PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-7 (7)
 

Clipboard (0)
None

Select a Filter Below

Journals
Year of Publication
Document Types
author:("tint, Michele")
1.  ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome 
The dimeric 14-3-3 proteins dock onto pairs of phosphorylated Ser and Thr residues on hundreds of proteins, and thereby regulate many events in mammalian cells. To facilitate global analyses of these interactions, we developed a web resource named ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome, which integrates multiple data sets on 14-3-3-binding phosphoproteins. ANIA also pinpoints candidate 14-3-3-binding phosphosites using predictor algorithms, assisted by our recent discovery that the human 14-3-3-interactome is highly enriched in 2R-ohnologues. 2R-ohnologues are proteins in families of two to four, generated by two rounds of whole genome duplication at the origin of the vertebrate animals. ANIA identifies candidate ‘lynchpins’, which are 14-3-3-binding phosphosites that are conserved across members of a given 2R-ohnologue protein family. Other features of ANIA include a link to the catalogue of somatic mutations in cancer database to find cancer polymorphisms that map to 14-3-3-binding phosphosites, which would be expected to interfere with 14-3-3 interactions. We used ANIA to map known and candidate 14-3-3-binding enzymes within the 2R-ohnologue complement of the human kinome. Our projections indicate that 14-3-3s dock onto many more human kinases than has been realized. Guided by ANIA, PAK4, 6 and 7 (p21-activated kinases 4, 6 and 7) were experimentally validated as a 2R-ohnologue family of 14-3-3-binding phosphoproteins. PAK4 binding to 14-3-3 is stimulated by phorbol ester, and involves the ‘lynchpin’ site phosphoSer99 and a major contribution from Ser181. In contrast, PAK6 and PAK7 display strong phorbol ester-independent binding to 14-3-3, with Ser113 critical for the interaction with PAK6. These data point to differential 14-3-3 regulation of PAKs in control of cell morphology.
Database URL: https://ania-1433.lifesci.dundee.ac.uk/prediction/webserver/index.py
doi:10.1093/database/bat085
PMCID: PMC3914767  PMID: 24501395
2.  Evolution of signal multiplexing by 14-3-3-binding 2R-ohnologue protein families in the vertebrates 
Open Biology  2012;2(7):120103.
14-3-3 proteins regulate cellular responses to stimuli by docking onto pairs of phosphorylated residues on target proteins. The present study shows that the human 14-3-3-binding phosphoproteome is highly enriched in 2R-ohnologues, which are proteins in families of two to four members that were generated by two rounds of whole genome duplication at the origin of the vertebrates. We identify 2R-ohnologue families whose members share a ‘lynchpin’, defined as a 14-3-3-binding phosphosite that is conserved across members of a given family, and aligns with a Ser/Thr residue in pro-orthologues from the invertebrate chordates. For example, the human receptor expression enhancing protein (REEP) 1–4 family has the commonest type of lynchpin motif in current datasets, with a phosphorylatable serine in the –2 position relative to the 14-3-3-binding phosphosite. In contrast, the second 14-3-3-binding sites of REEPs 1–4 differ and are phosphorylated by different kinases, and hence the REEPs display different affinities for 14-3-3 dimers. We suggest a conceptual model for intracellular regulation involving protein families whose evolution into signal multiplexing systems was facilitated by 14-3-3 dimer binding to lynchpins, which gave freedom for other regulatory sites to evolve. While increased signalling complexity was needed for vertebrate life, these systems also generate vulnerability to genetic disorders.
doi:10.1098/rsob.120103
PMCID: PMC3411107  PMID: 22870394
Branchiostoma; Ciona; hereditary spastic paraplegia; RAB3GAP1; RAB3GAP2
3.  Visualization and Biochemical Analyses of the Emerging Mammalian 14-3-3-Phosphoproteome* 
Molecular & Cellular Proteomics : MCP  2011;10(10):M110.005751.
Hundreds of candidate 14-3-3-binding (phospho)proteins have been reported in publications that describe one interaction at a time, as well as high-throughput 14-3-3-affinity and mass spectrometry-based studies. Here, we transcribed these data into a common format, deposited the collated data from low-throughput studies in MINT (http://mint.bio.uniroma2.it/mint), and compared the low- and high-throughput data in VisANT graphs that are easy to analyze and extend. Exploring the graphs prompted questions about technical and biological specificity, which were addressed experimentally, resulting in identification of phosphorylated 14-3-3-binding sites in the mitochondrial import sequence of the iron-sulfur cluster assembly enzyme (ISCU), cytoplasmic domains of the mitochondrial fission factor (MFF), and endoplasmic reticulum-tethered receptor expression-enhancing protein 4 (REEP4), RNA regulator SMAUG2, and cytoskeletal regulatory proteins, namely debrin-like protein (DBNL) and kinesin light chain (KLC) isoforms. Therefore, 14-3-3s undergo physiological interactions with proteins that are destined for diverse subcellular locations. Graphing and validating interactions underpins efforts to use 14-3-3-phosphoproteomics to identify mechanisms and biomarkers for signaling pathways in health and disease.
doi:10.1074/mcp.M110.005751
PMCID: PMC3205853  PMID: 21725060
4.  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
5.  Diverse driving forces underlie the invariant occurrence of the T42A, E139D, I282V and T468M SHP2 amino acid substitutions causing Noonan and LEOPARD syndromes 
Human Molecular Genetics  2008;17(13):2018-2029.
Missense PTPN11 mutations cause Noonan and LEOPARD syndromes (NS and LS), two developmental disorders with pleiomorphic phenotypes. PTPN11 encodes SHP2, an SH2 domain-containing protein tyrosine phosphatase functioning as a signal transducer. Generally, different substitutions of a particular amino acid residue are observed in these diseases, indicating that the crucial factor is the residue being replaced. For a few codons, only one substitution is observed, suggesting the possibility of specific roles for the residue introduced. We analyzed the biochemical behavior and ligand-binding properties of all possible substitutions arising from single-base changes affecting codons 42, 139, 279, 282 and 468 to investigate the mechanisms underlying the invariant occurrence of the T42A, E139D and I282V substitutions in NS and the Y279C and T468M changes in LS. Our data demonstrate that the isoleucine-to-valine change at codon 282 is the only substitution at that position perturbing the stability of SHP2's closed conformation without impairing catalysis, while the threonine-to-alanine change at codon 42, but not other substitutions of that residue, promotes increased phosphopeptide-binding affinity. The recognition specificity of the C-SH2 domain bearing the E139D substitution differed substantially from its wild-type counterpart acquiring binding properties similar to those observed for the N-SH2 domain, revealing a novel mechanism of SHP2's functional dysregulation. Finally, while functional selection does not seem to occur for the substitutions at codons 279 and 468, we point to deamination of the methylated cytosine at nucleotide 1403 as the driving factor leading to the high prevalence of the T468M change in LS.
doi:10.1093/hmg/ddn099
PMCID: PMC2900904  PMID: 18372317
6.  VirusMINT: a viral protein interaction database 
Nucleic Acids Research  2008;37(Database issue):D669-D673.
Understanding the consequences on host physiology induced by viral infection requires complete understanding of the perturbations caused by virus proteins on the cellular protein interaction network. The VirusMINT database (http://mint.bio.uniroma2.it/virusmint/) aims at collecting all protein interactions between viral and human proteins reported in the literature. VirusMINT currently stores over 5000 interactions involving more than 490 unique viral proteins from more than 110 different viral strains. The whole data set can be easily queried through the search pages and the results can be displayed with a graphical viewer. The curation effort has focused on manuscripts reporting interactions between human proteins and proteins encoded by some of the most medically relevant viruses: papilloma viruses, human immunodeficiency virus 1, Epstein–Barr virus, hepatitis B virus, hepatitis C virus, herpes viruses and Simian virus 40.
doi:10.1093/nar/gkn739
PMCID: PMC2686573  PMID: 18974184
7.  ProtNet: a tool for stochastic simulations of protein interaction networks dynamics 
BMC Bioinformatics  2007;8(Suppl 1):S4.
Background
Protein interactions support cell organization and mediate its response to any specific stimulus. Recent technological advances have produced large data-sets that aim at describing the cell interactome. These data are usually presented as graphs where proteins (nodes) are linked by edges to their experimentally determined partners. This representation reveals that protein-protein interaction (PPI) networks, like other kinds of complex networks, are not randomly organized and display properties that are typical of "hierarchical" networks, combining modularity and local clustering to scale free topology. However informative, this representation is static and provides no clue about the dynamic nature of protein interactions inside the cell.
Results
To fill this methodological gap, we designed and implemented a computer model that captures the discrete and stochastic nature of protein interactions. In ProtNet, our simplified model, the intracellular space is mapped onto either a two-dimensional or a three-dimensional lattice with each lattice site having a linear size (5 nm) comparable to the diameter of an average globular protein. The protein filled lattice has an occupancy (e.g. 20%) compatible with the estimated crowding of proteins in the cell cytoplasm. Proteins or protein complexes are free to translate and rotate on the lattice that represents a sort of naïve unstructured cell (devoid of compartments). At each time step, molecular entities (proteins or complexes) that happen to be in neighboring cells may interact and form larger complexes or dissociate depending on the interaction rules defined in an experimental protein interaction network. This whole procedure can be seen as a sort of "discrete molecular dynamics" applied to interacting proteins in a cell.
We have tested our model by performing different simulations using as interaction rules those derived from an experimental interactome of Saccharomyces cerevisiae (1378 nodes, 2491 edges) and we have compared the dynamics of complex formation in a two and a three dimensional lattice model.
Conclusion
ProtNet is a cellular automaton model, where each protein molecule or complex is explicitly represented and where simple interaction rules are applied to populations of discrete particles. This tool can be used to simulate the dynamics of protein interactions in the cell.
doi:10.1186/1471-2105-8-S1-S4
PMCID: PMC1885856  PMID: 17430571

Results 1-7 (7)