Simultaneous Genome-Wide Inference of Physical, Genetic, Regulatory, and Functional Pathway Components

Biomolecular pathways are built from diverse types of pairwise interactions, ranging from physical protein-protein interactions and modifications to indirect regulatory relationships. One goal of systems biology is to bridge three aspects of this complexity: the growing body of high-throughput data assaying these interactions; the specific interactions in which individual genes participate; and the genome-wide patterns of interactions in a system of interest. Here, we describe methodology for simultaneously predicting specific types of biomolecular interactions using high-throughput genomic data. This results in a comprehensive compendium of whole-genome networks for yeast, derived from ∼3,500 experimental conditions and describing 30 interaction types, which range from general (e.g. physical or regulatory) to specific (e.g. phosphorylation or transcriptional regulation). We used these networks to investigate molecular pathways in carbon metabolism and cellular transport, proposing a novel connection between glycogen breakdown and glucose utilization supported by recent publications. Additionally, 14 specific predicted interactions in DNA topological change and protein biosynthesis were experimentally validated. We analyzed the systems-level network features within all interactomes, verifying the presence of small-world properties and enrichment for recurring network motifs. This compendium of physical, synthetic, regulatory, and functional interaction networks has been made publicly available through an interactive web interface for investigators to utilize in future research at http://function.princeton.edu/bioweaver/

The MIA pathway: a key regulator of mitochondrial oxidative protein folding and biogenesis

Mitochondria are fundamental intracellular organelles with key roles in important cellular processes like energy production, Fe/S cluster biogenesis, and homeostasis of lipids and inorganic ions. Mitochondrial dysfunction is consequently linked to many human pathologies (cancer, diabetes, neurodegeneration, stroke) and apoptosis. Mitochondrial biogenesis relies on protein import as most mitochondrial proteins (about 10-15% of the human proteome) are imported after their synthesis in the cytosol. Over the last several years many mitochondrial translocation pathways have been discovered. Among them, the import pathway that targets proteins to the intermembrane space (IMS) stands out as it is the only one that couples import to folding and oxidation and results in the covalent modification of the incoming precursor that adopt internal disulfide bonds in the process (the MIA pathway). The discovery of this pathway represented a significant paradigm shift as it challenged the prevailing dogma that the endoplasmic reticulum is the only compartment of eukaryotic cells where oxidative folding can occur. The concept of the oxidative folding pathway was first proposed on the basis of folding and import data for the small Tim proteins that have conserved cysteine motifs and must adopt intramolecular disulfides after import so that they are retained in the organelle. The introduction of disulfides in the IMS is catalyzed by Mia40 that functions as a chaperone inducing their folding. The sulfhydryl oxidase Erv1 generates the disulfide pairs de novo using either molecular oxygen or, cytochrome c and other proteins as terminal electron acceptors that eventually link this folding process to respiration. The solution NMR structure of Mia40 (and supporting biochemical experiments) showed that Mia40 is a novel type of disulfide donor whose recognition capacity for its substrates relies on a hydrophobic binding cleft found adjacent to a thiol active CPC motif. Targeting of the substrates to this pathway is guided by a novel type of IMS targeting signal called ITS or MISS. This consists of only 9 amino acids, found upstream or downstream of a unique Cys that is primed for docking to Mia40 when the substrate is accommodated in the Mia40 binding cleft. Different routes exist to complete the folding of the substrates and their final maturation in the IMS. Identification of new Mia40 substrates (some even without the requirement of their cysteines) reveals an expanded chaperone-like activity of this protein in the IMS. New evidence on the targeting of redox active proteins like thioredoxin, glutaredoxin, and peroxiredoxin into the IMS suggests the presence of redox-dependent regulatory mechanisms of the protein folding and import process in mitochondria. Maintenance of redox balance in mitochondria is crucial for normal cell physiology and depends on the cross-talk between the various redox signaling processes and the mitochondrial oxidative folding pathway

A yeast-based assay for protein tyrosine kinase substrate specificity and inhibitor resistance

Phosphorylation of tyrosines by protein kinases is a fundamental mode of signal transduction in all eukaryotic cells, leading to a wide variety of cellular outcomes, including proliferation, differentiation, transcriptional activation, and programmed cell death. Perturbations to tyrosine kinase signaling networks by activation, overexpression, or mutation is the driving factor in many diseases, most notably cancers. The development of tyrosine kinase inhibitors, 37 of which are currently FDA-approved, has led to a revolution in cancer treatment. Imatinib, the first FDA-approved kinase inhibitor, has drastically improved prognosis for patients with Bcr-abl-positive leukemias. Despite this unprecedented success, however, up to one-third of patients lose response to imatinib due to mutations within the tyrosine kinase domain of Bcr-abl. Subsequent generations of Bcr-abl inhibitors, including dasatinib and ponatinib, have been developed to overcome these resistance mutations, but in each case, novel resistance mutations have arisen. We present a high-throughput yeast-based assay for the prediction of dasatinib- and ponatinib-resistant mutations in the ABL1 kinase domain. Our results not only recapitulate all known dasatinib-resistant mutations, but confirm recent patient data emphasizing the importance of compound mutations in ponatinib resistance. Furthermore, with hundreds of kinase inhibitors in development for the treatment of a wide range of diseases, understanding the cellular pathway of each kinase is critically important to the selection of ideal drug targets and avoiding potentially toxic side effects. Discovery of novel tyrosine kinase substrates is hindered by the presence of 90 human tyrosine kinases, which are often active in the same pathways. Phosphoproteomics, chemical genetics, and in vitro assays have been used to great success, yet only 30% of phosphorylated tyrosines in the human proteome have been assigned to a specific kinase. Recent advances in predicting tyrosine kinase substrates have been made by combining large data sets on kinase domain specificity, cellular localization, and protein-protein interactions in probabilistic algorithms. However, the high-quality data sets required for accurate predictions are often lacking. In chapter 2, we present a high-throughput yeastbased assay for screening millions of putative kinase substrates, which we then use to build a probabilistic model to accurately predict the in vitro phosphorylation of candidate substratesBiochemistr

Dynamic, but not necessarily disordered, human-virus interactions mediated through slims in viral proteins

Most viruses have small genomes that encode proteins needed to perform essential enzy-matic functions. Across virus families, primary enzyme functions are under functional constraint; however, secondary functions mediated by exposed protein surfaces that promote interactions with the host proteins may be less constrained. Viruses often form transient interactions with host proteins through conformationally flexible interfaces. Exposed flexible amino acid residues are known to evolve rapidly suggesting that secondary functions may generate diverse interaction potentials between viruses within the same viral family. One mechanism of interaction is viral mimicry through short linear motifs (SLiMs) that act as functional signatures in host proteins. Viral SLiMs display specific patterns of adjacent amino acids that resemble their host SLiMs and may occur by chance numerous times in viral proteins due to mutational and selective processes. Through mimicry of SLiMs in the host cell proteome, viruses can interfere with the protein interaction network of the host and utilize the host-cell machinery to their benefit. The overlap between rapidly evolving protein regions and the location of functionally critical SLiMs suggest that these motifs and their functional potential may be rapidly rewired causing variation in pathogenicity, infectivity, and virulence of related viruses. The following review provides an overview of known viral SLiMs with select examples of their role in the life cycle of a virus, and a discussion of the structural properties of experimentally validated SLiMs highlighting that a large portion of known viral SLiMs are devoid of predicted intrinsic disorder based on the viral SLiMs from the ELM database

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk

LocateP: Genome-scale subcellular-location predictor for bacterial proteins

Contains fulltext : 69477.pdf ( ) (Open Access)BACKGROUND: In the past decades, various protein subcellular-location (SCL) predictors have been developed. Most of these predictors, like TMHMM 2.0, SignalP 3.0, PrediSi and Phobius, aim at the identification of one or a few SCLs, whereas others such as CELLO and Psortb.v.2.0 aim at a broader classification. Although these tools and pipelines can achieve a high precision in the accurate prediction of signal peptides and transmembrane helices, they have a much lower accuracy when other sequence characteristics are concerned. For instance, it proved notoriously difficult to identify the fate of proteins carrying a putative type I signal peptidase (SPIase) cleavage site, as many of those proteins are retained in the cell membrane as N-terminally anchored membrane proteins. Moreover, most of the SCL classifiers are based on the classification of the Swiss-Prot database and consequently inherited the inconsistency of that SCL classification. As accurate and detailed SCL prediction on a genome scale is highly desired by experimental researchers, we decided to construct a new SCL prediction pipeline: LocateP. RESULTS: LocateP combines many of the existing high-precision SCL identifiers with our own newly developed identifiers for specific SCLs. The LocateP pipeline was designed such that it mimics protein targeting and secretion processes. It distinguishes 7 different SCLs within Gram-positive bacteria: intracellular, multi-transmembrane, N-terminally membrane anchored, C-terminally membrane anchored, lipid-anchored, LPxTG-type cell-wall anchored, and secreted/released proteins. Moreover, it distinguishes pathways for Sec- or Tat-dependent secretion and alternative secretion of bacteriocin-like proteins. The pipeline was tested on data sets extracted from literature, including experimental proteomics studies. The tests showed that LocateP performs as well as, or even slightly better than other SCL predictors for some locations and outperforms current tools especially where the N-terminally anchored and the SPIase-cleaved secreted proteins are concerned. Overall, the accuracy of LocateP was always higher than 90%. LocateP was then used to predict the SCLs of all proteins encoded by completed Gram-positive bacterial genomes. The results are stored in the database LocateP-DB http://www.cmbi.ru.nl/locatep-db1. CONCLUSION: LocateP is by far the most accurate and detailed protein SCL predictor for Gram-positive bacteria currently available

Refining Protein Subcellular Localization

The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality