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

Article (PDF Available)inNucleic Acids Research 34(3):1066-80 · February 2006with22 Reads
DOI: 10.1093/nar/gkj494 · Source: PubMed
Abstract
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.

Figures

Comprehensive genome analysis of 203 genomes
provides structural genomics with new insights
into protein family space
Russell L. Marsden*, David Lee, Michael Maibaum, Corin Yeats and Christine A. Orengo
Department of Biochemistry and Molecular Biology, University College London, Gower Street, London WC1E 6BT, UK
Received September 21, 2005; Revised December 16, 2005; Accepted January 20, 2006
ABSTRACT
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 pro-
teomes can be assigned to fewer than 6000 well-
characterized domain families with the remaining
domain-like regions belonging to a much larger num-
ber of small uncharacterized families that are largely
species specific. Our comprehensive domain annota-
tion 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 struc-
turally 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.
INTRODUCTION
A fundamental bridge that enables us to link a protein
sequence to its function is knowledge of its structure. It is
well known that protein structure tends to be conserved to
a greater degree than protein sequence and comparison of
protein structure is often able to reveal functional relationships
that are hidden at the sequence level (1). The identification of
links between structural relatives can be a powerful method to
infer function, in many cases it has been shown that a small
number of residues within a protein’s active site or binding
pocket are critical for biological activity and such residues
may only appear to be conserved through structural analysis
(2,3). Structural biology faces the task of characterizing the
shapes and dynamics of the entire protein repertoire of whole
genomes in order to facilitate an understanding of biochemical
functions and their mechanisms of action within the cell.
However, with the ever-growing disparity between the number
of known sequences and known structures, the need to struc-
turally and functionally annotate sequence space appears more
pressing than ever. Structural genomics projects were instig-
ated to address this issue through the large-scale determination
of protein 3D structure (4–8). To solve a structure for each
genome sequence would be experimentally, practically and
financially prohibitive (9). Rather, many structural genomics
initiatives aim to fill in areas of fold space and in doing so,
provide structures that will cover surrounding sequence space
by acting as a structural template for comparative modelling
and fold recognition (1,10,11). Increasing the coverage of
structure annotations will reveal new insights between protein
sequence, structure and function, which in turn will expedite
our understanding of protein function on the molecular level
and improve the methods by which we can automatically
provide structure-guided functional annotations to new protein
structures (12–15).
A variety of structural genomics initiatives are in progress
around the world, including the United States, where the Pro-
tein Structure Initiative (PSI) funded by the National Institute
for General Medical Sciences (NIGMS) under the National
Institute of Health (NIH) began its pilot phase in 2000
(16,17). Among its principal aims was the development of
bioinformatics-based target selection and monitoring strat-
egies that were able to meet the demands of the large amounts
of data required for high-throughput genome-scale structure
determination (18,19). Traditional biology has now been solv-
ing protein structures for several decades; however, without
*To whom correspondence should be addressed. Tel: +44 207 679 2171; Fax: +44 207 679 7193; Email: marsden@biochem.ucl.ac.uk
The Author 2006. Published by Oxford University Press. All rights reserved.
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1066–1080 Nucleic Acids Research, 2006, Vol. 34, No. 3
doi:10.1093/nar/gkj494
a ‘global target plan’, solved structures tend to represent
the interests of individual researchers, rather than specifically
aiming to enrich our knowledge of structure space. Further-
more, a single structure is often solved more than once, bound
to different ligands or with a range of amino acid substitutions.
While these studies are fundamental to molecular biology,
such endeavours would be considered to be redundant
under the guise of many structural genomics projects. In
order to map protein structure space more efficiently, most
structural genomics groups apply a target selection strategy
that increases the likelihood that a new structure will exhibit
a novel fold or provide a new homologous superfamily in a
previously observed fold group. Accordingly, a central step
in target selection is the use of comparative sequence analysis
to identify and exclude sequences that have a relative of
known structure in the Protein Data Bank (PDB) (20).
However, there is no guarantee that all the remaining target
sequences will be amenable to high-throughput analysis—
the high attrition rate of target proteins in high-throughput
structural genomics pipelines has been well documented
[e.g. (21,22)]. Many target selection protocols have attempted
to reduce the number of these difficult proteins by excluding
or truncating sequences that are predicted to contain
regions of low-complexity, coiled-coils, and transmembrane
helices.
Increasingly, target selection is concerned with the organ-
ization of genome sequences into protein families (17,23–27).
These families can then be prioritized according to a range of
properties, such as size, taxonomic distribution and suitability
of family representatives for structure determination, directing
efforts towards well-considered regions of sequence space.
Although these principles of target selection are widely
employed in the structural genomics community, a varied
array of target selection strategies have also been developed
to meet the particular requirements of different initiatives.
Such considerations have included the prioritization of repre-
sentatives from large families or the identification of ORFan
sequences for which little is known in terms of their origin
and function. Proteins have also been targeted according
to their species distribution, which may correlate to their
general function, e.g. proteins found in all three super
kingdoms of life or those found only in single pathogenic
organisms.
Recently, Chandonia and Brenner (27) proposed the
Pfam5000 target selection strategy which aims to provide a
roadmap for coordinated target selection in the second phase
of the PSI. This approach aims to guide the selection of a
manageable number of target proteins from a list of the largest
5000 Pfam families, many of which lack a member of known
structure. By solving structures, such that the top 5000 largest
Pfam families have at least one structural representative, it
was shown that at least 1-fold assignment could be achieved
for 68 and 61% of all prokaryotic and eukaryotic proteins,
respectively.
A diverse range of methods have been applied to the prob-
lem of automatically clustering large collections of protein
sequences, such as Swiss-Prot/TrEMBL (28), into families.
Such methods include ProDom (29), DIVCLUS (30), ProtoNet
(31), GeneRAGE (32), SYSTERS (33), ADDA (34) and
CHOP (35). These methods first aim to define domain regions
in protein sequences which are then clustered based on some
measure of relatedness shared between domain sequences. The
TribeMCL algorithm, developed by Enright and co-workers
(36), aims to assign complete protein sequences into families
that correlate closely with overall domain architecture. This
family assignment protocol has been used to create the
Gene3D database (37,38), which has been used extensively
in the target selection pipeline used by the Midwest Center
for Structural Genomics (MCSG). Resources such as 3D-
GENOMICS (39) and SUPERFAMILY (40) aim to provide
domain annotations to genome sequences using sensitive
sequence profile methods such as PSI-BLAST (41) and hidden
Markov models (HMMs) (42). Similarly, sequences in the
Gene3D database are annotated at the domain level, using a
HMM library of CATH and Pfam domains, which enables us
to provide a wide variety of whole-gene and domain level
protein family assignments.
Many families of protein domains, particularly in the case of
large families, display divergence in their molecular function
(43). It has been proposed that a future direction for target
selection in second phase of the protein structure initiative
(PSI2) should include fine grain targets, selected from large
families of proteins in order to provide a more thorough cov-
erage of functional space as it relates to protein structure. The
level of granularity required for the selection of additional
targets must consider the number of sequences that can be
computationally modelled with ‘useful’ accuracy from each
solved structure, based on the medical and biological relev-
ance of individual protein families. It is generally accepted that
sequences sharing 30% or more sequence identity are likely to
share a similar fold (44) and accordingly, to confidently con-
struct models of reasonable accuracy, at least 30% sequence
identity must be shared between the sequence to be modelled
and the structural template (11).
Here, we present an analysis of sequences from 203 com-
pleted genomes clustered in the Gene3D resource. We analyse
the growth in new families together with the distributions of
singleton sequences as the number of available completed
genomes has increased. Over 90% of the genomes sequences
can be provided with CATH, Pfam, transmembrane-helix,
coiled-coil, low complexity or N-terminal signal peptide
annotations. The mapping of CATH and Pfam domains to
the sequences held in Gene3D has enabled us to measure
genome coverage provided by the largest CATH and Pfam
domain families, where we aim to calculate coverage on the
basis of domain sequences, rather than whole gene sequences.
We find that many of the remaining unannotated sequence
regions appear domain-like in length and belong to a large
number of small families that tend to be species specific.
The comprehensive domain annotation of a large number of
genomes has also enabled us to calculate more accurate estim-
ates of the number of multi-domain proteins in eukaryotes
and prokaryotes than previous studies. We also show that
while over 70% of domain sequences in 203 completed
genomes fall into just 2000 CATH and Pfam domain families,
the number of structures that would be required to provide
useful homology models for these sequences approaches
90 000, clearly an unachievable demand for structural genom-
ics. With this in mind it will be important to develop
and improve methods to identify shifts in function across
large superfamilies in order to suggest additional relatives
for structure determination.
Nucleic Acids Research, 2006, Vol. 34, No. 3 1067
MATERIALS AND METHODS
Gene3D database
The protein families in the Gene3D database are built using the
TribeMCL algorithm developed by Enright and co-workers
(36). This method applies the Markov cluster algorithm
(45) (http://www.library.uu.nl/digiarchief/dip/diss/1895620/
inhoud.htm) that simulates flow in a protein similarity
graph and assigns complete protein sequences into families
based on the density and strengths of links between them.
BLAST sequence comparison is used to identify links between
the completed genome sequences using an E-value cutoff of
0.001 (38).
Since Gene3D was first developed many more completed
genomes have become available while other genomes have
been revised. Completed genomes are downloaded from
Integr8 (http://www.ebi.ac.uk/integr8/). A new protocol has
been developed to update the Gene3D families and provide
methods for maintaining the resource. Novel sequences from
the updated or newly released genomes are scanned against
sequences already held in Gene3D using BLAST. An 80%
overlap cutoff is used to select only those matches representing
whole chain matches. New sequences are then assigned into
the best hit family for each of the new sequences using
inCluster (in-house program). If a sequence cannot be assigned
to an existing Gene3D family, a new family is created.
Where possible, CATH and Pfam domains are assigned to
the genome sequences in Gene3D using Markov models
(HMMs). Domain family assignments are made by scanning
whole sequences against CATH and Pfam HMM libraries,
built using the SAM-T99 protocol developed by Karplus
et al. (46) where the fw0.5 script is used as recommended
in the SAM-T99 documentation. The domain assignments
held in the current release of Gene3D correspond to CATH
version 2.6, represented by 4596 models, one for each close
sequence family, and Pfam version 18, represented by 7677
models. The model libraries are scanned using hmmscore and
domain matches are accepted and processed using the
DomainFinder algorithm (47).
Prediction methods
The Memsat program (48) was used to identify transmembrane
helices using default thresholds. We used the COILS2
algorithm (49) to identify coiled-coil regions, using a probab-
ility cutoff of 0.9 and a window size of 28 residues and the
SEG program (50) with default parameters to identify regions
of low complexity. The SignalP (51) program was used to
predict the presence of signal peptides. SignalP was run
with default parameters according to the organism type
(gram-positive gram-negative, eukaryotic). SignalP v2.0
runs two prediction methods; a Neural Net (NN) based method
and an HMM-based method. A NN prediction was assigned as
‘true’ when both the Y-max score and the S-mean score were
above the threshold values. In cases where S-mean is above
the threshold, while Y-max is not, it is possible that the protein
may contain some sort of membrane anchor (as this can give
a high S-mean score if the membrane anchor has a hydro-
phobic N-terminus). HMM signal peptide predictions were
assigned when both the HMM C-max and S-prob are above
their threshold values. The NN method tends to assign more
accurate cleavage positions for prokaryotic sequences than
the HMM method. Correspondingly, in cases where both
the NN and HMM method assign a signal peptide, the cleavage
position was taken from the NN prediction.
Greedy coverage
A greedy coverage algorithm was run on sequence relatives
assigned to CATH, Pfam and NewFam families as follows:
Links between family members were assigned, using an imple-
mentation of the Needleman and Wunsch global alignment
sequence comparison algorithm, in cases where sequence
identity and overlap were found to be >30% and >80%,
respectively. The sequence (representing a possible homology
modelling template) with the highest number of links is first
selected, and removed, along with all those sequences to which
it is linked, from further calculations. This step is repeated
until no sequences are left in the family.
Calculating homology modelling coverage of
CATH superfamilies
The homology modelling coverage of sequence relatives
assigned to the top 20 largest CATH domain superfamilies
was estimated using a non-identical (N-rep) set of
superfamily-specific HMMs taken from CATH version 2.6.
The HMMs were constructed using the SAM-T99 software as
described above. Here, we calculated homology modelling
coverage at the domain level, since the modular nature of
many domains means they are often found in different contexts
within larger multi-domain structures. As a consequence, we
may slightly underestimate our coverage, as the CATH
domain database tends to fall behind the PDB in terms of
completeness, due to the manual stages that are involved in
its curation. Each CATH N-rep HMM was scanned against
sequence relatives assigned to its corresponding superfamily
and ‘modellable’ sequence regions were assigned where an
overlap >80% and sequence identity of >30% existed
between domain template and match sequence. Though the
approach taken to calculate homology modelling coverage did
not necessitate the identification of remote homologues, SAM-
T99 sequence comparison attempts to address the inherent
difficulty in identifying sequence relatives of discontinuous
domains (46).
RESULTS
Analysis of protein families and singletons in Gene3D
Entire genomes are now sequenced and released on an increas-
ingly frequent basis and in response, resources such as
Gene3D must be updated on a regular basis in order to com-
pete with the expansion in sequence data. At the time of
writing the Gene3D database currently holds 203 completed
genomes; 170 bacteria, 16 archaea and 17 eukaryotes, which
are represented by 633 546 non-identical sequences. Whole
gene clustering of these sequences formed 51 778 Gene3D
families containing two or more sequence members while
158 798 sequences could not be assigned to a Gene3D family.
These sequences are described as Gene3D-singletons,
i.e. whole gene sequences for which no related sequence
1068 Nucleic Acids Research, 2006, Vol. 34, No. 3
can be detected by the TribeMCL algorithm used to construct
the Gene3D families.
As the number of clustered genomes increases in the
Gene3D database, we are able to assess the rate of discovery
of new Gene3D families over time. A previous analysis by
Kunin and co-workers (53) demonstrated a constant rate in
the discovery of new protein families as the genomes of 80
eukaryotic and prokaryotic organisms were released. This
observed trend was contrary to some expectations that the
rate of discovery of new protein families would slow as
sequence space became better sampled with the increasing
number of completed genomes (24,53).
Here, we repeat this analysis for genomes clustered into
protein families in Gene3D. We analysed each genome in
Gene3D in order of their date of release (as listed on the
GOLD website http://www.genomesonline.org) to calculate
the rate of increase in new families as assigned by the
Gene3D protocol. Where more than one strain of a genome
was published, we considered only one strain, reducing the
number of genomes analysed from 203 to 169. Despite the fact
that over 100 additional genomes were included compared
with the analysis by Kunin and co-workers, it appears that,
as yet, the increase in the discovery of new families is still
relatively constant.
Figure 1a shows the number of Gene3D families after addi-
tion of each newly released genome. A steady increase in
Gene3D families can be observed as each of the 169 genomes
is added to the database, (correlation coefficient with respect
to the order of released genomes is R
2
> 0.95). The addition
of the eukaryotic genomes (the largest of which are indicated
in Figure 1a) results in sizeable increases in the number of
new Gene3D families; however, analysis of the prokaryotic
genomes alone reveals that the rate of discovery of new
families in bacteria and archaea is as high (R
2
> 0.99) con-
curring with the observation by Kunin et al. (53) that the
sequence diversity across the prokaryotes is as pronounced
as it is in the few eukaryotic genomes that have so far been
sequenced.
0
10000
20000
30000
40000
50000
60000
0 20 40 60 80 100 120 140 160
Genomes ordered by release date
Number of families
C. elegans
D. melanogaster
A. thaliana
Human
M. musculus
S. cerevisiae
0
10000
20000
30000
40000
50000
60000
0 20 40 60 80 100 120 140 160
Genomes ordered by release date
Number of families
C. elegans
D. melanogaster
A. thaliana
Human
M. musculus
S. cerevisiae
0
100000
200000
300000
400000
500000
600000
700000
0 20 40 60 80 100 120 140 160
Genomes ordered by r elease date
Number of sequences
0
10
20
30
40
50
60
70
80
90
100
Percentage of singletons
number of sequences
number of singletons
percentage of singletons
a
b
Figure 1. (a) The accumulation of Gene3D protein families over time, represented by the order in which newly completed genomes have been released. Some of the
largest eukaryotic genomes are labelled, each of which contributes a large number of new Gene3D families. (b) With the release of each new genome, the number of
singleton sequences is increasing in the Gene3D database, while the overall percentage of sequences assigned as singletons is gradually decreasing.
Nucleic Acids Research, 2006, Vol. 34, No. 3 1069
The continued growth in protein families suggests that
sequence space is more diverse than might have initially
been thought, with new and distinct protein families appearing
in new genomes and taxonomic groups. The additional obser-
vation that many genome sequences cannot be assigned to
either an existing or new protein families is also suggestive
of this diversity. Though it has been argued that some single-
tons may correspond to highly divergent sequences that we
are unable to assign to known families (53), increasing evid-
ence has indicated that many singletons represent sequences
unique to each organism representing single member families
(52,54–55). Even in cases where structure genomics efforts are
largely directed towards the coverage of large families, the
structural characterization of singleton sequences will still
remain of significant interest, as it is likely that many singleton
targets will correspond to real proteins with novel functions
or distant homologues of existing families that can only be
identified through structure comparison (56).
Earlier work by Siew and Fischer (57) analysed the changes
in the distribution of singletons over time as the sequences
from 60 completed genomes were released. In a similar ana-
lysis, we have calculated the total number of sequences
assigned as singletons in Gene3D as each newly released
genome has been clustered into the database. The correspond-
ing percentage of singletons, as a fraction of the overall num-
ber of sequences in the growing database, has also been
calculated. If a sequence that was previously described as a
singleton was assigned to a new Gene3D family on addition
of a new genome, its status was changed to non-singleton.
As genomes are added to the database (Figure 1b) there is a
gradual increase in the total number of singleton sequences.
While there are new matches to old singletons, forming new
Gene3D families, their number do not cancel out the addition
of new singletons provided by each new genome. However,
the percentage of all singletons is gradually declining, having
fallen to 24% of all sequences in the protein family database.
The number of singletons initially found in each genome and
subsequently reassigned to new families on addition of further
genomes is likely to be related to the taxonomic distance
between genomes added to the Gene3D database. It is of
note that while there are three times as many genomes
in our analysis, our observations describe a continuation in
the trends observed by Siew and Fischer (57) using the
sequences of 60 completed genomes. The steady growth in
singleton sequences, rather than a general reduced presence in
the more recently sequenced organisms, suggests that these
novel sequences form a fundamental part of the genomes, with
recent analysis showing that many of these singletons repres-
ent real, functional and sometimes essential proteins (55).
Assignment of CATH and Pfam domain annotations
In addition to the division of sequences into gene families,
Gene3D aims to determine the domain architecture (domain
content and their order in sequence), of each member
sequence. In the current version of Gene3D, we search a lib-
rary of HMMs relating to 1572 CATH version 2.6 (58) and
7677 Pfam version 18 (59) families against the sequences of
the completed genomes using the SAM protocol (46). These
well established domain databases provide a comprehensive
set of domain families whose members are classified using
automated protocols together with a considerable amount of
manual validation.
It is necessary to take a hierarchical approach to domain
assignment in cases where CATH and Pfam domain annota-
tions are found to overlap. Such conflicts were resolved by
calculating a hybrid of CATH and Pfam assignments. CATH
domain matches are given priority over Pfam domain matches
since domains in the CATH database are identified from both
sequence and structure, which is generally considered to be a
more reliable approach for protein domain delineation than
their identification from sequence. The general approach is
illustrated in Figure 2, and described in more detail in the
corresponding legend. We found that over 80% of the
CATH domain families overlap a Pfam domain assignment
by over 90% of their length (just under 10% having no Pfam
counterpart). A total of 26% of Pfam domain families over-
lapped 90% or more residues in a corresponding CATH
Assignments
N C
10 190 210 300 430
CATH-1
Pfam-2
Pfam-1
Unassigned-
region
Residue #
Pfam CATH Pfam
(A) (B) (C) (D) (E)
Unassigned-
region
90
Figure 2. Assignment of CATH, Pfam and unassigned-regions to Gene3D sequences. A hierarchical scheme is used, where CATH domains are first assigned,
followed by non-overlapping Pfam domain assignments. In this example, a single CATH domain and two Pfam domains have matched a single sequence. CATH-1
and Pfam-1 overlap, with Pfam-1 extending beyond the CATH domain by 80 residues towards the N-terminus and 20 residues towards the C-terminus. According to
the hierarchy, the CATH assignment (region B) is given priority and therefore assigned. Pfam-1 is then considered. When assigning domains we apply a conservative
length cut-off of >50 residues. Consequently residues 10–89 corresponding to a fragment of Pfam-1 (region A), are assigned as a Pfam-1-subdomain whereas
residues 191–201 (region C) are not. There is no such clash for Pfam-2 (region E) and this assignment is accepted. Residues 211–300 (region D) cannot be assigned to
a CATH or Pfam family, yet form a sequence fragment of >50 consecutive residues. We refer to such sequences as ‘unassigned-regions’.
1070 Nucleic Acids Research, 2006, Vol. 34, No. 3
assignment, while 60% of Pfam domain families showed little
or no overlap with a CATH domain mapping.
Annotation coverage of genome sequences
In addition to the assignment of CATH and Pfam domains,
a series of prediction-based sequence annotations were
also made to each sequence; transmembrane helices are
predicted using the MEMSAT algorithm (48), coiled-coils
using COILS2 (50), regions of low complexity by the
SEG program (50) and N-terminal signal peptides by SignalP
(51). The annotation coverage of the non-redundant set
of sequences from the 203 genomes in Gene3D is tabulated
in Table 1. Coverage is calculated on both a whole-sequence
and residue basis with the contribution of each feature
type given cumulatively and non-cumulatively. Cumulative
totals are calculated on a hierarchical basis as ordered in
column 1.
The contribution of a particular feature, as a function
of sequences, gives a higher value than the per residue
counterpart. For example, while over 26% of sequences
from all genomes were predicted to contain two or more trans-
membrane helices, this corresponds to 7% of residues in the
203 completed genomes. Over 90% of all sequences, corres-
ponding to nearly 63% of residues, can be assigned one or
more feature types. CATH domain annotations can be pro-
vided for 47.8% of sequences (33.3% of residues) with just
under 70% of sequences (46.1% of residues) having one or
more matches to a Pfam domain family. The higher level of
domain annotation by Pfam is expected as the CATH domain
database classifies only sequences of known structure. Assign-
ing CATH domains first, and subsequently counting only non-
overlapping domain-like Pfam annotations, provides 76.5% of
sequences (55.5% of residues) with a CATH and/or Pfam
domain assignment.
Figure 3a and b shows annotation coverage for 30 randomly
selected genomes in the Gene3D database, 10 from each king-
dom of life. In this case, values are expressed on a per residue
basis, as this may be considered to give a more meaningful
measure of coverage.
Table 1. Annotation coverage of 663 546 sequences from 203 completed genomes in Gene3D
Annotation Coverage per sequence Coverage per residue
Non-cumulative Cumulative Non-cumulative Cumulative
No. of
sequences
Percentage of
sequences
No. of
sequences
Percentage of
sequences
No. of
residues
Percentage of
residues
No. of
residues
Percentage of
residues
CATH 317 055 47.8 317 055 47.8 78 877 234 33.3 78 877 234 33.3
Pfam 462 820 69.7 170 297 25.7 109 103 870 46.1 52 509 661 22.2
Transmembrane helices
a
175 096 26.4 34 119 5.1 16 474 777 7.0 5 385 960 2.3
Coiled-coil 38 038 5.7 10 207 1.5 2 451 346 1.0 1 796 539 0.8
Regions of low complexity 404 232 60.9 60 684 9.1 17 043 439 7.2 9 406 682 4.0
N-terminal signal peptide 72 261 10.9 5894 0.9 2 053 702 0.9 998 697 0.4
Total 598 256 90.2 148 974 773 62.9
Coverage is calculated as a percentage of sequences and percentage of residues. Values are also shown on a non-cumulative basis, showing the relative contribution of
each annotation type, and cumulatively, calculated on a hierarchical basis, as ordered in column 1. See Materials and Methods for details of the prediction
methods used.
a
Transmembrane helices were only considered in cases where two or more were assigned to a sequence.
Figure 3. Residue annotations of 30 representative completed genomes; 10 archaeal (top), 10 bacterial (middle) and 10 eukaryotic (bottom). (a) Cumulative residue
coverage. (b) Non-cumulative residue coverage. The contribution of each type of annotation is shown, to account for the fact that a given residue may be annotated by
more than one prediction method. Accordingly, the totals may not reflect the percentage of annotated residues.
Nucleic Acids Research, 2006, Vol. 34, No. 3 1071
The reliability of membrane-helix prediction methods
permits us to make some general observations on the distri-
bution of multi-spanning membrane proteins. As reported in
Table 1, 26.4% of the proteins encoded by the 203 completed
genomes are predicted to contain transmembrane helices, in
agreement with previous estimates (60–62). Comparable
distributions of membrane proteins were also observed across
the prokaryotic and eukaryotic genomes with respective aver-
ages of 26.5 and 24.7%. A series of studies have previously
demonstrated a roughly linear relationship between the num-
ber open reading frames (ORFs) in each genome and the
number of helical membrane proteins (60,62). A similar effect
was observed in our analysis—it is notable however that
although a similar percentage of ORFs appear to encode mem-
brane proteins across the single and multi-cellular genomes,
the overall fraction of residues assigned to membrane-helices
(as a percentage of all residues in a genome) appears lower
for the eukaryotic genomes, see Figure 3. This might in
part be an indication that many membrane-spanning proteins
in the eukaryotes have an additional series of globular
domains.
Figure 4 shows the distribution of membrane spanning heli-
ces across proteins encoded by prokaryotic or eukaryotic
genomes. As described in Materials and Methods, proteins
predicted to contain a single membrane helix are treated as
a separate class in our target selection pipeline, as these may
well be membrane-anchored globular domains. While the
number of membrane proteins falls off rapidly with an increas-
ing number of spanning helices, a number of exceptions are
apparent; eukaryotic proteins have a greater representation
of 7-transmembrane-helices (G-protein coupled receptors),
prokaryotes have a greater fraction of 6, 10 and 12-trans-
membrane-helical proteins (permease and transporter-like
proteins). Similar biases have been reported before, despite
the use of different transmembrane prediction algorithms and
smaller genome datasets (25,60–63).
Coiled-coils were predicted in 5.7% of the sequences across
all genomes. Eukaryotic proteins tend to have, on average, a
larger fraction of proteins with coiled-coil regions, with 10.6%
of eukaryotic sequences predicted to contain coiled-coils com-
pared with 3.8% of the prokaryotic sequences. The vast major-
ity of coiled-coil regions were over 28 residues in length.
Fewer than 11% of sequences were assigned an N-terminal
signal peptide by the SignalP algorithm, though a number of
genomes contained a significantly higher number of secreted
proteins, including Aeropyrum pernix. Altogether, 69% of
sequences (458 228 of 663 546) were predicted to contain
one or more regions corresponding to transmembrane helices,
coiled-coils, regions of low complexity or N-terminal signal
peptide predictions.
Clustering and filtering the unassigned-regions
As described above, the mapping of CATH and Pfam domains
to the genome sequences leaves a subset of sequences for
which a domain family assignment cannot be provided,
even by sensitive sequence comparison algorithms, such as
HMMs. This subset is populated by whole sequences lacking
CATH or Pfam domain assignments, or partial sequences
that remain after CATH and/or Pfam domain assignments
have been calculated. The length distribution of these
unassigned-regions is shown in Figure 5a, (grey line) and
can be compared with the length distribution of domains
assigned in the CATH domain database (black line). A signi-
ficant proportion of unassigned-region sequences appear
domain-like in their length when compared with the length
distribution of domains assigned in the CATH database,
though it is also notable that nearly 7% of the unassigned
sequence regions are greater than 450 residues in length
(roughly three times the CATH domain length average) and
are likely to represent many of the whole sequences to which
no CATH or Pfam domain assignments could be made.
0
5
10
15
20
25
30
35
40
45
234567891011121314151617181920
Number of transmembrane-helices
Percentage frequency
Prokaryotes
Eukaryotes
Figure 4. The distribution of the number of membrane spanning helices (n) in prokaryotic and eukaryotic proteins. The percentage frequency of whole-gene
sequences predicted to contain n membrane helices is shown on the y-axis. Eukaryotes appear to have a greater number of 7-transmembrane-helices (7-TM-helices),
while prokaryotes tend to require a higher fraction of 6, 10 and 12-TM-helical proteins.
1072 Nucleic Acids Research, 2006, Vol. 34, No. 3
We filtered the unassigned regions by including only
those sequences that contained 50 or more consecutive resi-
dues free from any transmembrane helix, coiled-coil, low
complexity or signal peptide annotation, a similar approach
has previously been described by Liu and Rost (25). Such
sequences (referred to as filtered-unassigned-regions) might
be expected to have an increased likelihood of forming a
globular state on folding and as such are more desirable for
structure determination. This filtering reduced the number of
shorter unassigned-regions, Figure 5a dark-grey line, with
an overall reduction of the unassigned sequences from
468 860 to 371 059, with nearly 30% of residues in the
unassigned-regions assigned to one of the feature types.
The mean sequence length for domains in CATH is just
above 160 residues, while the unassigned-regions and filtered
unassigned-regions have mean lengths of 200 and 220 resi-
dues, respectively. A large proportion of the unassigned
regions have a similar length distribution to single domain
proteins, but without further analysis (such as more rigorous
domain boundary identification), we cannot be certain
that all unassigned regions are single domain. Nevertheless,
for simplicity we approximate each unassigned region
as a domain. As such, hereinafter we describe all CATH,
Pfam and unassigned sequence assignments as domain
sequences.
Once identified, all 468 860 unassigned-regions were clus-
tered using the TribeMCL algorithm (see methods) to generate
56 854 families, described as NewFam clusters, containing
two or more members (corresponding to 204 414 sequences)
with 256 446 domain sequences remaining unclustered,
described as NewFam singletons. The largest NewFam cluster
contained 366 sequences. The length distribution of the New-
Fam members and singletons is shown in Figure 5b, where it
is apparent that many of the shortest unassigned-regions
remained unclustered by the TribeMCL algorithm.
CATH, Pfam and NewFam coverage of the
genome sequences
Generating NewFam clusters enabled the calculation of
CATH, Pfam and NewFam coverage of sequences from the
203 completed genomes, summarized in Table 2. Coverage is
calculated as the percentage of all domain sequences, rather
than whole sequences. Table 2a shows that 39 and 24% of
a
b
0
2
4
6
8
10
12
0 100 200 300 400 500 600 700 800
Length (amino acids)
Percentage of sequences
Unassigned-regions
Filtered-unassigned regions
CATH domains
0
2
4
6
8
10
12
0 100 200 300 400 500 600 700 800
Len
g
th (amino acids)
Percentage of sequences
Unassigned-regions belonging
to a NewFam cluster
Singleton unassigned-regions
Figure 5. (a) The length distribution of unassigned regions in Gene3D sequences compared with domains in the CATH domain database. (b) Distribution of lengths
for sequences belonging to NewFam clusters compared to NewFam singleton sequences.
Nucleic Acids Research, 2006, Vol. 34, No. 3 1073
sequence fragments are assigned to CATH and Pfam domains,
respectively, with per residue coverage illustrated in Table 2b.
Table 2c gives the CATH and Pfam domain coverage as a
percentage of non-singleton sequence fragments; that is
CATH, Pfam and NewFam singletons are excluded from
this calculation. The targeting of such domain sequences
that cannot be assigned to a family is given a low priority
in family based target selection strategies. As expected, an
increase in coverage of both CATH and Pfam domains is
seen, with 48 and 31% of sequence fragments being assigned
to their respective CATH and Pfam families. Finally, Table 2e
and f shows the per sequence and residue assignment with
the exclusion of singleton sequences and using filtered-
unassigned-regions. An increase in the percentage in CATH
and Pfam coverage is seen, in accordance with the decrease in
the total number of domain-like sequences, with 84% of the
domain sequences covered by CATH and non-overlapping
Pfam domain families.
Frequency of multi-domain proteins in the three
kingdoms of life
Using the extensive domain annotations described above, we
have attempted to estimate the number of number of multi-
domain proteins in the three kingdoms of life. Previous studies
(60,64) suggested that 80% of proteins in the eukaryotes and
65% of proteins in prokaryotes contain more than one domain.
Work by Harrison et al. (65) estimated that almost 66%
of eukaryotic genome sequences form multi-domain proteins.
However, these measurements were extrapolated from a few
genomes based only on the subset of proteins for which
domain assignments could be provided.
In our analysis, we first identified sequences fully covered
by a CATH or Pfam domain assignment, which we described
as single-domain, and those sequences assigned a combination
of two or more CATH, PFam and NewFam domains, which
we described as multi-domain. For this calculation, we also
attempted to further subdivide the remaining sequences for
which no CATH or Pfam assignment could be made, as
some of these proteins may contain more than one domain.
The mean length and corresponding standard deviation of
the single-domain proteins found in each individual genome
was calculated to provide a genome-specific length threshold
(mean length plus 1 standard deviation) that was used to
subdivide, where possible, the whole-gene unannotated
sequences. Though a relatively simple approach, this method
was considered to achieve a better overall estimate of the num-
ber of multi-domain sequences, as protein domains have been
shown to follow relatively narrow length distributions (66,67).
Table 2. Coverage of CATH and Pfam domain assignments
110,958,552119,539,415203,522,715Total
residues
Per residue
937,032989,6461,254,428Total
sequences
Per
sequence
Excluding singletons &
filtering unassigned-
regions
b
Excluding singleto n
sequences
a
All CATH, Pfam and
NewFam sequences
Coverage
42%
21%
37%
48%
31%
21%
51%
33%
16%
45%
31%
24%
48%
32%
20%
a
b
c
d
e
f
CATH Pfam Unassigned-region
39%
24%
37%
a
Singletons are defined as those sequences belonging to single member CATH, Pfam or NewFam families.
b
The unassigned-regions were filtered by including only those sequences containing 50 or more consecutive residues devoid of any transmembrane-helix, coiled-coil
or low complexity annotations.
1074 Nucleic Acids Research, 2006, Vol. 34, No. 3
A significant majority of whole-gene unassigned-regions were
assigned as single-domain proteins.
We estimate that 67% of eukaryotic proteins are multi-
domain with 54 and 57% of archaeal and bacterial sequences
forming multi-domain proteins. Interestingly, we also found
that the percentage of multi-domain proteins within individual
eukaryotic genomes is relatively unvaried compared to
bacteria, see Figure 6. In fact, our estimates show that the
number of multi-domain proteins in bacterial genomes vary
widely, ranging from 32% of sequences to almost 75% (as
high as some eukaryotes). It also appears likely that the dis-
tribution of multi-domain proteins in archaeal genomes would
follow a similar distribution to that of the bacteria if a similar
number of archaeal genomes were available for analysis,
(i.e. hundreds rather than tens). Though a similar discrepancy
in sample size also exists between bacterial and eukaryotic
genomes, the differences in average percentages of multi-
domain proteins between eukaryotes and prokaryotes appear
significant. Recently, work by Bjorklund et al. (68) calculated
that 65% of proteins in eukaryotes and 40% of proteins in
prokaryotes are multi-domain. However, their lower numbers
are likely to be attributable to the lower minimum domain
length cutoff they use (100 residues) and also the use of
21 genomes in their analysis compared to the 203 used in
this study. Their results are very similar—though possibly
slightly lower—when they use, in our view, a more reasonable
sequence length cutoff of 50 residues, as applied in this
analysis.
Identification of domain families for structure
determination
It is quite clear that structural genomics cannot support the
experimental determination of all proteins, and so it is hoped
that by solving a few thousand well chosen structures we can
go someway towards filling in the remaining structure space
by computational homology modelling (1). Structural genom-
ics must therefore sample a broad range of sequence families
in order to optimize the number of sequences that can be
modelled. Here, we aim to deduce the number of experimental
structures that are required to provide structural annotation to
the genome sequences. Coverage is calculated on a domain
sequence and residue basis.
The coverage of space, as represented by the 203 completed
genomes, in CATH, Pfam and NewFam domain families
is shown in Figure 7a (per domain sequence) and Figure 7B
(per residue), where domain families are ordered according
to size on the x-axis, with the largest first. The exclusion
of singletons from the coverage calculations suggests that
if the top 2000 largest domain families include at least
one member of known structure (with 1328 already doing
so), we can cover 70% of domain family members (inclu-
ding unassigned-regions belonging to NewFam clusters).
If only prokaryotic targets are used, the coverage of the
correspondingly largest 2000 families only falls to 67% of
the domain sequences. Alternatively, ensuring that the 2000
largest domain families found in the Human genome have
at least one member of known structure would provide
coverage for 57% of all domain family members in the
Human genome.
Increasing the number of families to 5000 gives a com-
paratively lower increase of coverage to 80% of all the
non-singleton domain sequences assigned in the 203
genomes, Figure 7a. A rapid gain in sequence coverage
is initially achieved by targeting the largest families
first, an effect that tails off as smaller families are consi-
dered. Coverage is also shown for all sequences, including
singletons (black line), representing a rather more pessi-
mistic view, with coverage of a little over 55% for the
2000 largest families. Figure 7b shows coverage on a per
residue basis, with similar values to the coverage given on
a sequence level.
Calculation of coverage on the basis of one structure per
domain family provides a lower bound in terms of effort
required by the structural genomics initiatives. The domain
families represent a ‘coarse-grained’ division of sequence
space into broad sequence families containing all relatives
sharing a common ancestor. Targeting large families in
Gene3D provides greater structural coverage of all proteins
but a large proportion of the structural models generated will
only be moderately accurate for distant homologues in the
family. It does not account for the fact that many domain
families will contain considerable structural and functional
variation that cannot be resolved by the determination of a
single structure. In view of this, it is necessary to calculate the
number of protein structures that are required to provide
homology models, of reasonable accuracy, for the remainder
of sequence space. Homology modelling uses experimentally
determined structures (templates) to predict the 3D structure of
another protein (target) with a related amino acid sequence.
Protein structure can reveal the chemical principles underlying
protein function and this structure-derived functional output
can be transferred to related sequences. The level to which
such annotations can be achieved in homology modelling is
generally considered to be dictated by the level of sequence
identity that is shared between template and modelled
sequence. We assume that at 30% sequence identity or
above, reasonably accurate models can be produced to enable
structural variations within a given family to be correlated
with putative functional variation.
The members of each CATH, Pfam and NewFam family
were sub-clustered using the greedy coverage algorithm
0
10
20
30
40
50
60
30 35 40 45 50 55 60 65 70 75 80
Percentage multi-domain proteins
Number of genomes
Bacteria
Archaea
Eukaryotes
Figure 6. Percentage of multi-domain protein sequences in each of 203
completed genomes from the three kingdoms of life.
Nucleic Acids Research, 2006, Vol. 34, No. 3 1075
described by (69). Greedy coverage first identifies the
sequence with the largest number of relatives above a given
threshold, in this case 30% sequence identity. These sequences
are assigned to the first cluster and removed, while the process
is repeated iteratively on the remaining sequences until no
sequences remain. Such clustering is suitable for the needs
of structural genomics, where the prioritization of targets that
provide the highest number of homology models as possible is
a significant goal of target selection. Figure 8 shows genome
coverage on the basis of the subfamilies identified within the
domain families. Note that singletons are defined as those
sequences belonging to single-member CATH, Pfam and
NewFam families rather than single-member subfamily clus-
ters. Excluding such singletons, the number of structures
required to provide accurate models (>30% sequence identity)
for 70% of the CATH, Pfam and NewFam sequences is
90 000. Clearly, this is significant increase in the number
of targets in comparison to the figures presented in Figure 6.
Similar analysis, albeit using differing approaches, including
those by Vitkup et al. (24), Liu and Rost (62) and Chandonia
and Brenner (27) have also demonstrated large-scale require-
ments for the significant coverage of sequence space at similar
modelling densities. Achieving these numbers of structural
determinations is clearly not a realistic goal for any structural
genomics effort.
A more rational methodology is required that targets
additional family members in a manner that will provide
broader structural insights in the most functionally diverse
domain families. Comparative genome analysis has shown
that a large proportion of these families are very large, having
expanded significantly during evolution through extensive
gene duplication within a genome (70–72). Increasing our
knowledge on the diverse functional roles across subfamilies
will have a greater medical and biological value than random
fine-grained target selection.
Already, many of the largest domain superfamilies in nature
are classified in CATH and SCOP (73), since they have at least
one relative of known structure. However, for many of these
large superfamilies, this structural data can only be extrapol-
ated to a small percentage of the remaining sequences in the
superfamily through homology modelling. This can be high-
lighted by calculating the homology modelling coverage of
the twenty most frequently occurring CATH domain super-
families in the 203 completed genomes, illustrated in Figure 9.
Homology modelling coverage was measured at the domain
level, using a subset of non-identical superfamily-specific
Coverage per sequence
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000 4000 5000 6000
Families, ordered by size
Percentage of sequences
All CATH, Pfam and NewFam sequences
Excluding singletons
Excluding singletons and filtering
unassigned-regions
Coverage per residue
0
10
20
30
40
50
60
70
80
90
0 1000 2000 3000 4000 5000 6000
Families, ordered by size
Percentage of sequences
All CATH, Pfam and NewFam sequences
Excluding singletons
Excluding singletons and filtering
unassigned-regions
a
b
Figure 7. Coverage of protein space, represented by 203 complete genomes. CATH, Pfam and NewFam families are ordered by size (x-axis). Coverage is shown on a
sequence (a) and residue (b) basis.
1076 Nucleic Acids Research, 2006, Vol. 34, No. 3
domains from CATH to act as structural templates, using an
overlap of 80% and a minimum sequence identity of 30% to be
shared between the template and target structure (see Materials
and Methods). This may provide a slight underestimate in the
fraction of sequences for which a model can be provided, since
the CATH database is partially reliant on manual curation, and
is not entirely up to date with the PDB.
The level of modelling coverage varies across the super-
families: while as many as 80% of sequence relatives in the
Phosphorylase kinase domain 1 superfamily (3.30.200.20) can
be modelled based on a sequence identity of 30% or more,
structural data are extrapolated to a much smaller percentage
of the sequence members in many other of the largest CATH
domain families. For example, just 16% of the sequence relat-
ives in the equivalently sized Hydrolase superfamily
(3.40.50.1820) can be structurally annotated. This effect is
more pronounced when calculating modelling coverage of
these large superfamilies above 60% sequence identity.
Such a level of sequence identity is required to give higher
quality models that are essential for reliable drug design and
ligand docking.
A closer examination shows that the level of modelling
coverage within each superfamily is tends to be underpinned
by the number of unique known structures and sequence
0
10
20
30
40
50
60
70
80
90
100
Percentage of sequence relatives
> 30% sequence identity
> 60% sequence identity
P-loop hydrolases
(3.40.50.300)
Rossmann-like
domain (3.40.50.720)
Winged helix DNA
repressor (1.10.10.10)
Immunoglobulins
(2.60.40.10)
Periplasmic binding
protein l ike II (3.40.190.10)
Transferase
(3.40.50.150)
Oxidoreductase
(3.50.50.60)
Repressor regulator
(3.40.50.2600)
Homeodomain like
(1.10.10.60)
Ligase (3.40.50.620)
Hydrolase (3.40.50.182 0)
Nucleic acid binding
proteins (2.40.50.140)
Type I PLP-dependent
(3.40.640.10)
Aldolase class 1
(3.20.20.70)
Phophorylase kinase
domain 1 (3.30.200.20)
Phosphotransferase
domain 1 (1.10.510. 10)
Chaperone (3.30.565.10)
Transferase (3.40.630.30)
Peptide synthase
(3.40.50.980)
(34238,
5017)
(17542,
2621)
(12711,
2481)
(7418,
1517)
(6417,
2307)
(5940,
2129)
(5887,
1215)
(5446,
683)
(5386,
1061)
(4987,
749)
(4792,
1860)
(4761,
767)
(4662,
486)
(4616,
727)
(4545,
968)
(4282,
884)
(3983,
889)
(3455,
622)
(3455,
1302)
(3424
700)
Lyase (3.90.1150.1 0)
CATH supe rfamily
118
125
84
137
51
40
40
11
40
38
55
59
32
58
37
27
12
33
22
6
Figure 9. Homology modelling coverage calculated for sequence relatives in the twenty largest CATH domain families found in 203 completed genomes in
Gene3D. Each bar of the histogram represents the percentage of superfamily sequence relatives that can be modelled at relatively high accuracy (>30% sequence
identity shared between target and template). The black regions represent the subset of these sequences that can be modelled at >60% sequence identity. The
value shown within each histogram bar indicates the number of known unique-domain structures (sharing <30% sequence identity) within each superfamily.
The number of sequences relatives assigned to each superfamily, and the number of sequence families they cluster into (at 30% sequence identity) is shown in
brackets above each bar. Though these superfamilies contain many relatives of known structure, we are still unable to accurately model many of the sequence
relatives, suggesting additional experimentally determined structures are still required to increase our structural understanding of these highly recurrent domain
families.
0 20000 40000
60000
80000 100000
SubFamilies, ordered b
y
size
0
10
20
30
40
50
60
70
80
Percentage of sequences
Excluding singletons and filtering unassigned-regions
Excluding singletons
All CATH, Pfam and NewFam sequences
Coverage per sequence
Figure 8. Fine-grained coverage of protein space. We applied a greedy cover-
age algorithm to calculate the number of structures required to model all
members of CATH, Pfam and NewFam families based on a cutoff >30%
sequence identity. This measure suggests many more structural determinations
will be required to enable accurate modelling of a large percentage of protein
sequences compared with the numbers suggested by a coarse-grained coverage
approach.
Nucleic Acids Research, 2006, Vol. 34, No. 3 1077
diversity (here measured by the number of 30% sequence
identity families, Figure 9, values in bold). In general, those
superfamilies with a similar number of unique solved
structures and a similar level of sequence diversity share
comparable levels of modelling coverage. In contrast, a higher
sequence diversity in a given superfamily can lead to a lower
modelling coverage despite a similar number of known struc-
tures. For example, the Hydrolases (3.40.50.1820) and the
Aldolase Class-1 superfamily (3.20.20.70) differ greatly in
modelling coverage (18 and 69%, respectively) while contain-
ing a similar number of unique solved structures (55 and 58).
In turn, a lower modelling coverage can occur with a lower
number of unique solved structures despite similar levels of
sequence diversity, e.g. Peptide synthase (3.40.50.980) and
Aldolase Class-1 (3.20.20.70).
Though many CATH or SCOP superfamilies contain relat-
ives of known structure, it is clear that we are still unable
to accurately model many of the sequence relatives, suggesting
additional experimentally determined structures are still
required to increase our structural understanding of these
highly recurrent domain families. Therefore, to provide
good structural models for a significant proportion of all
proteome sequences, it will be essential to target additional
sequence subfamilies with no structural relatives in the large
superfamilies. Determining structures of sequence-distant
members of these highly recurrent superfamilies will in
turn have a significant impact on the understanding of
their structural and functional evolution and is essential for
the continued development of tools that enable us to predict
function from structure.
DISCUSSION
In the past few years, we have witnessed a steady increase in
the number of structural genomics projects, and though many
differ in their aims and approaches, all are united by a common
goal to extend the repertoire of structure space. Recent ana-
lysis of a number of initiatives shows an encouraging start to
these endeavours, while also highlighting the requirement for
the continued development and application of rigorous target
selection strategies to enable the efficient coverage of protein
space (18,74).
In this paper, we have described some of the approaches that
have been applied within the MCSG consortium, including the
use of the Gene3D database to enable a family-based approach
to our selection of target sequences. The latest version of
Gene3D provides sequence similarity derived protein family
clusters for 203 completed genomes, which in turn have
been annotated by structural and sequence domain family
assignments. These large-scale CATH and Pfam domain
assignments have allowed us to extrapolate the extent to
which sequence space can be effectively covered by solving
new structures, and how suitable targets might be derived
from the huge array of uncharacterized sequences that are
available to us.
We have shown that at a coarse-level of granularity, a struc-
tural representative for each of the 2000 largest CATH and
Pfam families will give a coverage of over 70% of the domain-
like sequences found in 203 completed genomes. This level
of annotation would require the derivation of roughly 1000
structures, corresponding to Pfam families lacking a member
of known structure. It is intended that many of the protein
structures solved by the structural genomics initiatives will be
used as fold templates to build models for additional proteins
related by sequence similarity with structural comparison then
facilitating the assignment of function from structures with a
characterized function. Even though the continued improve-
ment in homology modelling and fold recognition methods
will permit structural annotations to be made at increasingly
low levels of sequence similarity, the characterization of one
structure per domain family will ultimately leave large areas of
sequence space beyond the limits of our modelling pipelines.
Consequently, we calculated the number of structures that
would be required to provide models for a fine-grained cov-
erage of sequence space, as represented by the 203 genomes in
Gene3D. By applying a greedy coverage algorithm to each
CATH, Pfam and NewFam family in Gene3D, to simulate a
modelling density cutoff of 30% sequence identity, we found
considerably more structures (over 90 000) would be required
to provide reasonable homology models for 70% of domain-
like sequences. With this in mind it is clear that the diversity of
structures that can be provided for the reliable modelling of
sequence space must be brought into context with the require-
ments of biological and biomedical sciences to enable fold
space to be targeted in an effective way.
The data from this analysis, together with related work by
Vitkup (24) and Chandonia and Brenner (27), suggest that the
derivation of a target list that includes representatives from the
largest domain families can serve as a convenient platform for
target selection between different structural genomic projects.
This coarse-grained target selection should then work along
side a fine-grained approach allowing additional targets to be
selected from families with particular biomedical or biochem-
ical importance or families that cover diverse areas of
sequence and function space. The derivation of new or addi-
tional representative structures for sequence families should
result in iterative updates of such target lists to concentrating
on the most relevant areas of sequence space.
We are currently investigating the use of simple methods
such as sequence clustering, comparison of domain archi-
tecture, and functional annotation [using GO (www.
geneontology.org), COG (75) and KEGG (76)] to enable
the identification of sequences likely to represent structurally
uncharacterized function space. For example, the 2000 largest
CATH and Pfam domain superfamilies contain 4790 COG
functional groups that have no close structural homologue
(>30% sequence identity). The more specific annotations
and groupings tend to belong within a limited number of
broader categories, though there is not a neat hierarchical
nesting of all clusters and some overlap between levels is
seen. Scoring schemes may therefore be required to prioritize
targets according to their membership of different categories
of diversity within a given family. This would provide a more
directed approach to pick out additional high-value targets
within large sequence families, as opposed to a more random
approach.
While structural genomics has often been considered to be
synonymous with the pursuit of new folds, it is clear that the
characterization of structures distantly related to existing
domain superfamilies or forming novel superfamilies is of
equal importance, because it enables us to gain new view-
points on the evolution of protein structure and function. In
1078 Nucleic Acids Research, 2006, Vol. 34, No. 3
turn, the need for a more comprehensive coverage of particular
sequence families from a modelling point of view will be of
increasing importance. By aiming to understand the structure
and function of each gene product, we can also begin to
unravel the huge variety of protein interactions and assemblies
that occur within the cellular environment. It is clear that
future structure genomics efforts must target proteins that
fulfil many of these requirements.
ACKNOWLEDGEMENTS
The authors thank Steve Brenner and colleagues for discussions
on these topics. The authors also wish to thank Tony Lewis,
Mark Dibley and Timothy Dallman for valuable comments and
discussion. This work was funded by the NIGMS of the NIH
through the Midwest Center for Structural Genomics, the
Wellcome Trust and the EU funded BioSapiens project.
Conflict of interest statement. None declared.
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1080 Nucleic Acids Research, 2006, Vol. 34, No. 3
    • "The concept of orphan genes was first described by Fischer and Eisenberg in 1999 from studies of microbial genomes (Fischer and Eisenberg, 1999). Although many have predicted that genes considered species specific would later turn out to be an artifact of sparse genome sequence, this has proved not to be the case (Arendsee et al., 2014; Gollery et al., 2006 Gollery et al., , 2007 Marsden et al., 2006; Neme and Tautz, 2013; Silveira et al., 2013; Tautz and Domazet-Loso, 2011). Orphan genes appear to be present in all species, and represent a significant fraction (approximately 0.5% to >8%) of analysed eukaryotic and prokaryotic genomes. "
    [Show abstract] [Hide abstract] ABSTRACT: The genome of each species contains as high as 8% of genes that are uniquely present in that species. Little is known about the functional significance of these so-called species specific or orphan genes. The Arabidopsis thaliana gene Qua-Quine Starch (QQS) is species specific. Here, we show that altering QQS expression in Arabidopsis affects carbon partitioning to both starch and protein. We hypothesized QQS may be conserved in a feature other than primary sequence, and as such could function to impact composition in another species. To test the potential of QQS in affecting composition in an ectopic species, we introduced QQS into soybean. Soybean T1 lines expressing QQS have up to 80% decreased leaf starch and up to 60% increased leaf protein; T4 generation seeds from field-grown plants contain up to 13% less oil, while protein is increased by up to 18%. These data broaden the concept of QQS as a modulator of carbon and nitrogen allocation, and demonstrate that this species-specific gene can affect the seed composition of an agronomic species thought to have diverged from Arabidopsis 100 million years ago.
    Full-text · Article · Aug 2014
    • "This unidentifiable, speciesspecific number of genes has been estimated to be about 40% (e.g. Eichinger et al., 2005; Marsden et al., 2006), if no genome of the same taxon is completely determined. Species-specific gene family extensions would lead to a slight underestimation of genome size. "
    Full-text · Dataset · Nov 2012 · BMC Structural Biology
    • "In 2004 about half of all first structures for Pfam families were from SG rather than SB. Marsden et al. [13] analyzed 203 complete genomes in the Gene3D resource [14] to provide new insights into protein family space. The number of protein families was found to be continually expanding with time but a significant proportion of the proteomes could be assigned to relatively few large, well-characterized domain families while the vast majority of domain families were relatively rare and often species specific. "
    [Show abstract] [Hide abstract] ABSTRACT: The Midwest Center for Structural Genomics (MCSG) is one of the large-scale centres of the Protein Structure Initiative (PSI). During the first two phases of the PSI the MCSG has solved over a thousand protein structures. A criticism of structural genomics is that target selection strategies mean that some structures are solved without having a known function and thus are of little biomedical significance. Structures of unknown function have stimulated the development of methods for function prediction from structure. We show that the MCSG has met the stated goals of the PSI and use online resources and readily available function prediction methods to provide functional annotations for more than 90% of the MCSG structures. The structure-to-function prediction method ProFunc provides likely functions for many of the MCSG structures that cannot be annotated by sequence-based methods. Although the focus of the PSI was structural coverage, many of the structures solved by the MCSG can also be associated with functional classes and biological roles of possible biomedical value.
    Full-text · Article · Jan 2011
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