ABSTRACT
SUMMARY: CycSim is a web application dedicated to in silico experiments with genome-scale metabolic models coupled to the exploration of knowledge from BioCyc and KEGG. Specifically, CycSim supports the design of knockout experiments: simulation of growth phenotypes of single or multiple gene deletions mutants on specified media, comparison of these predictions with experimental phenotypes and direct visualization of both on metabolic maps. The web interface is designed for simplicity, putting constraint-based modelling techniques within easier reach of biologists. CycSim also functions as an online repository of genome-scale metabolic models. AVAILABILITY: http://www.genoscope.cns.fr/cycsim.
Subject(s)
Genome , Genomics/methods , Metabolism/genetics , Software , Computational Biology , Databases, Genetic , Internet , User-Computer InterfaceSubject(s)
Computational Biology/methods , Proteins/chemistry , Algorithms , Amino Acid Sequence , Dimerization , Gene Library , Helicobacter pylori/metabolism , Models, Biological , Molecular Sequence Data , Protein Binding , Protein Structure, Tertiary , Sequence Homology, Amino Acid , Software , Two-Hybrid System TechniquesABSTRACT
UNLABELLED: A number of predictive methods have been designed to predict protein interaction from sequence or expression data. On the experimental front, however, high-throughput proteomics technologies are starting to yield large volumes of protein-protein interaction data. High-quality experimental protein interaction maps constitute the natural dataset upon which to build interaction predictions. Thus the motivation to develop the first interaction-based protein interaction map prediction algorithm. A technique to predict protein-protein interaction maps across organisms is introduced, the 'interaction-domain pair profile' method. The method uses a high-quality protein interaction map with interaction domain information as input to predict an interaction map in another organism. It combines sequence similarity searches with clustering based on interaction patterns and interaction domain information. We apply this approach to the prediction of an interaction map of Escherichia coli from the recently published interaction map of the human gastric pathogen Helicobacter pylori. Results are compared with predictions of a second inference method based only on full-length protein sequence similarity - the "naive" method. The domain-based method is shown to i) eliminate a significant amount of false-positives of the naive method that are the consequences of multi-domain proteins; ii) increase the sensitivity compared to the naive method by identifying new potential interactions. AVAILABILITY: Contact the authors.
Subject(s)
Algorithms , Proteins/chemistry , Proteins/metabolism , Amino Acid Sequence , Binding Sites , Computational Biology , Databases, Protein , Escherichia coli Proteins/chemistry , Escherichia coli Proteins/genetics , Escherichia coli Proteins/metabolism , Macromolecular Substances , Molecular Sequence Data , Molecular Structure , Peptide Mapping/statistics & numerical data , Protein Structure, Tertiary , Proteins/genetics , Software , Software DesignABSTRACT
With the availability of complete DNA sequences for many prokaryotic and eukaryotic genomes, and soon for the human genome itself, it is important to develop reliable proteome-wide approaches for a better understanding of protein function. As elementary constituents of cellular protein complexes and pathways, protein-protein interactions are key determinants of protein function. Here we have built a large-scale protein-protein interaction map of the human gastric pathogen Helicobacter pylori. We have used a high-throughput strategy of the yeast two-hybrid assay to screen 261 H. pylori proteins against a highly complex library of genome-encoded polypeptides. Over 1,200 interactions were identified between H. pylori proteins, connecting 46.6% of the proteome. The determination of a reliability score for every single protein-protein interaction and the identification of the actual interacting domains permitted the assignment of unannotated proteins to biological pathways.
Subject(s)
Bacterial Proteins/metabolism , Helicobacter pylori/metabolism , Amino Acid Sequence , Binding Sites , Databases, Factual , Escherichia coli/genetics , Gene Library , Humans , Internet , Molecular Sequence Data , Protein Binding , Proteome , Sequence Alignment , Software , Urease/metabolismABSTRACT
Recent advances in genomics have led to the accumulation of an unprecedented amount of data about genes. Proteins, not genes, however, sustain function. The traditional approach to protein function analysis has been the design of smart genetic assays and powerful purification protocols to address very specific questions concerning cellular mechanisms. Lately, a number of proteome-wide functional strategies have emerged, giving rise to a new field in biology, proteomics, that addresses the biology of a cell as a whole.
Subject(s)
Molecular Biology/methods , Proteins/genetics , Proteins/metabolism , Molecular Biology/trends , Mutation , Peptide Library , Transcription, Genetic , Two-Hybrid System TechniquesABSTRACT
In the wake of sequencing projects, protein function analysis is evolving fast, from the careful design of assays that address specific questions to 'large-scale' proteomics technologies that yield proteome-wide maps of protein expression or interaction. As these new technologies depend heavily on information storage, representation and analysis, existing databases and software tools are being adapted, while new resources are emerging. This paper describes the proteomics databases and software available through the World-Wide Web, focusing on their present use and applicability. As the resource situation is highly transitory, trends and probable evolutions are discussed whenever applicable.
