Serena Paoluzi

Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins.

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.

Recognition specificity of individual EH domains of mammals and yeast

The Eps homology (EH) domain is a recently described protein binding module that is found, in multiple or single copies, in several proteins in species as diverse as human and yeast. In this work, we have investigated the molecular details of recognition specificity mediated by this domain family by characterizing the peptide‐binding preference of 11 different EH domains from mammal and yeast proteins. Ten of the eleven EH domains could bind at least some peptides containing an Asn‐Pro‐Phe (NPF) motif. By contrast, the first EH domain of End3p preferentially binds peptides containing an His‐Thr/Ser‐Phe (HT/SF) motif. Domains that have a low affinity for the majority of NPF peptides reveal some affinity for a third class of peptides that contains two consecutive amino acids with aromatic side chains (FW or WW). This is the case for the third EH domain of Eps15 and for the two N‐terminal domains of YBL47c. The consensus sequences derived from the peptides selected from phage‐displayed peptide libraries allows for grouping of EH domains into families that are characterized by different NPF‐context preference. Finally, comparison of the primary sequence of EH domains with similar or divergent specificity identifies a residue at position +3 following a conserved tryptophan, whose chemical characteristics modulate binding preference.

Selectivity and promiscuity in the interaction network mediated by protein recognition modules

A substantial fraction of protein interactions in the cell is mediated by families of protein modules binding to relatively short linear peptides. Many of these interactions have a high dissociation constant and are therefore suitable for supporting the formation of dynamic complexes that are assembled and disassembled during signal transduction. Extensive work in the past decade has shown that, although member domains within a family have some degree of intrinsic peptide recognition specificity, the derived interaction networks display substantial promiscuity. We review here recent advances in the methods for deriving the portion of the protein network mediated by these domain families and discuss how specific biological outputs could emerge in vivo despite the observed promiscuity in peptide recognition in vitro.

Alternative Bacteriophage Display Systems

Filamentous phage has been extensively used to implement various aspects of phage display technology. The success of these organisms as vectors to present foreign peptides and to link them to their coding sequences is a consequence of their structural and biological characteristics. Some of these properties, however, represent a limitation when one attempts to display proteins that cannot be efficiently exported through the bacterial membrane or do not fold properly in the periplasm. Thus, the desirability of developing alternative display systems was recognised recently and led to the development of a different class of display vectors that assemble their capsid in the cytoplasm and are released via cell lysis. This review describes and compares the properties of these alternative display systems.

The SH2 Domain Interaction Landscape

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 high-density peptide chip technology that allows for probing of 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 predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the extracellular signal-regulated kinase activation loop was validated by experiments in living cells.

The protein interaction network mediated by human SH3 domains

Families of conserved protein domains, specialized in mediating interactions with short linear peptide motifs, are responsible for the formation of a variety of dynamic complexes in the cell. An important subclass of these motifs are characterized by a high proline content and play a pivotal role in biological processes requiring the coordinated assembly of multi-protein complexes. This is achieved via interaction of proteins containing modules such as Src Homology-3 (SH3) or WW domains and specific proline rich patterns. Here we make available via a publicly accessible database a synopsis of our current understanding of the interaction landscape of the human SH3 protein family. This is achieved by integrating an information extraction strategy with a new experimental approach. In a first approach we have used a text mining strategy to capture a large number of manuscripts reporting interactions between SH3 domains and target peptides. Relevant information was annotated in the MINT database. In a second experimental approach we have used a variant of the WISE (Whole Interactome Scanning Experiment) strategy to probe a large number of naturally occurring and chemically-synthesized peptides arrayed at high density on a glass surface. By this method we have tested 60 human SH3 domains for their ability to bind a collection of 9192 poly-proline containing peptides immobilized on a glass chip. To evaluate the quality of the resulting interaction dataset, we retested some of the interactions on a smaller scale and performed a series of pull down experiments on native proteins. Peptide chips, pull down assays, SPOT synthesis and phage display experiments have allowed us to further characterize the specificity and promiscuity of proline-rich binding domains and to map their interaction network. Both the information captured from the literature and the interactions inferred from the peptide chip experiments were collected and stored in the PepspotDB (http://mint.bio.uniroma2.it/PepspotDB/).

Combining peptide recognition specificity and context information for the prediction of the 14-3-3-mediated interactome in S. cerevisiae and H. sapiens†

Large‐scale interaction studies contribute the largest fraction of protein interactions information in databases. However, co‐purification of non‐specific or indirect ligands, often results in data sets that are affected by a considerable number of false positives. For the fraction of interactions mediated by short linear peptides, we present here a combined experimental and computational strategy for ranking the reliability of the inferred partners. We apply this strategy to the family of 14‐3‐3 domains. We have first characterized the recognition specificity of this domain family, largely confirming the results of previous analyses, while revealing new features of the preferred sequence context of 14‐3‐3 phospho‐peptide partners. Notably, a proline next to the carboxy side of the phospho‐amino acid functions as a potent inhibitor of 14‐3‐3 binding. The position‐specific information about residue preference was encoded in a scoring matrix and two regular expressions. The integration of these three features in a single predictive model outperforms publicly available prediction tools. Next we have combined, by a naïve Bayesian approach, these “peptide features” with “protein features”, such as protein co‐expression and co‐localization. Our approach provides an orthogonal reliability assessment and maps with high confidence the 14‐3‐3 peptide target on the partner proteins.

Domain repertoires as a tool to derive protein recognition rules.

Several approaches, some of which are described in this issue, have been proposed to assemble a complete protein interaction map. These are often based on high throughput methods that explore the ability of each gene product to bind any other element of the proteome of the organism. Here we propose that a large number of interactions can be inferred by revealing the rules underlying recognition specificity of a small number (a few hundreds) of families of protein recognition modules. This can be achieved through the construction and characterization of domain repertoires. A domain repertoire is assembled in a combinatorial fashion by allowing each amino acid position in the binding site of a given protein recognition domain to vary to include all the residues allowed at that position in the domain family. The repertoire is then searched by phage display techniques with any target of interest and from the primary structure of the binding site of the selected domains one derives rules that are used to infer the formation of complexes between natural proteins in the cell.

Metformin Induces Apoptosis and Downregulates Pyruvate Kinase M2 in Breast Cancer Cells Only When Grown in Nutrient-Poor Conditions

Introduction Metformin is proposed as adjuvant therapy in cancer treatment because of its ability to limit cancer incidence by negatively modulating the PI3K/AKT/mTOR pathway. In vitro, in addition to inhibiting cancer cell proliferation, metformin can also induce apoptosis. The molecular mechanism underlying this second effect is still poorly characterized and published data are often contrasting. We investigated how nutrient availability can modulate metformin-induced apoptosis in three breast cancer cell lines. Material and Methods MCF7, SKBR3 and MDA-MB-231 cells were plated in MEM medium supplemented with increasing glucose concentrations or in DMEM medium and treated with 10 mM metformin. Cell viability was monitored by Trypan Blue assay and treatment effects on Akt/mTOR pathway and on apoptosis were analysed by Western Blot. Moreover, we determined the level of expression of pyruvate kinase M2 (PKM2), a well-known glycolytic enzyme expressed in cancer cells. Results Our results showed that metformin can induce apoptosis in breast cancer cells when cultured at physiological glucose concentrations and that the pro-apoptotic effect was completely abolished when cells were grown in high glucose/high amino acid medium. Induction of apoptosis was found to be dependent on AMPK activation but, at least partially, independent of TORC1 inactivation. Finally, we showed that, in nutrient-poor conditions, metformin was able to modulate the intracellular glycolytic equilibrium by downregulating PKM2 expression and that this mechanism was mediated by AMPK activation. Conclusion We demonstrated that metformin induces breast cancer cell apoptosis and PKM2 downregulation only in nutrient-poor conditions. Not only glucose levels but also amino acid concentration can influence the observed metformin inhibitory effect on the mTOR pathway as well as its pro-apoptotic effect. These data demonstrate that the reduction of nutrient supply in tumors can increase metformin efficacy and that modulation of PKM2 expression/activity could be a promising strategy to boost metformin anti-cancer effect.

Mapping the human phosphatome on growth 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.