A especificidade espacial e temporal subjacente à diversidade de processos de
plasticidade sináptica que ocorrem no sistema nervoso central está profundamente
relacionada com a disponibilidade da proteína brain-derived neurotrophic factor
(BDNF) em domínios sub-celulares distintos, especialmente na área pós-sináptica.
Contudo, os mecanismos moleculares que regulam a síntese proteica de BDNF nas
dendrites estão ainda por desvendar. Assim, o principal objectivo deste trabalho foi
investigar alguns dos mecanismos de regulação da síntese de BDNF em diferentes
regiões das dendrites. Em particular, foram estudados os mecanismos envolvidos na
regulação dos níveis de BDNF em resposta à estimulação eléctrica in vitro e a um
estímulo epileptogénico in vivo, e a contribuição da maquinaria de síntese proteica para
essas alterações. Usando imunocitoquímica demonstrámos que o aumento dos níveis da
proteína BDNF resultante da actividade neuronal está dependente da acção de alguns
elementos no processo de síntese proteica. Em particular, a proteína ribossomal S6
parece ter um papel preponderante nas fases iniciais da tradução, enquanto a cinase de
proteínas Aurora A, envolvida em mecanismos de tradução dependentes de 3’UTR...
A extração de Interações Proteína-Proteína (IPPs) a partir de texto é um problema relevante na área biomédica e um desafio na área de aprendizado de máquina. Na área biomédica, as IPPs são fundamentais para compreender o funcionamento dos seres vivos. No entanto, o número de artigos relacionados com IPPs está aumentando rapidamente, sendo impraticável identicá-las e catalogá-las manualmente. Por exemplo, no caso das IPPs humanas apenas 10% foram catalogadas. Por outro lado, em aprendizado de máquina, métodos baseados em kernels são frequentemente empregados para extrair automaticamente IPPs, atingindo resultados considerados estado da arte. Esses métodos usam informações léxicas, sintáticas ou semânticas como características. Entretanto, os resultados ainda são insuficientes, atingindo uma taxa relativamente baixa, em termos da medida F, devido à complexidade do problema. Apesar dos esforços em produzir kernels, cada vez mais sofisticados, usando árvores sintáticas como árvores constituintes ou de dependência, pouco é conhecido sobre o desempenho de outras abordagens de aprendizado de máquina como, por exemplo, as redes bayesianas. As àrvores constituintes são estruturas de grafos que contêm informação importante da gramática subjacente as sentenças de textos contendo IPPs. Por outro lado...
The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology.
As whole-genome protein–protein interaction datasets become available for a wide range of species, evolutionary biologists have the opportunity to address some of the unanswered questions surrounding the evolution of these complex systems. Protein interaction networks from divergent organisms may be compared to investigate how gene duplication, deletion, and rewiring processes have shaped the evolution of their contemporary structures. However, current approaches for comparing observed networks from multiple species lack the phylogenetic context necessary to reconstruct the evolutionary history of a network. Here we show how probabilistic modeling can provide a platform for the quantitative analysis of multiple protein interaction networks. We apply this technique to the reconstruction of ancestral networks for the bZIP family of transcription factors and find that excellent agreement is obtained with an alternative sequence-based method for the prediction of leucine zipper interactions. Further analysis shows our probabilistic method to be significantly more robust to the presence of noise in the observed network data than a simple parsimony-based approach. In addition, the integration of evidence over multiple species means that the same method may be used to improve the quality of noisy interaction data for extant species. The ancestral states of a protein interaction network have been reconstructed here by using an explicit probabilistic model of network evolution. We anticipate that this model will form the basis of more general methods for probing the evolutionary history of biochemical networks.
Motivation: Aligning protein–protein interaction (PPI) networks of different species has drawn a considerable interest recently. This problem is important to investigate evolutionary conserved pathways or protein complexes across species, and to help in the identification of functional orthologs through the detection of conserved interactions. It is, however, a difficult combinatorial problem, for which only heuristic methods have been proposed so far.
The study of protein-protein interactions is essential to define the molecular networks that contribute to maintain homeostasis of an organism’s body functions. Disruptions in protein interaction networks have been shown to result in diseases in both humans and animals. Monogenic diseases disrupting biochemical pathways such as hereditary coagulopathies (e.g. hemophilia), provided a deep insight in the biochemical pathways of acquired coagulopathies of complex diseases. Indeed, a variety of complex liver diseases can lead to decreased synthesis of the same set of coagulation factors as in hemophilia. Similarly, more complex diseases such as different cancers have been shown to result from malfunctions of common proteins pathways. In order to discover, in high throughput, the molecular underpinnings of poorly characterized diseases, we present a statistical method to identify shared protein interaction network(s) between diseases. Integrating (i) a protein interaction network with (ii) disease to protein relationships derived from mining Gene Ontology annotations and the biomedical literature with natural language understanding (PhenoGO), we identified protein-protein interactions that were associated with pairs of diseases and calculated the statistical significance of the occurrence of interactions in the protein interaction knowledgebase. Significant correlations between diseases and shared protein networks were identified and evaluated in this study...
The densely connected structure of protein-protein interaction (PPI) networks reflects on the functional need of proteins to co-operate in cellular processes. However, PPI networks do not adequately capture the competition in protein binding. By contrast, the interface interaction network (IIN) studied here resolves the modular character of protein-protein binding and distinguishes between simultaneous and exclusive interactions that underlie both co-operation and competition. We show that the topology of the IIN is under evolutionary pressure and we connect topological features of the IIN to specific biological functions. To reveal the forces shaping the network topology, we use a sequence-based computational model of interface binding along with network analysis. We find that the more fragmented structure of IINs, in contrast to the dense PPI networks, arises in large part from the competition between specific and nonspecific binding. The need to minimize nonspecific binding favors specific network motifs, including a minimal number of cliques (i.e., fully connected subgraphs) and many disconnected fragments. Validating the model, we find that these network characteristics are closely mirrored in the IIN of clathrin-mediated endocytosis. Features unexpected on the basis of our motif analysis are found to indicate either exceptional binding selectivity or important regulatory functions.
Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.
Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20...
Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis.
The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.
Protein–protein interactions (PPIs) and their networks play a central role in all biological processes. Akin to the complete sequencing of genomes and their comparative analysis, complete descriptions of interactomes and their comparative analysis is fundamental to a deeper understanding of biological processes. A first step in such an analysis is to align two or more PPI networks. Here, we introduce an algorithm, IsoRank, for global alignment of multiple PPI networks. The guiding intuition here is that a protein in one PPI network is a good match for a protein in another network if their respective sequences and neighborhood topologies are a good match. We encode this intuition as an eigenvalue problem in a manner analogous to Google's PageRank method. Using IsoRank, we compute a global alignment of the Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens PPI networks. We demonstrate that incorporating PPI data in ortholog prediction results in improvements over existing sequence-only approaches and over predictions from local alignments of the yeast and fly networks. Previous methods have been effective at identifying conserved, localized network patterns across pairs of networks. This work takes the further step of performing a global alignment of multiple PPI networks. It simultaneously uses sequence similarity and network data and...
Background: Genome-wide libraries of yeast deletion strains have been used to screen for genes that drive phenotypes such as stress response. A surprising observation emerging from these studies is that the genes with the largest changes in mRNA expression during a state transition are not those that drive that transition. Here, we show that integrating gene expression data with context-independent protein interaction networks can help prioritize master regulators that drive biological phenotypes. Results: Genes essential for survival had previously been shown to exhibit high centrality in protein interaction networks. However, the set of genes that drive growth in any specific condition is highly context-dependent. We inferred regulatory networks from gene expression data and transcription factor binding motifs in Saccharomyces cerevisiae, and found that high-degree nodes in regulatory networks are enriched for transcription factors that drive the corresponding phenotypes. We then found that using a metric combining protein interaction and transcriptional networks improved the enrichment for drivers in many of the contexts we examined. We applied this principle to a dataset of gene expression in normal human fibroblasts expressing a panel of viral oncogenes. We integrated regulatory interactions inferred from this data with a database of yeast two-hybrid protein interactions and ranked 571 human transcription factors by their combined network score. The ranked list was significantly enriched in known cancer genes that could not be found by standard differential expression or enrichment analyses. Conclusions: There has been increasing recognition that network-based approaches can provide insight into critical cellular elements that help define phenotypic state. Our analysis suggests that no one network...
Networks have in recent years emerged as an invaluable tool for describing
and quantifying complex systems in many branches of science. Recent studies
suggest that networks often exhibit hierarchical organization, where vertices
divide into groups that further subdivide into groups of groups, and so forth
over multiple scales. In many cases these groups are found to correspond to
known functional units, such as ecological niches in food webs, modules in
biochemical networks (protein interaction networks, metabolic networks, or
genetic regulatory networks), or communities in social networks. Here we
present a general technique for inferring hierarchical structure from network
data and demonstrate that the existence of hierarchy can simultaneously explain
and quantitatively reproduce many commonly observed topological properties of
networks, such as right-skewed degree distributions, high clustering
coefficients, and short path lengths. We further show that knowledge of
hierarchical structure can be used to predict missing connections in partially
known networks with high accuracy, and for more general network structures than
competing techniques. Taken together, our results suggest that hierarchy is a
central organizing principle of complex networks...
The degree distribution of many biological and technological networks has
been described as a power-law distribution. While the degree distribution does
not capture all aspects of a network, it has often been suggested that its
functional form contains important clues as to underlying evolutionary
processes that have shaped the network. Generally, the functional form for the
degree distribution has been determined in an ad-hoc fashion, with clear
power-law like behaviour often only extending over a limited range of
connectivities. Here we apply formal model selection techniques to decide which
probability distribution best describes the degree distributions of protein
interaction networks. Contrary to previous studies this well defined approach
suggests that the degree distribution of many molecular networks is often
better described by distributions other than the popular power-law
distribution. This, in turn, suggests that simple, if elegant, models may not
necessarily help in the quantitative understanding of complex biological
Protein-protein interaction networks associated with diseases have gained
prominence as an area of research. We investigate algebraic and topological
indices for protein-protein interaction networks of 11 human cancers derived
from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. We find a
strong correlation between relative automorphism group sizes and topological
network complexities on the one hand and five year survival probabilities on
the other hand. Moreover, we identify several protein families (e.g. PIK, ITG,
AKT families) that are repeated motifs in many of the cancer pathways.
Interestingly, these sources of symmetry are often central rather than
peripheral. Our results can aide in identification of promising targets for
anti-cancer drugs. Beyond that, we provide a unifying framework to study
protein-protein interaction networks of families of related diseases (e.g.
neurodegenerative diseases, viral diseases, substance abuse disorders).; Comment: 15 pages, 4 figures
Protein interaction networks (PIN) are popular means to visualize the
proteome. However, PIN datasets are known to be noisy, incomplete and biased by
the experimental protocols used to detect protein interactions. This paper aims
at understanding the connection between true protein interactions and the
protein interaction datasets that have been obtained using the most popular
experimental techniques, i.e. mass spectronomy (MS) and yeast two-hybrid (Y2H).
We show that the most natural adjacency matrix of protein interaction networks
has a separable form, and this induces precise relations between moments of the
degree distribution and the number of short loops. These relations provide
powerful tools to test the reliability of datasets and hint at the underlying
biological mechanism with which proteins and complexes recruit each other.; Comment: 38 pages, 10 figures
We present a method that compares the protein interaction networks of two
species to detect functionally similar (conserved) protein modules between
them. The method is based on an algorithm we developed to identify matching
subgraphs between two graphs. Unlike previous network comparison methods, our
algorithm has provable guarantees on correctness and efficiency. Our algorithm
framework also admits quite general connectivity and local matching criteria
that define when two subgraphs match and constitute a conserved module.
We apply our method to pairwise comparisons of the yeast protein network with
the human, fruit fly and nematode worm protein networks, using a lenient
criterion based on connectedness and matching edges, coupled with a betweenness
clustering heuristic. We evaluate the detected conserved modules against
reference yeast protein complexes using sensitivity and specificity measures.
In these evaluations, our method performs competitively with and sometimes
better than two previous network comparison methods. Further under some
conditions (proper homolog and species selection), our method performs better
than a popular single-species clustering method. Beyond these evaluations, we
discuss the biology of a couple of conserved modules detected by our method. We
demonstrate the utility of network comparison for transferring annotations from
yeast proteins to human ones...
A major issue in biology is the understanding of the interactions between
proteins. These interactions can be described by a network, where the proteins
are modeled by nodes and the interactions by edges. The origin of these protein
networks is not well understood yet. Here we present a two-step model, which
generates clusters with the same topological properties as networks for
protein-protein interactions, namely, the same degree distribution, cluster
size distribution, clustering coefficient and shortest path length. The
biological and model networks are not scale free but exhibit small world
features. The model allows the fitting of different biological systems by
tuning a single parameter.; Comment: 5 pages, 5 figures
Understanding of how protein interaction networks (PIN) of living organisms
have evolved or are organized can be the first stepping stone in unveiling how
life works on a fundamental ground. Here we introduce a hybrid network model
composed of the yeast PIN and the protein family interaction network. The
essential ingredient of the model includes the protein family identity and its
robustness under evolution, as well as the three previously proposed ones: gene
duplication, divergence, and mutation. We investigate diverse structural
properties of our model with parameter values relevant to yeast, finding that
the model successfully reproduces the empirical data.; Comment: 5 pages, 5 figures, 1 table. Title changed. Final version published