基于蛋白质互作知识的生物学通路扩充新方法

生物学通路被广泛应用于基因功能学研究,但现有的生物学通路知识并不完善,仍需进一步扩充。生物信息学预测为通路扩充提供了一种有效且经济的途径。文章提出了一种融合蛋白质?蛋白质互作知识以及Gene Ontology(GO)数据库信息进行基因通路预测的新方法。首先选取目标基因在蛋白质?蛋白质互作层面上的邻居所在的Kyoto Encyclopedia of Genes and Genomes(KEGG)通路为候选通路,然后通过检验候选通路中的基因是否在与目标基因关联的GO节点富集来判断目标基因的通路归属。分别利用Human Protein Reference Database(HPRD)和Biological General Repository for Interaction Datasets(BioGRID)数据库中的蛋白质?蛋白质互作信息进行预测。结果表明,在两套数据中,随着互作邻居个数的增加,预测的平均准确率(在所有目标基因注释的通路中被成功预测的比例)及相对准确率(在至少有一个注释通路被成功预测的基因集中,所有注释通路均被预测正确的基因所占的比例)均呈现上升趋势。当互作邻居个数达到22时,预测的平均准确率分别达到96.2%(HPRD)和96.3%(BioGRID),而相对准确率分别为93.3%(HPRD)和84.1%(BioGRID)。进一步利用新版数据库对旧版数据库中被更新的89个基因进行验证,至少有一个更新通路被预测正确的基因有50个,其中43个基因的更新通路被完全正确预测,相对准确率为86.0%。这些结果显示该方法是一种可靠且有效的通路扩充方法。

蛋白质互作组学技术及其在植物研究中的应用进展

蛋白质互作组学技术是一门鉴定和量化蛋白质与其他代谢物或蛋白质等分子相互作用的前沿技术,已成为研究植物系统生物学和多组学研究的重要组成部分。近年来,基于质谱的组学技术迅速发展,也促进蛋白质-代谢物相互作用(Protein-metabolite interaction,PMI)、蛋白质-蛋白质相互作用(Protein-protein interaction,PPI)的发现和验证方法取得巨大进步,这些蛋白质互作组学技术在功能基因组和功能代谢组研究中逐渐展示出巨大的应用潜力。系统总结了过去10年不同蛋白质互作组学技术(主要包括PMI和PPI)的分析策略,并详细分析了它们各自的优缺点和适用的相互作用类型,综述了蛋白质互作组学技术在植物研究领域的应用进展,对植物蛋白质互作组学技术的应用策略和需要攻克的关键技术瓶颈进行了总结。蛋白质互作组学技术的不断发展将进一步推动植物胞内信号转导及代谢调控通路的解析,而精准解析信号网络中关键相互作用将为植物自身生长发育以及适应外界环境等机制研究提供重要的信息。

人工智能在宿主与病原体蛋白质互作预测中的应用进展

宿主与病原体之间的蛋白质-蛋白质互作(Host-pathogen protein-protein interaction,HP-PPI)是病原体感染宿主的关键分子事件,准确识别HP-PPI对于理解宿主的免疫防御机制、病原体的致病机制,以及研发抗感染药物都具有重要意义。近年来,蛋白质互作实验技术的发展及其在宿主与病原体互作研究中的应用,积累了大量的HPPPI数据,于是人工智能技术逐渐在HP-PPI预测研究领域中脱颖而出。本文综述了人工智能方法在HP-PPI预测研究领域中的应用,首先介绍了基于人工智能方法识别HP-PPI的任务流程,总结了收录HP-PPI数据的常用数据库;然后重点对机器学习和深度学习两大类人工智能方法在HP-PPI预测研究领域中的应用进行分类归纳,介绍了部分经典算法模型的基本原理、特征选择和模型评估结果等;最后,分析了人工智能方法预测HP-PPI面临的问题及挑战,以期为宿主与病原体互作研究领域的科研人员提供思路和参考。

基于网络药理学分析白头翁汤治疗猪腹泻的作用机制

试验利用网络药理学研究方法尝试揭示白头翁汤治疗猪腹泻的活性成分、作用靶点及其基因功能、信号通路的机制。首先在中药系统药理学分析数据库中检索白头翁汤的所有化学成分、作用靶点,进而利用STRING、DAVID、NCBI数据库,Cytoscape软件构建化合物—靶点网络、蛋白质—蛋白质相互作用(PPI)网络、靶点—通路网络,研究白头翁汤治疗猪腹泻的作用机制。通过化合物的口服利用度和类药性筛选得出白头翁汤11个活性化合物,化合物—靶点网络结果显示,11个活性化合物含有63个相应靶点;白头翁汤作用于猪腹泻的PPI网络图包含45个靶点,主要靶点是雌激素受体1(estrogen receptor,ESR1)、CREB结合蛋白(CREB binding protein,CREBBP)、丝裂原活化蛋白激酶1(mitogen-activated protein kinase 1,MAPK1)、雄激素受体(androgen receptor,AR)等,与猪腹泻靶点基因直接相关的靶点是拓扑异构酶Ⅱβ(DNA topoisomeraseⅡ,TOP2B)、ESR1、ESR2、糖皮质激素受体(glucocorticoid receptor,NR3C1)、AR和细胞核受体共激活剂2(nuclear receptor coactivator 2,NCOA2);白头翁汤—猪腹泻PPI网络图关键靶点GO富集条目为5个,其中生物过程、分子功能、细胞组成相关的条目分别有3、1、1个;白头翁汤作用于猪腹泻PPI网络图KEGG信号通路有1条。白头翁汤可能主要通过金鱼草素、掌叶防己碱、8-异戊烯基二氢茆酚-7-葡糖苷、延胡索乙素、足叶草脂素、黄麻苷和8-羟基松脂醇等调控TOP2B、ESR1、ESR2、NR3C1、AR和NCOA2等靶点,基因功能富集于磷脂酶C激活G蛋白偶联受体信号通路、腺苷酸环化酶激活肾上腺素能受体信号通路、DNA模板转录、类固醇结合、细胞膜的组成部分,以及通过KEGG信号通路中神经活性的配体—受体相互作用信号通路来治疗猪腹泻。

Multifunctionality of the linker histones： an emerging role for protein-protein interactions

Linker histones, e.g., H1, are best known for their ability to bind to nucleosomes and stabilize both nucleosome structure and condensed higher-order chromatin structures. However, over the years many investigators have reported specific interactions between linker histones and proteins involved in important cellular processes. The purpose of this review is to highlight evidence indicating an important alternative mode of action for H1, namely protein-protein interactions. We first review key aspects of the traditional view of linker histone action, including the importance of the H1 C-terminal domain. We then discuss the current state of knowledge of linker histone interactions with other proteins, and, where possible, highlight the mechanism of linker histone-mediated protein-protein interactions. Taken together, the data suggest a combinatorial role for the linker histones, functioning both as primary chromatin architectural proteins and simultaneously as recruitment hubs for proteins involved in accessing and modifying the chromatin fiber.

A Co-expression System Based on Phage and Phagemid to Select Cognate Antibody-antigen Pairs in vivo

A modified selectively infective phage (SIP) is developed to facilitate the selection of interacting antibody antigen pairs from a large single chain antibody (scFv) library in vivo. The system is constructed with a modified helper phage M13KO7 and phagemid pCANTAB 5 E. The antigen fused to the C terminal of N1 N2 domain and the scFv to the N terminal of CT domain of the gIIIp of filamentous phage are encoded on the phage and phagemid vectors respectively. The phages produced by co transformants restore infectivity via interaction between antigen and antibody fusions in the cell periplasm. In a model system, the scFv fragment of the anti hemagglutinin 17/9 antibody and its corresponding antigen are detected in the presence of a 10 5 fold excess of a non interacting control pairs, which demonstrates this system to be very sensitive and facile to screen a large single chain antibody library.

Chemical Physics in Living Cells-using Light to Visualize and Control Intracellular Signal Transduction

Cells are crowded microenvironments filled with macromolecules undergoing constant phys- ical and chemical interactions. The physicochemical makeup of the cells aff）cts various cellular responses, determines cell-cell interactions and influences cell decisions. Chemical and physical properties diff）r between cells and within cells. Moreover, these properties are subject to dynamic changes in response to environmental signals, which often demand adjustments in the chemical or physical states of intracellular molecules. Indeed, cellular responses such as gene expression rely on the faithful relay of information from the outside to the inside of the cell, a process terrned signal transduction. The signal often traverses a complex path across subcellular spaces with variable physical chemistry, sometimes even influencing it. Understanding the molecular states of such signaling molecules and their intracellular environments is vital to our understanding of the cell. Exploring such intricate spaces is possible today largely because of experimental and theoretical tools. Here, we focus on one tool that is commonly used in chemical physics studies light. We summarize recent work which uses light to both visualize the cellular environment and also control intracel- lular processes along the axis of signal transduction. We highlight recent accomplishments in optical microscopy and optogenetics, an emerging experimental strategy which utilizes light to control the molecular processes in live cells. We believe that optogenetics lends un- precedented spatiotemporal precision to the manipulation of physicochemical properties in biological contexts. We hope to use this work to demonstrate new opportunities for chemical physicists who are interested in pursuing biological and biomedical questions.

基于知识融合策略构建双相障碍致病基因网络

【目的】提出基于知识融合策略构建基因网络方法 ,并应用于双相障碍相关的致病基因网络分析。【方法】将Wellcome Trust Case Control Consortium(WTCCC)提供的双相障碍全基因组单核苷酸多态(SNP)数据与人类蛋白质-蛋白质互作数据库对应的基因做交集。通过单体型全模型logistic回归模型检验获得经多重检验校正统计学显著的基因互作对子,并由此构建致病基因网络以及挖掘连通度显著高于理论分布的核心致病基因。【结果】采用知识融合的方法,将数据维度从482 248个SNP位点降至98 157。经统计模型检验获得3 841个互作基因用于构建双相障碍致病基因网络,并挖掘出115个核心致病基因。其中,在连通度高于30的29个核心基因中,有12个重复了以前的报道(PRKCA,EGFR,ESR1,ATXN1,FYN,CREBBP,TP53,AKT1,CSNK2A1,DLG1,PTN和LYN),另外17个未被报道过的基因从其生物功能以及致病分子机制上看,可能是新的双相障碍易感基因(SMAD3,SRC,GRB2,PIK3R1,ZBTB16,ABL1,APP,EP300,TGFBR1,SYK,YWHAZ,INSR,MAPK1,PRKCB,PRKCD,SMAD2和SVIL)。【结论】本文提出的基于蛋白质-蛋白质互作知识引导的基因网络构建方法是一种可靠的系统性分析方法,有助于全面地了解复杂疾病的分子网络机制和确立核心风险基因。

基于生物分子网络分析的精神分裂症功能模块挖掘

精神分裂症(schizophrenia, SCZ)是一种影响人类生命健康和生存质量的复杂性精神疾病,其遗传机制涉及多个生物分子的相互作用。本研究从分子水平挖掘与SCZ相关的功能模块,可为SCZ的基础研究提供新思路。首先,通过蛋白质-蛋白质互作知识引导扩展SCZ风险基因,构建SCZ特异性基因网络;随后,利用Newman分解算法挖掘功能模块,并通过拓扑学分析及泊松分布检验确定每个功能模块的拓扑属性和核心基因;最后,对功能模块进行功能学分析,根据富集到的通路类别评估功能模块之间的相互作用。结果显示,共提取了14个功能模块,均具备无标度网络性质。对所得功能模块进行拓扑学分析,共挖掘出102个核心基因,包括已知与精神分裂症相关的基因(例如EGFR、HAX1、IL1R1、RALGDS等)和尚未有相关报道的基因(例如SVIL、DNAJA1、RABAC1、STX6等)。通过富集分析发现这些功能模块参与多条生物学通路,包括细胞凋亡、ErbB信号通路、细胞周期、磷脂酶D信号通路、PI3K-Akt信号通路等。功能模块分析显示,大部分功能模块不是单独发挥作用的,它们共同影响SCZ的发生发展,具有共享的发病机制。

Interaction of δ-opioid Receptor with Membrane Transporters: Possible Mechanisms in Pain Suppression by Acupuncture

Objective： To investigate the possible mechanisms in acupuncture analgesia by interaction of δ-opioid receptor with neurotransmitter transport proteins or the Na^＋-K^＋ pump. Methods： Microinjection of respective heterologous cRNA into the Xenopus oocytes as a model system, and measurement of steady-state currents under two-electrode voltage clamp. Results： The co-expression of the 8-opioid receptor with GAT1, EAAC 1 or the sodium pump resulted in reducing activity of the respective transporter. Opioid receptor activation affected transporter activity in different ways： 1） GAT1 was further inhibited; 2） EAAC1 was stimulated; 3） Na^＋-K^＋ pump activity interfered with agonist sensitivity of DOR. Pump inhibition led to higher sensitivity for DPDPE. Conclusion： GABA transporter inhibition and glutamate transporter stimulation may counteract pain sensation by affecting the neurotransmitter concentration in the synaptic cleft and, therefore, may contribute synergistically to pain suppression by acupuncture. Sodium pump inhibition by endogenous ouabain may amplify these effects. These synergistic effects may be the molecular mechanism of inhibiting pain sense and/or acupuncture analgesia.

Homogeneous label-free fluorescent assay of small molecule-protein interactions using protein binding-inhibited transcription nanomachine

Quantitative analysis of interactions between small molecules and proteins is a central challenge in chemical genetics, molecular diagnostics and drug developments. Here, we developed a RNA transcription nanomachine by assembling T7 RNA polymerase on a small molecule-labeled DNA heteroduplex. The nanomachine, of which the RNA transcription activity can be quantitatively inhibited by protein binding, showed a great potential for small molecule-protein interaction assay. This finding enabled us to develop a novel homogeneous label-free strategy for assays of interactions between small molecules and their protein receptors. Three small molecule compounds and their protein receptors have been used to demonstrate the developed strategy. The results revealed that the protein-small molecule interaction assay strategy shows dynamic responses in the concentration range from 0.5 to 64 nM with a detection limit of 0.2 nM. Due to its label-free, homogeneous, and fluorescence-based detection format, besides its desirable sensitivity this technique could be greatly robust, cost-efficient and readily automated, implying that the developed small molecule-protein interaction assay strategy might create a new methodology for developing intrinsically robust, sensitive and selective platforms for homogeneous protein detection.

Identification of interaction between HIV-1 glycoprotein 41 and integrase

Human immunodeficiency virus-1 （HIV-1）encodes 15 viral proteins. Protein-protein interactions play a large role in the function of these proteins. In this study, we attempted to identify novel interactions between the HIV-1 proteins to better understand the role played by viral protein-protein interactions in the life cycle of HIV-I. Genes encoding the 15 viral proteins from the HIV-1 strain AD8 were inserted into the plasmids of a yeast two-hybrid system. By screening 120 pairs of proteins, interactions between seven pairs were found. This led to the discovery of an interaction between the HIV-1 proteins integrase （IN） and glycoprotein 41 （gp41）, which was confirmed by both co-immunoprecipitation （Co-IP） assays and fluorescence resonance energy transfer （FRET） imaging in live cells. In addition, it was found that the amino acids at positions 76-100 of gp41 are required for it to bind to IN. Deletion of this region from gp41 prevented its interaction with IN and reduced the production of HIV-1 in 293T cells. This study provides new information on HIV-1 protein-protein interactions which improves the understanding of the biological functions of gp41 and IN during the virus life cycle.

ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEIN INTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION

Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding （LLE） kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interactions （PPI）. The author first construct kernels based on PPI networks, then apply support vector machine （SVM） techniques to classify proteins into different functional groups. The 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chi-square methods. Finally, the authors conduct predictions of functions of some unknown genes and verify the preciseness of our prediction in part by the information of other data source.

Insights into the function of tegument proteins from the varicella zoster virus

Chickenpox(varicella) is caused by primary infection with varicella zoster virus(VZV), which can establish long-term latency in the host ganglion. Once reactivated, the virus can cause shingles(zoster) in the host. VZV has a typical herpesvirus virion structure consisting of an inner DNA core, a capsid, a tegument, and an outer envelope. The tegument is an amorphous layer enclosed between the nucleocapsid and the envelope, which contains a variety of proteins. However, the types and functions of VZV tegument proteins have not yet been completely determined. In this review, we describe the current knowledge on the multiple roles played by VZV tegument proteins during viral infection. Moreover, we discuss the VZV tegument protein-protein interactions and their impact on viral tissue tropism in SCID-hu mice. This will help us develop a better understanding of how the tegument proteins aid viral DNA replication, evasion of host immune response, and pathogenesis.

IgG and fibrinogen driven nanoparticle aggregation

A thorough understanding of how proteins induce nanoparticle （NP） aggregation is crucial when designing in vitro and in vivo assays and interpreting experimental results. This knowledge is also crucial when developing nano-applications and formulation for drug delivery systems. In this study, we found that extraction of immunoglobulin G （IgG） from cow serum results in lower polystyrene NPs aggregation. Moreover, addition of isolated IgG or fibrinogen to fetal cow serum enhanced this aggregation, thus demonstrating that these factors are major drivers of NP aggregation in serum. Counter-intuitively, NP aggregation was inversely dependent on protein concentration; i.e., low protein concentrations induced large aggregates, whereas high protein concentrations induced small aggregates. Protein-induced NP aggregation and aggregate size were monitored by absorbance at 400 nm and dynamic light scattering, respectively. Here, we propose a mechanism behind the protein concentration dependent aggregation; this mechanism involves the effects of multiple protein interactions on the NP surface, surface area limitations, aggregation kinetics, and the influence of other serum proteins.

Disease gene identification by using graph kernels and Markov random fields

Genes associated with similar diseases are often functionally related.This principle is largely supported by many biological data sources,such as disease phenotype similarities,protein complexes,protein-protein interactions,pathways and gene expression profiles.Integrating multiple types of biological data is an effective method to identify disease genes for many genetic diseases.To capture the gene-disease associations based on biological networks,a kernel-based Markov random field(MRF)method is proposed by combining graph kernels and the MRF method.In the proposed method,three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks,respectively,and a novel weighted MRF method is developed to integrate those data.In addition,an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF method.Numerical experiments are carried out by integrating known gene-disease associations,protein complexes,protein-protein interactions,pathways and gene expression profiles.The proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm,achieving an AUC score of 0.771 when integrating all those biological data in our experiments,which indicates that our proposed method is very promising compared with many existing methods.

Current advances in the application of proteomics in apoptosis research

Apoptosis,or programmed cell death,is a complex,genetically-determined process involved in the development and maintenance of homeostasis in multicellular organisms.Dysregulation of apoptosis has been implicated in a number of diseases,including cancer and autoimmune disease.Thus,the investigation of apoptotic regulation has evoked considerable interest.Many apoptotic proteins have been shown to be post-translationally modulated,such as by protein cleavage,translocation,protein-protein interaction,and various post-translational modifications,which fall precisely within the range of proteomic analysis.Recently,contemporary proteomic technologies have achieved significant advances and have accelerated research in functional and chemical proteomics,which have been applied to the field of apoptosis research and have the potential to be a driving force for the field.This review highlights some of the major achievements in the application of proteomics in apoptosis research and discusses new directions and challenges for the near future.

Kicking down the ladder： adding cleavable features to genetically encoded photocrosslinkers

Protein-protein interactions(PPIs)play vital roles in biological processes.However,the discovery of transient and biologically relevant PPIs remain a formidable challenge using conventional strategies such as co-immunoprecipitation(CoIP),yeast two hybrids(Y2H)and Forster resonance energy transfer(FRET)[1].Genetically encoded photocrosslinkers have recently emerged as a powerful approach for probing intracellular PPIs with the capability for in situ studies,low perturbation to cells as well as the wide generality.This facile strategy also demonstrated an advantage of high spatiotemporal resolution,which offers a robust capture strategy for the discovery of transient and/or weak PPIs with lowered false-positive backgrounds[2].