Baldo Oliva

Prediction of protein–protein interactions using distant conservation of sequence patterns and structure relationships

MOTIVATION Given that association and dissociation of protein molecules is crucial in most biological processes several in silico methods have been recently developed to predict protein-protein interactions. Structural evidence has shown that usually interacting pairs of close homologs (interologs) physically interact in the same way. Moreover, conservation of an interaction depends on the conservation of the interface between interacting partners. In this article we make use of both, structural similarities among domains of known interacting proteins found in the Database of Interacting Proteins (DIP) and conservation of pairs of sequence patches involved in protein-protein interfaces to predict putative protein interaction pairs. RESULTS We have obtained a large amount of putative protein-protein interaction (approximately 130,000). The list is independent from other techniques both experimental and theoretical. We separated the list of predictions into three sets according to their relationship with known interacting proteins found in DIP. For each set, only a small fraction of the predicted protein pairs could be independently validated by cross checking with the Human Protein Reference Database (HPRD). The fraction of validated protein pairs was always larger than that expected by using random protein pairs. Furthermore, a correlation map of interacting protein pairs was calculated with respect to molecular function, as defined in the Gene Ontology database. It shows good consistency of the predicted interactions with data in the HPRD database. The intersection between the lists of interactions of other methods and ours produces a network of potentially high-confidence interactions.

Predicting cancer involvement of genes from heterogeneous data

BackgroundSystematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.ResultsWe implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature.ConclusionOur approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.

PIANA: protein interactions and network analysis

UNLABELLED We present a software framework and tool called Protein Interactions And Network Analysis (PIANA) that facilitates working with protein interaction networks by (1) integrating data from multiple sources, (2) providing a library that handles graph-related tasks and (3) automating the analysis of protein-protein interaction networks. PIANA can also be used as a stand-alone application to create protein interaction networks and perform tasks such as predicting protein interactions and helping to identify spots in a 2D electrophoresis gel. AVAILABILITY PIANA is under the GNU GPL. Source code, database and detailed documentation may be freely downloaded from http://sbi.imim.es/piana.

CDK2-dependent activation of PARP-1 is required for hormonal gene regulation in breast cancer cells

Eukaryotic gene regulation implies that transcription factors gain access to genomic information via poorly understood processes involving activation and targeting of kinases, histone-modifying enzymes, and chromatin remodelers to chromatin. Here we report that progestin gene regulation in breast cancer cells requires a rapid and transient increase in poly-(ADP)-ribose (PAR), accompanied by a dramatic decrease of cellular NAD that could have broad implications in cell physiology. This rapid increase in nuclear PARylation is mediated by activation of PAR polymerase PARP-1 as a result of phosphorylation by cyclin-dependent kinase CDK2. Hormone-dependent phosphorylation of PARP-1 by CDK2, within the catalytic domain, enhances its enzymatic capabilities. Activated PARP-1 contributes to the displacement of histone H1 and is essential for regulation of the majority of hormone-responsive genes and for the effect of progestins on cell cycle progression. Both global chromatin immunoprecipitation (ChIP) coupled with deep sequencing (ChIP-seq) and gene expression analysis show a strong overlap between PARP-1 and CDK2. Thus, progestin gene regulation involves a novel signaling pathway that connects CDK2-dependent activation of PARP-1 with histone H1 displacement. Given the multiplicity of PARP targets, this new pathway could be used for the pharmacological management of breast cancer.

Exploiting Protein-Protein Interaction Networks for Genome-Wide Disease-Gene Prioritization

Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimers disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.

Backup in gene regulatory networks explains differences between binding and knockout results

The complementarity of gene expression and protein–DNA interaction data led to several successful models of biological systems. However, recent studies in multiple species raise doubts about the relationship between these two datasets. These studies show that the overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets. In addition, we show that incorporating protein interaction networks provides physical explanations for knockout effects. New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional.

Biana: a software framework for compiling biological interactions and analyzing networks

BackgroundThe analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.ResultsWe introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php.ConclusionsBIANAs approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

ADP-ribose–derived nuclear ATP synthesis by NUDIX5 is required for chromatin remodeling

A nuclear power source in the cell DNA is packaged onto nucleosomes, the principal component of chromatin. This chromatin must be remodeled to allow gene transcription, DNA replication, and DNA repair machineries access to the enclosed DNA. Chromatin-remodeling complexes require high levels of cellular energy to do their job. Wright et al. show that the energy needed to remodel chromatin can be derived from a source, poly-ADP-ribose, in the cell nucleus, rather than by diffusion of ATP from mitochondria in the cytoplasm, the usual powerhouse of the cell. Poly-ADP-ribose is converted to ADP-ribose and then to ATP, which can be used to fuel chromatin remodeling within the nucleus. Science, this issue p. 1221 Energy needed to remodel chromatin to make DNA accessible can be generated in situ in the nucleus from ADP-ribose. Key nuclear processes in eukaryotes, including DNA replication, repair, and gene regulation, require extensive chromatin remodeling catalyzed by energy-consuming enzymes. It remains unclear how the ATP demands of such processes are met in response to rapid stimuli. We analyzed this question in the context of the massive gene regulation changes induced by progestins in breast cancer cells and found that ATP is generated in the cell nucleus via the hydrolysis of poly(ADP-ribose) to ADP-ribose. In the presence of pyrophosphate, ADP-ribose is used by the pyrophosphatase NUDIX5 to generate nuclear ATP. The nuclear source of ATP is essential for hormone-induced chromatin remodeling, transcriptional regulation, and cell proliferation.

Role of motif B loop in allosteric regulation of RNA-dependent RNA polymerization activity

Increasing amounts of data show that conformational dynamics are essential for protein function. Unveiling the mechanisms by which this flexibility affects the activity of a given enzyme and how it is controlled by other effectors opens the door to the design of a new generation of highly specific drugs. Viral RNA-dependent RNA polymerases (RdRPs) are not an exception. These enzymes, essential for the multiplication of all RNA viruses, catalyze the formation of phosphodiester bonds between ribonucleotides in an RNA-template-dependent fashion. Inhibition of RdRP activity will prevent genome replication and virus multiplication. Thus, RdRPs, like the reverse transcriptase of retroviruses, are validated targets for the development of antiviral therapeutics. X-ray crystallography of RdRPs trapped in multiple steps throughout the catalytic process, together with NMR data and molecular dynamics simulations, have shown that all polymerase regions contributing to conserved motifs required for substrate binding, catalysis and product release are highly flexible and some of them are predicted to display correlated motions. All these dynamic elements can be modulated by external effectors, which appear as useful tools for the development of effective allosteric inhibitors that block or disturb the flexibility of these enzymes, ultimately impeding their function. Among all movements observed, motif B, and the B-loop at its N-terminus in particular, appears as a new potential druggable site.

BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference

Protein–protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.