Biomedical subjects
Publications and source records attributed to Julian Gough.
Most proteins have been formed by gene duplication, recombination, and divergence. Proteins of known structure can be matched to about 50% of genome sequences, and these data provide a quantitative description and can suggest hypotheses about the origins of these processes.
A general overview of the protein sequence set for the mouse transcriptome produced during the FANTOM2 sequencing project is presented here. We applied different algorithms to characterize protein sequences derived from a nonredundant representative protein set (RPS) and a variant protein set (VPS) of the mouse transcriptome. The functional characterization and assignment of Gene Ontology terms was done by analysis of the proteome using InterPro. The Superfamily database analyses gave a detailed structural classification according to SCOP and provide additional evidence for the functional characterization of the proteome data. The MDS database analysis revealed new domains which are not presented in existing protein domain databases. Thus the transcriptome gives us a unique source of data for the detection of new functional groups. The data obtained for the RPS and VPS sets facilitated the comparison of different patterns of protein expression. A comparison of other existing mouse and human protein sequence sets (e.g., the International Protein Index) demonstrates the common patterns in mammalian proteomes. The analysis of the membrane organization within the transcriptome of multiple eukaryotes provides valuable statistics about the distribution of secretory and transmembrane proteins
Manual curation has long been held to be the "gold standard" for functional annotation of DNA sequence. Our experience with the annotation of more than 20,000 full-length cDNA sequences revealed problems with this approach, including inaccurate and inconsistent assignment of gene names, as well as many good assignments that were difficult to reproduce using only computational methods. For the FANTOM2 annotation of more than 60,000 cDNA clones, we developed a number of methods and tools to circumvent some of these problems, including an automated annotation pipeline that provides high-quality preliminary annotation for each sequence by introducing an "uninformative filter" that eliminates uninformative annotations, controlled vocabularies to accurately reflect both the functional assignments and the evidence supporting them, and a highly refined, Web-based manual annotation tool that allows users to view a wide array of sequence analyses and to assign gene names and putative functions using a consistent nomenclature. The ultimate utility of our approach is reflected in the low rate of reassignment of automated assignments by manual curation. Based on these results, we propose a new standard for large-scale annotation, in which the initial automated annotations are manually investigated and then computational methods are iteratively modified and improved based on the results of manual curation.
The SUPERFAMILY hidden Markov model library representing all proteins of known structure predicts the domain architecture of protein sequences and classifies them at the SCOP superfamily level. This analysis has been carried out on all completely sequenced genomes. The ways in which the database can be useful to crystallographers is discussed, in particular with a view to high-throughput structure determination. The application of the SUPERFAMILY database to different target-selection strategies is suggested: novel folds, novel domain combinations and targeted attacks on genomes. Use of the database for more general inquiry in the context of structural studies is also explained. The database provides evolutionary relationships between target proteins and other proteins of known structure through the SCOP database, genome assignments and multiple sequence alignments.
Profile hidden Markov models (HMMs) are amongst the most successful procedures for detecting remote homology between proteins. There are two popular profile HMM programs, HMMER and SAM. Little is known about their performance relative to each other and to the recently improved version of PSI-BLAST. Here we compare the two programs to each other and to non-HMM methods, to determine their relative performance and the features that are important for their success. The quality of the multiple sequence alignments used to build models was the most important factor affecting the overall performance of profile HMMs. The SAM T99 procedure is needed to produce high quality alignments automatically, and the lack of an equivalent component in HMMER makes it less complete as a package. Using the default options and parameters as would be expected of an inexpert user, it was found that from identical alignments SAM consistently produces better models than HMMER and that the relative performance of the model-scoring components varies. On average, HMMER was found to be between one and three times faster than SAM when searching databases larger than 2000 sequences, SAM being faster on smaller ones. Both methods were shown to have effective low complexity and repeat sequence masking using their null models, and the accuracy of their E-values was comparable. It was found that the SAM T99 iterative database search procedure performs better than the most recent version of PSI-BLAST, but that scoring of PSI-BLAST profiles is more than 30 times faster than scoring of SAM models.
Now that complete genome sequences are available for a variety of organisms, the elucidation of potential gene products function is a central goal in the post-genome era. Domain fusion analysis has been proposed recently to infer the functional association of the component proteins. Here, we took a new approach to the analysis of the structural features of the proteins involved in fusion events. An exhaustive survey of fusion events within 30 completely sequenced genomes and subsequent structure annotations to the component proteins at a SCOP superfamily level with hidden Markov models was carried out. A domain fusion map was then constructed. The results revealed that proteins with the class alpha/beta fold are frequently involved in fusion events, around 86% of the total 676 assigned single-domain fusion pairs including at least one component protein belonging to the alpha/beta fold class. Moreover, the domain fusion map in our work may offer an attractive framework for designing chimeric enzymes following Nature's lead, and may give useful hints for exploring the evolutionary history of proteins. (c) 2002 Elsevier Science Ltd.
The SUPERFAMILY database contains a library of hidden Markov models representing all proteins of known structure. The database is based on the SCOP 'superfamily' level of protein domain classification which groups together the most distantly related proteins which have a common evolutionary ancestor. There is a public server at http://supfam.org which provides three services: sequence searching, multiple alignments to sequences of known structure, and structural assignments to all complete genomes. Given an amino acid or nucleotide query sequence the server will return the domain architecture and SCOP classification. The server produces alignments of the query sequences with sequences of known structure, and includes multiple alignments of genome and PDB sequences. The structural assignments are carried out on all complete genomes (currently 59) covering approximately half of the soluble protein domains. The assignments, superfamily breakdown and statistics on them are available from the server. The database is currently used by this group and others for genome annotation, structural genomics, gene prediction and domain-based genomic studies.
The comparison of the small molecule metabolism pathways in Escherichia coli and Saccharomyces cerevisiae (yeast) shows that 271 enzymes are common to both organisms. These common enzymes involve 384 gene products in E. coli and 390 in yeast, which are between one half and two thirds of the gene products of small molecule metabolism in E. coli and yeast, respectively. The arrangement and family membership of the domains that form all or part of 374 E. coli sequences and 343 yeast sequences was determined. Of these, 70% consist entirely of homologous domains, and 20% have homologous domains linked to other domains that are unique to E. coli, yeast, or both. Over two thirds of the enzymes common to the two organisms have sequence identities between 30% and 50%. The remaining groups include 13 clear cases of nonorthologous displacement. Our calculations show that at most one half to two thirds of the gene products involved in small molecule metabolism are common to E. coli and yeast. We have shown that the common core of 271 enzymes has been largely conserved since the separation of prokaryotes and eukaryotes, including modifications for regulatory purposes, such as gene fusion and changes in the number of isozymes in one of the two organisms. Only one fifth of the common enzymes have nonhomologous domains between the two organisms. Around the common core very different extensions have been made to small molecule metabolism in the two organisms.