ToxoNet: A high confidence map of protein-protein interactions in Toxoplasma gondii.

The apicomplexan intracellular parasite Toxoplasma gondii is a major food borne pathogen that is highly prevalent in the global population. The majority of the T. gondii proteome remains uncharacterized and the organization of proteins into complexes is unclear. To overcome this knowledge gap, we used a biochemical fractionation strategy to predict interactions by correlation profiling. To overcome the deficit of high-quality training data in non-model organisms, we complemented a supervised machine learning strategy, with an unsupervised approach, based on similarity network fusion. The resulting combined high confidence network, ToxoNet, comprises 2,063 interactions connecting 652 proteins. Clustering identifies 93 protein complexes. We identified clusters enriched in mitochondrial machinery that include previously uncharacterized proteins that likely represent novel adaptations to oxidative phosphorylation. Furthermore, complexes enriched in proteins localized to secretory organelles and the inner membrane complex, predict additional novel components representing novel targets for detailed functional characterization. We present ToxoNet as a publicly available resource with the expectation that it will help drive future hypotheses within the research community.

Human dermal fibroblast-derived secretory proteins for regulating nerve restoration: A bioinformatic approach.

BACKGROUND: Human dermal fibroblasts secrete diverse proteins that regulate wound repair and tissue regeneration. METHODS: In this study, dermal fibroblast-conditioned medium (DFCM) proteins potentially regulating nerve restoration were bioinformatically selected among the 337 protein lists identified by quantitative liquid chromatography-tandem mass spectrometry. Using these proteins, protein-protein interaction network analysis was conducted. In addition, the roles of DFCM proteins were reviewed according to their protein classifications. RESULTS: Gene Ontology protein classification categorized these 57 DFCM proteins into various classes, including protein-binding activity modulator (N = 11), cytoskeletal protein (N = 8), extracellular matrix protein (N = 6), metabolite interconversion enzyme (N = 5), chaperone (N = 4), scaffold/adapter protein (N = 4), calcium-binding protein (N = 3), cell adhesion molecule (N = 2), intercellular signal molecule (N = 2), protein modifying enzyme (N = 2), transfer/carrier protein (N = 2), membrane traffic protein (N = 1), translational protein (N = 1), and unclassified proteins (N = 6). Further protein-protein interaction network analysis of 57 proteins revealed significant interactions among the proteins that varied according to the settings of confidence score. CONCLUSIONS: Our bioinformatic analysis demonstrated that DFCM contains many secretory proteins that form significant protein-protein interaction networks crucial for regulating nerve restoration. These findings underscore DFCM proteins' critical roles in various nerve restoration stages during the wound repair process.

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure.

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective.

Novel biochemical, structural, and systems insights into inflammatory signaling revealed by contextual interaction proteomics.

Protein-protein interactions (PPIs) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components but mostly lack the context of cellular states. However, the generation of context-dependent PPI networks is essential for structural and systems-level modeling of biological processes-a goal that remains an unsolved challenge. Here we describe an experimental/computational strategy to achieve a modeling of PPIs that considers contextual information. This strategy defines the composition, stoichiometry, temporal organization, and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function.

From complete cross-docking to partners identification and binding sites predictions.

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.

Systematic analysis of the potential off-target activities of osimertinib by computational target fishing.

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor used to treat non-small cell lung cancer. However, its off-targets are obscure, and systematic analysis of off-target activities remains to be performed. Here, we identified the off-targets of osimertinib using PharmMapper and DRAR-CPI and analyzed the intersected targets using the GeneMANIA and DAVID servers. A drug-target-pathway network was constructed to visualize the associations. The results showed that osimertinib is associated with 31 off-targets, 40 Kyoto Encyclopedia of Genes and Genomes pathways, and 9 diseases. Network analysis revealed that the targets were involved in cancer and other physiological processes. In addition to EGFR, molecular docking analysis showed that seven proteins, namely Janus kinase 3, peroxisome proliferator-activated receptor alpha, renin, mitogen-activated protein kinases, lymphocyte-specific protein tyrosine kinase, cell division protein kinase 2 and proto-oncogene tyrosine-protein kinase Src, could also be potential targets of osimertinib. In conclusion, osimertinib is predicted to target multiple proteins and pathways, resulting in the formation of an action network via which it exerts systematic pharmacological effects.

The emerging roles of OSBP-related proteins in cancer: Impacts through phosphoinositide metabolism and protein-protein interactions.

Oxysterol-binding protein -related proteins (ORPs) form a large family of intracellular lipid binding/transfer proteins. A number of ORPs are implicated in inter-organelle lipid transfer over membrane contacts sites, their mode of action involving in several cases the transfer of two lipids in opposite directions, termed countercurrent lipid transfer. A unifying feature appears to be the capacity to bind phosphatidylinositol polyphosphates (PIPs). These lipids are in some cases transported by ORPs from one organelle to another to drive the transfer of another lipid against its concentration gradient, while they in other cases may act as allosteric regulators of ORPs, or an ORP may introduce a PIP to an enzyme for catalysis. Dysregulation of several ORP family members is implicated in cancers, ORP3, -4, -5 and -8 being thus far the most studied examples. The most likely mechanisms underlying their associations with malignant growth are (i) impacts on PIP-mediated signaling events resulting in altered Ca2+ homeostasis, bioenergetics, cell survival, proliferation, and migration, (ii) protein-protein interactions affecting the activity of signaling factors, and (iii) modification of cellular lipid transport in a way that facilitates the proliferation of malignant cells. In this review I discuss the existing functional evidence for the involvement of ORPs in cancerous growth, discuss the findings in the light of the putative mechanisms outlined above and the possibility of employing ORPs as targets of anti-cancer therapy.

The expression and interaction proteins analysis of BjuFKF1/LKP2 in B. juncea.

Brassica juncea is one of a unique vegetable in China, its tumorous stem can be processed into pickle or as fresh vegetable. For a long time, early-bolting as a main factor affects yield and quality of B. juncea, which happens about 15% all year round. As plant specific blue light receptors, FKF1/LKP2 involved in photoperiod flowering. To analyze the expression levels of BjuFKF1/BjuLKP2 and screen their interaction proteins in B. juncea, qRT-PCR and yeast two hybrid assays were recruited. qRT-PCR assays found that the expression levels of BjuFKF1 and BjuLKP2 were up-regulated expressed under both white and blue light. When under different light, BjuFKF1 was significantly increased at vegetative growth stage, but decreased in flowers under blue light. For BjuLKP2, its expression levels did not show significant changes under different light treatment. To investigate interaction proteins, BjuFKF1 and BjuLKP2 were used as bait proteins, and nine potential proteins were screened from yeast library. Yeast two hybrid assays was recruited to further verify their interaction, the results showed that both BjuFKF1 and BjuLKP2 interacted with BjuCOL, BjuCOL3, BjuCOL5, BjuAP2, BjuAP2-1 and BjuSKP1f, only BjuLKP2 interacted with BjuSVP-1 and BjuCDF1 in vivo. In this study, BjuFKF1 and BjuLKP2 were up-regulated expressed under both white and blue light. Yeast two hybrid results verified that BjuFKF1 and BjuLKP2 interacted with six and eight of those nine proteins in vivo, respectively. All of those results will provided reference genes to study BjuFKF1/BjuLKP2 regulated flowering pathway in B. juncea.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease.

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Structural Insights Support Targeting ASK1 Kinase for Therapeutic Interventions.

Apoptosis signal-regulating kinase (ASK) 1, a member of the mitogen-activated protein kinase kinase kinase (MAP3K) family, modulates diverse responses to oxidative and endoplasmic reticulum (ER) stress and calcium influx. As a crucial cellular stress sensor, ASK1 activates c-Jun N-terminal kinases (JNKs) and p38 MAPKs. Their excessive and sustained activation leads to cell death, inflammation and fibrosis in various tissues and is implicated in the development of many neurological disorders, such as Alzheimer's, Parkinson's and Huntington disease and amyotrophic lateral sclerosis, in addition to cardiovascular diseases, diabetes and cancer. However, currently available inhibitors of JNK and p38 kinases either lack efficacy or have undesirable side effects. Therefore, targeted inhibition of their upstream activator, ASK1, stands out as a promising therapeutic strategy for treating such severe pathological conditions. This review summarizes recent structural findings on ASK1 regulation and its role in various diseases, highlighting prospects for ASK1 inhibition in the treatment of these pathologies.

Screening and Identification of Candidate GUN1-Interacting Proteins in Arabidopsis thaliana.

Chloroplasts are semi-autonomous organelles governed by the precise coordination between the genomes of their own and the nucleus for functioning correctly in response to developmental and environmental cues. Under stressed conditions, various plastid-to-nucleus retrograde signals are generated to regulate the expression of a large number of nuclear genes for acclimation. Among these retrograde signaling pathways, the chloroplast protein GENOMES UNCOUPLED 1 (GUN1) is the first component identified. However, in addition to integrating aberrant physiological signals when chloroplasts are challenged by stresses such as photooxidative damage or the inhibition of plastid gene expression, GUN1 was also found to regulate other developmental processes such as flowering. Several partner proteins have been found to interact with GUN1 and facilitate its different regulatory functions. In this study, we report 15 possible interacting proteins identified through yeast two-hybrid (Y2H) screening, among which 11 showed positive interactions by pair-wise Y2H assay. Through the bimolecular fluorescence complementation assay in Arabidopsis protoplasts, two candidate proteins with chloroplast localization, DJC31 and HCF145, were confirmed to interact with GUN1 in planta. Genes for these GUN1-interacting proteins showed different fluctuations in the WT and gun1 mutant under norflurazon and lincomycin treatments. Our results provide novel clues for a better understanding of molecular mechanisms underlying GUN1-mediated regulations.

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning.

Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.

LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning.

Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA-protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features, hand-designed features and structure features, called LGFC-CNN, to predict lncRNA-protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA-protein interactions. Compared with other state-of-the-art methods on three lncRNA-protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA-protein interactions by combining raw sequence composition features, hand-designed features and structure features.

Arabidopsis bioinformatics: tools and strategies.

The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other '-omic' data. In this review, we cover some more recent tools (and highlight the 'classics') for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co-expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein-protein and protein-DNA interactions. We will touch on tools that integrate different data sets at the end of the article.

Critical Protein-Protein Interactions Determine the Biological Activity of Elk-1, a Master Regulator of Stimulus-Induced Gene Transcription.

Elk-1 is a transcription factor that binds together with a dimer of the serum response factor (SRF) to the serum-response element (SRE), a genetic element that connects cellular stimulation with gene transcription. Elk-1 plays an important role in the regulation of cellular proliferation and apoptosis, thymocyte development, glucose homeostasis and brain function. The biological function of Elk-1 relies essentially on the interaction with other proteins. Elk-1 binds to SRF and generates a functional ternary complex that is required to activate SRE-mediated gene transcription. Elk-1 is kept in an inactive state under basal conditions via binding of a SUMO-histone deacetylase complex. Phosphorylation by extracellular signal-regulated protein kinase, c-Jun N-terminal protein kinase or p38 upregulates the transcriptional activity of Elk-1, mediated by binding to the mediator of RNA polymerase II transcription (Mediator) and the transcriptional coactivator p300. Strong and extended phosphorylation of Elk-1 attenuates Mediator and p300 recruitment and allows the binding of the mSin3A-histone deacetylase corepressor complex. The subsequent dephosphorylation of Elk-1, catalyzed by the protein phosphatase calcineurin, facilitates the re-SUMOylation of Elk-1, transforming Elk-1 back to a transcriptionally inactive state. Thus, numerous protein-protein interactions control the activation cycle of Elk-1 and are essential for its biological function.

Identification of GOLPH3 Partners in Drosophila Unveils Potential Novel Roles in Tumorigenesis and Neural Disorders.

Golgi phosphoprotein 3 (GOLPH3) is a highly conserved peripheral membrane protein localized to the Golgi apparatus and the cytosol. GOLPH3 binding to Golgi membranes depends on phosphatidylinositol 4-phosphate [PI(4)P] and regulates Golgi architecture and vesicle trafficking. GOLPH3 overexpression has been correlated with poor prognosis in several cancers, but the molecular mechanisms that link GOLPH3 to malignant transformation are poorly understood. We recently showed that PI(4)P-GOLPH3 couples membrane trafficking with contractile ring assembly during cytokinesis in dividing Drosophila spermatocytes. Here, we use affinity purification coupled with mass spectrometry (AP-MS) to identify the protein-protein interaction network (interactome) of Drosophila GOLPH3 in testes. Analysis of the GOLPH3 interactome revealed enrichment for proteins involved in vesicle-mediated trafficking, cell proliferation and cytoskeleton dynamics. In particular, we found that dGOLPH3 interacts with the Drosophila orthologs of Fragile X mental retardation protein and Ataxin-2, suggesting a potential role in the pathophysiology of disorders of the nervous system. Our findings suggest novel molecular targets associated with GOLPH3 that might be relevant for therapeutic intervention in cancers and other human diseases.

Metabolic channeling: predictions, deductions, and evidence.

With the elucidation of myriad anabolic and catabolic enzyme-catalyzed cellular pathways crisscrossing each other, an obvious question arose: how could these networks operate with maximal catalytic efficiency and minimal interference? A logical answer was the postulate of metabolic channeling, which in its simplest embodiment assumes that the product generated by one enzyme passes directly to a second without diffusion into the surrounding medium. This tight coupling of activities might increase a pathway's metabolic flux and/or serve to sequester unstable/toxic/reactive intermediates as well as prevent their access to other networks. Here, we present evidence for this concept, commencing with enzymes that feature a physical molecular tunnel, to multi-enzyme complexes that retain pathway substrates through electrostatics or enclosures, and finally to metabolons that feature collections of enzymes assembled into clusters with variable stoichiometric composition. Lastly, we discuss the advantages of reversibly assembled metabolons in the context of the purinosome, the purine biosynthesis metabolon.

Quantitative Proteomics Analysis of Susceptibility and Resilience to Stress in a Rat model of PTSD.

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder and sometimes deadly consequence of exposure to severe psychological trauma. However, there has been little known about the definitive molecular changes involved in determining vulnerability to PTSD. In the current study, we used proteomics to quantify protein changes in the hippocampus of foot shocks rats. A total of 6151 proteins were quantified and 97 proteins were significantly differentially expressed. The protein-protein interaction (PPI) analysis showed that oxidation-reduction process and glutathione homeostasis may be the potential key progress of being vulnerable to PTSD. The Gene Ontology analysis revealed enriched GO terms in the protein groups of Susceptible group vs Control group rats for glutathione binding,oligopeptide binding,modified amino acid binding,and glutathione transferase activity for their molecular functions (MF) and in the process of cellular response to toxic substance,xenobiotic metabolic process, urea metabolic process, and response to drug for the biological process (BP).SIGNIFICANCE:In recent years, there has been a growing interest in mental illness associated with trauma exposure. We found that stress susceptibility was associated with increased expression of arginase 1 indicated as a potential treatment target. Our results also proposed that carbonic anhydrases 3 could be a biomarker for the development of PTSD. This research helps to explain the potential molecular mechanism in PTSD and supply a new method for ameliorating PTSD.

Proteome Profile of Trigeminal Ganglion in Murine Model of Allergic Contact Dermatitis: Complement 3 Pathway Contributes to Itch and Pain Sensation.

Allergic contact dermatitis (ACD) is a common inflammatory dermatosis characterized by persistent itch and pain after topical contact with reactive chemicals. Although it has been long recognized as a type-IV hypersensitivity, its complexity of pathophysiology mechanism makes it still a clinical aporia in treatment. In this study, we aimed to identify crucial proteins involved in the nociceptive sensation of ACD. Based on a chemical-induced ACD murine model, we collected trigeminal ganglions of ACD and control mice for quantitative tandem mass tag (TMT)-labeling proteomic analysis. Immunohistochemistry was further practiced to validate the bioinformatic analysis. A total of 7685 proteins were identified and analyzed. Sixty-four proteins were significantly upregulated, and 75 proteins were downregulated in ACD mice. GO analysis demonstrated that the changed proteins were significantly enriched in terms of immune and peptidase activity in ACD mice. Proteins involved in the complement and coagulation cascades were notably changed in the KEGG enrichment analysis. The upregulation of complement component 3 (C3) in trigeminal satellite cells of ACD mice was further confirmed by immunohistochemistry. ACD upregulated C3 in trigeminal satellite cells. The complement system in sensory ganglion might play an essential role in forming pruritic and nociceptive sensations in ACD.

Identification of COVID-19 and Dengue Host Factor Interaction Networks Based on Integrative Bioinformatics Analyses.

Background: The outbreak of Coronavirus disease 2019 (COVID-19) has become an international public health crisis, and the number of cases with dengue co-infection has raised concerns. Unfortunately, treatment options are currently limited or even unavailable. Thus, the aim of our study was to explore the underlying mechanisms and identify potential therapeutic targets for co-infection. Methods: To further understand the mechanisms underlying co-infection, we used a series of bioinformatics analyses to build host factor interaction networks and elucidate biological process and molecular function categories, pathway activity, tissue-specific enrichment, and potential therapeutic agents. Results: We explored the pathologic mechanisms of COVID-19 and dengue co-infection, including predisposing genes, significant pathways, biological functions, and possible drugs for intervention. In total, 460 shared host factors were collected; among them, CCL4 and AhR targets were important. To further analyze biological functions, we created a protein-protein interaction (PPI) network and performed Molecular Complex Detection (MCODE) analysis. In addition, common signaling pathways were acquired, and the toll-like receptor and NOD-like receptor signaling pathways exerted a significant effect on the interaction. Upregulated genes were identified based on the activity score of dysregulated genes, such as IL-1, Hippo, and TNF-α. We also conducted tissue-specific enrichment analysis and found ICAM-1 and CCL2 to be highly expressed in the lung. Finally, candidate drugs were screened, including resveratrol, genistein, and dexamethasone. Conclusions: This study probes host factor interaction networks for COVID-19 and dengue and provides potential drugs for clinical practice. Although the findings need to be verified, they contribute to the treatment of co-infection and the management of respiratory disease.