Protein-protein interactions: Methods, databases, and applications in virus-host study

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.

Protein-protein interaction map is a key gateway into liver regeneration

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.

Protein-protein Interaction Between Domains of PDZ and BAR from PICK1

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.

nNOS-mediated protein-protein interactions:promising targets for treating neurological and neuropsychiatric disorders

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.

A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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.

Transcriptome Profile Based on Protein-Protein Interaction Networks Provides a Set of Core Genes for Understanding the Metabolic Mechanisms of the Egg-Protecting Behavior in Amphioctopus fangsiao

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.

Yeast protein-protein interaction network model based on biological experimental data

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.

Integrated network analysis of transcriptomic and protein-protein interaction data in taurine-treated hepatic stellate cells

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.

Protein-Protein Interaction Extraction Based on Convex Combination Kernel Function

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.

Construct Protein-Protein Interaction Network by Mining Domain-Domain Interactions

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.

Prediction of Protein-Protein Interactions by a Novel Model Based on Domain Information

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.

Predicting residue contacts for protein-protein interactions by integration of multiple information

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.

Searching maximum quasi-bicliques from protein-protein interaction network

Searching the maximum bicliques or bipartite subgraphs in a graph is a tough question. We proposed a new and efficient method, Searching Quasi-Bicliques (SQB) algorithm, to detect maximum quasi-bicliques from protein-protein interaction network. As a Divide-and-Conquer method, SQB consists of three steps: first, it divides the protein-protein interaction network into a number of Distance-2-Subgraphs;second, by combining top-down and branch-and-bound methods, SQB seeks quasi-bicliques from every Distance-2-Subgraph;third, all the redundant results are removed. We successfully applied our method on the Saccharomyces cerevisiae dataset and obtained 2754 distinct quasi-bicliques.

Dynamic protein-protein interaction subnetworks of lung cancer in cases with smoking history

Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases.However,how lung cancer develops in patients with smoking history remains unclear.Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods.We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs.By defining expression variance (EV),we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database,and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history.We also determined the primary functions of each subnetwork:signal transduction,apoptosis,and cell migration and adhesion for subnetwork A;cell-sustained angiogenesis for subnetwork B;apoptosis for subnetwork C;and,finally,signal transduction and cell replication and proliferation for subnetworks D-G.The probability distribution of the degree of dynamic protein and static protein differed,clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins.There were high correlations among the dynamic proteins,suggesting that the dynamic proteins tend to form specific dynamic modules.We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.

Genome-Wide Identification,Expression Profiling and Protein-Protein Interaction Properties of the BEL-Like Homeodomain Gene Family in Apple

BEL1-like homeodomain(BLH)family proteins are homeodomain transcription factors,which are found ubiquitously in plants and play important roles in regulating meristem and flower development.Although BLH proteins have been reported in some plant species,there is very little information available for plants in the Malus genus(e.g.,apple tree:Malus domestica).In the present study,we identified 19 apple MdBLH genes.Phylogenetic analysis revealed that the MdBLH genes could be divided into five groups.Analysis of gene structure showed that MdBLH gene has four exons,and the third exon was 61 bp in length.Chromosomal location analysis suggested that the MdBLH genes are not distributed uniformly on 12 chromosomes.Eleven MdBLH genes were cloned by RT-PCR,and their expression patterns were also determined.Among them,the expression levels of MdBLH4.1 and MdBLH9.1 could be induced by sodium chloride stress,while the expression levels of MdATH1.1,MdBLH8.1,MdBLH8.3,and MdBLH11.1 were down-regulated by such stress.Transcriptional levels of MdATH1.1 and MdBLH7.2 were down-regulated by mannitol stress.The result of yeast two-hybrid experiment showed that MdBEL1.1 interacted with apple ovate family proteins 6(MdOFP6),and MdBLH3.1 interacted with the MdOFP4,MdOFP6,MdOFP13,and MdOFP16 proteins.Our results provide a strong theoretical basis and a valuable reference for analyzing of the biological functions of MdBLH proteins as transcription factors in apple growth,development,and stress and also for the construction of regulatory networks.

Using the theory of coevolution to predict protein-protein interactions in non-small cell lung cancer

Systems biology has become an effective approach for understanding the molecular mechanisms underlying the development of lung cancer.In this study,sequences of 100 non-small cell lung cancer (NSCLC)-related proteins were downloaded from the National Center for Biotechnology Information (NCBI) databases.The Theory of Coevolution was then used to build a protein-protein interaction (PPI) network of NSCLC.Adopting the reverse thinking approach,we analyzed the NSCLC proteins one at a time.Fifteen key proteins were identified and categorized into a special protein family F(K),which included Cyclin D1 (CCND1),E-cadherin (CDH1),Cyclin-dependent kinase inhibitor 2A (CDKN2A),chemokine (C-X-C motif) ligand 12 (CXCL12),epidermal growth factor (EGF),epidermal growth factor receptor (EGFR),TNF receptor superfamily,member 6(FAS),FK506 binding protein 12-rapamycin associated protein 1 (FRAP1),O-6-methylguanine-DNA methyltransferase (MGMT),parkinson protein 2,E3 ubiquitin protein ligase (PARK2),phosphatase and tensin homolog (PTEN),calcium channel voltage-dependent alpha 2/delta subunit 2 (CACNA2D2),tubulin beta class I (TUBB),SWI/SNF-related,matrix-associated,actin-dependent regulator of chromatin,subfamily a,member 2 (SMARCA2),and wingless-type MMTV integration site family,member 7A (WNT7A).Seven key nodes of the sub-network were identified,which included PARK2,WNT7A,SMARCA2,FRAP1,CDKN2A,CCND1,and EGFR.The PPI predictions of EGFR-EGF,PARK2-FAS,PTEN-FAS,and CACNA2D2-CDH1 were confirmed experimentally by retrieving the Biological General Repository for Interaction Datasets (BioGRID) and PubMed databases.We proposed that the 7 proteins could serve as potential diagnostic molecular markers for NSCLC.In accordance with the developmental mode of lung cancer established by Sekine et al.,we assumed that the occurrence and development of lung cancer were linked not only to gene loss in the 3p region (WNT7A,3p25) and genetic mutations in the 9p region but also to similar events in the regions of 1p36.2 (FRAP1),6q25.2-q27 (PARK2),and 11q13 (CCND1).Lastly,the invasion or metastasis of lung cancer happened.

Analysis of mechanism on Indigo Naturalis in treating chronic myelocytic leukemia based on two-dimentional model of protein-protein interaction network-moleculardocking technique

运用分子对接技术探讨青黛干预CMLK562细胞的有效分子学机制.使用在线人类孟德尔遗传数据库（OMIM）筛选CML相关基因,使用String10.0用于进一步文本挖掘并构建CML可视化蛋白质相互作用网络,将数据读入Cytoscape3.4.0,通过插件CentiScaPe2.1实现网络拓扑分析.青黛活性小分子物质从第三方数据库获取,运用Chemoffice8.0与Sybyl8.1对其再构建和优化,得到青黛小分子配体结构库,通过SurflexDock模块与受体革巴点进行分子对接,打分后得到关键靶标.构建了由425个节点（蛋白质）和2799条边（相互作用）组成的蛋白质相互作用网络,分析得出关键靶标JAK2.CML是一个受多基因调控的复杂疾病,JAK2很可能是其关键节点.

Determining human-coronavirus protein-protein interaction using machine intelligence

The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities globally.Recent research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral pathogenesis.However,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical interactions.This article elucidates the host-viral PPI through Machine Learning(ML)lenses and validates the biological significance of the same using web-based tools.ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work.The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment analysis.Consequently,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

RF-PSSM:A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

The identification of hepatitis C virus(HCV)virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets.An increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases,facilitating studies based on computational methods.In this study,we proposed a new computational approach,rotation forest position-specific scoring matrix(RF-PSSM),to predict the interactions among HCV and human proteins.In particular,PSSM was used to characterize each protein,two-dimensional principal component analysis(2DPCA)was then adopted for feature extraction of PSSM.Finally,rotation forest(RF)was used to implement classification.The results of various ablation experiments show that on independent datasets,the accuracy and area under curve(AUC)value of RF-PSSM can reach 93.74% and 94.29%,respectively,outperforming almost all cutting-edge research.In addition,we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1,which can provide theoretical guidance for future experimental studies.

Biological Interaction and Imaging of Ultrasmall Gold Nanoparticles

Ultrasmall gold nanoparticles(AuNPs)typically includes atomically precise gold nanoclusters(AuNCs)and AuNPs with a core size below 3 nm.Serving as a bridge between small molecules and traditional inorganic nanoparticles,the ultrasmall AuNPs show the unique advantages of both small molecules(e.g.,rapid distribution,renal clearance,low non-specific organ accumulation)and nanoparticles(e.g.,long blood circulation and enhanced permeability and retention effect).The emergence of ultrasmall AuNPs creates significant opportunities to address many challenges in the health field including disease diagnosis,monitoring and treatment.Since the nano–bio interaction dictates the overall biological applications of the ultrasmall AuNPs,this review elucidates the recent advances in the biological interactions and imaging of ultrasmall AuNPs.We begin with the introduction of the factors that influence the cellular interactions of ultrasmall AuNPs.We then discuss the organ interactions,especially focus on the interactions of the liver and kidneys.We further present the recent advances in the tumor interactions of ultrasmall AuNPs.In addition,the imaging performance of the ultrasmall AuNPs is summarized and discussed.Finally,we summarize this review and provide some perspective on the future research direction of the ultrasmall AuNPs,aiming to accelerate their clinical translation.