Structural proteomics of minimal organisms: Conservation of protein fold usage and evolutionary implications
© Chandonia and Kim; licensee BioMed Central Ltd. 2006
Received: 20 October 2005
Accepted: 28 March 2006
Published: 28 March 2006
Determining the complete repertoire of protein structures for all soluble, globular proteins in a single organism has been one of the major goals of several structural genomics projects in recent years.
We report that this goal has nearly been reached for several "minimal organisms" – parasites or symbionts with reduced genomes – for which over 95% of the soluble, globular proteins may now be assigned folds, overall 3-D backbone structures. We analyze the structures of these proteins as they relate to cellular functions, and compare conservation of fold usage between functional categories. We also compare patterns in the conservation of folds among minimal organisms and those observed between minimal organisms and other bacteria.
We find that proteins performing essential cellular functions closely related to transcription and translation exhibit a higher degree of conservation in fold usage than proteins in other functional categories. Folds related to transcription and translation functional categories were also overrepresented in minimal organisms compared to other bacteria.
The availability of complete genome sequences opened up a new era in biology, providing a global and systems view of the range of genome sizes in different organisms, the presence or absence of genes involved in various cellular functions, the genes involved in particular cellular functions, and the relative abundance of different gene families. This new global view is creating major new areas of research such as functional genomics . At the time of this writing, over 224 prokaryotic genomes and over 22 complete eukaryotic genomes have been sequenced . Just as the field of sequence genomics has yielded complete genome sequences for a variety of organisms, the field of structural genomics aims to provide structures for the complete array of biological macromolecules found in nature, [3–7]. The first phase of structural genomics focused only on proteins (not RNAs), and has proven to be an efficient means of providing structural information for new protein families [8–10].
After the first sequencing of a complete genome of Haemophilus influenzae , some of the earliest subsequent genomes sequenced were from the "minimal organisms" Mycoplasma genitalium and M. pneumoniae [12, 13]. Minimal organisms have been the subject of numerous experimental and computational genomic studies because of the possibility of identifying the minimal complement of genes necessary for sustaining life [14–16]. Because of their small size, organisms with minimal genomes have also been popular for structure and function prediction [13, 17–24]. The minimal organisms M. genitalium (~486 protein-encoding genes) and M. pneumoniae (~690 genes) have also been the focus of structural genomics research at the Berkeley Structural Genomics Center [25, 26].
Other minimal organisms that have been sequenced more recently include the aphid symbiont Buchnera aphidicola (~572 genes) , the ant symbiont Candidatus Blochmannia floridanus (~583 genes) , the tsetse fly symbiont Wigglesworthia glossinidia brevipalpis (~612 genes) , and the Whipple's disease parasite Tropheryma whipplei (~781 genes) . Comparative analysis of the first three symbiont genomes and M. genitalium has demonstrated that the symbionts are closely related, sharing 313 orthologous genes (51–55% of each genome), and that they share 179 genes with M. genitalium . However, a broader comparison of all five species, including T. whipplei, indicated significant variability in the functional repertoire of proteins in these organisms, suggesting that minimal genomes are not the result of a unique reductive evolutionary pathway, but the products of reductive evolution in specific environments .
A recent survey of proteins from 238 complete genomes revealed that fold assignments (approximate 3-D backbone structures) can be made for the majority of non-membrane proteins of minimal organisms . Statistically significant sequence similarity to a protein of known structure allows homology (evolutionary relatedness) to be inferred, thus enabling the fold of the homologous proteins to be assigned even in cases where the degree of sequence similarity is insufficiently high to allow accurate modeling .
Fold assignment of a protein has implications for functional annotation, because the link between molecular function and structure is well known. Todd and colleagues showed that while the majority of superfamilies display variation in enzyme function (i.e., molecular function), the biochemical mechanisms (as represented by the Enzyme Commission [EC] number) are almost always conserved between proteins with 40% sequence identity or above . More recent work has shown that conserved domain combinations, or supradomains, are more likely to maintain a conserved molecular function even at lower sequence identity . A study in two proteomes (yeast and Escherichia coli) found clear tendencies for fold-function association across a broad range of molecular functions . The latter study also found the fold distributions in the two proteomes surveyed did not vary significantly from the average across all sequenced proteomes, although the study was based on fold assignments for less than 10% of the total number of proteins.
We now report that recent efforts in structural biology and structural genomics have succeeded in enabling fold assignments for over ~90% of soluble, globular proteins in the five minimal organisms described above. In this report, we survey the classes of protein folds found in each organism, and examine the conservation in fold usage of proteins in several broad categories of cellular function. We find that the degree of conservation of fold usage varies among cellular functional categories, with the most conserved categories of proteins performing essential cellular functions closely related to transcription and translation. Finally, we compare the degree of conservation in cellular functions and fold usage among the five minimal organisms and E. coli, a non-minimal organism.
Results and discussion
Near-complete coverage of soluble, globular proteomes of "minimal" organisms
Status of near-complete structural proteomes as of 22 February 2005. How many proteins may be assigned folds in near-complete proteomes? The status for five near-complete prokaryotes are shown. E. coli, a well-studied bacteria that is not considered a minimal organism, is included for comparison.
Total # of proteins
# of soluble, globular proteins
# of soluble, non-globular proteins
# of membrane proteins
# of folds assigned
% folds assigned (of total)
% folds assigned(of soluble, globular)
# of remaining soluble, globular proteins
# of remaining soluble, non-globular proteins
# of remaining membrane proteins
Candidatus Blochmannia floridanus
Wigglesworthia glossinidia brevipalpis
Buchnera aphidicola (subsp. Acyrthosiphon pisum)
Tropheryma whipplei (strain TW08/27)
α/β fold class is the most common category of fold
Usage of protein fold classes are conserved for key cellular processes
In order to analyze how the annotated cellular function of each protein correlates with its structure, we examined the "functional role" annotation for each protein as provided in the TIGR database . We found that the distribution of proteins among SCOP fold classes was highly conserved within some roles and showed much more variability in others.
Cellular functions with most conserved SCOP fold usage
Previous comparative sequence genomic analyses of symbionts have shown that the number of proteins in most cellular function categories varies little between symbiont proteomes, and that many of the most highly conserved proteins have cellular functions related to information storage and processing, particularly translation and ribosomal structure . We calculated the coefficient of variation (CV) in the number of proteins in each functional role category (N1 for the first species, N2 for the second species, etc.), as shown in Equation 1.
Variation within functional categories based on sequence and structure. Which functional categories show the most variation in fold usage between organisms? The first column lists 17 TIGR cellular function categories, and an additional category composed of all proteins in each proteome. The "fold-based variation" column is based on a calculation of the coefficient of variation in the number of structurally characterized domains in each functional role in each of the first 7 SCOP classes (all-α, all-β, α/β, α+β, multi- domain, membrane, small). As described in Equation 2, the coefficient of variation is calculated separately for each of the 7 classes, and then averaged across all 7 classes to produce CVstructure. The "sequence-based variation" column gives the coefficient of variation in the number of proteins in each category (CVsequence, Equation 1). The "fold-based rank" and "sequenced-based rank" show the ranking of functional categories based on the amount of fold-based and sequence-based variation, from lowest amount of variation to the highest. Cellular function categories are ordered in the table according to their fold-based rank.
Average # of Proteins
Fold-based variation (CVstructure)
Sequence-based variation (CVsequence)
Fold-based Rank/Sequence-based Rank
Purines, pyrimidines, nucleosides, and nucleotides
Amino acid biosynthesis
Central intermediary metabolism
Fatty acid and phospholipid metabolism
Biosynthesis of cofactors, prosthetic groups, and carriers
Transport and binding proteins
We also calculated the coefficient of variation in the number of protein domains assigned to each SCOP class (N1,all-α for the first species in the all-α class, N2,all-α for the second species in the all-α class, etc), then averaged that data across all 7 structural classes, as shown in Equation 2.
Variation within functional categories in minimal organisms. Which functional categories show the most variation in fold usage between minimal organisms? The data are calculated as in Table 2, but ignore data from E. coli. The structure-based variation when E coli data are included (from Table 2) is provided for comparison.
Average # of Proteins
Fold-based variation (CVstructure)
Fold-based variation, including E. coli
Fold-based Rank/Sequence-based Rank
Fatty acid and phospholipid metabolism
Purines, pyrimidines, nucleosides, and nucleotides
Transport and binding proteins
Biosynthesis of cofactors, prosthetic groups, and carriers
Amino acid biosynthesis
Central intermediary metabolism
Common and overrepresented folds in minimal organisms
Most common SCOP folds in minimal organisms. Which SCOP folds are most common in minimal organisms? The first column gives the name and SCOP sccs identifier for folds classified in SCOP 1.67. The second column gives the total number of domains assigned to each fold among the five minimal organisms. The third column is calculated as the average number of domains among the five minimal organisms studied that were assigned to each fold, divided by the number of domains in E. coli assigned to the same fold.
P-loop containing nucleoside triphosphate hydrolases (c.37)
TIM beta/alpha-barrel (c.1)
OB (Oligonucleotide/oligosaccharide-binding) fold (b.40)
Adenine nucleotide alpha hydrolase-like (c.26)
Ribonuclease H-like motif (c.55)
NAD(P)-binding Rossmann-fold domains (c.2)
Class II aaRS and biotin synthetases (d.104)
DNA/RNA-binding 3-helical bundle (a.4)
Reductase/isomerase/elongation factor common domain (b.43)
Over-represented SCOP folds in minimal organisms. Which SCOP folds are most over-represented in minimal organisms, relative to E. coli? The first column gives the name and SCOP sccs identifier for folds from SCOP 1.67. The second column gives the total number of domains with each fold among the five organisms. The third column is calculated as the average number of domains among the five minimal organisms studied that were assigned to each fold, divided by the number of domains in E. coli assigned to the same fold. 37 other folds also have a ratio of 1.0 and 1 representative in each minimal organism.
DNA primase core (e.13)
An anticodon-binding domain of class I aminoacyl-tRNA synthetases (a.97)
Head domain of nucleotide exchange factor GrpE (b.73)
Ribosomal proteins L23 and L15e (d.12)
DNA clamp (d.131)
ValRS/IleRS/LeuRS editing domain (b.51)
S-adenosylmethionine synthetase (d.130)
Dihydrofolate reductases (c.71)
Ribosomal protein L6 (d.141)
beta and beta-prime subunits of DNA dependent RNA- polymerase (e.29)
Interestingly, all 47 SCOP folds present in equal or greater numbers in all minimal organisms as in E. coli are also folds for which only a single superfamily is characterized in SCOP; i.e., all proteins sharing the fold are also annotated as evolutionarily related to each other. The case of multiple superfamilies sharing one fold may arise from two alternative causes: convergent evolution of two or more families to one fold, or a single family that has diverged enough that homology between different branches of the family are no longer evident even from structure (in this case, each branch would be classified as a different superfamily in SCOP). These data imply that proteins that play sufficiently important roles to avoid elimination during reductive evolution have also not diverged as much as other protein families due to this same evolutionary pressure.
SCOP folds in minimal organisms but not E. coli. Which SCOP folds are found in minimal organisms, but not E. coli? The total number of domains from all five minimal organisms that were assigned to each fold is given in the second column.
alpha-2-Macroglobulin receptor associated protein (RAP) domain (a.13)
DBL homology domain (DH-domain) (a.87)
Non-globular all-alpha subunits of globular proteins (a.137)
GatB/YqeY domain (a.182)
SMAD/FHA domain (b.26)
C-terminal autoproteolytic domain of nucleoporin nup98 (b.119)
Nucleoplasmin-like/VP (viral coat and capsid proteins) (b.121)
Hypothetical protein TM1070 (b.123)
Hypothetical protein YojF (b.128)
Amidase signature (AS) enzymes (c.117)
Urease, gamma-subunit (d.8)
Penicillin-binding protein 2x (pbp-2x), c-terminal domain (d.11)
MHC antigen-recognition domain (d.19)
Thymidylate synthase-complementing protein Thy1 (d.207)
Smc hinge domain (d.215)
Polo-box domain (d.223)
After five years of progress in structural genomics, near-complete structural complements of the soluble proteins of several "minimal organisms" are now known. A complete set of fold assignments for nearly all soluble, globular proteins in a proteome is providing a global view of how minimal organisms are using various protein fold classes for different cellular functions and how the fold usage in each class is conserved.
Data from near-complete structural proteomes can yield hypotheses on protein evolution at a global level. Simple statistical analyses of the variation in numbers of structures in each structural and functional category can shed light on which functional categories are more or less conserved in minimal organisms. For example, the functional categories that showed the least variability in both sequence- and structure-based analyses were involved in essential cellular functions such as transcription and translation. Furthermore, every SCOP fold identified in equal or greater numbers in minimal organisms as in E. coli was the product of a single protein family, indicating that the proteins retained during reductive evolution of minimal organisms also tend to be from slow-evolving families. The latter observation was expected, as essential genes in other species have previously been shown to evolve more slowly than non-essential genes [41, 42].
Such observations may be followed up with more detailed studies based on phylogenetic modeling of protein families  or the construction of atomic models of proteins in those categories. Detailed atomic modeling of all proteins in a biochemical pathway will be useful to study the plasticity of these pathways in response to evolutionary pressures imposed by different organisms' environments .
Our database of known protein structures, knownstr, was created on 22 Feb 2005. This database contained sequences of every protein chain released by the PDB , including those of obsolete entries, sequences of proteins deposited in the PDB and made available while the structures were still on hold, and sequences from TargetDB , for which a structure had been solved by a participating structural genomics center.
Pfam  classification of known structures was evaluated using Pfam version 16.0. The HMMER tool (version 2.3.2)  was used to compare the Pfam_ls library of hidden Markov models to the knownstr database, using the family-specific "trusted cutoff" score as a cutoff for assigning significance.
INTEGR8 version 12  was used for sequence data. The Integr8 database contains data for 238 complete proteomes, including 19 eukaryotes. The proteome for each organism is composed of proteins curated from the Swiss-Prot and TrEMBL databases. All proteins were annotated with hidden Markov models [48, 49] from the InterPro  database. Since InterPro includes models from Pfam, we used the supplied InterPro annotations to map Pfam domains onto each protein. The version of InterPro used to annotate Integr8 version 12 includes Pfam 16.0
SUPERFAMILY  version 1.67 contains hidden Markov models based on superfamilies from the SCOP database [38, 52], also version 1.67. Recent versions of SUPERFAMILY  provide pre-calculated annotations of genomes downloaded from NCBI with all the superfamily models. We used these precalculated annotations to assign SCOP domains to sequences from minimal organisms and E. coli, as described below. The false positive rate for SUPERFAMILY annotations is estimated to be less than 1% .
The Comprehensive Microbial Resource  contains annotations of TIGR role categories in its OMNIOME database. We obtained TIGR role annotations from the version of OMNIOME downloaded on 12 May 2005. Of 19 TIGR role categories, two ("signal transduction" and "other categories") were found in low average abundance in the proteomes we analyzed (averaging 0.7 and 9.0 proteins per proteome, respectively), and these categories were excluded from our analysis. The remaining 17 categories are listed in Table 2.
To use annotations from the SUPERFAMILY and OMNIOME databases, we mapped proteins from the Integr8 database onto corresponding proteins in the NCBI and CMR Locus databases, respectively. In most cases, this was done by mapping identical sequences from the corresponding genome. However, in some cases, the gene or ORF annotations of the same genomes varied between the databases, resulting in different protein sequences. In these cases, we used BLAST  version 2.2.9 to map each Integr8 sequence to the most similar sequence in the other databases. We mapped each protein in Integr8 that could not be mapped by direct sequence match to the most significant BLAST hit in the other database, provided the BLAST E-value of the hit at least as significant as an empirically chosen threshold of 10-10. An average of 16.3 proteins in each proteome could not be mapped to any of the functional categories in OMNIOME, and were not included in this analysis.
Predicting tractability in high-throughput experiments
We identified all proteins with a predicted transmembrane helix, or with 20% or more residues in low complexity regions, or with 20% or more residues in coiled coil regions, as likely to be intractable in high-throughput experiments. Other proteins were annotated as soluble, globular proteins. The 20% threshold were used in more recent target selection rounds at the Berkeley Structural Genomics Center . Similar thresholds have also been justified by recent comprehensive crystallization trials on the Thermotoga maritima proteome .
The "seg" program  (version dated 5/24/2000) was run on all sequences in Integr8 to identify putative low complexity regions. The "ccp" program  (version dated 6/14/1998) was used to predict coiled coil regions in all sequences, and TMHMM 2.0a  was used to predict the locations of transmembrane helices. TMHMM can distinguish between soluble and membrane proteins with both specificity and sensitivity greater than 99%, but frequently produces false positive predictions when signal peptides are present. Default options were used for all programs.
This work is supported by grants from the NIH (1-P50-GM62412) and the U.S.
Department of Energy under Contract No. DE-AC02-05CH11231.
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