Both Intrinsic Substrate Preference and Network Context Contribute to Substrate Selection of Classical Tyrosine Phosphatases.

Reversible tyrosine phosphorylation is a widespread post-translational modification mechanism underlying cell physiology. Thus, understanding the mechanisms responsible for substrate selection by kinases and phosphatases is central to our ability to model signal transduction at a system level. Classical protein-tyrosine phosphatases can exhibit substrate specificity in vivo by combining intrinsic enzymatic specificity with the network of protein-protein interactions, which positions the enzymes in close proximity to their substrates. Here we use a high throughput approach, based on high density phosphopeptide chips, to determine the in vitro substrate preference of 16 members of the protein-tyrosine phosphatase family. This approach helped identify one residue in the substrate binding pocket of the phosphatase domain that confers specificity for phosphopeptides in a specific sequence context. We also present a Bayesian model that combines intrinsic enzymatic specificity and interaction information in the context of the human protein interaction network to infer new phosphatase substrates at the proteome level.

Structural studies and SH3 domain binding properties of a human antiviral salivary proline-rich peptide.

Human saliva contains hundreds of small proline-rich peptides originated by the proteolytic cleavage of the salivary basic Proline-Rich Proteins. Nevertheless only for few of them a specific biological activity has been assigned to date. Among them, the 1932 Da peptide (p1932) has been patented as an anti-HIV agent. In order to shed light on the possible mechanism of action of this peptide, we assessed in this study, by means of molecular dynamics calculations, circular dichroism and FTIR spectroscopic techniques, that p1932 has an intrinsic propensity to adopt a polyproline-II helix arrangement. This structural feature combined with the presence of PxxP motifs in its primary structure, represents an essential property for the exploitation of several biological activities. Next to these findings, we recently demonstrated the ability of this peptide to be internalized within cells of the oral mucosa, thus we focused onto a possible intracellular target, represented by the SH3 domains family. Its ability to interact with selected SH3 domains was finally assayed by Surface Plasmon Resonance spectroscopy. As a result, only Fyn, Hck, and c-Src SH3 domains gave positive results in terms of interaction, showing dissociation constants ranging from nanomolar to micromolar values having the best performer a KD of 148 nM. It is noteworthy that all the interacting domains belong to the Src kinases family, suggesting a role for p1932 as a modulator of the signal transduction pathways mediated by these kinases. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 714-725, 2016.

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.

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

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.

A Resource for the Network Representation of Cell Perturbations Caused by SARS-CoV-2 Infection.

The coronavirus disease 2019 (COVID-19) pandemic has caused more than 2.3 million casualties worldwide and the lack of effective treatments is a major health concern. The development of targeted drugs is held back due to a limited understanding of the molecular mechanisms underlying the perturbation of cell physiology observed after viral infection. Recently, several approaches, aimed at identifying cellular proteins that may contribute to COVID-19 pathology, have been reported. Albeit valuable, this information offers limited mechanistic insight as these efforts have produced long lists of cellular proteins, the majority of which are not annotated to any cellular pathway. We have embarked in a project aimed at bridging this mechanistic gap by developing a new bioinformatic approach to estimate the functional distance between a subset of proteins and a list of pathways. A comprehensive literature search allowed us to annotate, in the SIGNOR 2.0 resource, causal information underlying the main molecular mechanisms through which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and related coronaviruses affect the host-cell physiology. Next, we developed a new strategy that enabled us to link SARS-CoV-2 interacting proteins to cellular phenotypes via paths of causal relationships. Remarkably, the extensive information about inhibitors of signaling proteins annotated in SIGNOR 2.0 makes it possible to formulate new potential therapeutic strategies. The proposed approach, which is generally applicable, generated a literature-based causal network that can be used as a framework to formulate informed mechanistic hypotheses on COVID-19 etiology and pathology.

Myo-REG: A Portal for Signaling Interactions in Muscle Regeneration.

Muscle regeneration is a complex process governed by the interplay between several muscle-resident mononuclear cell populations. Following acute or chronic damage these cell populations are activated, communicate via cell-cell interactions and/or paracrine signals, influencing fate decisions via the activation or repression of internal signaling cascades. These are highly dynamic processes, occurring with distinct temporal and spatial kinetics. The main challenge toward a system level description of the muscle regeneration process is the integration of this plethora of inter- and intra-cellular interactions. We integrated the information on muscle regeneration in a web portal. The scientific content annotated in this portal is organized into two information layers representing relationships between different cell types and intracellular signaling-interactions, respectively. The annotation of the pathways governing the response of each cell type to a variety of stimuli/perturbations occurring during muscle regeneration takes advantage of the information stored in the SIGNOR database. Additional curation efforts have been carried out to increase the coverage of molecular interactions underlying muscle regeneration and to annotate cell-cell interactions. To facilitate the access to information on cell and molecular interactions in the context of muscle regeneration, we have developed Myo-REG, a web portal that captures and integrates published information on skeletal muscle regeneration. The muscle-centered resource we provide is one of a kind in the myology field. A friendly interface allows users to explore, approximately 100 cell interactions or to analyze intracellular pathways related to muscle regeneration. Finally, we discuss how data can be extracted from this portal to support in silico modeling experiments.

Characterization by mass cytometry of different methods for the preparation of muscle mononuclear cells.

Biological processes that are mediated by cell-cell interactions in heterogeneous populations are best approached by methods that have single cell resolution. Most of these methods rely on the preparation, from solid tissues, of cell suspensions by enzymatic digestion, followed by analysis of single cell reactivity to an antibody panel that allows the discrimination of cell populations and characterization of their activation state. Thus for any specific biological problem, both efficient and at the same time mild, protocols for cell separation, together with tissue specific panels of antibodies, need to be developed and optimized. Here we characterize an antibody panel that permits the discrimination of mononuclear muscle cell populations by mass cytometry and use it to characterize the cell populations obtained by three different cell extraction procedures from muscle fibers. We show that our panel of antibodies, albeit limited and incomplete, is sufficient to discriminate most of the mononuclear muscle cell populations and that each cell extraction method yields heterogeneous cell populations with a different relative abundance of the distinct cell types.

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.

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.

Combining affinity proteomics and network context to identify new phosphatase substrates and adapters in growth pathways.

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.

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/).

A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules.

Peptide recognition modules mediate many protein-protein interactions critical for the assembly of macromolecular complexes. Complete genome sequences have revealed thousands of these domains, requiring improved methods for identifying their physiologically relevant binding partners. We have developed a strategy combining computational prediction of interactions from phage-display ligand consensus sequences with large-scale two-hybrid physical interaction tests. Application to yeast SH3 domains generated a phage-display network containing 394 interactions among 206 proteins and a two-hybrid network containing 233 interactions among 145 proteins. Graph theoretic analysis identified 59 highly likely interactions common to both networks. Las17 (Bee1), a member of the Wiskott-Aldrich Syndrome protein (WASP) family of actin-assembly proteins, showed multiple SH3 interactions, many of which were confirmed in vivo by coimmunoprecipitation.