family GTPases (Ras GEF-like domain), named novel SH2-containing proteins (NSP). NSPs, including SH2D3C (NSP3), are adaptor proteins involved in the regulation of adhesion, migration, tissue organization, and immune response. We have shown that SH2D3C binds to DPP3 through its C-terminal Ras GEF-like domain, detected the colocalization of the proteins in living cells, and confirmed direct interaction in the cytosol and membrane ruffles. Computational analysis also confirmed the binding of the C-terminal domain of SH2D3C to DPP3, but the exact model could not be discerned. This is the first indication that DPP3 and SH2D3C are interacting partners, and further studies to elucidate the physiological significance of this interaction are on the way. [ABSTRACT FROM AUTHOR]

The development of severe COVID-19, which is a complex multisystem disease, is thought to be associated with many genes whose action is modulated by numerous environmental and genetic factors. In this study, we focused on the ideas of the omnigenic model of heritability of complex traits, which assumes that a small number of core genes and a large pool of peripheral genes expressed in disease-relevant tissues contribute to the genetics of complex traits through interconnected networks. We hypothesized that primary immunodeficiency disease (PID) genes may be considered as core genes in severe COVID-19, and their functional partners (FPs) from protein–protein interaction networks may be considered as peripheral near-core genes. We used whole-exome sequencing data from patients aged ≤ 45 years with severe (n = 9) and non-severe COVID-19 (n = 11), and assessed the cumulative contribution of rare high-impact variants to disease severity. In patients with severe COVID-19, an excess of rare high-impact variants was observed at the whole-exome level, but maximal association signals were detected for PID + FP gene subsets among the genes intolerant to LoF variants, haploinsufficient and essential. Our exploratory study may serve as a model for new directions in the research of host genetics in severe COVID-19. [ABSTRACT FROM AUTHOR]

More than 930,000 protein–protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein–cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein–peptide complexes and one protein–protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) is an arbovirus of the Flaviviridae genus that has rapidly disseminated from across the Pacific to the Americas. Robust evidence has indicated a crucial role of ZIKV in congenital virus syndrome, including neonatal microcephaly. Moreover, emerging evidence suggests an association between ZIKV infection and the development of an extensive spectrum of central nervous system inflammatory demyelinating diseases (CNS IDD), such as multiple sclerosis–like clinical phenotypes. However, the underlying mechanisms of host-pathogen neuro-immune interactions remain to be elucidated. This study aimed to identify common transcriptional signatures between multiple sclerosis (MS) and ZIKV infection to generate molecular interaction networks, thereby leading to the identification of deregulated processes and pathways, which could give an insight of these underlying molecular mechanisms. Our investigation included publicly available transcriptomic data from MS patients in either relapse or remission (RR-MS) and datasets of subjects acutely infected by ZIKV for both immune peripheral cells and central nervous system cells. The protein-protein interaction (PPI) analysis showed upregulated AP-1 transcription factors (JUN and FOS) among the top hub and bottleneck genes in RR-MS and ZIKV data. Gene enrichment analysis retrieved a remarkable presence of ontologies and pathways linked to oxidative stress responses, immune cell function, inflammation, interleukin signaling, cell division, and transcriptional regulation commonly enriched in both scenarios. Considering the recent findings concerning AP-1 function in immunological tolerance breakdown, regulation of inflammation, and its function as an oxidative stress sensor, we postulate that the ZIKV trigger may contribute as a boost for the activation of such AP-1-regulated mechanisms that could favor the development of MS-like phenotypes following ZIKV infection in a genetically susceptible individual. [ABSTRACT FROM AUTHOR]

The study of protein-protein interactions (PPIs) and predicting the protein structure plays a critical role in understanding cellular processes and designing therapeutic interventions. In this research, we explore the application of quantum algorithms, specifically Grover’s algorithm, in improving the accuracy and efficiency of PPI prediction. By harnessing the inherent parallelism and quantum search capabilities of Grover’s algorithm, we aim to enhance the identification of interacting protein pairs from large-scale datasets. We demonstrate the effectiveness of using this algorithm through an extensive approach, comparing the performance of Grover’s algorithm with classical machine learning algorithms. Our results reveal that the quantum algorithm offers significant improvements in prediction accuracy, enabling the identification of previously undetected PPIs. Moreover, we discuss the advantages and limitations of using Grover’s algorithm in PPI prediction and provide insights into its potential for accelerating research in protein interaction networks. This research highlights the potential of quantum algorithms in advancing the field of bioinformatics and protein interaction analysis.

Deciphering coevolutionary signals in protein sequences and applying deep learning methods such as AlphaFold have led to breakthroughs in modeling protein structures and interactions. The accuracy of interaction partner detection and structural modeling or protein complexes by computational methods now approaches experimental methods, and we are entering a new era where computation will play an essential role in both tasks. We expect rapid progress in characterizing human PPIs, thus enabling biomedical applications such as interpreting pathogenic variants, developing drugs to target PPIs, and designing protein binders to regulate protein function. We still face challenges in modeling transient and weak interactions, understanding the interactions mediated by intrinsically disordered regions (IDRs), expanding to other molecules such as polysaccharides and lipids, and moving towards modeling the entire cell. Protein–protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale. [ABSTRACT FROM AUTHOR]

Triple-negative breast cancer (TNBC) is a highly aggressive and metastasizing cancer that has the worst prognosis out of all breast cancer subtypes. The epithelial–mesenchymal transition (EMT) and cancer stem cells (CSCs) have been proposed as important mechanisms underlying TNBC metastasis. CDK9 is highly expressed in breast cancer, including TNBC, where it promotes EMT and induces cancer cell stemness. In this study, we have identified a tetrahydroisoquinoline derivative (compound 1) as a potent and selective CDK9-cyclin T1 inhibitor via virtual screening. Interestingly, by targeting the ATP binding site, compound 1 not only inhibited CDK9 activity but also disrupted the CDK9-cyclin T1 protein–protein interaction (PPI). Mechanistically, compound 1 reversed EMT and reduced the ratio of CSCs by blocking the CDK9-cyclin T1 interaction, leading to reduced TNBC cell proliferation and migration. To date, compound 1 is the first reported tetrahydroisoquinoline-based CDK9-cyclin T1 ATP-competitive inhibitor that also interferes with the interaction between CDK9 and cyclin T1. Compound 1 may serve as a promising scaffold for developing more selective and potent anti-TNBC agents. Our work also provides insight into the role of the CDK9-cyclin T1 PPI on EMT and CSCs and highlights the feasibility and significance of targeting CDK9 for the treatment of TNBC.

To understand protein function deeply, it is important to identify how it interacts physically with its target. Phyllogen is a phyllody-inducing effector that interacts with the K domain of plant MADS-box transcription factors (MTFs), which is followed by proteasome-mediated degradation of the MTF. Although several amino acid residues of phyllogen have been identified as being responsible for the interaction, the exact interface of the interaction has not been elucidated. In this study, we comprehensively explored interface residues based on random mutagenesis using error-prone PCR. Two novel residues, at which mutations enhanced the affinity of phyllogen to MTF, were identified. These residues, and all other known interaction-involved residues, are clustered together at the surface of the protein structure of phyllogen, indicating that they constitute the interface of the interaction. Moreover, in silico structural prediction of the protein complex using ColabFold suggested that phyllogen interacts with the K domain of MTF via the putative interface. Our study facilitates an understanding of the interaction mechanisms between phyllogen and MTF. [ABSTRACT FROM AUTHOR]

The voltage-gated sodium (NaV) channel subtype NaV1.7 plays a critical role in pain signaling, making it an important drug target. Here we studied the molecular interactions between µ-Conotoxin KIIIA (KIIIA) and the human NaV1.7 channel (hNaV1.7). We developed a structural model of hNaV1.7 using Rosetta computational modeling and performed in silico docking of KIIIA using RosettaDock to predict residues forming specific pairwise contacts between KIIIA and hNaV1.7. We experimentally validated these contacts using mutant cycle analysis. Comparison between our KIIIA-hNaV1.7 model and the cryo-EM structure of KIIIA-hNaV1.2 revealed key similarities and differences between NaV channel subtypes with potential implications for the molecular mechanism of toxin block. The accuracy of our integrative approach, combining structural data with computational modeling, experimental validation, and molecular dynamics simulations, suggests that Rosetta structural predictions will be useful for rational design of novel biologics targeting specific NaV channels. [ABSTRACT FROM AUTHOR]

Motivation Biological experimental approaches to protein–protein interaction (PPI) site prediction are critical for understanding the mechanisms of biochemical processes but are time-consuming and laborious. With the development of Deep Learning (DL) techniques, the most popular Convolutional Neural Networks (CNN)-based methods have been proposed to address these problems. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in protein sequences. Current methods cannot efficiently explore the nature of Position Specific Scoring Matrix (PSSM), secondary structure and raw protein sequences by processing them all together. For PPI site prediction, how to effectively model the PPI context with attention to prediction remains an open problem. In addition, the long-distance dependencies of PPI features are important, which is very challenging for many CNN-based methods because the innate ability of CNN is difficult to outperform auto-regressive models like Transformers. Results To effectively mine the properties of PPI features, a novel hybrid neural network named HN-PPISP is proposed, which integrates a Multi-layer Perceptron Mixer (MLP-Mixer) module for local feature extraction and a two-stage multi-branch module for global feature capture. The model merits Transformer, TextCNN and Bi-LSTM as a powerful alternative for PPI site prediction. On the one hand, this is the first application of an advanced Transformer (i.e. MLP-Mixer) with a hybrid network for sequence-based PPI prediction. On the other hand, unlike existing methods that treat global features altogether, the proposed two-stage multi-branch hybrid module firstly assigns different attention scores to the input features and then encodes the feature through different branch modules. In the first stage, different improved attention modules are hybridized to extract features from the raw protein sequences, secondary structure and PSSM, respectively. In the second stage, a multi-branch network is designed to aggregate information from both branches in parallel. The two branches encode the features and extract dependencies through several operations such as TextCNN, Bi-LSTM and different activation functions. Experimental results on real-world public datasets show that our model consistently achieves state-of-the-art performance over seven remarkable baselines. Availability The source code of HN-PPISP model is available at https://github.com/ylxu05/HN-PPISP. [ABSTRACT FROM AUTHOR]

Introduction: Antimicrobial resistance (AMR) is a serious global threat. Identification of novel antibacterial targets is urgently warranted to help antimicrobial drug discovery programs. This study attempted identification of potential targets in two important pathogens Pseudomonas aeruginosa and Staphylococcus aureus. Methods: Transcriptomes of P. aeruginosa and S. aureus exposed to two different quorum-modulatory polyherbal formulations were subjected to network analysis to identify the most highly networked differentially expressed genes (hubs) as potential anti-virulence targets. Results: Genes associated with denitrification and sulfur metabolism emerged as the most important targets in P. aeruginosa. Increased buildup of nitrite (NO2) in P. aeruginosa culture exposed to the polyherbal formulation Panchvalkal was confirmed through in vitro assay too. Generation of nitrosative stress and inducing sulfur starvation seemed to be effective anti-pathogenic strategies against this notorious gram-negative pathogen. Important targets identified in S. aureus were the transcriptional regulator sarA, immunoglobulin-binding protein Sbi, serine protease SplA, the saeR/S response regulator system, and gamma-hemolysin components hlgB and hlgC. Conclusion: Further validation of the potential targets identified in this study is warranted through appropriate in vitro and in vivo assays in model hosts. Such validated targets can prove vital to many antibacterial drug discovery programs globally. [ABSTRACT FROM AUTHOR]

Schizophrenia is a mental illness affecting the normal lifestyle of adults and early adolescents incurring major symptoms as jumbled speech, involvement in everyday activities eventually got reduced, patients always struggle with attention and memory, reason being both the genetic and environmental factors responsible for altered brain chemistry and structure, resulting in schizophrenia and associated orphan diseases. The network biology describes the interactions among genes/proteins encoding molecular mechanisms of biological processes, development, and diseases. Besides, all the molecular networks, protein‐protein Interaction Networks have been significant in distinguishing the pathogenesis of diseases and thereby drug discovery. The present meta‐analysis prioritizes novel disease indications viz. rare and orphan diseases associated with target Glutamate Ionotropic Receptor NMDA Type Subunit 1, GRIN1 using text mining knowledge‐based tools. Furthermore, ZINC database was virtually screened, and binding conformation of selected compounds was performed and resulted in the identification of Narciclasine (ZINC04097652) and Alvespimycin (ZINC73138787) as potential inhibitors. Furthermore, docked complexes were subjected to MD simulation studies which suggests that the identified leads could be a better potential drug to recuperate schizophrenia. [ABSTRACT FROM AUTHOR]

BackgroundThe pathophysiology of bone defects (BDs) is complex, and the treatment for bone defects, in particular massive bone defects, remains a major clinical challenge. Our study was conducted to explore the molecular events related to the progression of bone defects a common clinical condition.MethodsFirst, microarray data of GSE20980 were obtained from the Gene Expression Omnibus (GEO) database, where 33 samples in total were used to analyze the molecular biological processes related to bone defects. Next, the original data were normalized and differentially expressed genes (DEGs) were identified. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Finally, a protein–protein interaction (PPI) network was constructed and the trends of the different genes were confirmed.ResultsCompared with the samples of non-critical size defects (NCSD), the samples of critical size defects (CSD) had 2057, 827, and 1,024 DEGs at 7, 14, and 21 days post injury, respectively. At day 7, the DEGs were significantly enriched in metabolic pathways, at day 14 the DEGs were predominantly enriched in G-protein coupled signaling pathways and the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway, and at day 21 the DEGs were mainly enriched in circadian entrainment and synaptic-related functions. The PPI network showed similar results. Quantitative real-time PCR (qRT-PCR) and western blot (WB) were performed to validate the partial results of sequencing.ConclusionThis study provides some clues about the molecular mechanism behind bone defects, which should contribute to scientific research and clinical treatment of this condition.

Medicinal chemistry is constantly searching for new approaches to develop more effective and targeted therapeutic molecules. The design of peptidomimetics is a promising emerging strategy that is aimed at developing peptides that mimic or modulate the biological activity of proteins. Among these, stapled peptides stand out for their unique ability to stabilize highly frequent helical motifs, but they have failed to be systematically reported. Here, we exploit chemically diverse helix-inducing i, i + 4 constraints—lactam, hydrocarbon, triazole, double triazole and thioether—on two distinct short sequences derived from the N-terminal peptidase domain of hACE2 upon structural characterization and in silico alanine scan. Our overall objective was to provide a sequence-independent comparison of α-helix-inducing staples using circular dichroism (CD) and nuclear magnetic resonance (NMR) spectroscopy. We identified a 9-mer lactam stapled peptide derived from the hACE2 sequence (His34-Gln42) capable of reaching its maximal helicity of 55% with antiviral activity in bioreporter- and pseudovirus-based inhibition assays. To the best of our knowledge, this study is the first comprehensive investigation comparing several cyclization methods with the goal of generating stapled peptides and correlating their secondary structures with PPI inhibitions using a highly topical model system (i.e., the interaction of SARS-CoV-2 Spike RBD with hACE2).

The voltage-gated sodium (NaV) channel subtype NaV1.7 plays a critical role in pain signaling, making it an important drug target. Here we studied the molecular interactions between μ-Conotoxin KIIIA (KIIIA) and the human NaV1.7 channel (hNaV1.7). We developed a structural model of hNaV1.7 using Rosetta computational modeling and performed in silico docking of KIIIA using RosettaDock to predict residues forming specific pairwise contacts between KIIIA and hNaV1.7. We experimentally validated these contacts using mutant cycle analysis. Comparison between our KIIIA-hNaV1.7 model and the cryo-EM structure of KIIIA-hNaV1.2 revealed key similarities and differences between NaV channel subtypes with potential implications for the molecular mechanism of toxin block. The accuracy of our integrative approach, combining structural data with computational modeling, experimental validation, and molecular dynamics simulations, suggests that Rosetta structural predictions will be useful for rational design of novel biologics targeting specific NaV channels.

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

Protein–protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein–protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein–protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

Enterococcus faecalis has recently shown signs of high antibiotic resistance. These bacteria can endure extremes of temperature and this may be due to the high thermostability of its proteins. E. faecalis has two acyl carrier proteins (ACPs), AcpA (EfAcpA), which is essential for de novo fatty acid synthesis (FAS), and EfAcpB, which plays an auxiliary role in the incorporation of exogenous fatty acids. Structural studies on EfAcpA and its interaction with FAS enzymes have not yet been reported. Here, we investigated the structures of EfAcpA using NMR spectroscopy, showing that EfAcpA consists of three α-helices with a long α 2 α 3 loop, while the other ACPs have four α-helices. CD experiments showed that the melting temperature of EfAcpA is 76.3 °C and the Ala mutation for Ile10 reduced it dramatically by 29.5 °C. Highly conserved Ile10 of EfAcpA mediates compact intramolecular packing and promotes high thermostability. A docking simulation of EfAcpA and β-ketoacyl-ACP synthase III (EfKAS III) showed that the α 2 α 3 loop of EfAcpA contributes to specific protein–protein interactions (PPI) with EfKAS III. Unconserved charged residues, Lys52 and Glu54, in the α 2 α 3 loop of EfAcpA formed specific electrostatic interactions with Asp 226 and Arg217 of EfKAS III, respectively. Binding interactions between EfAcpA and EfKASIII may provide insights for designing PPI inhibitors targeting FAS in E. faecalis to overcome its antibacterial resistance. [Display omitted] • NMR structure of E. faecalis ACP (EfAcpA) in fatty acid synthesis was determined. • EfAcpA lacks characteristic helix III structure but harbors a long α 2 α 3 loop. • Unconserved charged residues in the loop interact with β-ketoacyl-ACP synthase III. • Our findings may help in designing novel antibiotics against E. faecalis. [ABSTRACT FROM AUTHOR]

Trypsin can significantly improve the storage quality of Hylocereus undatus (H. undatus). To verify the hub WRKY gene of H. undatus in trypsin preservation, joint analysis of transcriptome and protein–protein interaction (PPI) network was carried out, and virus‐induced gene silencing (VIGS) was conducted. In the transcriptome of H. undatus, GO directed acyclic graph (DAG) showed that the GO terms of 55 WRKY genes were mainly enriched in sequence‐specific DNA binding, DNA binding transcription factor activity, and so on. The GO enrichment chord diagram showed that HuWRKY40 was significantly up‐regulated in the enriched top10 GO terms. KEGG enrichment analysis showed that 55 WRKY genes were mainly enriched in plant–pathogen interaction and MAPK pathway. The results of PPI network showed that HuWRKY40 was a hub protein of WRKY transcription factors (TFs) family regulated by trypsin, which was consistent with the results of transcriptome analysis. Bioinformatics analysis showed that HuWRKY40 of H. undatus had the highest homology with Beta vulgaris L. and Spinacia oleracea L. The function of the core regulatory protein HuWRKY40 was further clarified by VIGS technology. The results of VIGS showed that there was a big difference between the phenotype of the pTRV2‐HuWRKY40 group and that of the control group. Finally, it was confirmed that HuWRKY40 accelerated the synthesis of flavonoids and improved the fruit quality during the storage of H. undatus. This study found that trypsin may regulate HuWRKY40 activity through the MAPK cascade pathway, affect the participation of flavonoid synthesis, and then delay fruit corruption. Practical applications: With attention of people to the safety and freshness of fruits and vegetables, biological preservation technology has become one of the hotspots in the field of preservation in recent years. Trypsin can significantly improve the antioxidant capacity of fruits and vegetables. As a new biological preservative, it is convenient to operate and economical. In the current work, the mechanism of trypsin on the WRKY TFs during H. undatus storage was investigated. The application of trypsin would provide a new strategy for the storage quality control of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Background: Renal fibrosis is a widely used pathological indicator of progressive chronic kidney disease (CKD), and renal fibrosis mediates most progressive renal diseases as a final pathway. Nevertheless, the key genes related to the host response are still unclear. In this study, the potential gene network, signaling pathways, and key genes under unilateral ureteral obstruction (UUO) model in mouse kidneys were investigated by integrating two transcriptional data profiles. Methods: The mice were exposed to UUO surgery in two independent experiments. After 7 days, two datasets were sequenced from mice kidney tissues, respectively, and the transcriptome data were analyzed to identify the differentially expressed genes (DEGs). Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were executed. A Protein-Protein Interaction (PPI) network was constructed based on an online database STRING. Additionally, hub genes were identified and shown, and their expression levels were investigated in a public dataset and confirmed by quantitative real time-PCR (qRT-PCR) in vivo. Results: A total of 537 DEGs were shared by the two datasets. GO and the KEGG analysis showed that DEGs were typically enriched in seven pathways. Specifically, five hub genes (Bmp1, CD74, Fcer1g, Icam1, H2-Eb1) were identified by performing the 12 scoring methods in cytoHubba, and the receiver operating characteristic (ROC) curve indicated that the hub genes could be served as biomarkers. Conclusion: A gene network reflecting the transcriptome signature in CKD was established. The five hub genes identified in this study are potentially useful for the treatment and/or diagnosis CKD as biomarkers. [ABSTRACT FROM AUTHOR]

Simple Summary: Dynamic O-GlcNAc modification regulates the functions of proteins in a broad range of cellular processes. Dysregulation of the sole enzymes responsible for O-GlcNAc cycling, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), and the associated cellular O-GlcNAc profile is a common feature across nearly every cancer type. Here, we highlight recent studies on the structural features of OGT and OGA, as well as the emerging roles and molecular mechanisms of their aberrant protein–protein interactions (PPIs) in rewiring cancer networks, to help identify key protein contacts and functional modules that drive malignancies and to promote cancer therapeutic innovations. The dynamic O-GlcNAc modification of intracellular proteins is an important nutrient sensor for integrating metabolic signals into vast networks of highly coordinated cellular activities. Dysregulation of the sole enzymes responsible for O-GlcNAc cycling, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), and the associated cellular O-GlcNAc profile is a common feature across nearly every cancer type. Many studies have investigated the effects of aberrant OGT/OGA expression on global O-GlcNAcylation activity in cancer cells. However, recent studies have begun to elucidate the roles of protein–protein interactions (PPIs), potentially through regions outside of the immediate catalytic site of OGT/OGA, that regulate greater protein networks to facilitate substrate-specific modification, protein translocalization, and the assembly of larger biomolecular complexes. Perturbation of OGT/OGA PPI networks makes profound changes in the cell and may directly contribute to cancer malignancies. Herein, we highlight recent studies on the structural features of OGT and OGA, as well as the emerging roles and molecular mechanisms of their aberrant PPIs in rewiring cancer networks. By integrating complementary approaches, the research in this area will aid in the identification of key protein contacts and functional modules derived from OGT/OGA that drive oncogenesis and will illuminate new directions for anti-cancer drug development. [ABSTRACT FROM AUTHOR]

Bacterial resistance represents a major health problem worldwide and there is an urgent need to develop first-in-class compounds directed against new therapeutic targets. We previously developed a drug-discovery platform to identify new antimicrobials able to disrupt the protein–protein interaction between the β' subunit and the σ70 initiation factor of bacterial RNA polymerase, which is essential for transcription. As a follow-up to such work, we have improved the discovery strategy to make it less time-consuming and more cost-effective. This involves three sequential assays, easily scalable to a high-throughput format, and a subsequent in-depth characterization only limited to hits that passed the three tests. This optimized workflow, applied to the screening of 5360 small molecules from three synthetic and natural compound libraries, led to the identification of six compounds interfering with the β'–σ70 interaction, and thus was capable of inhibiting promoter-specific RNA transcription and bacterial growth. Upon supplementation with a permeability adjuvant, the two most potent transcription-inhibiting compounds displayed a strong antibacterial activity against Escherichia coli with minimum inhibitory concentration (MIC) values among the lowest (0.87–1.56 μM) thus far reported for β'–σ PPI inhibitors. The newly identified hit compounds share structural feature similarities with those of a pharmacophore model previously developed from known inhibitors. [ABSTRACT FROM AUTHOR]

Abstract Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection. Result Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors. Conclusions Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes.

The development of severe COVID-19, which is a complex multisystem disease, is thought to be associated with many genes whose action is modulated by numerous environmental and genetic factors. In this study, we focused on the ideas of the omnigenic model of heritability of complex traits, which assumes that a small number of core genes and a large pool of peripheral genes expressed in disease-relevant tissues contribute to the genetics of complex traits through interconnected networks. We hypothesized that primary immunodeficiency disease (PID) genes may be considered as core genes in severe COVID-19, and their functional partners (FPs) from protein–protein interaction networks may be considered as peripheral near-core genes. We used whole-exome sequencing data from patients aged ≤ 45 years with severe (n = 9) and non-severe COVID-19 (n = 11), and assessed the cumulative contribution of rare high-impact variants to disease severity. In patients with severe COVID-19, an excess of rare high-impact variants was observed at the whole-exome level, but maximal association signals were detected for PID + FP gene subsets among the genes intolerant to LoF variants, haploinsufficient and essential. Our exploratory study may serve as a model for new directions in the research of host genetics in severe COVID-19.

Dipeptidyl peptidase 3 (DPP3) is a zinc-dependent exopeptidase with broad specificity for four to eight amino acid residue substrates. It has a role in the regulation of oxidative stress response NRF2–KEAP1 pathway through the interaction with KEAP1. We have conducted stable isotope labeling by amino acids in a cell culture coupled to mass spectrometry (SILAC-MS) interactome analysis of TRex HEK293T cells using DPP3 as bait and identified SH2 Domain-Containing Protein 3C (SH2D3C) as prey. SH2D3C is one of three members of a family of proteins that contain both the SH2 domain and a domain similar to guanine nucleotide exchange factor domains of Ras family GTPases (Ras GEF-like domain), named novel SH2-containing proteins (NSP). NSPs, including SH2D3C (NSP3), are adaptor proteins involved in the regulation of adhesion, migration, tissue organization, and immune response. We have shown that SH2D3C binds to DPP3 through its C-terminal Ras GEF-like domain, detected the colocalization of the proteins in living cells, and confirmed direct interaction in the cytosol and membrane ruffles. Computational analysis also confirmed the binding of the C-terminal domain of SH2D3C to DPP3, but the exact model could not be discerned. This is the first indication that DPP3 and SH2D3C are interacting partners, and further studies to elucidate the physiological significance of this interaction are on the way.