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The advent of sequencing and structural genomics projects has provided a dramatic boost in the number of protein structures and sequences. Due to the high-throughput nature of these projects, many of the molecules are uncharacterised and their functions unknown. This, in turn, has led to the need for a greater number and diversity of tools and databases providing annotation through transfer based on homology and prediction methods. Though many such tools to annotate protein sequence and structure exist, they are spread throughout the world, often with dedicated individual web pages. This situation does not provide a consensus view of the data and hinders comparison between methods. Integration of these methods is needed. So far this has not been possible since there was no common vocabulary available that could be used as a standard language. A variety of terms could be used to describe any particular feature ranging from different spellings to completely different terms. The Protein Feature Ontology (http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=BS) is a structured controlled vocabulary for features of a protein sequence or structure. It provides a common language for tools and methods to use, so that integration and comparison of their annotations is possible. The Protein Feature Ontology comprises approximately 100 positional terms (located in a particular region of the sequence), which have been integrated into the Sequence Ontology (SO). 40 non-positional terms which describe general protein properties have also been defined and, in addition, post-translational modifications are described by using an already existing ontology, the Protein Modification Ontology (MOD). The Protein Feature Ontology has been used by the BioSapiens Network of Excellence, a consortium comprising 19 partner sites in 14 European countries generating over 150 distinct annotation types for protein sequences and structures.

The CATH database of domain structures has been used to explore the structural variation of homologous domains in 294 well populated domain structure superfamilies, each containing at least three sequence diverse relatives. Our analyses confirm some previously detected trends relating sequence divergence to structural variation but for a much larger dataset and in some superfamilies the new data reveal exceptional structural variation. Use of a new algorithm (2DSEC) to analyse variability in secondary structure compositions across a superfamily sheds new light on how structures evolve. 2DSEC detects inserted secondary structures that embellish the core of conserved secondary structures found throughout the superfamily. Analysis showed that for 56% of highly populated superfamilies (>9 sequence diverse relatives), there are twofold or more increases in the numbers of secondary structures in some relatives. In some families fivefold increases occur, sometimes modifying the fold of the domain. Manual inspection of secondary structure insertions or embellishments in 48 particularly variable superfamilies revealed that although these insertions were usually discontiguous in the sequence they were often co-located in 3D resulting in a larger structural motif that often modified the geometry of the active site or the surface conformation promoting diverse domain partnerships and protein interactions. These observations, supported by automatic analysis of all well populated CATH families, suggest that accretion of small secondary structure insertions may provide a simple mechanism for evolving new functions in diverse relatives. Some layered domain architectures (e.g. mainly-beta and alpha-beta sandwiches) that recur highly in the genomes more frequently exploit these types of embellishments to modify function. In these architectures, aggregation occurs most often at the edges, top or bottom of the beta-sheets. Information on structural variability across domain superfamilies has been made available through the CATH Dictionary of Homologous Structures (DHS).

In order to support the structural genomic initiatives, both by rapidly classifying newly determined structures and by suggesting suitable targets for structure determination, we have recently developed several new protocols for classifying structures in the CATH domain database (http://www.biochem.ucl.ac.uk/bsm/cath). These aim to increase the speed of classification of new structures using fast algorithms for structure comparison (GRATH) and to improve the sensitivity in recognising distant structural relatives by incorporating sequence information from relatives in the genomes (DomainFinder). In order to ensure the integrity of the database given the expected increase in data, the CATH Protein Family Database (CATH-PFDB), which currently includes 25 320 structural domains and a further 160 000 sequence relatives has now been installed in a relational ORACLE database. This was essential for developing more rigorous validation procedures and for allowing efficient querying of the database, particularly for genome analysis. The associated Dictionary of Homologous Superfamilies [Bray,J.E., Todd,A.E., Pearl,F.M.G., Thornton,J.M. and Orengo,C.A. (2000) Protein Eng., 13, 153–165], which provides multiple structural alignments and functional information to assist in assigning new relatives, has also been expanded recently and now includes information for 903 homo­logous superfamilies. In order to improve coverage of known structures, preliminary classification levels are now provided for new structures at interim stages in the classification protocol. Since a large proportion of new structures can be rapidly classified using profile-based sequence analysis [e.g. PSI-BLAST: Altschul,S.F., Madden,T.L., Schaffer,A.A., Zhang,J., Zhang,Z., Miller,W. and Lipman,D.J. (1997) Nucleic Acids Res., 25, 3389–3402], this provides preliminary classification for easily recognisable homologues, which in the latest release of CATH (version 1.7) represented nearly three-quarters of the non-identical structures.

Recent years have seen a huge increase in the generation of genomic and proteomic data. This has been due to improvements in current biological methodologies, the development of new experimental techniques and the use of computers as support tools. All these raw data are useless if they cannot be properly analysed, annotated, stored and displayed. Consequently, a vast number of resources have been created to present the data to the wider community. Annotation tools and databases provide the means to disseminate these data and to comprehend their biological importance. This review examines the various aspects of annotation: type, methodology and availability. Moreover, it puts a special interest on novel annotation fields, such as that of phenotypes, and highlights the recent efforts focused on the integrating annotations.

Background The Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources. DAS continues to expand its applicability and evolve in response to new challenges facing integrative bioinformatics. Results Here we describe the various infrastructure components of DAS and present a new extended version of the DAS specification. Version 1.53E incorporates several recent developments, including its extension to serve new data types and an ontology for protein features. Conclusion Our extensions to the DAS protocol have facilitated the integration of new data types, and our improvements to the existing DAS infrastructure have addressed recent challenges. The steadily increasing numbers of available data sources demonstrates further adoption of the DAS protocol.

1 Introduction 2 Prediction of variant location 3 Prediction of variant function – analysis of the role of alternative splicing in changing function by modulation of functional residues 3.1 Functions associated with alternative splicing 3.2 Functional adaptation through alternative splicing 3.2.1 Tafazzin 3.2.2 Phosphoribosylglycinamide formyltransferase (GARS-AIRS-GART) 3.3 Analysis across the ENCODE dataset 4 Prediction of variant structure 5 Summary of effects of alternative splicing 6 Prediction of principal isoforms 6.1 A series of automatic methods for predicting the principal isoform 6.1.1 Methods 6.1.2 Evaluation of pipeline definitions 7 The ENCODE pipeline – an automated workflow for analysis of human splice isoforms 7.1 Behind EPipe 7.2 Example workflow: IFN alpha/beta receptor protein 7.3 Future perspectives

Understanding human variation and disease often requires knowledge of a broad array of biomolecular data items, down to the role of an individual amino acid in a protein, and how mutations or alternative splicing events can change function and phenotype. There are a number of key databases that collect biomolecular information; the EMBL DNA database (Cochrane et al. 2006) and Ensembl (Flicek et al. 2007) collect annotations on genomic sequence features, the UniProt knowledge base (Bairoch et al. 2005) provides detailed annotation on protein sequences, and the Worldwide PDB member databases (Berman et al. 2007) provide protein structural information. Whilst these databases house a great deal of information on sequences and structures, the advent of high throughput methods in genome sequencing and structural genomics initiatives has produced an explosion in the quantity of uncharacterised data. As a result, the development of tools which annotate these sequences and structures by prediction or transfer of information from homologous relatives has also increased in number and diversity. These methods are crucial in order to fill in the functional space between characterised and uncharacterised protein sequences and structures.

Alternative premessenger RNA splicing enables genes to generate more than one gene product. Splicing events that occur within protein coding regions have the potential to alter the biological function of the expressed protein and even to create new protein functions. Alternative splicing has been suggested as one explanation for the discrepancy between the number of human genes and functional complexity. Here, we carry out a detailed study of the alternatively spliced gene products annotated in the ENCODE pilot project. We find that alternative splicing in human genes is more frequent than has commonly been suggested, and we demonstrate that many of the potential alternative gene products will have markedly different structure and function from their constitutively spliced counterparts. For the vast majority of these alternative isoforms, little evidence exists to suggest they have a role as functional proteins, and it seems unlikely that the spectrum of conventional enzymatic or structural functions can be substantially extended through alternative splicing.

Owing to the ongoing success of the genome sequencing and structural genomics projects, the increase in both sequence and structural data is rapid. The development of tools for the annotation of sequence and structural data has become more important in the hope of keeping up with this data explosion. Scientists in this field have addressed these issues over the last 10 years and there now exists a wealth of methods and approaches to help interpret these data. However, there is no current way in which these methods can be incorporated easily so that the resulting annotations can be viewed together. This review discusses the development of these annotation methods and introduces the BioSapiens Network of Excellence, which has been formed in order to integrate the methods which have been developed in Europe.

New directions in biology are being driven by the complete sequencing of genomes, which has given us the protein repertoires of diverse organisms from all kingdoms of life. In tandem with this accumulation of sequence data, worldwide structural genomics initiatives, advanced by the development of improved technologies in X-ray crystallography and NMR, are expanding our knowledge of structural families and increasing our fold libraries. Methods for detecting remote sequence similarities have also been made more sensitive and this means that we can map domains from these structural families onto genome sequences to understand how these families are distributed throughout the genomes and reveal how they might influence the functional repertoires and biological complexities of the organisms. We have used robust protocols to assign sequences from completed genomes to domain structures in the CATH database, allowing up to 60% of domain sequences in these genomes, depending on the organism, to be assigned to a domain family of known structure. Analysis of the distribution of these families throughout bacterial genomes identified more than 300 universal families, some of which had expanded significantly in proportion to genome size. These highly expanded families are primarily involved in metabolism and regulation and appear to make major contributions to the functional repertoire and complexity of bacterial organisms. When comparisons are made across all kingdoms of life, we find a smaller set of universal domain families (approx. 140), of which families involved in protein biosynthesis are the largest conserved component. Analysis of the behaviour of other families reveals that some (e.g. those involved in metabolism, regulation) have remained highly innovative during evolution, making it harder to trace their evolutionary ancestry. Structural analyses of metabolic families provide some insights into the mechanisms of functional innovation, which include changes in domain partnerships and significant structural embellishments leading to modulation of active sites and protein interactions.

The CATH database of protein domain structures (http://www.biochem.ucl.ac.uk/bsm/cath/) currently contains 43 229 domains classified into 1467 superfamilies and 5107 sequence families. Each structural family is expanded with sequence relatives from GenBank and completed genomes, using a variety of efficient sequence search protocols and reliable thresholds. This extended CATH protein family database contains 616 470 domain sequences classified into 23 876 sequence families. This results in the significant expansion of the CATH HMM model library to include models built from the CATH sequence relatives, giving a 10% increase in coverage for detecting remote homologues. An improved Dictionary of Homologous superfamilies (DHS) (http://www.biochem.ucl.ac.uk/bsm/dhs/) containing specific sequence, structural and functional information for each superfamily in CATH considerably assists manual validation of homologues. Information on sequence relatives in CATH superfamilies, GenBank and completed genomes is presented in the CATH associated DHS and Gene3D resources. Domain partnership information can be obtained from Gene3D (http://www.biochem.ucl.ac.uk/bsm/cath/Gene3D/). A new CATH server has been implemented (http://www.biochem.ucl.ac.uk/cgi-bin/cath/CathServer.pl) providing automatic classification of newly determined sequences and structures using a suite of rapid sequence and structure comparison methods. The statistical significance of matches is assessed and links are provided to the putative superfamily or fold group to which the query sequence or structure is assigned.

In this article we present a review of the methods used for comparing and classifying protein structures. We discuss the hierarchies and populations of fold groups and evolutionary families in some of the major classifications and we consider some of the problems confronting any general analyses of structural evolution in protein families. We also review some more recent analyses that have expanded these classifications by identifying sequence relatives in the genomes and thereby reveal interesting trends in fold usage and recurrence.