Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes...Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.展开更多
Background:Triple-negative breast cancer(TNBC),characterized by its lack of traditional hormone receptors and HER2,presents a significant challenge in oncology due to its poor response to conventional therapies.Autoph...Background:Triple-negative breast cancer(TNBC),characterized by its lack of traditional hormone receptors and HER2,presents a significant challenge in oncology due to its poor response to conventional therapies.Autophagy is an important process for maintaining cellular homeostasis,and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors.In contrast to targeting protein activity,intervention with proteinprotein interaction(PPI)can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways.Methods:Here,we employed Naive Bayes,Decision Tree,and k-Nearest Neighbors to elucidate the complex PPI network associated with autophagy in TNBC,aiming to uncover novel therapeutic targets.Meanwhile,the candidate proteins interacting with Beclin 2 were initially screened in MDA-MB-231 cells using Beclin 2 as bait protein by immunoprecipitation-mass spectrometry assay,and the interaction relationship was verified by molecular docking and CO-IP experiments after intersection.Colony formation,cellular immunofluorescence,cell scratch and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT)tests were used to predict the clinical therapeutic effects of manipulating candidate PPI.Results:By developing three PPI classification models and analyzing over 13,000 datasets,we identified 3733 previously unknown autophagy-related PPIs.Our network analysis revealed the central role of Beclin 2 in autophagy regulation,uncovering its interactions with 39 newly identified proteins.Notably,the CO-IP studies identified the substantial interaction between Beclin 2 and Ubiquilin 1,which was anticipated by our model and discovered in immunoprecipitation-mass spectrometry assay results.Subsequently,in vitro investigations showed that overexpressing Beclin 2 increased Ubiquilin 1,promoted autophagy-dependent cell death,and inhibited proliferation and metastasis in MDA-MB-231 cells.Conclusions:This study not only enhances our understanding of autophagy regulation in TNBC but also identifies the Beclin 2-Ubiquilin 1 axis as a promising target for precision therapy.These findings open new avenues for drug discovery and offer inspiration for more effective treatments for this aggressive cancer subtype.展开更多
Recent studies indicate that the process of liver regeneration involves multiple signaling pathways and a variety of genes,cytokines and growth factors. Protein-protein interactions(PPIs)play a role in nearly all even...Recent studies indicate that the process of liver regeneration involves multiple signaling pathways and a variety of genes,cytokines and growth factors. Protein-protein interactions(PPIs)play a role in nearly all events that take place within the cell and PPI maps should be helpful in further understanding the process of liver regeneration.In this review,we discuss recent progress in understanding the PPIs that occur during liver regeneration especially those in the transforming growth factorβsignaling pathways.We believe the use of large-scale PPI maps for integrating the information already known about the liver regeneration is a useful approach in understanding liver regeneration from the standpoint of systems biology.展开更多
Two DNA fragments encoding PDZ domain （21-110 residues） and BAR domain （ 150-360 residues） from PICK1 （1-416 residues） were amplified by PCR and then introduced into vectors, pET-32M and pMAL-e2X respectively to...Two DNA fragments encoding PDZ domain （21-110 residues） and BAR domain （ 150-360 residues） from PICK1 （1-416 residues） were amplified by PCR and then introduced into vectors, pET-32M and pMAL-e2X respectively to generate recombinant plasmids, pE-pdz and pM-bar. Having been separately transferred into the hosts E. coli BL21 and E. coli JM109, these two strains can express fusion proteins： His-tagged PDZ（PDZ domain） and maltose binding protein-BAR（ MBP-BAR domain） respectively, as confirmed by both SDS-PAGE and Wostem blotting. The interaction between these two domains is dose-dependence, as identified by a pull-down test. Moreover, it has been shown from the ELISA analysis that the actual amount of PDZ bound to MBP-BAR-amylose beads reaches （ 16 ± 0. 5）%, as calculated by the molar ratio of PDZ to MBP-BAR. In addition, the interaction between BAR（bait） and PDZ（prey） in vivo was also examined with a yeast two-hybrid system.展开更多
Neurological and neuropsychiatric disorders are one of the leading causes of disability worldwide and affect the health of billions of people.Nitric oxide(NO),a free gas with multitudinous bioactivities,is mainly prod...Neurological and neuropsychiatric disorders are one of the leading causes of disability worldwide and affect the health of billions of people.Nitric oxide(NO),a free gas with multitudinous bioactivities,is mainly produced from the oxidation of L-arginine by neuronal nitric oxide synthase(nNOS)in the brain.Inhibiting nNOS benefits a variety of neurological and neuropsychiatric disorders,including stroke,depression and anxiety disorders,posttraumatic stress disorder,Parkinson’s disease,Alzheimer’s disease,chronic pain,and drug addiction.Due to critical roles of nNOS in learning and memory and synaptic plasticity,direct inhibition of nNOS may cause severe side effects.Importantly,interactions of several proteins,including post-synaptic density 95(PSD-95),carboxyterminal PDZ ligand of nNOS(CAPON)and serotonin transporter(SERT),with the PSD/Disc-large/ZO-1 homologous(PDZ)domain of nNOS have been demonstrated to influence the subcellular distribution and activity of the enzyme in the brain.Therefore,it will be a preferable means to interfere with nNOS-mediated proteinprotein interactions(PPIs),which do not lead to undesirable effects.Herein,we summarize the current literatures on nNOS-mediated PPIs involved in neurological and neuropsychiatric disorders,and the discovery of drugs targeting the PPIs,which is expected to provide potential targets for developing novel drugs and new strategy for the treatment of neurological and neuropsychiatric disorders.展开更多
Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interacti...Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.展开更多
Peptide stapling strategy has been proven a promising solution in addressing two major pharmacological hurdles, proteolytic stability and membrane permeability, for small peptides as therapeutics. This stapling peptid...Peptide stapling strategy has been proven a promising solution in addressing two major pharmacological hurdles, proteolytic stability and membrane permeability, for small peptides as therapeutics. This stapling peptides feature a covalent cross-link of side chains, thus effectively mimicking α-helix as inhibitors of protein-protein interactions. In this review, we category and analyze key examples of various peptide stapling strategies based on different cross-links aligned on the side chain of peptides mainly in the last three years.展开更多
Protein-protein interactions(PPIs) are recognized as attractive therapeutic targets. However targeting PPIs especially intracellular ones has been proven extremely difficult for conventional drug-like small molecules,...Protein-protein interactions(PPIs) are recognized as attractive therapeutic targets. However targeting PPIs especially intracellular ones has been proven extremely difficult for conventional drug-like small molecules, and biological drugs such as monoclonal antibodies have difficulty in reaching intracellular targets. Macrocyclic peptides are promising candidates of PPI regulators for their potential in combining high potency and biological stability together. Cell permeability of macrocyclic peptides may also be achieved by structural modifications or conjugation to a cell-penetrating sequence. Significant progress has been made in this research area in recent years. Important technology progress and recent examples of macrocyclic peptide PPI modulators are reviewed.展开更多
Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special si...Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special significance.Meanwhile, there is free information on the Internet, such as databases and algorithms of protein-protein interactions(PPIs).In this paper, a novel method which integrates distributed heterogeneous data sources and algorithms to predict PPIs for alpha-synuclein in silico is proposed.The PPIs generated by the method take advantage of various experimental data, and indicate new information about PPIs for alpha-synuclein.In the end of this paper, the result illustrates that the method is practical.It is hoped that the prediction result obtained by this method can provide guidance for biological experiments of PPIs for alpha-synuclein to reveal possible mechanisms of PD.展开更多
Proteins are major functional units that are tightly connected to form complex and dynamic networks.These networks enable cells and organisms to operate properly and respond efficiently to environmental cues.Over the ...Proteins are major functional units that are tightly connected to form complex and dynamic networks.These networks enable cells and organisms to operate properly and respond efficiently to environmental cues.Over the past decades,many biochemical methods have been developed to search for protein-binding partners in order to understand how protein networks are constructed and connected.At the same time,rapid development in proteomics and mass spectrometry(MS)techniques makes it possible to identify interacting proteins and build comprehensive protein-protein interaction networks.The resulting interactomes and networks have proven informative in the investigation of biological functions,such as in the field of DNA damage repair.In recent years,a number of proteins involved in DNA damage response and DNA repair pathways have been uncovered with MS-based protein-protein interaction studies.As the technologies for enriching associated proteins and MS become more sophisticated,the studies of protein-protein interactions are entering a new era.In this review,we summarize the strategies and recent developments for exploring protein-protein interaction.In addition,we discuss the application of these tools in the investigation of protein-protein interaction networks involved in DNA damage response and DNA repair.展开更多
Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms ass...Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.展开更多
High-throughput techniques,such as the yeast-two-hybrid system,produce mass protein-protein interaction data. The new technique makes it possible to predict protein complexes by com-putation. A novel method,named DSDA...High-throughput techniques,such as the yeast-two-hybrid system,produce mass protein-protein interaction data. The new technique makes it possible to predict protein complexes by com-putation. A novel method,named DSDA,has been put forward to predict protein complexes via dense subgraph because the proteins among a protein complex have a much tighter relation among them than with others. This method chooses a node with its neighbors to form the initial subgraph,and chooses a node which has the tightest relation with the subgraph according to greedy strategy,then the chosen node is added into the initial subgraph until the subgraph density is below the threshold value. The ob-tained subgraph is then removed from the network and the process continues until no subgraph can be detected. Compared with other algorithms,DSDA can predict not only non-overlap protein com-plexes but also overlap protein complexes. The experiment results show that DSDA predict as many protein complexes as possible. And in Y78K network the accuracy of DSDA is as twice times as that of RNSC and MCL.展开更多
Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has ...Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.展开更多
Domain-domain interactions are important clues to inferring protein-protein interactions. Although about 8 000 domain-domain interactions are discovered so far,they are just the tip of the iceberg. Because domains are...Domain-domain interactions are important clues to inferring protein-protein interactions. Although about 8 000 domain-domain interactions are discovered so far,they are just the tip of the iceberg. Because domains are conservative and commonplace in proteins,domain-domain interactions are discovered based on pairs of domains which significantly co-exist in proteins. Meanwhile,it is realized that:( 1) domain-domain interactions may exist within the same proteins or across different proteins;( 2) only the domain-domain interactions across different proteins can mediate interactions between proteins;( 3) domains have biases to interact with other domains. And then,a novel method is put forward to construct protein-protein interaction network by using domain-domain interactions. The method is validated by experiments and compared with the state- of-art methods in the field. The experimental results suggest that the method is reasonable and effectiveness on constructing Protein-protein interactions network.展开更多
BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated anti...BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.展开更多
Coevolution can be seen as the interdependency between evolutionary histories. In the context of protein evolution, functional correlation proteins are ever-present coordinated evolutionary characters without disrupti...Coevolution can be seen as the interdependency between evolutionary histories. In the context of protein evolution, functional correlation proteins are ever-present coordinated evolutionary characters without disruption of organismal integrity. As to complex system, there are two forms of protein-protein interactions in vivo, which refer to inter-complex interaction and intra-complex interaction. In this paper, we studied the difference of coevolution characters between inter-complex interaction and intra-complex interaction using ＂Mirror tree＂ method on the respiratory chain （RC） proteins. We divided the correlation coefficients of every pairwise RC proteins into two groups corresponding to the binary protein--protein interaction in intra-complex and the binary protein--protein interaction in inter-complex, respectively. A dramatical discrepancy is detected between the coevolution characters of the two sets of protein interactions （Wilcoxon test, p-value = 4.4 × 10 6）. Our finding reveals some critical information on coevolutionary study and assists the mechanical investigation of protein-protein interaction. Furthermore, the results also provide some unique clue for supramolecular organization of protein complexes in the mitochondrial inner membrane. More detailed binding sites map and genome information of nuclear encoded RC proteins will be extraordinary valuable for the further mitochondria dynamics study.展开更多
Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the opti...Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.展开更多
OBJECTIVE： To construct a protein-protein interaction（PPI） network in hypertension patients with blood-stasis syndrome（BSS） by using digital gene expression（DGE） sequencing and database mining techniques.METHOD...OBJECTIVE： To construct a protein-protein interaction（PPI） network in hypertension patients with blood-stasis syndrome（BSS） by using digital gene expression（DGE） sequencing and database mining techniques.METHODS： DGE analysis based on the Solexa Genome Analyzer platform was performed on vascular endothelial cells incubated with serum of hypertension patients with BSS. The differentially expressed genes were f iltered by comparing the expression levels between the different experimental groups. Then functional categories and e nriched pathways of the unique genes for BSS were analyzed using Database for Annotation, Visualization and Integrated Discovery（DAVID） to select those in the enrichment pathways. I nterologous Interaction Database（I2D） was used to construct PPI networks with the selected genes for hypertension patients with BSS. The potential candidate genes related to BSS were identif ied by comparing the number of relationships among genes. Confi rmed by quantitative reverse transcription-polymerase chain reaction（q RTPCR）, gene ontology（GO） analysis was used to infer the functional annotations of the potential candidate genes for BSS.RESULTS： With gene enrichment analysis using DAVID, a list of 58 genes was chosen from the unique genes. The selected 58 genes were analyzed using I2 D, and a PPI network was constructed. Based on the network analysis results, candidate genes for BSS were identifi ed：DDIT3, JUN, HSPA8, NFIL3, HSPA5, HIST2H2 BE, H3F3 B, CEBPB, SAT1 and GADD45 A. Verif ied through qRT-PCR and analyzed by GO, the functional annotations of the potential candidate genes were explored.CONCLUSION： Compared with previous methodologies reported in the literature, the present DGE analysis and data mining method have shown a great improvement in analyzing BSS.展开更多
Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspe...Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.展开更多
Detailed knowledge of interfacial region between interacting proteins is not only helpful in annotating function for proteins, but also very important for structure-based drug design and disease treatment. However, th...Detailed knowledge of interfacial region between interacting proteins is not only helpful in annotating function for proteins, but also very important for structure-based drug design and disease treatment. However, this is one of the most difficult tasks and current methods are constrained by some factors. In this study, we developed a new method to predict residue-residue contacts of two interacting protein domains by integrating information about evolutionary couplings andamino acid pairwise contact potentials, as well as domain-domain interaction interfaces. The experimental results showed that our proposed method outperformed the previous method with the same datasets. Moreover, the method promises an improvement in the source of template-based protein docking.展开更多
基金National Natural Science Foundation of China,No.31971180 and No.11474013.
文摘Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.
基金the National Natural Science Foundation of China(Nos.22307009,82374155,82073997,82104376)the Sichuan Science and Technology Program(Nos.2023NSFSC1108,2024NSFTD0023)+1 种基金the Postdoctoral Research Project of Sichuan Provincethe Xinglin Scholar Research Promotion Project of Chengdu University of TCM.
文摘Background:Triple-negative breast cancer(TNBC),characterized by its lack of traditional hormone receptors and HER2,presents a significant challenge in oncology due to its poor response to conventional therapies.Autophagy is an important process for maintaining cellular homeostasis,and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors.In contrast to targeting protein activity,intervention with proteinprotein interaction(PPI)can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways.Methods:Here,we employed Naive Bayes,Decision Tree,and k-Nearest Neighbors to elucidate the complex PPI network associated with autophagy in TNBC,aiming to uncover novel therapeutic targets.Meanwhile,the candidate proteins interacting with Beclin 2 were initially screened in MDA-MB-231 cells using Beclin 2 as bait protein by immunoprecipitation-mass spectrometry assay,and the interaction relationship was verified by molecular docking and CO-IP experiments after intersection.Colony formation,cellular immunofluorescence,cell scratch and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide(MTT)tests were used to predict the clinical therapeutic effects of manipulating candidate PPI.Results:By developing three PPI classification models and analyzing over 13,000 datasets,we identified 3733 previously unknown autophagy-related PPIs.Our network analysis revealed the central role of Beclin 2 in autophagy regulation,uncovering its interactions with 39 newly identified proteins.Notably,the CO-IP studies identified the substantial interaction between Beclin 2 and Ubiquilin 1,which was anticipated by our model and discovered in immunoprecipitation-mass spectrometry assay results.Subsequently,in vitro investigations showed that overexpressing Beclin 2 increased Ubiquilin 1,promoted autophagy-dependent cell death,and inhibited proliferation and metastasis in MDA-MB-231 cells.Conclusions:This study not only enhances our understanding of autophagy regulation in TNBC but also identifies the Beclin 2-Ubiquilin 1 axis as a promising target for precision therapy.These findings open new avenues for drug discovery and offer inspiration for more effective treatments for this aggressive cancer subtype.
基金Supported by Chinese Human Liver Proteome Project,No.2004BA711A19-08National 863 Project,No.2007AA02Z100
文摘Recent studies indicate that the process of liver regeneration involves multiple signaling pathways and a variety of genes,cytokines and growth factors. Protein-protein interactions(PPIs)play a role in nearly all events that take place within the cell and PPI maps should be helpful in further understanding the process of liver regeneration.In this review,we discuss recent progress in understanding the PPIs that occur during liver regeneration especially those in the transforming growth factorβsignaling pathways.We believe the use of large-scale PPI maps for integrating the information already known about the liver regeneration is a useful approach in understanding liver regeneration from the standpoint of systems biology.
基金the National Natural Science Foundation of China(No 30400065)
文摘Two DNA fragments encoding PDZ domain （21-110 residues） and BAR domain （ 150-360 residues） from PICK1 （1-416 residues） were amplified by PCR and then introduced into vectors, pET-32M and pMAL-e2X respectively to generate recombinant plasmids, pE-pdz and pM-bar. Having been separately transferred into the hosts E. coli BL21 and E. coli JM109, these two strains can express fusion proteins： His-tagged PDZ（PDZ domain） and maltose binding protein-BAR（ MBP-BAR domain） respectively, as confirmed by both SDS-PAGE and Wostem blotting. The interaction between these two domains is dose-dependence, as identified by a pull-down test. Moreover, it has been shown from the ELISA analysis that the actual amount of PDZ bound to MBP-BAR-amylose beads reaches （ 16 ± 0. 5）%, as calculated by the molar ratio of PDZ to MBP-BAR. In addition, the interaction between BAR（bait） and PDZ（prey） in vivo was also examined with a yeast two-hybrid system.
基金supported by grants from National Natural Science Foundation of China (82090042, 31530091,81870912)National Key Research and Development Program of China (2016YFC1306703)。
文摘Neurological and neuropsychiatric disorders are one of the leading causes of disability worldwide and affect the health of billions of people.Nitric oxide(NO),a free gas with multitudinous bioactivities,is mainly produced from the oxidation of L-arginine by neuronal nitric oxide synthase(nNOS)in the brain.Inhibiting nNOS benefits a variety of neurological and neuropsychiatric disorders,including stroke,depression and anxiety disorders,posttraumatic stress disorder,Parkinson’s disease,Alzheimer’s disease,chronic pain,and drug addiction.Due to critical roles of nNOS in learning and memory and synaptic plasticity,direct inhibition of nNOS may cause severe side effects.Importantly,interactions of several proteins,including post-synaptic density 95(PSD-95),carboxyterminal PDZ ligand of nNOS(CAPON)and serotonin transporter(SERT),with the PSD/Disc-large/ZO-1 homologous(PDZ)domain of nNOS have been demonstrated to influence the subcellular distribution and activity of the enzyme in the brain.Therefore,it will be a preferable means to interfere with nNOS-mediated proteinprotein interactions(PPIs),which do not lead to undesirable effects.Herein,we summarize the current literatures on nNOS-mediated PPIs involved in neurological and neuropsychiatric disorders,and the discovery of drugs targeting the PPIs,which is expected to provide potential targets for developing novel drugs and new strategy for the treatment of neurological and neuropsychiatric disorders.
基金This work was supported in part by the National Natural Science Foundation of China(61772493)the CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2020-004B)+4 种基金the Natural Science Foundation of Chongqing(China)(cstc2019jcyjjqX0013)Chongqing Research Program of Technology Innovation and Application(cstc2019jscx-fxydX0024,cstc2019jscx-fxydX0027,cstc2018jszx-cyzdX0041)Guangdong Province Universities and College Pearl River Scholar Funded Scheme(2019)the Pioneer Hundred Talents Program of Chinese Academy of Sciencesthe Deanship of Scientific Research(DSR)at King Abdulaziz University(G-21-135-38).
文摘Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins.With the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them efficiently.To address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using MapReduce.To do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction.Respective solutions are then devised to overcome these limitations.In particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins.After that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy.Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.
文摘Peptide stapling strategy has been proven a promising solution in addressing two major pharmacological hurdles, proteolytic stability and membrane permeability, for small peptides as therapeutics. This stapling peptides feature a covalent cross-link of side chains, thus effectively mimicking α-helix as inhibitors of protein-protein interactions. In this review, we category and analyze key examples of various peptide stapling strategies based on different cross-links aligned on the side chain of peptides mainly in the last three years.
基金supported by Principle Training Program of Education Department of Sichuan Province (No. 18CZ0042)Fundamental Research Fund of Chengdu University (No. 2081916027)Undergraduate Innovation Programm of Chengdu University (Nos. CDU_CX_2018250, CDU_CX_2018251)
文摘Protein-protein interactions(PPIs) are recognized as attractive therapeutic targets. However targeting PPIs especially intracellular ones has been proven extremely difficult for conventional drug-like small molecules, and biological drugs such as monoclonal antibodies have difficulty in reaching intracellular targets. Macrocyclic peptides are promising candidates of PPI regulators for their potential in combining high potency and biological stability together. Cell permeability of macrocyclic peptides may also be achieved by structural modifications or conjugation to a cell-penetrating sequence. Significant progress has been made in this research area in recent years. Important technology progress and recent examples of macrocyclic peptide PPI modulators are reviewed.
基金supported by the National Basic Research Program of China (Grant No.2006CB500702)the Shanghai Lead-ing Academic Discipline Project (Grant No.J50103)Shanghai University Systems Biology Reasearch Funding (GrantNo.SBR08001)
文摘Alpha-synuclein plays an important role in Parkinson's disease(PD).The current study of alpha-synuclein mainly concentrates at the gene level.However, it is found that the study at the protein level has special significance.Meanwhile, there is free information on the Internet, such as databases and algorithms of protein-protein interactions(PPIs).In this paper, a novel method which integrates distributed heterogeneous data sources and algorithms to predict PPIs for alpha-synuclein in silico is proposed.The PPIs generated by the method take advantage of various experimental data, and indicate new information about PPIs for alpha-synuclein.In the end of this paper, the result illustrates that the method is practical.It is hoped that the prediction result obtained by this method can provide guidance for biological experiments of PPIs for alpha-synuclein to reveal possible mechanisms of PD.
基金support from the Pamela and Wayne Garrison Distinguished Chair in Cancer Research,the Cancer Prevention&Research Institute of Texas(Nos.RP160667 and RP180813)the National Institutes of Health(NIH)(Nos.CA210929,CA216911,and CA216437)。
文摘Proteins are major functional units that are tightly connected to form complex and dynamic networks.These networks enable cells and organisms to operate properly and respond efficiently to environmental cues.Over the past decades,many biochemical methods have been developed to search for protein-binding partners in order to understand how protein networks are constructed and connected.At the same time,rapid development in proteomics and mass spectrometry(MS)techniques makes it possible to identify interacting proteins and build comprehensive protein-protein interaction networks.The resulting interactomes and networks have proven informative in the investigation of biological functions,such as in the field of DNA damage repair.In recent years,a number of proteins involved in DNA damage response and DNA repair pathways have been uncovered with MS-based protein-protein interaction studies.As the technologies for enriching associated proteins and MS become more sophisticated,the studies of protein-protein interactions are entering a new era.In this review,we summarize the strategies and recent developments for exploring protein-protein interaction.In addition,we discuss the application of these tools in the investigation of protein-protein interaction networks involved in DNA damage response and DNA repair.
基金supported by the earmarked fund for the Modern Agro-industry Technology Research System(No.CARS-49)the Natural Science Foundation of Shan-dong Province(No.ZR2019BC052)the National Natural Science Foundation of China(No.42006077).
文摘Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.
基金Supported by the National Natural Science Foundation of China (60803025)
文摘High-throughput techniques,such as the yeast-two-hybrid system,produce mass protein-protein interaction data. The new technique makes it possible to predict protein complexes by com-putation. A novel method,named DSDA,has been put forward to predict protein complexes via dense subgraph because the proteins among a protein complex have a much tighter relation among them than with others. This method chooses a node with its neighbors to form the initial subgraph,and chooses a node which has the tightest relation with the subgraph according to greedy strategy,then the chosen node is added into the initial subgraph until the subgraph density is below the threshold value. The ob-tained subgraph is then removed from the network and the process continues until no subgraph can be detected. Compared with other algorithms,DSDA can predict not only non-overlap protein com-plexes but also overlap protein complexes. The experiment results show that DSDA predict as many protein complexes as possible. And in Y78K network the accuracy of DSDA is as twice times as that of RNSC and MCL.
基金Project supported by the National Natural Science Foundation of China(No.11172158)
文摘Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61271346,61571163,61532014,91335112 and 61402132)the Fundamental Research Funds for the Central Universities(Grant No.DB13AB02)
文摘Domain-domain interactions are important clues to inferring protein-protein interactions. Although about 8 000 domain-domain interactions are discovered so far,they are just the tip of the iceberg. Because domains are conservative and commonplace in proteins,domain-domain interactions are discovered based on pairs of domains which significantly co-exist in proteins. Meanwhile,it is realized that:( 1) domain-domain interactions may exist within the same proteins or across different proteins;( 2) only the domain-domain interactions across different proteins can mediate interactions between proteins;( 3) domains have biases to interact with other domains. And then,a novel method is put forward to construct protein-protein interaction network by using domain-domain interactions. The method is validated by experiments and compared with the state- of-art methods in the field. The experimental results suggest that the method is reasonable and effectiveness on constructing Protein-protein interactions network.
基金the National Natural Science Foundation of China,No.81360595 and No.81860790Guangxi Natural Science Foundation Program,No.KJT13066+2 种基金the Bagui Scholars Foundation Program of Guangxithe Special-term Experts Foundation Program of Guangxithe Project of Guangxi Young Teacher Fundamental Ability Promotion,No.2017KY0298
文摘BACKGROUND Studies show that the antifibrotic mechanism of taurine may involve its inhibition of the activation and proliferation of hepatic stellate cells(HSCs). Since the molecular mechanism of taurine-mediated antifibrotic activity has not been fully unveiled and is little studied, it is imperative to use "omics" methods to systematically investigate the molecular mechanism by which taurine inhibits liver fibrosis.AIM To establish a network including transcriptomic and protein-protein interaction data to elucidate the molecular mechanism of taurine-induced HSC apoptosis.METHODS We used microarrays, bioinformatics, protein-protein interaction(PPI) network,and sub-modules to investigate taurine-induced changes in gene expression in human HSCs(LX-2). Subsequently, all of the differentially expressed genes(DEGs) were subjected to gene ontology function and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. Furthermore, the interactions of DEGs were explored in a human PPI network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software.RESULTS A total of 635 DEGs were identified in taurine-treated HSCs when compared with the controls. Of these, 304 genes were statistically significantly up-regulated, and 331 down-regulated. Most of these DEGs were mainly located on the membrane and extracellular region, and are involved in the biological processes of signal transduction, cell proliferation, positive regulation of extracellular regulated protein kinases 1(ERK1) and ERK2 cascade, extrinsic apoptotic signaling pathway and so on. Fifteen significantly enriched pathways with DEGs were identified, including mitogen-activated protein kinase(MAPK) signaling pathway, peroxisome proliferators-activated receptor signaling pathway,estrogen signaling pathway, Th1 and Th2 cell differentiation, cyclic adenosine monophosphate signaling pathway and so on. By integrating the transcriptomics and human PPI data, nine critical genes, including MMP2, MMP9, MMP21,TIMP3, KLF10, CX3CR1, TGFB1, VEGFB, and EGF, were identified in the PPI network analysis.CONCLUSION Taurine promotes the apoptosis of HSCs via up-regulating TGFB1 and then activating the p38 MAPK-JNK-Caspase9/8/3 pathway. These findings enhance the understanding of the molecular mechanism of taurine-induced HSC apoptosis and provide references for liver disorder therapy.
基金supported by the grants from the National Science Foundation of China(No.31071163)the National Basic Research Program(973 Program)(No.2010CB126604)the Ministry of Science and Technology of the People's Republic of China
文摘Coevolution can be seen as the interdependency between evolutionary histories. In the context of protein evolution, functional correlation proteins are ever-present coordinated evolutionary characters without disruption of organismal integrity. As to complex system, there are two forms of protein-protein interactions in vivo, which refer to inter-complex interaction and intra-complex interaction. In this paper, we studied the difference of coevolution characters between inter-complex interaction and intra-complex interaction using ＂Mirror tree＂ method on the respiratory chain （RC） proteins. We divided the correlation coefficients of every pairwise RC proteins into two groups corresponding to the binary protein--protein interaction in intra-complex and the binary protein--protein interaction in inter-complex, respectively. A dramatical discrepancy is detected between the coevolution characters of the two sets of protein interactions （Wilcoxon test, p-value = 4.4 × 10 6）. Our finding reveals some critical information on coevolutionary study and assists the mechanical investigation of protein-protein interaction. Furthermore, the results also provide some unique clue for supramolecular organization of protein complexes in the mitochondrial inner membrane. More detailed binding sites map and genome information of nuclear encoded RC proteins will be extraordinary valuable for the further mitochondria dynamics study.
文摘Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.
基金supported by the National Natural Science Foundation of China (No. 81173157)the Guangdong Natural Science Foundation (No. 10151063201000045)
文摘OBJECTIVE： To construct a protein-protein interaction（PPI） network in hypertension patients with blood-stasis syndrome（BSS） by using digital gene expression（DGE） sequencing and database mining techniques.METHODS： DGE analysis based on the Solexa Genome Analyzer platform was performed on vascular endothelial cells incubated with serum of hypertension patients with BSS. The differentially expressed genes were f iltered by comparing the expression levels between the different experimental groups. Then functional categories and e nriched pathways of the unique genes for BSS were analyzed using Database for Annotation, Visualization and Integrated Discovery（DAVID） to select those in the enrichment pathways. I nterologous Interaction Database（I2D） was used to construct PPI networks with the selected genes for hypertension patients with BSS. The potential candidate genes related to BSS were identif ied by comparing the number of relationships among genes. Confi rmed by quantitative reverse transcription-polymerase chain reaction（q RTPCR）, gene ontology（GO） analysis was used to infer the functional annotations of the potential candidate genes for BSS.RESULTS： With gene enrichment analysis using DAVID, a list of 58 genes was chosen from the unique genes. The selected 58 genes were analyzed using I2 D, and a PPI network was constructed. Based on the network analysis results, candidate genes for BSS were identifi ed：DDIT3, JUN, HSPA8, NFIL3, HSPA5, HIST2H2 BE, H3F3 B, CEBPB, SAT1 and GADD45 A. Verif ied through qRT-PCR and analyzed by GO, the functional annotations of the potential candidate genes were explored.CONCLUSION： Compared with previous methodologies reported in the literature, the present DGE analysis and data mining method have shown a great improvement in analyzing BSS.
基金National Natural Science Foundations of China(Nos.61503116,61402007)Foundation for Young Talents in the Colleges of Anhui Province Committee,China(No.2013SQRL097ZD)+1 种基金Natural Science Foundation of Anhui Educational Committee,China(No.KJ2014A198)Natural Science Foundation of Anhui Province,China(No.1408085QF108)
文摘Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.
文摘Detailed knowledge of interfacial region between interacting proteins is not only helpful in annotating function for proteins, but also very important for structure-based drug design and disease treatment. However, this is one of the most difficult tasks and current methods are constrained by some factors. In this study, we developed a new method to predict residue-residue contacts of two interacting protein domains by integrating information about evolutionary couplings andamino acid pairwise contact potentials, as well as domain-domain interaction interfaces. The experimental results showed that our proposed method outperformed the previous method with the same datasets. Moreover, the method promises an improvement in the source of template-based protein docking.
