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1.  Tools and data services registry: a community effort to document bioinformatics resources 
Nucleic Acids Research  2015;44(Database issue):D38-D47.
Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.
Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.
As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.
doi:10.1093/nar/gkv1116
PMCID: PMC4702812  PMID: 26538599
2.  SIGNOR: a database of causal relationships between biological entities 
Nucleic Acids Research  2015;44(Database issue):D548-D554.
Assembly of large biochemical networks can be achieved by confronting new cell-specific experimental data with an interaction subspace constrained by prior literature evidence. The SIGnaling Network Open Resource, SIGNOR (available on line at http://signor.uniroma2.it), was developed to support such a strategy by providing a scaffold of prior experimental evidence of causal relationships between biological entities. The core of SIGNOR is a collection of approximately 12 000 manually-annotated causal relationships between over 2800 human proteins participating in signal transduction. Other entities annotated in SIGNOR are complexes, chemicals, phenotypes and stimuli. The information captured in SIGNOR can be represented as a signed directed graph illustrating the activation/inactivation relationships between signalling entities. Each entry is associated to the post-translational modifications that cause the activation/inactivation of the target proteins. More than 4900 modified residues causing a change in protein concentration or activity have been curated and linked to the modifying enzymes (about 351 human kinases and 94 phosphatases). Additional modifications such as ubiquitinations, sumoylations, acetylations and their effect on the modified target proteins are also annotated. This wealth of structured information can support experimental approaches based on multi-parametric analysis of cell systems after physiological or pathological perturbations and to assemble large logic models.
doi:10.1093/nar/gkv1048
PMCID: PMC4702784  PMID: 26467481
3.  The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells 
BMC Systems Biology  2015;9(Suppl 3):S3.
Background
Cell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks - namely non-random degree distribution and high clustering coefficient - are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization.
Methods
We have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks.
Results
By using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations.
Conclusions
The analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property.
doi:10.1186/1752-0509-9-S3-S3
PMCID: PMC4464023  PMID: 26050708
4.  Metformin Protects Skeletal Muscle from Cardiotoxin Induced Degeneration 
PLoS ONE  2014;9(12):e114018.
The skeletal muscle tissue has a remarkable capacity to regenerate upon injury. Recent studies have suggested that this regenerative process is improved when AMPK is activated. In the muscle of young and old mice a low calorie diet, which activates AMPK, markedly enhances muscle regeneration. Remarkably, intraperitoneal injection of AICAR, an AMPK agonist, improves the structural integrity of muscles of dystrophin-deficient mdx mice. Building on these observations we asked whether metformin, a powerful anti-hyperglycemic drug, which indirectly activates AMPK, affects the response of skeletal muscle to damage. In our conditions, metformin treatment did not significantly influence muscle regeneration. On the other hand we observed that the muscles of metformin treated mice are more resilient to cardiotoxin injury displaying lesser muscle damage. Accordingly myotubes, originated in vitro from differentiated C2C12 myoblast cell line, become more resistant to cardiotoxin damage after pre-incubation with metformin. Our results indicate that metformin limits cardiotoxin damage by protecting myotubes from necrosis. Although the details of the molecular mechanisms underlying the protective effect remain to be elucidated, we report a correlation between the ability of metformin to promote resistance to damage and its capacity to counteract the increment of intracellular calcium levels induced by cardiotoxin treatment. Since increased cytoplasmic calcium concentrations characterize additional muscle pathological conditions, including dystrophies, metformin treatment could prove a valuable strategy to ameliorate the conditions of patients affected by dystrophies.
doi:10.1371/journal.pone.0114018
PMCID: PMC4252070  PMID: 25461598
5.  VirusMentha: a new resource for virus-host protein interactions 
Nucleic Acids Research  2014;43(Database issue):D588-D592.
Viral infections often cause diseases by perturbing several cellular processes in the infected host. Viral proteins target host proteins and either form new complexes or modulate the formation of functional host complexes. Describing and understanding the perturbation of the host interactome following viral infection is essential for basic virology and for the development of antiviral therapies. In order to provide a general overview of such interactions, a few years ago we developed VirusMINT. We have now extended the scope and coverage of VirusMINT and established VirusMentha, a new virus–virus and virus–host interaction resource build on the detailed curation protocols of the IMEx consortium and on the integration strategies developed for mentha. VirusMentha is regularly and automatically updated every week by capturing, via the PSICQUIC protocol, interactions curated by five different databases that are part of the IMEx consortium. VirusMentha can be freely browsed at http://virusmentha.uniroma2.it/ and its complete data set is available for download.
doi:10.1093/nar/gku830
PMCID: PMC4384001  PMID: 25217587
6.  The SH2 domain interaction landscape 
Cell reports  2013;3(4):1293-1305.
Summary
Members of the SH2 domain family modulate signal transduction by binding to short peptides containing phosphorylated tyrosines. Each domain displays a distinct preference for the sequence context of the phosphorylated residue. We have developed a new high-density peptide chip technology that allows probing the affinity of most SH2 domains for a large fraction of the entire complement of tyrosine phosphopeptides in the human proteome. Using this technique we have experimentally identified thousands of putative SH2- peptide interactions for more than 70 different SH2 domains. By integrating this rich data set with orthogonal context-specific information, we have assembled an SH2 mediated probabilistic interaction network, which we make available as a community resource in the PepSpotDB database. A new predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the ERK activation loop was validated by experiments in living cells.
doi:10.1016/j.celrep.2013.03.001
PMCID: PMC4110347  PMID: 23545499
SH2; protein interaction domains; protein networks; domain recognition specificity
7.  3D hydrogel environment rejuvenates aged pericytes for skeletal muscle tissue engineering 
Skeletal muscle tissue engineering is a promising approach for the treatment of muscular disorders. However, the complex organization of muscle, combined with the difficulty in finding an appropriate source of regenerative cells and in providing an adequate blood supply to the engineered tissue, makes this a hard task to face. In the present work, we describe an innovative approach to rejuvenate adult skeletal muscle-derived pericytes (MP) based on the use of a PEG-based hydrogel scaffold. MP were isolated from young (piglet) and adult (boar) pigs to assess whether aging affects tissue regeneration efficiency. In vitro, MP from boars had similar morphology and colony forming capacity to piglet MP, but an impaired ability to form myotubes and capillary-like structures. However, the use of a PEG-based hydrogel to support adult MP significantly improved their myogenic differentiation and angiogenic potentials in vitro and in vivo. Thus, PEG-based hydrogel scaffolds may provide a progenitor cell “niche” that promotes skeletal muscle regeneration and blood vessel growth, and together with pericytes may be developed for use in regenerative applications.
doi:10.3389/fphys.2014.00203
PMCID: PMC4039010  PMID: 24910618
stem cells; perycite; skeletal muscle; myogenic differentiation; tissue engineering; PEG-firbinogen; biomaterials
8.  Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways 
Frontiers in Genetics  2014;5:115.
Protein phosphorylation homoeostasis is tightly controlled and pathological conditions are caused by subtle alterations of the cell phosphorylation profile. Altered levels of kinase activities have already been associated to specific diseases. Less is known about the impact of phosphatases, the enzymes that down-regulate phosphorylation by removing the phosphate groups. This is partly due to our poor understanding of the phosphatase-substrate network. Much of phosphatase substrate specificity is not based on intrinsic enzyme specificity with the catalytic pocket recognizing the sequence/structure context of the phosphorylated residue. In addition many phosphatase catalytic subunits do not form a stable complex with their substrates. This makes the inference and validation of phosphatase substrates a non-trivial task. Here, we present a novel approach that builds on the observation that much of phosphatase substrate selection is based on the network of physical interactions linking the phosphatase to the substrate. We first used affinity proteomics coupled to quantitative mass spectrometry to saturate the interactome of eight phosphatases whose down regulations was shown to affect the activation of the RAS-PI3K pathway. By integrating information from functional siRNA with protein interaction information, we develop a strategy that aims at inferring phosphatase physiological substrates. Graph analysis is used to identify protein scaffolds that may link the catalytic subunits to their substrates. By this approach we rediscover several previously described phosphatase substrate interactions and characterize two new protein scaffolds that promote the dephosphorylation of PTPN11 and ERK by DUSP18 and DUSP26, respectively.
doi:10.3389/fgene.2014.00115
PMCID: PMC4019850  PMID: 24847354
phosphatase; signal transduction; systems biology; cell biology; protein protein interaction
9.  The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases 
Nucleic Acids Research  2013;42(Database issue):D358-D363.
IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).
doi:10.1093/nar/gkt1115
PMCID: PMC3965093  PMID: 24234451
10.  Protein Interaction Data Curation - The International Molecular Exchange Consortium (IMEx) 
Nature methods  2012;9(4):345-350.
The IMEx consortium is an international collaboration between major public interaction data providers to share curation effort and make a non-redundant set of protein interactions available in a single search interface on a common website (www.imexconsortium.org). Common curation rules have been developed and a central registry is used to manage the selection of articles to enter into the dataset. The advantages of such a service to the user, quality control measures adopted and data distribution practices are discussed.
doi:10.1038/nmeth.1931
PMCID: PMC3703241  PMID: 22453911
11.  Reactive Oxygen Species and Epidermal Growth Factor Are Antagonistic Cues Controlling SHP-2 Dimerization 
Molecular and Cellular Biology  2012;32(10):1998-2009.
The SHP-2 tyrosine phosphatase plays key regulatory roles in the modulation of the cell response to growth factors and cytokines. Over the past decade, the integration of genetic, biochemical, and structural data has helped in interpreting the pathological consequences of altered SHP-2 function. Using complementary approaches, we provide evidence here that endogenous SHP-2 can dimerize through the formation of disulfide bonds that may also involve the catalytic cysteine. We show that the fraction of dimeric SHP-2 is modulated by growth factor stimulation and by the cell redox state. Comparison of the phosphatase activities of the monomeric self-inhibited and dimeric forms indicated that the latter is 3-fold less active, thus pointing to the dimerization process as an additional mechanism for controlling SHP-2 activity. Remarkably, dimers formed by different SHP-2 mutants displaying diverse biochemical properties were found to respond differently to epidermal growth factor (EGF) stimulation. Although this differential behavior cannot be rationalized mechanistically yet, these findings suggest a possible regulatory role of dimerization in SHP-2 function.
doi:10.1128/MCB.06674-11
PMCID: PMC3347403  PMID: 22411627
12.  Mapping the human phosphatome on growth pathways 
Phosphatases control cell growth by a variety of mechanisms. A novel strategy is presented that combines multiparametric analysis of cell perturbations with logic modeling to achieve a detailed mapping of human phosphatase function on growth pathways.
siRNA-mediated downregulation of 298 phosphatase and phosphatase-related genes coupled to automated microscopy was used to characterize their impact on key growth pathways.In parallel, a literature-derived signed directed network was derived and optimized by training with experimental data.The resulting logic-based growth model was used to infer the cell state upon perturbation of each signaling node and compare it with the profiles obtained upon phosphatase perturbation.Mapping of 67% of the protein phosphatase onto the growth model shows that phosphatases are key modulators of growth pathways and affect cell-cycle progression.This novel approach is general and enables to efficiently map proteins onto complex pathways.
Large-scale siRNA screenings allow linking the function of poorly characterized genes to phenotypic readouts. According to this strategy, genes are associated with a function of interest if the alteration of their expression perturbs the phenotypic readouts. However, given the intricacy of the cell regulatory network, the mapping procedure is low resolution and the resulting models provide little mechanistic insights. We have developed a new strategy that combines multiparametric analysis of cell perturbation with logic modeling to achieve a more detailed functional mapping of human genes onto complex pathways. A literature-derived optimized model is used to infer the cell activation state following upregulation or downregulation of the model entities. By matching this signature with the experimental profile obtained in the high-throughput siRNA screening it is possible to infer the target of each protein, thus defining its ‘entry point' in the network. By this novel approach, 41 phosphatases that affect key growth pathways were identified and mapped onto a human epithelial cell-specific growth model, thus providing insights into the mechanisms underlying their function.
doi:10.1038/msb.2012.36
PMCID: PMC3435503  PMID: 22893001
cancer; computational biology; functional genomics; imaging; modeling
13.  The human phosphatase interactome: An intricate family portrait 
Febs Letters  2012;586(17):2732-2739.
The concerted activities of kinases and phosphatases modulate the phosphorylation levels of proteins, lipids and carbohydrates in eukaryotic cells. Despite considerable effort, we are still missing a holistic picture representing, at a proteome level, the functional relationships between kinases, phosphatases and their substrates. Here we focus on phosphatases and we review and integrate the available information that helps to place the members of the protein phosphatase superfamilies into the human protein interaction network. In addition we show how protein interaction domains and motifs, either covalently linked to the phosphatase domain or in regulatory/adaptor subunits, play a prominent role in substrate selection.
doi:10.1016/j.febslet.2012.05.008
PMCID: PMC3437441  PMID: 22626554
PTP, protein tyrosine phosphatase; LP, lipid phosphatase; PPP, phosphoprotein phosphatases; PPM, metallo-dependent protein phosphatase; HAD, haloacid dehalogenase; RS, regulatory subunit; Human phosphatome; Phosphatase family classification; Substrate recognition specificity
14.  Oxidative Stress, DNA Damage, and c-Abl Signaling: At the Crossroad in Neurodegenerative Diseases? 
The c-Abl tyrosine kinase is implicated in diverse cellular activities including growth factor signaling, cell adhesion, oxidative stress, and DNA damage response. Studies in mouse models have shown that the kinases of the c-Abl family play a role in the development of the central nervous system. Recent reports show that aberrant c-Abl activation causes neuroinflammation and neuronal loss in the forebrain of transgenic adult mice. In line with these observations, an increased c-Abl activation is reported in human neurodegenerative pathologies, such as Parkinson's, and Alzheimer's diseases. This suggests that aberrant nonspecific posttranslational modifications induced by c-Abl may contribute to fuel the recurrent phenotypes/features linked to neurodegenerative disorders, such as an impaired mitochondrial function, oxidative stress, and accumulation of protein aggregates. Herein, we review some reports on c-Abl function in neuronal cells and we propose that modulation of different aspects of c-Abl signaling may contribute to mediate the molecular events at the interface between stress signaling, metabolic regulation, and DNA damage. Finally, we propose that this may have an impact in the development of new therapeutic strategies.
doi:10.1155/2012/683097
PMCID: PMC3385657  PMID: 22761618
15.  Counteracting Effects Operating on Src Homology 2 Domain-containing Protein-tyrosine Phosphatase 2 (SHP2) Function Drive Selection of the Recurrent Y62D and Y63C Substitutions in Noonan Syndrome*♦ 
The Journal of Biological Chemistry  2012;287(32):27066-27077.
Background: Disease-associated PTPN11 mutations enhance the function of SHP2 by destabilizing its inactive state or increasing binding to phosphotyrosyl-containing partners.
Results: Amino acid substitutions at codons 62 and 63 have a profound and complex effect on SHP2 structure and function.
Conclusion: A selection-by-function mechanism acting on mutations at those codons implies balancing of counteracting effects operating on the activity of SHP2.
Significance: An unanticipated functional behavior underlies disease-causing weak hypermorphs.
Activating mutations in PTPN11 cause Noonan syndrome, the most common nonchromosomal disorder affecting development and growth. PTPN11 encodes SHP2, an Src homology 2 (SH2) domain-containing protein-tyrosine phosphatase that positively modulates RAS function. Here, we characterized functionally all possible amino acid substitutions arising from single-base changes affecting codons 62 and 63 to explore the molecular mechanisms lying behind the largely invariant occurrence of the Y62D and Y63C substitutions recurring in Noonan syndrome. We provide structural and biochemical data indicating that the autoinhibitory interaction between the N-SH2 and protein-tyrosine phosphatase (PTP) domains is perturbed in both mutants as a result of an extensive structural rearrangement of the N-SH2 domain. Most mutations affecting Tyr63 exerted an unpredicted disrupting effect on the structure of the N-SH2 phosphopeptide-binding cleft mediating the interaction of SHP2 with signaling partners. Among all the amino acid changes affecting that codon, the disease-causing mutation was the only substitution that perturbed the stability of the inactive conformation of SHP2 without severely impairing proper phosphopeptide binding of N-SH2. On the other hand, the disruptive effect of the Y62D change on the autoinhibited conformation of the protein was balanced, in part, by less efficient binding properties of the mutant. Overall, our data demonstrate that the selection-by-function mechanism acting as driving force for PTPN11 mutations affecting codons 62 and 63 implies balancing of counteracting effects operating on the allosteric control of the function of SHP2.
doi:10.1074/jbc.M112.350231
PMCID: PMC3411048  PMID: 22711529
Genetic Diseases; Protein Structure; SH2 Domains; Signal Transduction; Protein-tyrosine Phosphatase (Tyrosine Phosphatase); Noonan Syndrome; SHP2
17.  Structural and functional protein network analyses predict novel signaling functions for rhodopsin 
Proteomic analyses, literature mining, and structural data were combined to generate an extensive signaling network linked to the visual G protein-coupled receptor rhodopsin. Network analysis suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking.
Using a shotgun proteomic approach, we identified the protein inventory of the light sensing outer segment of the mammalian photoreceptor.These data, combined with literature mining, structural modeling, and computational analysis, offer a comprehensive view of signal transduction downstream of the visual G protein-coupled receptor rhodopsin.The network suggests novel signaling branches downstream of rhodopsin to cytoskeleton dynamics and vesicular trafficking.The network serves as a basis for elucidating physiological principles of photoreceptor function and suggests potential disease-associated proteins.
Photoreceptor cells are neurons capable of converting light into electrical signals. The rod outer segment (ROS) region of the photoreceptor cells is a cellular structure made of a stack of around 800 closed membrane disks loaded with rhodopsin (Liang et al, 2003; Nickell et al, 2007). In disc membranes, rhodopsin arranges itself into paracrystalline dimer arrays, enabling optimal association with the heterotrimeric G protein transducin as well as additional regulatory components (Ciarkowski et al, 2005). Disruption of these highly regulated structures and processes by germline mutations is the cause of severe blinding diseases such as retinitis pigmentosa, macular degeneration, or congenital stationary night blindness (Berger et al, 2010).
Traditionally, signal transduction networks have been studied by combining biochemical and genetic experiments addressing the relations among a small number of components. More recently, large throughput experiments using different techniques like two hybrid or co-immunoprecipitation coupled to mass spectrometry have added a new level of complexity (Ito et al, 2001; Gavin et al, 2002, 2006; Ho et al, 2002; Rual et al, 2005; Stelzl et al, 2005). However, in these studies, space, time, and the fact that many interactions detected for a particular protein are not compatible, are not taken into consideration. Structural information can help discriminate between direct and indirect interactions and more importantly it can determine if two or more predicted partners of any given protein or complex can simultaneously bind a target or rather compete for the same interaction surface (Kim et al, 2006).
In this work, we build a functional and dynamic interaction network centered on rhodopsin on a systems level, using six steps: In step 1, we experimentally identified the proteomic inventory of the porcine ROS, and we compared our data set with a recent proteomic study from bovine ROS (Kwok et al, 2008). The union of the two data sets was defined as the ‘initial experimental ROS proteome'. After removal of contaminants and applying filtering methods, a ‘core ROS proteome', consisting of 355 proteins, was defined.
In step 2, proteins of the core ROS proteome were assigned to six functional modules: (1) vision, signaling, transporters, and channels; (2) outer segment structure and morphogenesis; (3) housekeeping; (4) cytoskeleton and polarity; (5) vesicles formation and trafficking, and (6) metabolism.
In step 3, a protein-protein interaction network was constructed based on the literature mining. Since for most of the interactions experimental evidence was co-immunoprecipitation, or pull-down experiments, and in addition many of the edges in the network are supported by single experimental evidence, often derived from high-throughput approaches, we refer to this network, as ‘fuzzy ROS interactome'. Structural information was used to predict binary interactions, based on the finding that similar domain pairs are likely to interact in a similar way (‘nature repeats itself') (Aloy and Russell, 2002). To increase the confidence in the resulting network, edges supported by a single evidence not coming from yeast two-hybrid experiments were removed, exception being interactions where the evidence was the existence of a three-dimensional structure of the complex itself, or of a highly homologous complex. This curated static network (‘high-confidence ROS interactome') comprises 660 edges linking the majority of the nodes. By considering only edges supported by at least one evidence of direct binary interaction, we end up with a ‘high-confidence binary ROS interactome'. We next extended the published core pathway (Dell'Orco et al, 2009) using evidence from our high-confidence network. We find several new direct binary links to different cellular functional processes (Figure 4): the active rhodopsin interacts with Rac1 and the GTP form of Rho. There is also a connection between active rhodopsin and Arf4, as well as PDEδ with Rab13 and the GTP-bound form of Arl3 that links the vision cycle to vesicle trafficking and structure. We see a connection between PDEδ with prenyl-modified proteins, such as several small GTPases, as well as with rhodopsin kinase. Further, our network reveals several direct binary connections between Ca2+-regulated proteins and cytoskeleton proteins; these are CaMK2A with actinin, calmodulin with GAP43 and S1008, and PKC with 14-3-3 family members.
In step 4, part of the network was experimentally validated using three different approaches to identify physical protein associations that would occur under physiological conditions: (i) Co-segregation/co-sedimentation experiments, (ii) immunoprecipitations combined with mass spectrometry and/or subsequent immunoblotting, and (iii) utilizing the glycosylated N-terminus of rhodopsin to isolate its associated protein partners by Concanavalin A affinity purification. In total, 60 co-purification and co-elution experiments supported interactions that were already in our literature network, and new evidence from 175 co-IP experiments in this work was added. Next, we aimed to provide additional independent experimental confirmation for two of the novel networks and functional links proposed based on the network analysis: (i) the proposed complex between Rac1/RhoA/CRMP-2/tubulin/and ROCK II in ROS was investigated by culturing retinal explants in the presence of an ROCK II-specific inhibitor (Figure 6). While morphology of the retinas treated with ROCK II inhibitor appeared normal, immunohistochemistry analyses revealed several alterations on the protein level. (ii) We supported the hypothesis that PDEδ could function as a GDI for Rac1 in ROS, by demonstrating that PDEδ and Rac1 co localize in ROS and that PDEδ could dissociate Rac1 from ROS membranes in vitro.
In step 5, we use structural information to distinguish between mutually compatible (‘AND') or excluded (‘XOR') interactions. This enables breaking a network of nodes and edges into functional machines or sub-networks/modules. In the vision branch, both ‘AND' and ‘XOR' gates synergize. This may allow dynamic tuning of light and dark states. However, all connections from the vision module to other modules are ‘XOR' connections suggesting that competition, in connection with local protein concentration changes, could be important for transmitting signals from the core vision module.
In the last step, we map and functionally characterize the known mutations that produce blindness.
In summary, this represents the first comprehensive, dynamic, and integrative rhodopsin signaling network, which can be the basis for integrating and mapping newly discovered disease mutants, to guide protein or signaling branch-specific therapies.
Orchestration of signaling, photoreceptor structural integrity, and maintenance needed for mammalian vision remain enigmatic. By integrating three proteomic data sets, literature mining, computational analyses, and structural information, we have generated a multiscale signal transduction network linked to the visual G protein-coupled receptor (GPCR) rhodopsin, the major protein component of rod outer segments. This network was complemented by domain decomposition of protein–protein interactions and then qualified for mutually exclusive or mutually compatible interactions and ternary complex formation using structural data. The resulting information not only offers a comprehensive view of signal transduction induced by this GPCR but also suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking, predicting an important level of regulation through small GTPases. Further, it demonstrates a specific disease susceptibility of the core visual pathway due to the uniqueness of its components present mainly in the eye. As a comprehensive multiscale network, it can serve as a basis to elucidate the physiological principles of photoreceptor function, identify potential disease-associated genes and proteins, and guide the development of therapies that target specific branches of the signaling pathway.
doi:10.1038/msb.2011.83
PMCID: PMC3261702  PMID: 22108793
protein interaction network; rhodopsin signaling; structural modeling
18.  MINT, the molecular interaction database: 2012 update 
Nucleic Acids Research  2011;40(Database issue):D857-D861.
The Molecular INTeraction Database (MINT, http://mint.bio.uniroma2.it/mint/) is a public repository for protein–protein interactions (PPI) reported in peer-reviewed journals. The database grows steadily over the years and at September 2011 contains approximately 235 000 binary interactions captured from over 4750 publications. The web interface allows the users to search, visualize and download interactions data. MINT is one of the members of the International Molecular Exchange consortium (IMEx) and adopts the Molecular Interaction Ontology of the Proteomics Standard Initiative (PSI-MI) standards for curation and data exchange. MINT data are freely accessible and downloadable at http://mint.bio.uniroma2.it/mint/download.do. We report here the growth of the database, the major changes in curation policy and a new algorithm to assign a confidence to each interaction.
doi:10.1093/nar/gkr930
PMCID: PMC3244991  PMID: 22096227
19.  The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text 
BMC Bioinformatics  2011;12(Suppl 8):S3.
Background
Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.
Results
A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89% and the best AUC iP/R was 68%. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53%, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35%) the macro-averaged precision ranged between 50% and 80%, with a maximum F-Score of 55%.
Conclusions
The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows.
doi:10.1186/1471-2105-12-S8-S3
PMCID: PMC3269938  PMID: 22151929
20.  BioCreative III interactive task: an overview 
BMC Bioinformatics  2011;12(Suppl 8):S4.
Background
The BioCreative challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems applied to the biological domain. The biocurator community, as an active user of biomedical literature, provides a diverse and engaged end user group for text mining tools. Earlier BioCreative challenges involved many text mining teams in developing basic capabilities relevant to biological curation, but they did not address the issues of system usage, insertion into the workflow and adoption by curators. Thus in BioCreative III (BC-III), the InterActive Task (IAT) was introduced to address the utility and usability of text mining tools for real-life biocuration tasks. To support the aims of the IAT in BC-III, involvement of both developers and end users was solicited, and the development of a user interface to address the tasks interactively was requested.
Results
A User Advisory Group (UAG) actively participated in the IAT design and assessment. The task focused on gene normalization (identifying gene mentions in the article and linking these genes to standard database identifiers), gene ranking based on the overall importance of each gene mentioned in the article, and gene-oriented document retrieval (identifying full text papers relevant to a selected gene). Six systems participated and all processed and displayed the same set of articles. The articles were selected based on content known to be problematic for curation, such as ambiguity of gene names, coverage of multiple genes and species, or introduction of a new gene name. Members of the UAG curated three articles for training and assessment purposes, and each member was assigned a system to review. A questionnaire related to the interface usability and task performance (as measured by precision and recall) was answered after systems were used to curate articles. Although the limited number of articles analyzed and users involved in the IAT experiment precluded rigorous quantitative analysis of the results, a qualitative analysis provided valuable insight into some of the problems encountered by users when using the systems. The overall assessment indicates that the system usability features appealed to most users, but the system performance was suboptimal (mainly due to low accuracy in gene normalization). Some of the issues included failure of species identification and gene name ambiguity in the gene normalization task leading to an extensive list of gene identifiers to review, which, in some cases, did not contain the relevant genes. The document retrieval suffered from the same shortfalls. The UAG favored achieving high performance (measured by precision and recall), but strongly recommended the addition of features that facilitate the identification of correct gene and its identifier, such as contextual information to assist in disambiguation.
Discussion
The IAT was an informative exercise that advanced the dialog between curators and developers and increased the appreciation of challenges faced by each group. A major conclusion was that the intended users should be actively involved in every phase of software development, and this will be strongly encouraged in future tasks. The IAT Task provides the first steps toward the definition of metrics and functional requirements that are necessary for designing a formal evaluation of interactive curation systems in the BioCreative IV challenge.
doi:10.1186/1471-2105-12-S8-S4
PMCID: PMC3269939  PMID: 22151968
21.  Benchmarking of the 2010 BioCreative Challenge III text-mining competition by the BioGRID and MINT interaction databases 
BMC Bioinformatics  2011;12(Suppl 8):S8.
Background
The vast amount of data published in the primary biomedical literature represents a challenge for the automated extraction and codification of individual data elements. Biological databases that rely solely on manual extraction by expert curators are unable to comprehensively annotate the information dispersed across the entire biomedical literature. The development of efficient tools based on natural language processing (NLP) systems is essential for the selection of relevant publications, identification of data attributes and partially automated annotation. One of the tasks of the Biocreative 2010 Challenge III was devoted to the evaluation of NLP systems developed to identify articles for curation and extraction of protein-protein interaction (PPI) data.
Results
The Biocreative 2010 competition addressed three tasks: gene normalization, article classification and interaction method identification. The BioGRID and MINT protein interaction databases both participated in the generation of the test publication set for gene normalization, annotated the development and test sets for article classification, and curated the test set for interaction method classification. These test datasets served as a gold standard for the evaluation of data extraction algorithms.
Conclusion
The development of efficient tools for extraction of PPI data is a necessary step to achieve full curation of the biomedical literature. NLP systems can in the first instance facilitate expert curation by refining the list of candidate publications that contain PPI data; more ambitiously, NLP approaches may be able to directly extract relevant information from full-text articles for rapid inspection by expert curators. Close collaboration between biological databases and NLP systems developers will continue to facilitate the long-term objectives of both disciplines.
doi:10.1186/1471-2105-12-S8-S8
PMCID: PMC3269943  PMID: 22151178
22.  Identification of New Substrates of the Protein-tyrosine Phosphatase PTP1B by Bayesian Integration of Proteome Evidence* 
The Journal of Biological Chemistry  2010;286(6):4173-4185.
There is growing evidence that tyrosine phosphatases display an intrinsic enzymatic preference for the sequence context flanking the target phosphotyrosines. On the other hand, substrate selection in vivo is decisively guided by the enzyme-substrate connectivity in the protein interaction network. We describe here a system wide strategy to infer physiological substrates of protein-tyrosine phosphatases. Here we integrate, by a Bayesian model, proteome wide evidence about in vitro substrate preference, as determined by a novel high-density peptide chip technology, and “closeness” in the protein interaction network. This allows to rank candidate substrates of the human PTP1B phosphatase. Ultimately a variety of in vitro and in vivo approaches were used to verify the prediction that the tyrosine phosphorylation levels of five high-ranking substrates, PLC-γ1, Gab1, SHP2, EGFR, and SHP1, are indeed specifically modulated by PTP1B. In addition, we demonstrate that the PTP1B-mediated dephosphorylation of Gab1 negatively affects its EGF-induced association with the phosphatase SHP2. The dissociation of this signaling complex is accompanied by a decrease of ERK MAP kinase phosphorylation and activation.
doi:10.1074/jbc.M110.157420
PMCID: PMC3039405  PMID: 21123182
ERK; Phospholipase C; Ras; Receptor-tyrosine Kinase; Tyrosine-protein Phosphatase (Tyrosine Phosphatase); Gab1; PTP1B; SHP2
23.  Finding and sharing: new approaches to registries of databases and services for the biomedical sciences 
The recent explosion of biological data and the concomitant proliferation of distributed databases make it challenging for biologists and bioinformaticians to discover the best data resources for their needs, and the most efficient way to access and use them. Despite a rapid acceleration in uptake of syntactic and semantic standards for interoperability, it is still difficult for users to find which databases support the standards and interfaces that they need. To solve these problems, several groups are developing registries of databases that capture key metadata describing the biological scope, utility, accessibility, ease-of-use and existence of web services allowing interoperability between resources. Here, we describe some of these initiatives including a novel formalism, the Database Description Framework, for describing database operations and functionality and encouraging good database practise. We expect such approaches will result in improved discovery, uptake and utilization of data resources.
Database URL: http://www.casimir.org.uk/casimir_ddf
doi:10.1093/database/baq014
PMCID: PMC2911849  PMID: 20627863
24.  MINT, the molecular interaction database: 2009 update 
Nucleic Acids Research  2009;38(Database issue):D532-D539.
MINT (http://mint.bio.uniroma2.it/mint) is a public repository for molecular interactions reported in peer-reviewed journals. Since its last report, MINT has grown considerably in size and evolved in scope to meet the requirements of its users. The main changes include a more precise definition of the curation policy and the development of an enhanced and user-friendly interface to facilitate the analysis of the ever-growing interaction dataset. MINT has adopted the PSI-MI standards for the annotation and for the representation of molecular interactions and is a member of the IMEx consortium.
doi:10.1093/nar/gkp983
PMCID: PMC2808973  PMID: 19897547
25.  Bayesian Modeling of the Yeast SH3 Domain Interactome Predicts Spatiotemporal Dynamics of Endocytosis Proteins 
PLoS Biology  2009;7(10):e1000218.
A genome-scale specificity and interaction map for yeast SH3 domain-containing proteins reveal how family members show selective binding to target proteins and predicts the dynamic localization of new candidate endocytosis proteins.
SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast, Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes.
Author Summary
Significant diversity exists in protein structure and function, yet certain structural domains are used repeatedly across species to execute similar functions. The SH3 domain is one such common structural domain. It is found in signaling proteins and mediates protein–protein interactions by binding to short peptide sequences generally composed of proline. To investigate both the generality and selectivity of peptide binding by SH3 domains, we examined peptide specificity for almost all SH3 domains encoded within the proteome of the budding yeast, Saccharomyces cerevisiae, using a range of experimental methods. We found that although most of the intrinsic binding specificity for SH3 domains can be summarized by the two previously described canonical binding modes, each individual SH3 domain that we studied utilizes unique features of its cognate ligand to achieve binding selectivity. Moreover, some domains exhibit binding specificities that are distinct from the two canonical classes. We integrated peptide-SH3 domain binding data from three complementary screening techniques using a Bayesian statistical model to generate a protein–protein interaction network for the budding yeast SH3 domain family. This network was highly enriched in endocytosis proteins and their interactions. By examining these interactions in detail, we show that our SH3 domain network can be used to predict the temporal localization of several previously uncharacterized proteins to dynamic complexes that orchestrate the process of endocytosis.
doi:10.1371/journal.pbio.1000218
PMCID: PMC2756588  PMID: 19841731

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