Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer

Src activation by Chk1 promotes actin patch formation and prevents chromatin bridge breakage in cytokinesis

In cytokinesis with chromatin bridges, cells delay abscission and retain actin patches at the intercellular canal to prevent chromosome breakage. In this study, we show that inhibition of Src, a protein-tyrosine kinase that regulates actin dynamics, or Chk1 kinase correlates with chromatin breakage and impaired formation of actin patches but not with abscission in the presence of chromatin bridges. Chk1 is required for optimal localization and complete activation of Src. Furthermore, Chk1 phosphorylates human Src at serine 51, and phosphorylated Src localizes to actin patches, the cell membrane, or the nucleus. Nonphosphorylatable mutation of S51 to alanine reduces Src catalytic activity and impairs formation of actin patches, whereas expression of a phosphomimicking Src-S51D protein rescues actin patches and prevents chromatin breakage in Chk1-deficient cells. We propose that Chk1 phosphorylates Src-S51 to fully induce Src kinase activity and that phosphorylated Src promotes formation of actin patches and stabilizes chromatin bridges. These results identify proteins that regulate formation of actin patches in cytokinesis

Accurate Prediction of Peptide Binding Sites on Protein Surfaces

Many important protein–protein interactions are mediated by the binding of a short peptide stretch in one protein to a large globular segment in another. Recent efforts have provided hundreds of examples of new peptides binding to proteins for which a three-dimensional structure is available (either known experimentally or readily modeled) but where no structure of the protein–peptide complex is known. To address this gap, we present an approach that can accurately predict peptide binding sites on protein surfaces. For peptides known to bind a particular protein, the method predicts binding sites with great accuracy, and the specificity of the approach means that it can also be used to predict whether or not a putative or predicted peptide partner will bind. We used known protein–peptide complexes to derive preferences, in the form of spatial position specific scoring matrices, which describe the binding-site environment in globular proteins for each type of amino acid in bound peptides. We then scan the surface of a putative binding protein for sites for each of the amino acids present in a peptide partner and search for combinations of high-scoring amino acid sites that satisfy constraints deduced from the peptide sequence. The method performed well in a benchmark and largely agreed with experimental data mapping binding sites for several recently discovered interactions mediated by peptides, including RG-rich proteins with SMN domains, Epstein-Barr virus LMP1 with TRADD domains, DBC1 with Sir2, and the Ago hook with Argonaute PIWI domain. The method, and associated statistics, is an excellent tool for predicting and studying binding sites for newly discovered peptides mediating critical events in biology

Src homology 2 domain containing protein 5 (SH2D5) binds the breakpoint cluster region protein, BCR, and regulates levels of Rac1-GTP

SH2D5 is a mammalian-specific, uncharacterized adaptor-like protein that contains an N-terminal phosphotyrosine binding (PTB) domain and a C-terminal Src Homology 2 (SH2) domain. We show that SH2D5 is highly enriched in adult mouse brain, particularly in purkinjie cells in the cerebellum and the cornu ammonis of the hippocampus. Despite harboring two potential phosphotyrosine (pTyr) recognition domains, SH2D5 binds minimally to pTyr ligands, consistent with the absence of a conserved pTyr-binding arginine residue in the SH2 domain. Immunoprecipitation coupled to mass spectrometry (IP-MS) from cultured cells revealed a prominent association of SH2D5 with Breakpoint Cluster Region protein (BCR), a RacGAP that is also highly expressed in brain. This interaction occurred between the PTB domain of SH2D5 and an NxxF motif located within the N-terminal region of BCR. siRNA-mediated depletion of SH2D5 in a neuroblastoma cell line, B35, induced a cell rounding phenotype correlated with low levels of activated Rac1-GTP, suggesting that SH2D5 affects Rac1-GTP levels. Taken together, our data provide the first characterization of the SH2D5 signaling protein

Prediction of Signed Protein Kinase Regulatory Circuits.

Complex networks of regulatory relationships between protein kinases comprise a major component of intracellular signaling. Although many kinase-kinase regulatory relationships have been described in detail, these tend to be limited to well-studied kinases whereas the majority of possible relationships remains unexplored. Here, we implement a data-driven, supervised machine learning method to predict human kinase-kinase regulatory relationships and whether they have activating or inhibiting effects. We incorporate high-throughput data, kinase specificity profiles, and structural information to produce our predictions. The results successfully recapitulate previously annotated regulatory relationships and can reconstruct known signaling pathways from the ground up. The full network of predictions is relatively sparse, with the vast majority of relationships assigned low probabilities. However, it nevertheless suggests denser modes of inter-kinase regulation than normally considered in intracellular signaling research. A record of this paper's transparent peer review process is included in the Supplemental Information

Phosfinder: a web server for the identification of phosphate-binding sites on protein structures

Phosfinder is a web server for the identification of phosphate binding sites in protein structures. Phosfinder uses a structural comparison algorithm to scan a query structure against a set of known 3D phosphate binding motifs. Whenever a structural similarity between the query protein and a phosphate binding motif is detected, the phosphate bound by the known motif is added to the protein structure thus representing a putative phosphate binding site. Predicted binding sites are then evaluated according to (i) their position with respect to the query protein solvent-excluded surface and (ii) the conservation of the binding residues in the protein family. The server accepts as input either the PDB code of the protein to be analyzed or a user-submitted structure in PDB format. All the search parameters are user modifiable. Phosfinder outputs a list of predicted binding sites with detailed information about their structural similarity with known phosphate binding motifs, and the conservation of the residues involved. A graphical applet allows the user to visualize the predicted binding sites on the query protein structure. The results on a set of 52 apo/holo structure pairs show that the performance of our method is largely unaffected by ligand-induced conformational changes. Phosfinder is available at http://phosfinder.bio.uniroma2.it

Rosetta FlexPepDock web server—high resolution modeling of peptide–protein interactions

Peptide–protein interactions are among the most prevalent and important interactions in the cell, but a large fraction of those interactions lack detailed structural characterization. The Rosetta FlexPepDock web server (http://flexpepdock.furmanlab.cs.huji.ac.il/) provides an interface to a high-resolution peptide docking (refinement) protocol for the modeling of peptide–protein complexes, implemented within the Rosetta framework. Given a protein receptor structure and an approximate, possibly inaccurate model of the peptide within the receptor binding site, the FlexPepDock server refines the peptide to high resolution, allowing full flexibility to the peptide backbone and to all side chains. This protocol was extensively tested and benchmarked on a wide array of non-redundant peptide–protein complexes, and was proven effective when applied to peptide starting conformations within 5.5 Å backbone root mean square deviation from the native conformation. FlexPepDock has been applied to several systems that are mediated and regulated by peptide–protein interactions. This easy to use and general web server interface allows non-expert users to accurately model their specific peptide–protein interaction of interest

SuperTarget and Matador: resources for exploring drug-target relationships

The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use query interface enables the user to pose complex queries, for example to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.d

The identification of short linear motif-mediated interfaces within the human interactome

Motivation: Eukaryotic proteins are highly modular, containing multiple interaction interfaces that mediate binding to a network of regulators and effectors. Recent advances in high-throughput proteomics have rapidly expanded the number of known protein–protein interactions (PPIs); however, the molecular basis for the majority of these interactions remains to be elucidated. There has been a growing appreciation of the importance of a subset of these PPIs, namely those mediated by short linear motifs (SLiMs), particularly the canonical and ubiquitous SH2, SH3 and PDZ domain-binding motifs. However, these motif classes represent only a small fraction of known SLiMs and outside these examples little effort has been made, either bioinformatically or experimentally, to discover the full complement of motif instances