Comprehensive genome analysis of 203 genomes provides structural genomics with new insights into protein family space

We present an analysis of 203 completed genomes in the Gene3D resource (including 17 eukaryotes), which demonstrates that the number of protein families is continually expanding over time and that singleton-sequences appear to be an intrinsic part of the genomes. A significant proportion of the proteomes can be assigned to fewer than 6000 well-characterized domain families with the remaining domain-like regions belonging to a much larger number of small uncharacterized families that are largely species specific. Our comprehensive domain annotation of 203 genomes enables us to provide more accurate estimates of the number of multi-domain proteins found in the three kingdoms of life than previous calculations. We find that 67% of eukaryotic sequences are multi-domain compared with 56% of sequences in prokaryotes. By measuring the domain coverage of genome sequences, we show that the structural genomics initiatives should aim to provide structures for less than a thousand structurally uncharacterized Pfam families to achieve reasonable structural annotation of the genomes. However, in large families, additional structures should be determined as these would reveal more about the evolution of the family and enable a greater understanding of how function evolves.     

Target selection and deselection at the Berkeley Structural Genomics Center

At the Berkeley Structural Genomics Center (BSGC), our goal is to obtain a near-complete structural complement of proteins in the minimal organisms Mycoplasma genitalium and M. pneumoniae, two closely related pathogens. Current targets for structure determination have been selected in six major stages, starting with those predicted to be most tractable to high throughput study and likely to yield new structural information. We report on the process used to select these proteins, as well as our target deselection procedure. Target deselection reduces experimental effort by eliminating targets similar to those recently solved by the structural biology community or other centers. We measure the impact of the 69 structures solved at the BSGC as of July 2004 on structure prediction coverage of the M. pneumoniae and M. genitalium proteomes. The number of Mycoplasma proteins for which the fold could first be reliably assigned based on structures solved at the BSGC (24 M. pneumoniae and 21 M. genitalium) is approximately 25% of the total resulting from work at all structural genomics centers and the worldwide structural biology community (94 M. pneumoniae and 86 M. genitalium) during the same period. As the number of structures contributed by the BSGC during that period is less than 1% of the total worldwide output, the benefits of a focused target selection strategy are apparent. If the structures of all current targets were solved, the percentage of M. pneumoniae proteins for which folds could be reliably assigned would increase from approximately 57% (391 of 687) at present to around 80% (550 of 687), and the percentage of the proteome that could be accurately modeled would increase from around 37% (254 of 687) to about 64% (438 of 687). In M. genitalium, the percentage of the proteome that could be structurally annotated based on structures of our remaining targets would rise from 72% (348 of 486) to around 76% (371 of 486), with the percentage of accurately modeled proteins would rise from 50% (243 of 486) to 58% (283 of 486). Sequences and data on experimental progress on our targets are available in the public databases TargetDB and PEPCdb.     

Target space for structural genomics revisited

MOTIVATION: Structural genomics eventually aims at determining structures for all proteins. However, in the beginning experimentalists are likely to focus on globular proteins to achieve a rapid basic coverage of protein sequence space. How many proteins will structural genomics have to target? How many proteins will be excluded since we already have structural information for these or since they are not globular? We have to answer these questions in the context of our target selection for the North-East Structural Genomics Consortium (NESG). RESULTS: We estimated that structural information is available for about 6-38% of all proteins; 6% if we require high accuracy in comparative modelling, 38% if we are satisfied with having a rough idea about the fold. Excluding all regions that are not globular, we found that structural genomics may have to target about 48% of all proteins. This corresponded to a similar percentage of residues of the entire proteomes (52%). We explored a number of different strategies to cluster protein space in order to find the number of families representing these 48% of structurally unknown proteins. For the subset of all entirely sequenced eukaryotes, we found over 18 000 fragment clusters each of which may be a suitable target for structural genomics. AVAILABILITY: All data are available from the authors, most results are summarized at: http://cubic.bioc.columbia.edu/genomes/RES/2002_bioinformatics/     

SUPFAM—a database of potential protein superfamily relationships derived by comparing sequence-based and structure-based families: implications for structural genomics and function annotation in genomes

Members of a superfamily of proteins could result from divergent evolution of homologues with insignificant similarity in the amino acid sequences. A superfamily relationship is detected commonly after the three-dimensional structures of the proteins are determined using X-ray analysis or NMR. The SUPFAM database described here relates two homologous protein families in a multiple sequence alignment database of either known or unknown structure. The present release (1.1), which is the first version of the SUPFAM database, has been derived by analysing Pfam, which is one of the commonly used databases of multiple sequence alignments of homologous proteins. The first step in establishing SUPFAM is to relate Pfam families with the families in PALI, which is an alignment database of homologous proteins of known structure that is derived largely from SCOP. The second step involves relating Pfam families which could not be associated reliably with a protein superfamily of known structure. The profile matching procedure, IMPALA, has been used in these steps. The first step resulted in identification of 1280 Pfam families (out of 2697, i.e. 47%) which are related, either by close homologous connection to a SCOP family or by distant relationship to a SCOP family, potentially forming new superfamily connections. Using the profiles of 1417 Pfam families with apparently no structural information, an all-against-all comparison involving a sequence-profile match using IMPALA resulted in clustering of 67 homologous protein families of Pfam into 28 potential new superfamilies. Expansion of groups of related proteins of yet unknown structural information, as proposed in SUPFAM, should help in identifying ‘priority proteins’ for structure determination in structural genomics initiatives to expand the coverage of structural information in the protein sequence space. For example, we could assign 858 distinct Pfam domains in 2203 of the gene products in the genome of Mycobacterium tubercolosis. Fifty-one of these Pfam families of unknown structure could be clustered into 17 potentially new superfamilies forming good targets for structural genomics. SUPFAM database can be accessed at http://pauling.mbu.iisc.ernet.in/~supfam.     

A comparison of sequence and structure protein domain families as a basis for structural genomics.

MOTIVATION: Protein families can be defined based on structure or sequence similarity. We wanted to compare two protein family databases, one based on structural and one on sequence similarity, to investigate to what extent they overlap, the similarity in definition of corresponding families, and to create a list of large protein families with unknown structure as a resource for structural genomics. We also wanted to increase the sensitivity of fold assignment by exploiting protein family HMMs. RESULTS: We compared Pfam, a protein family database based on sequence similarity, to Scop, which is based on structural similarity. We found that 70% of the Scop families exist in Pfam while 57% of the Pfam families exist in Scop. Most families that occur in both databases correspond well to each other, but in some cases they are different. Such cases highlight situations in which structure and sequence approaches differ significantly. The comparison enabled us to compile a list of the largest families that do not occur in Scop; these are suitable targets for structure prediction and determination, and may be useful to guide projects in structural genomics. It can be noted that 13 out of the 20 largest protein families without a known structure are likely transmembrane proteins. We also exploited Pfam to increase the sensitivity of detecting homologs of proteins with known structure, by comparing query sequences to Pfam HMMs that correspond to Scop families. For SWISSPROT+TREMBL, this yielded an increase in fold assignment from 31% to 42% compared to using FASTA only. This method assigned a structure to 22% of the proteins in Saccharomyces cerevisiae, 24% in Escherichia coli, and 16% in Methanococcus jannaschii.     

Completeness in structural genomics

Structural genomics has the goal of obtaining useful, three-dimensional models of all proteins by a combination of experimental structure determination and comparative model building. We evaluate different strategies for optimizing information return on effort. The strategy that maximizes structural coverage requires about seven times fewer structure determinations compared with the strategy in which targets are selected at random. With a choice of reasonable model quality and the goal of 90% coverage, we extrapolate the estimate of the total effort of structural genomics. It would take approximately 16,000 carefully selected structure determinations to construct useful atomic models for the vast majority of all proteins. In practice, unless there is global coordination of target selection, the total effort will likely increase by a factor of three. The task can be accomplished within a decade provided that selection of targets is highly coordinated and significant funding is available.     

Automatic target selection for structural genomics on eukaryotes

A central goal of structural genomics is to experimentally determine representative structures for all protein families. At least 14 structural genomics pilot projects are currently investigating the feasibility of high-throughput structure determination; the National Institutes of Health funded nine of these in the United States. Initiatives differ in the particular subset of "all families" on which they focus. At the NorthEast Structural Genomics consortium (NESG), we target eukaryotic protein domain families. The automatic target selection procedure has three aims: 1) identify all protein domain families from currently five entirely sequenced eukaryotic target organisms based on their sequence homology, 2) discard those families that can be modeled on the basis of structural information already present in the PDB, and 3) target representatives of the remaining families for structure determination. To guarantee that all members of one family share a common foldlike region, we had to begin by dissecting proteins into structural domain-like regions before clustering. Our hierarchical approach, CHOP, utilizing homology to PrISM, Pfam-A, and SWISS-PROT chopped the 103,796 eukaryotic proteins/ORFs into 247,222 fragments. Of these fragments, 122,999 appeared suitable targets that were grouped into >27,000 singletons and >18,000 multifragment clusters. Thus, our results suggested that it might be necessary to determine >40,000 structures to minimally cover the subset of five eukaryotic proteomes.     

Proteome Analysis Database: online application of InterPro and CluSTr for the functional classification of proteins in whole genomes

The SWISS-PROT group at EBI has developed the Proteome Analysis Database utilising existing resources and providing comparative analysis of the predicted protein coding sequences of the complete genomes of bacteria, archaea and eukaryotes (http://www.ebi.ac. uk/proteome/). The two main projects used, InterPro and CluSTr, give a new perspective on families, domains and sites and cover 31-67% (InterPro statistics) of the proteins from each of the complete genomes. CluSTr covers the three complete eukaryotic genomes and the incomplete human genome data. The Proteome Analysis Database is accompanied by a program that has been designed to carry out InterPro proteome comparisons for any one proteome against any other one or more of the proteomes in the database.     

InterPro as a new tool for whole genome analysis. A comparative analysis of Mycobacterium tuberculosis, Bacillus subtilis and Escherichia coli as a case study

InterPro was developed as a new integrated documentation resource for protein families, domains and functional sites to rationalize the complementary efforts of the PROSITE, PRINTS, Pfam and ProDom database projects and has applications in computational functional classification of newly determined sequences lacking biochemical characterization and in comparative genome analysis. InterPro contains over 3500 entries, with more than 1000000 hits in SWISS-PROT and TrEMBL. The database is accessible for text- and sequence-based searches at http://www.ebi.ac.uk/interpro/. InterPro was used for whole proteome analysis of the pathogenic microorganism, Mycobacterium tuberculosis, and comparison with the predicted protein coding sequences of the complete genomes of Bacillus subtilis and Escherichia coli. 64.8% of the M. tuberculosis proteins in the proteome matched InterPro entries, and these could be classified according to function. The comparison with B. subtilis and E. coli provided information on the most common protein families and domains, and the most highly represented families in each organism. InterPro thus provides a useful tool for global views of whole proteomes and their compositions.     

Coverage of protein sequence space by current structural genomics targets

By its purest definition the ultimate goal of structural genomics (SG) is the determination of the structures of all proteins encoded by genomes. Most of these will be obtained by homology modeling using the structures of a set of target proteins for experimental determination. Thanks to the open exchange of SG target information, we are able to analyze the sequences of the current target list to evaluate the extent of its coverage of protein sequence space. The presence of homologous sequences currently either in the Protein Data Bank (PDB) or among SG targets has been determined for each of the protein sequences in several organisms. In this way we are able to evaluate the coverage by existing or targeted structural data for the non-membranous parts of entire proteomes. For small bacterial proteomes such as that of H. influenzae almost all proteins have homologous sequences among SG targets or in the PDB. There is significantly lower coverage for more complex organisms, such as C. elegans. We have mapped the SG target list onto the ProtoMap clustering of protein sequences. Clusters occupied by SG targets represent over 150,000 protein sequences, which is approximately 44% of the total protein sequences classified by ProtoMap. The mapping of SG targets also enables an evaluation of the degree of overlap within the target list. An SG target typically occupies a ProtoMap cluster with more than six other homologous targets.     

Vertebrate genomes code excess proteins with charge periodicity of 28 residues

All amino acid sequences derived from 248 prokaryotic genomes, 10 invertebrate genomes (plants and fungi) and 10 vertebrate genomes were analysed by the autocorrelation function of charge sequences. The analysis of the total amino acid sequences derived from the 268 biological genomes showed that a significant periodicity of 28 residues is observable for the vertebrate genomes, but not for the other genomes. When proteins with a charge periodicity of 28 residues (PCP28) were selected from the total proteomes, we found that PCP28 in fact exists in all proteomes, but the number of PCP28 is much larger for the vertebrate proteomes than for the other proteomes. Although excess PCP28 in the vertebrate proteomes are only poorly characterized, a detailed inspection of the databases suggests that most excess PCP28 are nuclear proteins.     

CHOP proteins into structural domain-like fragments

We developed a method CHOP dissecting proteins into domain-like fragments. The basic idea was to cut proteins beginning from very reliable experimental information (PDB), proceeding to expert annotations of domain-like regions (Pfam-A), and completing through cuts based on termini of known proteins. In this way, CHOP dissected more than two thirds of all proteins from 62 proteomes. Analysis of our structural domain-like fragments revealed four surprising results. First, >70% of all dissected proteins contained more than one fragment. Second, most domains spanned on average over approximately 100 residues. This average was similar for eukaryotic and prokaryotic proteins, and it is also valid-although previously not described-for all proteins in the PDB. Third, single-domain proteins were significant longer than most domains in multidomain proteins. Fourth, three fourths of all domains appeared shorter than 210 residues. We believe that our CHOP fragments constituted an important resource for functional and structural genomics. Nevertheless, our main motivation to develop CHOP was that the single-linkage clustering method failed to adequately group full-length proteins. In contrast, CLUP-the simple clustering scheme CLUP introduced here-succeeded largely to group the CHOP fragments from 62 proteomes such that all members of one cluster shared a basic structural core. CLUP found >63,000 multi- and >118,000 single-member clusters. Although most fragments were restricted to a particular cluster, approximately 24% of the fragments were duplicated in at least two clusters. Our thresholds for grouping two fragments into the same cluster were rather conservative. Nevertheless, our results suggested that structural genomics initiatives have to target >30,000 fragments to at least cover the multimember clusters in 62 proteomes.     

Functional coverage of the human genome by existing structures, structural genomics targets, and homology models

The bias in protein structure and function space resulting from experimental limitations and targeting of particular functional classes of proteins by structural biologists has long been recognized, but never continuously quantified. Using the Enzyme Commission and the Gene Ontology classifications as a reference frame, and integrating structure data from the Protein Data Bank (PDB), target sequences from the structural genomics projects, structure homology derived from the SUPERFAMILY database, and genome annotations from Ensembl and NCBI, we provide a quantified view, both at the domain and whole-protein levels, of the current and projected coverage of protein structure and function space relative to the human genome. Protein structures currently provide at least one domain that covers 37% of the functional classes identified in the genome; whole structure coverage exists for 25% of the genome. If all the structural genomics targets were solved (twice the current number of structures in the PDB), it is estimated that structures of one domain would cover 69% of the functional classes identified and complete structure coverage would be 44%. Homology models from existing experimental structures extend the 37% coverage to 56% of the genome as single domains and 25% to 31% for complete structures. Coverage from homology models is not evenly distributed by protein family, reflecting differing degrees of sequence and structure divergence within families. While these data provide coverage, conversely, they also systematically highlight functional classes of proteins for which structures should be determined. Current key functional families without structure representation are highlighted here; updated information on the "most wanted list" that should be solved is available on a weekly basis from http://function.rcsb.org:8080/pdb/function_distribution/index.html.     

Structural similarity to bridge sequence space: finding new families on the bridges

Structures for protein domains have increased rapidly in recent years owing to advances in structural biology and structural genomics projects. New structures are often similar to those solved previously, and such similarities can give insights into function by linking poorly understood families to those that are better characterized. They also allow the possibility of combing information to find still more proteins adopting a similar structure and sometimes a similar function, and to reprioritize families in structural genomics pipelines. We explore this possibility here by preparing merged profiles for pairs of structurally similar, but not necessarily sequence-similar, domains within the SMART and Pfam database by way of the Structural Classification of Proteins (SCOP). We show that such profiles are often able to successfully identify further members of the same superfamily and thus can be used to increase the sensitivity of database searching methods like HMMer and PSI-BLAST. We perform detailed benchmarks using the SMART and Pfam databases with four complete genomes frequently used as annotation benchmarks. We quantify the associated increase in structural information in Swissprot and discuss examples illustrating the applicability of this approach to understand functional and evolutionary relationships between protein families.     

This Déjà Vu Feeling—Analysis of Multidomain Protein Evolution in Eukaryotic Genomes

Evolutionary innovation in eukaryotes and especially animals is at least partially driven by genome rearrangements and the resulting emergence of proteins with new domain combinations, and thus potentially novel functionality. Given the random nature of such rearrangements, one could expect that proteins with particularly useful multidomain combinations may have been rediscovered multiple times by parallel evolution. However, existing reports suggest a minimal role of this phenomenon in the overall evolution of eukaryotic proteomes. We assembled a collection of 172 complete eukaryotic genomes that is not only the largest, but also the most phylogenetically complete set of genomes analyzed so far. By employing a maximum parsimony approach to compare repertoires of Pfam domains and their combinations, we show that independent evolution of domain combinations is significantly more prevalent than previously thought. Our results indicate that about 25% of all currently observed domain combinations have evolved multiple times. Interestingly, this percentage is even higher for sets of domain combinations in individual species, with, for instance, 70% of the domain combinations found in the human genome having evolved independently at least once in other species. We also show that previous, much lower estimates of this rate are most likely due to the small number and biased phylogenetic distribution of the genomes analyzed. The process of independent emergence of identical domain combination is widespread, not limited to domains with specific functional categories. Besides data from large-scale analyses, we also present individual examples of independent domain combination evolution. The surprisingly large contribution of parallel evolution to the development of the domain combination repertoire in extant genomes has profound consequences for our understanding of the evolution of pathways and cellular processes in eukaryotes and for comparative functional genomics.     

Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life

Intrinsically disordered proteins and intrinsically disordered protein regions are highly abundant in nature. However, the quantitative and qualitative measures of protein intrinsic disorder in species with known genomes are still not available. Furthermore, although the correlation between high fraction of disordered residues and advanced species has been reported, the details of this correlation and the connection between the disorder content and proteome complexity have not been reported as of yet. To fill this gap, we analysed entire proteomes of 3484 species from three domains of life (archaea, bacteria and eukaryotes) and from viruses. Our analysis revealed that the evolution process is characterized by distinctive patterns of changes in the protein intrinsic disorder content. We are showing here that viruses are characterized by the widest spread of the proteome disorder content (the percentage of disordered residues ranges from 7.3% in human coronavirus NL63 to 77.3% in Avian carcinoma virus). For several organisms, a clear correlation is seen between their disorder contents and habitats. In multicellular eukaryotes, there is a weak correlation between the complexity of an organism (evaluated as a number of different cell types) and its overall disorder content. For both the prokaryotes and eukaryotes, the disorder content is generally independent of the proteome size. However, disorder shows a sharp increase associated with the transition from prokaryotic to eukaryotic cells. This suggests that the increased disorder content in eukaryotic proteomes might be used by nature to deal with the increased cell complexity due to the appearance of the various cellular compartments.     

Functional annotation by sequence-weighted structure alignments: statistical analysis and case studies from the Protein 3000 structural genomics project in Japan

A method to functionally annotate structural genomics targets, based on a novel structural alignment scoring function, is proposed. In the proposed score, position-specific scoring matrices are used to weight structurally aligned residue pairs to highlight evolutionarily conserved motifs. The functional form of the score is first optimized for discriminating domains belonging to the same Pfam family from domains belonging to different families but the same CATH or SCOP superfamily. In the optimization stage, we consider four standard weighting functions as well as our own, the "maximum substitution probability," and combinations of these functions. The optimized score achieves an area of 0.87 under the receiver-operating characteristic curve with respect to identifying Pfam families within a sequence-unique benchmark set of domain pairs. Confidence measures are then derived from the benchmark distribution of true-positive scores. The alignment method is next applied to the task of functionally annotating 230 query proteins released to the public as part of the Protein 3000 structural genomics project in Japan. Of these queries, 78 were found to align to templates with the same Pfam family as the query or had sequence identities > or = 30%. Another 49 queries were found to match more distantly related templates. Within this group, the template predicted by our method to be the closest functional relative was often not the most structurally similar. Several nontrivial cases are discussed in detail. Finally, 103 queries matched templates at the fold level, but not the family or superfamily level, and remain functionally uncharacterized.     

Progress of structural genomics initiatives: an analysis of solved target structures

The explosion in gene sequence data and technological breakthroughs in protein structure determination inspired the launch of structural genomics (SG) initiatives. An often stated goal of structural genomics is the high-throughput structural characterisation of all protein sequence families, with the long-term hope of significantly impacting on the life sciences, biotechnology and drug discovery. Here, we present a comprehensive analysis of solved SG targets to assess progress of these initiatives. Eleven consortia have contributed 316 non-redundant entries and 323 protein chains to the Protein Data Bank (PDB), and 459 and 393 domains to the CATH and SCOP structure classifications, respectively. The quality and size of these proteins are comparable to those solved in traditional structural biology and, despite huge scope for duplicated efforts, only 14% of targets have a close homologue (>/=30% sequence identity) solved by another consortium. Analysis of CATH and SCOP revealed the significant contribution that structural genomics is making to the coverage of superfamilies and folds. A total of 67% of SG domains in CATH are unique, lacking an already characterised close homologue in the PDB, whereas only 21% of non-SG domains are unique. For 29% of domains, structure determination revealed a remote evolutionary relationship not apparent from sequence, and 19% and 11% contributed new superfamilies and folds. The secondary structure class, fold and superfamily distributions of this dataset reflect those of the genomes. The domains fall into 172 different folds and 259 superfamilies in CATH but the distribution is highly skewed. The most populous of these are those that recur most frequently in the genomes. Whilst 11% of superfamilies are bacteria-specific, most are common to all three superkingdoms of life and together the 316 PDB entries have provided new and reliable homology models for 9287 non-redundant gene sequences in 206 completely sequenced genomes. From the perspective of this analysis, it appears that structural genomics is on track to be a success, and it is hoped that this work will inform future directions of the field.     

The Proteome Analysis database: a tool for the in silico analysis of whole proteomes

The Proteome Analysis database (http://www.ebi.ac.uk/proteome/) has been developed by the Sequence Database Group at EBI utilizing existing resources and providing comparative analysis of the predicted protein coding sequences of the complete genomes of bacteria, archeae and eukaryotes. Three main projects are used, InterPro, CluSTr and GO Slim, to give an overview on families, domains, sites, and functions of the proteins from each of the complete genomes. Complete proteome analysis is available for a total of 89 proteome sets. A specifically designed application enables InterPro proteome comparisons for any one proteome against any other one or more of the proteomes in the database.