Search

DIMA-the domain interaction map has evolved from a simple web server for domain phylogenetic profiling into an integrative prediction resource combining both experimental data on domain-domain interactions and predictions from two different algorithms. With this update, DIMA obtains greatly improved coverage at the level of genomes and domains as well as with respect to available prediction approaches. The domain phylogenetic profiling method now uses SIMAP as its backend for exhaustive domain hit coverage: 7038 Pfam domains were profiled over 460 completely sequenced genomes. Domain pair exclusion predictions were produced from 83 969 distinct protein-protein interactions obtained from IntAct resulting in 21 513 domain pairs with significant domain pair exclusion algorithm scores. Additional predictions applying the same algorithm to predicted protein interactions from STRING yielded 2378 high-confidence pairs. Experimental data comes from iPfam (3074) and 3did (3034 pairs), two databases identifying domain contacts in solved protein structures. Taken together, these two resources yielded 3653 distinct interacting domain pairs. DIMA is available at http://mips.gsf.de/genre/proj/dima.

With the complete human genomic sequence being unraveled, the focus will shift to gene identification and to the functional analysis of gene products. The generation of a set of cDNAs, both sequences and physical clones, which contains the complete and noninterrupted protein coding regions of all human genes will provide the indispensable tools for the systematic and comprehensive analysis of protein function to eventually understand the molecular basis of man. Here we report the sequencing and analysis of 500 novel human cDNAs containing the complete protein coding frame. Assignment to functional categories was possible for 52% (259) of the encoded proteins, the remaining fraction having no similarities with known proteins. By aligning the cDNA sequences with the sequences of the finished chromosomes 21 and 22 we identified a number of genes that either had been completely missed in the analysis of the genomic sequences or had been wrongly predicted. Three of these genes appear to be present in several copies. We conclude that full-length cDNA sequencing continues to be crucial also for the accurate identification of genes. The set of 500 novel cDNAs, and another 1000 full-coding cDNAs of known transcripts we have identified, adds up to cDNA representations covering 2%–5 % of all human genes. We thus substantially contribute to the generation of a gene catalog, consisting of both full-coding cDNA sequences and clones, which should be made freely available and will become an invaluable tool for detailed functional studies. [The sequence data described in this paper have been submitted to the EMBL database under the accession nos. given in Table ​Table22.] Table 2 Functional Classification of Individual cDNAsa The recent past has witnessed major advances in the determination of the sequence of the human genome (Dunham et al. 1999; Hattori et al. 2000). Although the whole genomic sequence will be completely unraveled in the near future (Collins et al. 1998), the identification of genes and the deciphering of gene structures will extend for a prolonged time, and cDNA sequences will continue to be invaluable tools for this adventure, especially in view of alternative splicing. The primary focus will shift to the functional analysis of the genes and their protein products to finally understand the molecular basis of human life. Current estimates vary between 29,000 and >70,000 genes to constitute the protein coding repertoire of the human genome (Fields et al. 1994; Ewing and Green 2000; Liang et al. 2000; Roest Crollius et al. 2000). However, thus far only some 11,000 cDNA sequences have been deposited in public databases, which are supposed to contain the complete protein coding open reading frame (ORF). The majority of the respective cDNA clones are most likely not accessible. The generation of a physical clone set representing all human genes that should be made freely accessible is consequently regarded to have an extremely high impact (Schuler 1997; Pruitt et al. 2000). This would permit the establishment of a catalog of clones to provide the resources needed in the proteomics era where the functions of proteins, their action in pathways, and the possible disease relation are deciphered. Until recently, the long-cDNA sequencing project carried out at the Kazusa Institute (Nomura et al. 1994; Nagase et al. 2000) Consortium had been the only systematic full-length cDNA sequencing project with a significant output of novel sequence information. The initiation of a new large-scale cDNA sequencing project has been announced lately that is coordinated by the National Institute of Health (Strausberg et al. 1999). We founded a cDNA Consortium in 1997 as part of the German Genome Project and aim at the characterization of the complete sequences of novel human transcripts at the cDNA level. Here, we report the sequences and analysis of 500 novel human cDNAs that all contain the complete protein coding region. These cDNAs constitute the most valuable essence of 30,000 clones that have been EST sequenced and 3630 fully sequenced cDNAs. Over 1000 cDNAs that cover the complete coding sequence of already known transcripts have been identified in the EST-sequenced clone set. All clones are made available through the Resource Center of the German Genome Project (RZPD).

We describe a genome-wide characterization of mRNA transcript levels in yeast grown on the fatty acid oleate, determined using Serial Analysis of Gene Expression (SAGE). Comparison of this SAGE library with that reported for glucose grown cells revealed the dramatic adaptive response of yeast to a change in carbon source. A major fraction (>20%) of the 15,000 mRNA molecules in a yeast cell comprised differentially expressed transcripts, which were derived from only 2% of the total number of ∼6300 yeast genes. Most of the mRNAs that were differentially expressed code for enzymes or for other proteins participating in metabolism (e.g., metabolite transporters). In oleate-grown cells, this was exemplified by the huge increase of mRNAs encoding the peroxisomal β-oxidation enzymes required for degradation of fatty acids. The data provide evidence for the existence of redox shuttles across organellar membranes that involve peroxisomal, cytoplasmic, and mitochondrial enzymes. We also analyzed the mRNA profile of a mutant strain with deletions of the PIP2 and OAF1 genes, encoding transcription factors required for induction of genes encoding peroxisomal proteins. Induction of genes under the immediate control of these factors was abolished; other genes were up-regulated, indicating an adaptive response to the changed metabolism imposed by the genetic impairment. We describe a statistical method for analysis of data obtained by SAGE.

Background We have recently released a comprehensive, manually curated database of mammalian protein complexes called CORUM. Combining CORUM with other resources, we assembled a dataset of over 2700 mammalian complexes. The availability of a rich information resource allows us to search for organizational properties concerning these complexes. Results As the complexity of a protein complex in terms of the number of unique subunits increases, we observed that the number of such complexes and the mean non-synonymous to synonymous substitution ratio of associated genes tend to decrease. Similarly, as the number of different complexes a given protein participates in increases, the number of such proteins and the substitution ratio of the associated gene also tends to decrease. These observations provide evidence relating natural selection and the organization of mammalian complexes. We also observed greater homogeneity in terms of predicted protein isoelectric points, secondary structure and substitution ratio in annotated versus randomly generated complexes. A large proportion of the protein content and interactions in the complexes could be predicted from known binary protein-protein and domain-domain interactions. In particular, we found that large proteins interact preferentially with much smaller proteins. Conclusion We observed similar trends in yeast and other data. Our results support the existence of conserved relations associated with the mammalian protein complexes.

Conserved domains carry many of the functional features found in the proteins of an organism. This includes not only catalytic activity, substrate binding, and structural features but also molecular adapters, which mediate the physical interactions between proteins or proteins with other molecules. In addition, two conserved domains can be linked not by physical contact but by a common function like forming a binding pocket. Although a wealth of experimental data has been collected and carefully curated for protein-protein interactions, as of today little useful data is available from major databases with respect to relations on the domain level. This lack of data makes computational prediction of domain-domain interactions a very important endeavor. In this chapter, we discuss the available experimental data (iPfam) and describe some important approaches to the problem of identifying interacting and/or functionally linked domain pairs from different kinds of input data. Specifically, we will discuss phylogenetic profiling on the level of conserved protein domains on one hand and inference of domain-interactions from observed or predicted protein-protein interactions datasets on the other. We explore the predictive power of these predictions and point out the importance of deploying as many different methods as possible for the best results.

Conserved domains carry many of the functional features found in the proteins of an organism. This includes not only catalytic activity, substrate binding, and structural features but also molecular adapters, which mediate the physical interactions between proteins or proteins with other molecules. In addition, two conserved domains can be linked not by physical contact but by a common function like forming a binding pocket. Although a wealth of experimental data has been collected and carefully curated for protein-protein interactions, as of today little useful data is available from major databases with respect to relations on the domain level. This lack of data makes computational prediction of domain-domain interactions a very important endeavor. In this chapter, we discuss the available experimental data (iPfam) and describe some important approaches to the problem of identifying interacting and/or functionally linked domain pairs from different kinds of input data. Specifically, we will discuss phylogenetic profiling on the level of conserved protein domains on one hand and inference of domain-interactions from observed or predicted protein-protein interactions datasets on the other. We explore the predictive power of these predictions and point out the importance of deploying as many different methods as possible for the best results.

The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/.; International audience; The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/.

The Munich Information Center for Protein Sequences (MIPS-GSF), Neuherberg, Germany, provides protein sequence-related information based on whole-genome analysis. The main focus of the work is directed toward the systematic organization of sequence-related attributes as gathered by a variety of algorithms, primary information from experimental data together with information compiled from the scientific literature. MIPS maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the database of complete cDNAs (German Human Genome Project, NGFN), the database of mammalian protein-protein interactions (MPPI), the database of FASTA homologies (SIMAP), and the interface for the fast retrieval of protein-associated information (QUIPOS). The Arabidopsis thaliana database, the rice database, the plant EST databases (MATDB, MOsDB, SPUTNIK), as well as the databases for the comprehensive set of genomes (PEDANT genomes) are described elsewhere in the 2003 and 2004 NAR database issues, respectively. All databases described, and the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de).