Computational identification of protein-protein interactions in rice based on the predicted rice interactome network

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box domain-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.     

蛋白质相互作用的生物信息学研究进展

生命过程的分子基础在于生物分子之间的相互作用,其中蛋白质分子之间的相互作用占有极其重要的地位。研究蛋白质相互作用对于理解生命的真谛、探讨致病微生物的致病机理,以及研究新药提高人们的健康水平具有重要的作用。用生物信息学的方法研究蛋白质的相互作用已经取得了许多重要的成果,但也有很多问题还需解决。本文从蛋白质相互作用的数据库、预测方法、可预测蛋白质相互作用的网上服务、蛋白质相互作用网络等几方面,对蛋白质相互作用的生物信息学研究成果及其存在的问题做了概述。     

Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells

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Computational Prediction of Protein-Protein Interactions in Leishmania Predicted Proteomes

The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.     

InCeP: Intracellular Pathway Based on mKIAA Protein-Protein Interactions

Since December 2001, we have been conducting a project to isolateand determine entire sequences of mouse KIAA cDNA clones, whichencode polypeptides corresponding to human KIAA proteins. Theultimate goal of this project has been elucidation of the functionsof KIAA proteins. To address this issue, we have been generating‘libraries’ of antibodies against mKIAA proteins.We have, to date, already generated >800 antibodies. Usingour ‘libraries’ of antibodies, we are now identifyingendogenous mKIAA protein–protein interactions. In thepresent study, novel interactions were identified by MS/MS analysisfollowing immunoprecipitation with anti-mKIAA antibodies. Theinteractions with biologically known molecules should enableus to predict the function of mKIAA/KIAA proteins, includinghypothetical proteins identified in our cDNA project. Theseinteractions are subsequently used for construction of an intracellularpathway related to the mKIAA protein, and the pathway is distributedthrough the InCeP (IntraCellular Pathway based on mKIAA protein–proteininteractions) database. Users can freely access the InCeP throughthe internet and download the graphical display as well as thecurated information.     

基于支持向量机的蛋白质相互作用识别

支持向量机（SVM）是广泛应用于各个领域的分类算法,包括生物信息学。本研究应用SVM作为蛋白质相互作用的分类算法,所用蛋白质相互作用数据下载于墨尼黑生物信息学中心的酿酒酵母数据集,包含有6736条蛋白质,其中相互作用的有4837对,不相互作用的有9674对。提取蛋白质主要结构的电荷和等电位点特征,并应用SVM分类算法对此进行了分类。结果显示,分类的正确率在60％左右,但是较系统发育谱法还是获得了较高的分类正确率。     

Improving Identification of In-organello Protein-Protein Interactions Using an Affinity-enrichable,Isotopically Coded,and Mass Spectrometry-cleavable Chemical Crosslinker

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Highlights

• •Used affinity-enrichable, isotopically coded, and MS-cleavable crosslinker.
• •Targeted acquisition strategy based on isotopic-coding described and evaluated.
• •Novel data analysis pipeline developed provides improved crosslink identification.
• •Large dataset reveals hundreds of mitochondrial protein-protein interactions.

     

生物信息学方法预测蛋白质相互作用网络中的功能模块

蛋白质相互作用是大多数生命过程的基础。随着高通量实验技术和计算机预测方法的发展,在各种生物中已获得了数目十分庞大的蛋白质相互作用数据,如何从中提取出具有生物学意义的数据是一项艰巨的挑战。从蛋白质相互作用数据出发获得相互作用网络进而预测出其中的功能模块,对于蛋白质功能预测、揭示各种生化反应过程的分子机理都有着极大的帮助。我们分类概括了用生物信息学预测蛋白质相互作用功能模块的方法,以及对这些方法的评价,并介绍了蛋白质相互作用网络比较的一些方法。     

蛋白质－DNA相互作用与真核基因转录

结合最近几年对真核转录调节因子和ＤＮＡ的结构与机能研究,概述了蛋白质－蛋白质及蛋白质－ＤＮＡ相互作用方式以及介导相互作用的分子结构基础,论述了转录因子之间,转录因子与ＤＮＡ之间相互作用过程中的同与拮抗作用,发生机制及其在真核基因转录调节中的遍性和重要意义。     

蛋白质－DNA相互作用与真核基因转录

结合最近几年对真核转录调节因子和DNA的结构与机能研究,概述了蛋白质-蛋白质及蛋白质-DNA相互作用方式以及介导相互作用的分子结构基础,论述了转录因于之间、转录因子与DNA之间相互作用过程中的协同与拮抗作用、发生机制及其在真核基因转录调节中的普遍性和重要意义.     

Identification of significant pathways in gastric cancer based on protein-protein interaction networks and cluster analysis

Gastric cancer is one of the most common and lethal cancers worldwide. However, despite its clinical importance, the regulatory mechanisms involved in the aggressiveness of this cancer are still poorly understood. A better understanding of the biology, genetics and molecular mechanisms of gastric cancer would be useful in developing novel targeted approaches for treating this disease. In this study we used protein-protein interaction networks and cluster analysis to comprehensively investigate the cellular pathways involved in gastric cancer. A primary immunodeficiency pathway, focal adhesion, ECM-receptor interactions and the metabolism of xenobiotics by cytochrome P450 were identified as four important pathways associated with the progression of gastric cancer. The genes in these pathways, e.g., ZAP70, IGLL1, CD79A, COL6A3, COL3A1, COL1A1, CYP2C18 and CYP2C9, may be considered as potential therapeutic targets for gastric cancer.     

Comparative Analysis of Protein-Protein Interactions in Cancer-Associated Genes 25

Protein-protein interactions (PPIs) have been widely studied to understand the bi-ological processes or molecular functions associated with different disease systems like cancer. While focused studies on individual cancers have generated valuable in-formation, global and comparative analysis of datasets from different cancer types has not been done. In this work, we carried out bioinformatic analysis of PPIs corresponding to differentially expressed genes from microarrays of various tumor tissues (belonging to bladder, colon, kidney and thyroid cancers) and compared their associated biological processes and molecular functions (based on Gene On-tology terms). We identified a set of processes or functions that are common to all these cancers, as well as those that are specific to only one or partial cancer types. Similarly, protein interaction networks in nucleic acid metabolism were compared to identify the common/specific clusters of proteins across different cancer types. Our results provide a basis for further experimental investigations to study protein interaction networks associated with cancer. The methodology developed in this work can also be applied to study similar disease systems.     

Predicting Disease-Related Proteins Based on Clique Backbone in Protein-Protein Interaction Network

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.     

基于蛋白质网络功能模块的蛋白质功能预测

在破译了基因序列的后基因组时代,随着系统生物学实验的快速发展,产生了大量的蛋白质相互作用数据,利用这些数据寻找功能模块及预测蛋白质功能在功能基因组研究中具有重要意义.打破了传统的基于蛋白质间相似度的聚类模式,直接从蛋白质功能团的角度出发,考虑功能团间的一阶和二阶相互作用,提出了模块化聚类方法(MCM),对实验数据进行聚类分析,来预测模块内未知蛋白质的功能.通过超几何分布P值法和增、删、改相互作用的方法对聚类结果进行预测能力分析和稳定性分析.结果表明,模块化聚类方法具有较高的预测准确度和覆盖率,有很好的容错性和稳定性.此外,模块化聚类分析得到了一些具有高预测准确度的未知蛋白质的预测结果,将会对生物实验有指导意义,其算法对其他具有相似结构的网络也具有普遍意义.     

Identification of the ubiquitin interfacial residues in a ubiquitin-E2 covalent complex

One of the key intermediates formed during the protein ubiquitination cycle is a covalent complex between ubiquitin (Ub) and the conjugation enzyme, UBC1. In order to probe the interface between these two proteins we have formed the covalent complex in situ (in the NMR tube) using Ub, the catalytic domain of UBC1, UBC1450, an activation enzyme, E1, and Mg²⁺-ATP. The size of the Ub-UBC1450 complex (25 kDa) and its relatively short lifetime ( 4 h) makes assignment of the backbone resonances in the covalent species difficult. In order to monitor the formation and identify the interface in the complex we have used fast ¹H-¹⁵N HSQC spectra to monitor the decay of ¹H-¹⁵N correlations as a function of time until the complex formed reached about 90%. The residual peak intensities were used to probe the surface of interaction between Ub and UBC1450 and provided a clear surface of interaction on Ub.     

Distinctive Network Topology of Phase-Separated Proteins in Human Interactome

Liquid-liquid phase separation (LLPS) is an important mechanism that mediates the formation of biomolecular condensates. Despite the immense interest in LLPS, phase-separated proteins verified by experiments are still limited, and identification of phase-separated proteins at proteome-scale is a challenging task. Multivalent interaction among macromolecules is the driving force of LLPS, which suggests that phase-separated proteins may harbor distinct biological characteristics in protein–protein interactions (PPIs). In this study, we constructed an integrated human PPI network (HPIN) and mapped phase-separated proteins into it. Analysis of the network parameters revealed differences of network topology between phase-separated proteins and others. The results further suggested the efficiency when applying topological similarities in distinguishing components of MLOs. Furthermore, we found that affinity purification mass spectrometry (AP-MS) detects PPIs more effectively than yeast-two hybrid system (Y2H) in phase separation-driven condensates. Our work provides the first global view of the distinct network topology of phase-separated proteins in human interactome, suggesting incorporation of PPI network for LLPS prediction in further studies.     

Interactions of U1 RNP with heterogeneous nuclear RNA in rat Novikoff hepatoma nuclei

Summary The nature of the association of U1 RNA with rapidly sedimenting RNP structures in rat hepatoma nuclei was investigated. The effects of salt and proteinase K treatment on the stability of this bound form of U1 RNA were studied using sucrose density gradient analyses. Quantitation of the amount of U1 RNA remaining associated with large structures after treatment was used to assess the relative contribution of protein-protein(and protein-RNA) versus RNA-RNA interactions. Forty-eight percent of the total nuclear U1 RNA released by sonication was found in a bound form when the sonicate was centrifuged through gradients containing 50 mM NaCl. Fifty percent of this bound U1 RNA remained associated with rapidly sedimenting RNPs when the NaCl concentration was raised to 500 mM. To assess the contribution of protein independent interactions, large RNPs were completely deproteinized and their RNA moieties were then recentrifuged on gradients. By this analysis, 27% of the U1 RNA originally bound to hnRNPs was associated with rapidly sedimenting (>30 S) RNA (at 50 mM NaCl) suggesting their association by RNA-RNA hydrogen bonds. When the concentration of NaCl was 500 mM, 31% of the U1 RNA was associated with large RNA. Hence, approximately 30% of the U1 RNA molecules originally bound (or about 15% of the total nuclear U1 RNA) were found to be associated by RNA-RNA hydrogen bonds while the remainder of the binding of U1 RNP to hnRNP was by protein-protein and/or protein-RNA interactions.     

In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces

Unraveling the interaction network of molecules in-vivo is key to understanding the mechanisms that regulate cell function and metabolism. A multitude of methodological options for addressing molecular interactions in cells have been developed, but most of these methods suffer from being rather indirect and therefore hardly quantitative. On the contrary, a few high-end quantitative approaches were introduced, which however are difficult to extend to high throughput. To combine high throughput capabilities with the possibility to extract quantitative information, we recently developed a new concept for identifying protein-protein interactions (Schwarzenbacher et al., 2008). Here, we describe a detailed protocol for the design and the construction of this system which allows for analyzing interactions between a fluorophore-labeled protein ("prey") and a membrane protein ("bait") in-vivo. Cells are plated on micropatterned surfaces functionalized with antibodies against the bait exoplasmic domain. Bait-prey interactions are assayed via the redistribution of the fluorescent prey. The method is characterized by high sensitivity down to the level of single molecules, the capability to detect weak interactions, and high throughput capability, making it applicable as screening tool.     

Identification of the key LMO2-binding determinants on Ldb1

The overexpression of LIM-only protein 2 (LMO2) in T-cells, as a result of chromosomal translocations, retroviral insertion during gene therapy, or in transgenic mice models, leads to the onset of T-cell leukemias. LMO2 comprises two protein-binding LIM domains that allow LMO2 to interact with multiple protein partners, including LIM domain-binding protein 1 (Ldb1, also known as CLIM2 and NLI), an essential cofactor for LMO proteins. Sequestration of Ldb1 by LMO2 in T-cells may prevent it binding other key partners, such as LMO4. Here, we show using protein engineering and enzyme-linked immunosorbent assay (ELISA) methodologies that LMO2 binds Ldb1 with a twofold lower affinity than does LMO4. Thus, excess LMO2 rather than an intrinsically higher binding affinity would lead to sequestration of Ldb1. Both LIM domains of LMO2 are required for high-affinity binding to Ldb1 (K(D) = 2.0 x 10(-8) M). However, the first LIM domain of LMO2 is primarily responsible for binding to Ldb1 (K(D) = 2.3 x 10(-7) M), whereas the second LIM domain increases binding by an order of magnitude. We used mutagenesis in combination with yeast two-hybrid analysis, and phage display selection to identify LMO2-binding "hot spots" within Ldb1 that locate to the LIM1-binding region. The delineation of this region reveals some specific differences when compared to the equivalent LMO4:Ldb1 interaction that hold promise for the development of reagents to specifically bind LMO2 in the treatment of leukemia.