细胞黏附分子互作网络的构建与分析

借助网络分析可对基因调控、蛋白质互作和信号转导等细胞活动进行全局和局部性质分析.以细胞黏附的蛋白质相互作用为对象,通过数据挖掘和可视化软件构建了整合蛋白介导的黏附分子互作网络,该分子互作网络由156种蛋白质通过690种相互作用相连,其平均节点度为8.66、平均聚集系数为0.24,平均路径长度为2.6.黏附分子互作网络中包含数个功能模块,这些模块涉及网络内部多种分子相互作用的启动与停止,并进一步影响细胞的黏附、迁移和骨架组织.对黏附分子网络进行模体筛选和比较,发现一些数量相对较少、以三元复合物为主要结构的关键模体,同时对各网络模块和模体对细胞黏附的调控作用进行了探讨.     

The identification of similarities between biological networks: application to the metabolome and interactome

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE: Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to all life, such as purine, pyrimidine and amino acid metabolism. The most similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestingly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.     

Calcium signaling and molecular mechanisms underlying neurodegenerative diseases

Calcium (Ca²⁺) is a ubiquitous second messenger that regulates various activities in eukaryotic cells. Especially important role calcium plays in excitable cells. Neurons require extremely precise spatial-temporal control of calcium-dependent processes because they regulate such vital functions as synaptic plasticity. Recent evidence indicates that neuronal calcium signaling is abnormal in many of neurodegenerative disorders such as Alzheimer’s disease (AD), Huntington’s disease (HD) and Parkinson’s disease (PD). These diseases represent a major medical, social, financial and scientific problem, but despite enormous research efforts, they are still incurable and only symptomatic relief drugs are available. Thus, new approaches and targets are needed. This review highlight neuronal calcium-signaling abnormalities in these diseases, with particular emphasis on the role of neuronal store-operated Ca²⁺ entry (SOCE) pathway and its potential relevance as a therapeutic target for treatment of neurodegeneration.     

An atlas of protein-protein interactions across mouse tissues

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Systemizing the structures and structuring the system

This Keystone symposium, entitled ‘Biomolecular Interactions and Networks: function and disease’, was held in Quebec City, Canada, 7–12 March 2010. The conference was distinctive in that it bridged two fields that may be perceived as having little in common: structural and systems biology. However, the growth in structural and omics data brings these two fields closer and closer. Indeed, in two sections of this article we cover talks on systematic analyses of protein structures, as well as systems level approaches that incorporate structural information. In two other sections, we report studies that aim at charting and analyzing cellular systems, and finally we discuss talks that pointed to the issue of promiscuity in biological networks.     

Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions

The integration of molecular networks with other types of data, such as changing levels of gene expression or protein-structural features, can provide richer information about interactions than the simple node-and-edge representations commonly used in the network community. For example, the mapping of 3D-structural data onto networks enables classification of proteins into singlish- or multi-interface hubs (depending on whether they have >2 interfaces). Similarly, interactions can be classified as permanent or transient, depending on whether their interface is used by only one or by multiple partners. Here, we incorporate an additional dimension into molecular networks: dynamic conformational changes. We parse the entire PDB structural databank for alternate conformations of proteins and map these onto the protein interaction network, to compile a first version of the Dynamic Structural Interaction Network (DynaSIN). We make this network available as a readily downloadable resource file, and we then use it to address a variety of downstream questions. In particular, we show that multi-interface hubs display a greater degree of conformational change than do singlish-interface ones; thus, they show more plasticity which perhaps enables them to utilize more interfaces for interactions. We also find that transient associations involve smaller conformational changes than permanent ones. Although this may appear counterintuitive, it is understandable in the following framework: as proteins involved in transient interactions shuttle between interchangeable associations, they interact with domains that are similar to each other and so do not require drastic structural changes for their activity. We provide evidence for this hypothesis through showing that interfaces involved in transient interactions bind fewer classes of domains than those in a control set.     

Toward a systems level view of the ECM and related proteins: a framework for the systematic definition and analysis of biological systems

Advances in high throughput 'omic technologies are starting to provide unprecedented insights into how components of biological systems are organized and interact. Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems-level view of the human extracellular matrix (ECM)--a three-dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene-disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue-specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM.     

Computational active site analysis of molecular pathways to improve functional classification of enzymes

This study describes a method to computationally assess the function of homologous enzymes through small molecule binding interaction energy. Three experimentally determined X-ray structures and four enzyme models from ornithine cyclo-deaminase, alanine dehydrogenase, and mu-crystallin were used in combination with nine small molecules to derive a function score (FS) for each enzyme-model combination. While energy values varied for a single molecule-enzyme combination due to differences in the active sites, we observe that the binding energies for the entire pathway were proportional for each set of small molecules investigated. This proportionality of energies for a reaction pathway appears to be dependent on the amino acids in the active site and their direct interactions with the small molecules, which allows a function score (FS) to be calculated to assess the specificity of each enzyme. Potential of mean force (PMF) calculations were used to obtain the energies, and the resulting FS values demonstrate that a measurement of function may be obtained using differences between these PMF values. Additionally, limitations of this method are discussed based on: (a) larger substrates with significant conformational flexibility; (b) low homology enzymes; and (c) open active sites. This method should be useful in accurately predicting specificity for single enzymes that have multiple steps in their reactions and in high throughput computational methods to accurately annotate uncharacterized proteins based on active site interaction analysis.     

Protein interactions and disease: computational approaches to uncover the etiology of diseases

The genomic era has been characterised by vast amounts of data from diverse sources, creating a need for new tools to extract biologically meaningful information. Bioinformatics is, for the most part, responding to that need. The sparseness of the genomic data associated with diseases, however, creates a new challenge. Understanding the complex interplay between genes and proteins requires integration of data from a wide variety of sources, i.e. gene expression, genetic linkage, protein interaction, and protein structure among others. Thus, computational tools have become critical for the integration, representation and visualization of heterogeneous biomedical data. Furthermore, several bioinformatics methods have been developed to formulate predictions about the functional role of genes and proteins, including their role in diseases. After an introduction to the complex interplay between proteins and genetic diseases, this review explores recent approaches to the understanding of the mechanisms of disease at the molecular level. Finally, because most known mechanisms leading to disease involve some form of protein interaction, this review focuses on the recent methodologies for understanding diseases through their underlying protein interactions. Recent contributions from genetics, protein structure and protein interaction network analyses to the understanding of diseases are discussed here.     

Combining different 'omics' technologies to map and validate protein-protein interactions in humans.

The mapping of protein-protein interactions is key to understanding biological processes. Many technologies have been reported to map interactions and these have been systematically applied in yeast. To date, the number of reported yeast protein interactions that have been truly validated by at least one other approach is low. The mapping of human protein interaction networks is even more complicated. Thus, it is unreasonable to try to map the human interactome; instead, interaction mapping in human cell lines should be focused along the lines of diseases or changes that can be associated with specific cells. In this paper, an approach for combining different 'omics' technologies to achieve efficient mapping and validation of protein interactions in human cell lines is presented.     

The molecular basis of mechanisms underlying polarization vision

The underlying mechanisms of polarization sensitivity (PS) have long remained elusive. For rhabdomeric photoreceptors, questions remain over the high levels of PS measured experimentally. In ciliary photoreceptors, and specifically cones, little direct evidence supports any type of mechanism. In order to promote a greater interest in these fundamental aspects of polarization vision, we examined a varied collection of studies linking membrane biochemistry, protein-protein interactions, molecular ordering and membrane phase behaviour. While initially these studies may seem unrelated to polarization vision, a common narrative emerges. A surprising amount of evidence exists demonstrating the importance of protein-protein interactions in both rhabdomeric and ciliary photoreceptors, indicating the possible long-range ordering of the opsin protein for increased PS. Moreover, we extend this direction by considering how such protein paracrystalline organization arises in all cell types from controlled membrane phase behaviour and propose a universal pathway for PS to occur in both rhabdomeric and cone photoreceptors.     

Thermodynamic analysis of biodegradation pathways

Microorganisms provide a wealth of biodegradative potential in the reduction and elimination of xenobiotic compounds in the environment. One useful metric to evaluate potential biodegradation pathways is thermodynamic feasibility. However, experimental data for the thermodynamic properties of xenobiotics is scarce. The present work uses a group contribution method to study the thermodynamic properties of the University of Minnesota Biocatalysis/Biodegradation Database. The Gibbs free energies of formation and reaction are estimated for 914 compounds (81%) and 902 reactions (75%), respectively, in the database. The reactions are classified based on the minimum and maximum Gibbs free energy values, which accounts for uncertainty in the free energy estimates and a feasible concentration range relevant to biodegradation. Using the free energy estimates, the cumulative free energy change of 89 biodegradation pathways (51%) in the database could be estimated. A comparison of the likelihood of the biotransformation rules in the Pathway Prediction System and their thermodynamic feasibility was then carried out. This analysis revealed that when evaluating the feasibility of biodegradation pathways, it is important to consider the thermodynamic topology of the reactions in the context of the complete pathway. Group contribution is shown to be a viable tool for estimating, a priori, the thermodynamic feasibility and the relative likelihood of alternative biodegradation reactions. This work offers a useful tool to a broad range of researchers interested in estimating the feasibility of the reactions in existing or novel biodegradation pathways. Biotechnol. Bioeng. 2009;103: 532–541. © 2009 Wiley Periodicals, Inc.     

Toward a universal measure of robustness across model organs and systems

The development of an individual must be capable of resisting the harmful effects of internal and external perturbations. This capacity, called robustness, can make the difference between normal variation and disease. Some systems and organs are more resilient in their capacity to correct the effects of internal disturbances such as mutations. Similarly, organs and organisms differ in their capacity to be resilient against external disturbances, such as changes in temperature. Furthermore, all developmental systems must be somewhat flexible to permit evolutionary change, and understanding robustness requires a comparative framework. Over the last decades, most research on developmental robustness has been focusing on specific model systems and organs. Hence, we lack tools that would allow cross-species and cross-organ comparisons. Here, we emphasize the need for a uniform framework to experimentally test and quantify robustness across study systems and suggest that the analysis of fluctuating asymmetry might be a powerful proxy to do so. Such a comparative framework will ultimately help to resolve why and how organs of the same and different species differ in their sensitivity to internal (e.g., mutations) and external (e.g., temperature) perturbations and at what level of biological organization buffering capacities exist and therefore create robustness of the developmental system.     

Diverse molecular mechanisms of translation initiation in prokaryotes

More than 30 years ago Shine and Dalgarno proposed a classic model of prokaryotic translation initiation, based on the central role of the mRNA-16S rRNA interactions. Since then basic research has greatly extended the view of this process, owing to rapid progress in experimental techniques and genome sequencing. This review focuses on bioinformatic data and experimental results obtained in vitro and in vivo, demonstrating the diversity of molecular mechanisms for ribosome recruitment in prokaryotes.     

Structural and molecular analysis of a protective epitope of Lyme disease antigen OspA and antibody interactions

The murine monoclonal antibody LA‐2 recognizes a clinically protective epitope on outer surface protein (OspA) of Borrelia burgdorferi , the causative agent of Lyme disease in North America. Human antibody equivalence to LA‐2 is the best serologic correlate of protective antibody responses following OspA vaccination. Understanding the structural and functional basis of the LA‐2 protective epitope is important for developing OspA‐based vaccines and discovering prophylactic antibodies against Lyme disease. Here, we present a detailed structure‐based analysis of the LA‐2/OspA interaction interface and identification of residues mediating antibody recognition. Mutations were introduced into both OspA and LA‐2 on the basis of computational predictions on the crystal structure of the complex and experimentally tested for in vitro binding and borreliacidal activity. We find that Y32 and H49 on the LA‐2 light chain, N52 on the LA‐2 heavy chain and residues A208, N228 and N251 on OspA were the key constituents of OspA/LA‐2 interface. These results reveal specific residues that may be exploited to modulate recognition of the protective epitope of OspA and have implications for developing prophylactic passive antibodies.     

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http://dragon.bio.purdue.edu/pfp/.     

Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major

Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systems‐based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof‐of‐concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage‐specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.