Institute for Systems Biology

About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.

A network of protein?protein interactions in yeast

Abstract: A global analysis of 2,709 published interactions between proteins of the yeast Saccharomyces cerevisiae has been performed, enabling the establishment of a single large network of 2,358 interactions among 1,548 proteins. Proteins of known function and cellular location tend to cluster together, with 63% of the interactions occurring between proteins with a common functional assignment and 76% occurring between proteins found in the same subcellular compartment. Possible functions can be assigned to a protein based on the known functions of its interacting partners. This approach correctly predicts a functional category for 72% of the 1,393 characterized proteins with at least one partner of known function, and has been applied to predict functions for 364 previously uncharacterized proteins.

Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry

Abstract: Quantitative proteome profiling using stable isotope protein tagging and automated tandem mass spectrometry (MS/MS) is an emerging technology with great potential for the functional analysis of biological systems and for the detection of clinical diagnostic or prognostic marker proteins. Owing to the enormous complexity of proteomes, their comprehensive analysis is an as-yet-unresolved technical challenge. However, biologically or clinically important information can be obtained if specific, information-rich protein classes, or sub-proteomes, are isolated and analyzed. Glycosylation is the most common post-translational modification. Here we describe a method for the selective isolation, identification and quantification of peptides that contain N-linked carbohydrates. It is based on the conjugation of glycoproteins to a solid support using hydrazide chemistry, stable isotope labeling of glycopeptides and the specific release of formerly N-linked glycosylated peptides via peptide- N-glycosidase F (PNGase F). The recovered peptides are then identified and quantified by MS/MS. We applied the approach to the analysis of plasma membrane proteins and proteins contained in human blood serum.

Evolutionary and biomedical insights from the rhesus macaque genome

Abstract: The rhesus macaque (Macaca mulatta) is an abundant primate species that diverged from the ancestors of Homo sapiens about 25 million years ago. Because they are genetically and physiologically similar to humans, rhesus monkeys are the most widely used nonhuman primate in basic and applied biomedical research. We determined the genome sequence of an Indian-origin Macaca mulatta female and compared the data with chimpanzees and humans to reveal the structure of ancestral primate genomes and to identify evidence for positive selection and lineage-specific expansions and contractions of gene families. A comparison of sequences from individual animals was used to investigate their underlying genetic diversity. The complete description of the macaque genome blueprint enhances the utility of this animal model for biomedical research and improves our understanding of the basic biology of the species.

Protein tolerance to random amino acid change

Abstract: Mutagenesis of protein-encoding sequences occurs ubiquitously; it enables evolution, accumulates during aging, and is associated with disease Many biotechnological methods exploit random mutations to evolve novel proteins To quantitate protein tolerance to random change, it is vital to understand the probability that a random amino acid replacement will lead to a protein's functional inactivation We define this probability as the "x factor" Here, we develop a broadly applicable approach to calculate x factors and demonstrate this method using the human DNA repair enzyme 3-methyladenine DNA glycosylase (AAG) Three gene-wide mutagenesis libraries were created, each with 10(5) diversity and averaging 22, 46, and 62 random amino acid changes per mutant After determining the percentage of functional mutants in each library using high-stringency selection (>19,000-fold), the x factor was found to be 34% +/- 6% Remarkably, reanalysis of data from studies of diverse proteins reveals similar inactivation probabilities To delineate the nature of tolerated amino acid substitutions, we sequenced 244 surviving AAG mutants The 920 tolerated substitutions were characterized by substitutability index and mapped onto the AAG primary, secondary, and known tertiary structures Evolutionarily conserved residues show low substitutability indices In AAG, beta strands are on average less substitutable than alpha helices; and surface loops that are not involved in DNA binding are the most substitutable Our results are relevant to such diverse topics as applied molecular evolution, the rate of introduction of deleterious alleles into genomes in evolutionary history, and organisms' tolerance of mutational burden

Immunoaffinity profiling of tyrosine phosphorylation in cancer cells

Abstract: Tyrosine kinases play a prominent role in human cancer, yet the oncogenic signaling pathways driving cell proliferation and survival have been difficult to identify, in part because of the complexity of the pathways and in part because of low cellular levels of tyrosine phosphorylation. In general, global phosphoproteomic approaches reveal small numbers of peptides containing phosphotyrosine. We have developed a strategy that emphasizes the phosphotyrosine component of the phosphoproteome and identifies large numbers of tyrosine phosphorylation sites. Peptides containing phosphotyrosine are isolated directly from protease-digested cellular protein extracts with a phosphotyrosine-specific antibody and are identified by tandem mass spectrometry. Applying this approach to several cell systems, including cancer cell lines, shows it can be used to identify activated protein kinases and their phosphorylated substrates without prior knowledge of the signaling networks that are activated, a first step in profiling normal and oncogenic signaling networks.