Many parasitic organisms, eukaryotes as well as bacteria, possess surface antigens with amino acid repeats. Making up the interface between host and pathogen such repetitive proteins may be virulence factors involved in immune evasion or cytoadherence. They find immunological applications in serodiagnostics and vaccine development. Here we use proteins which contain perfect repeats as a basis for comparative genomics between parasitic and free-living organisms.
Results
We have developed Reptile http://reptile.unibe.ch, a program for proteome-wide probabilistic description of perfect repeats in proteins. Parasite proteomes exhibited a large variance regarding the proportion of repeat-containing proteins. Interestingly, there was a good correlation between the percentage of highly repetitive proteins and mean protein length in parasite proteomes, but not at all in the proteomes of free-living eukaryotes. Reptile combined with programs for the prediction of transmembrane domains and GPI-anchoring resulted in an effective tool for in silico identification of potential surface antigens and virulence factors from parasites.
Conclusion
Systemic surveys for perfect amino acid repeats allowed basic comparisons between free-living and parasitic organisms that were directly applicable to predict proteins of serological and parasitological importance. An on-line tool is available at http://genomics.unibe.ch/dora.
Background
Repetitive amino acid subsequences in polypeptides are of interest regarding the function as well as the evolution of proteins. At least 14% of all proteins contain internal repeats, the proportion being somewhat lower in prokaryote and higher in eukaryote proteomes [1]. Multicellular eukaryotes in particular, possess numerous adhesion proteins of repetitive nature in the extracellular matrix. Other highly repetitive proteins are those of the cytoskeleton [1, 2]. Typical motifs involved in protein-protein interaction are the tetratricopeptide repeat (34 aa), armadillo (47 aa), ankyrin (33 aa), and the leucine-rich repeat (about 20 aa) [3]. Several tools are available for the detection of repeats in proteins: Radar [4, 5], Repro [6, 7], Internal Repeats Finder [8, 9], TRIPS [10, 11], Trust [12, 13], Davros [14], RepSeq [15, 16], REP [2, 17], Repper [18, 19], and ProtRepeatsDB [20, 21]. Apart from simply counting repetitive occurrences of amino acid subsequences in polypeptides, repeats can be detected by self-alignment or – if they are evenly distributed – by Fourier transform. Here we present Reptile, a simple tool for quantitative proteome-wide surveys of perfect amino acid repeats, and its use for the prediction of surface antigens and virulence factors from parasites.
Pathogenic bacteria as well as eukaryotic parasites often possess surface proteins of repetitive nature, presumably to protect themselves against their hosts' defence responses [22, 23]. Examples are the procyclins of the sleeping sickness parasite Trypanosoma brucei with over twenty Glu-Pro (EP-type), respectively five Gly-Pro-Glu-Glu-Thr (GPEET-type) repeats [24, 25], the circumsporozoite protein of the malaria parasite Plasmodium falciparum with around forty Asn-Ala-Asn-Pro (NANP) repeats [26], or SdrE from Staphylococcus aureus, a determinant of staphylococcal sepsis with 83 Ser-Glu (SE) repeats [27]. Such short, perfect repeats are usually very immunogenic. They may serve for serological diagnostics – the presence of repeat-directed antibodies in the serum indicating infection – as is the case with PfHRP2 [28], a malaria antigen with over fifty Ala-His-His (AHH) repeats. Repetitive amino acid sequences also find applications in synthetic vaccines [29]. Furthermore, repeat-containing proteins from parasites may be virulence factors involved in immune evasion, cytoadherence, stress resistance, or biofilm formation [30–35]. The completion of the genome sequencing projects for P. falciparum, T. brucei, Leishmania major, and other parasites now permits systemic approaches to repeat-containing proteins. Here we identify all proteins from pathogens that contain repeats and use them for comparative genomics between parasitic and non-parasitic species. All data and programs are freely accessible via the world-wide web.
Results and Discussion
Probabilistic description of perfect repeats with Reptile
In order to scan whole proteomes for repeat-containing proteins, we created the tool Reptile. It uses a "brute-force" algorithm that detects all perfect repeats and enables direct calculation of a P-value. For each input sequence, Reptile generates all possible substrings from length 2 to a user-defined maximum (the default is 20) and counts their occurrences. After removing redundant repeats that are contained within longer ones, the repeated sequences are returned by ascending P-value. The probability P to find at least n repeats of length r in a random sequence of length L (with nr ≤ L ≤ n20r) equals the number of possible sequences that contain the desired repeat, divided by the total number of possible sequences (20L).
(1)
Where 20r is the number of possible repeat sequences, 20L-nr the number of possible sequences around the repeats, and the binomial equals the number of ways to place the n repeats in L. P* is an overestimate because the sequences with more than n repeats are counted too often. Taking this into account gives the correct formula for P:
(2)
Where i counts from n to the maximal number of repeats (L/r), switching signs with every increment according to the inclusion-exclusion principle [36]. For practical purposes calculation of P*, the first summand of P, is sufficient since further summands decrease rapidly with increasing number of repeats. Reptile returns all repeats below a user-defined cut-off P-value (the default is 10-5, corresponding to an expectancy of one false positive in 100'000 sequences). Direct repeats are marked. The P-value being independent of the actual sequence of a repeat, Reptile also returns a measure of whether a detected repeat consists of rare or frequent amino acids. This "Amino acid abundance measure" (AM) was defined as follows:
(3)
Where r is the length of the repeat and fi is the frequency in the corresponding proteome – respectively set of sequences submitted by the user – of the amino acid at position i of the repeat. AM is symmetric to zero, negative values indicating that a repeat predominantly consists of rare amino acids (and vice versa). Reptile is running on-line [37] and accepts batch input of up to 50,000 sequences in any of the commonly used formats.
Compared to other repeat-prediction programs (Table 1) the main strengths of Reptile are its quantitative assessment of the detected repeats and its infallibility regarding short perfect repeats, such as they occur in antigens from parasites. Reptile will spot in a given protein all recurring subsequences from length two to twenty, even if they are dispersed. In contrast to programs implementing self-alignment, however, Reptile does not properly recognize degenerate repeats. Though proteins harbouring degenerate repeats also exhibit low P-values and will not go unnoticed, Reptile will not identify the basic repetitive unit but several shorter ones contained within. Other programs (Table 1) should be used when studying large repeat regions or imperfect, diverging repeats.
Table 1
Comparison of programs for the detection of repetitive subsequences in proteins
Program
Method used
Detection of degenerate repeats
Calculation of a P-Value
Analysis of whole Proteomes
%Hits found in SwissProt
Detection of T. brucei procyclin
1
Reptile
Hashing2
No
Yes
Yes
153
Yes
REP [2]
Profiles of known repeats
Yes
No
No
1.1
No
RADAR [5]
Alignment
Yes
No
No
28
Yes
REPRO [7]
Alignment
Yes
No
No
n.a.
Yes
Internal Repeats finder [8]
Alignment
Yes
Yes
No
14
No
TRIPS [9]
Fourier transform
Yes
No
No
12
No
RepSeq [10]
Hashing
Yes
Yes
Yes
n.a.
Yes
ProtRepeatsDB [11]
Mixed
Yes
Yes
Yes
n.a.
Yes
Repper [12]
Fourier transform
Yes
No
No
n.a.
No
1The T. brucei surface protein (GenBank accession AAK62893) with five GPEET repeats [25] was used for benchmarking.
2Word count using a hash table.
3Using P < 0.001 (same as for Internal Repeats Finder).
Genome-wide surveys for highly repetitive proteins
We defined highly repetitive proteins as proteins that contain perfect repeats of a P-value below 10-10. Reptile was used to screen for such proteins in predicted proteomes from fully sequenced genomes. The median proportion of highly repetitive proteins was 2.7% in eukaryote proteomes and 0.43% in prokaryotes, confirming the notion [1] that eukaryotes possess more repetitive proteins than bacteria (p < 0.0001, Mann-Whitney test). The more repeats a protein has, the longer it becomes. In eukaryotic proteomes the percentage of highly-repetitive proteins correlated to some degree with the mean protein length (Spearman coefficient rS = 0.51, p = 0.011). When distinguishing free-living from (endo)parasitic eukaryotes (Table 2), it was evident that the correlation was caused entirely by the latter. Obligate parasites exhibited a good correlation between highly-repetitive proteins and mean protein length (rS = 0.82, p = 0.003) while free-living eukaryotes showed no correlation at all (Figure 1). The finding that the percentage of highly repetitive proteins predicts average protein length only in parasite proteomes reflects the significance of repeat-containing proteins for survival in the host, possibly counterbalanced by a selective pressure on parasites for shorter proteins [38].
Table 2
Eukaryotic proteomes analyzed
Organism
Kingdom
Type
Proteins
Homo sapiens
Metazoa
F
38220
Mus musculus
Metazoa
F
35593
Arabidopsis thaliana
Viridiplantae
F
34554
Caenorhabditis elegans
Metazoa
F
22431
Drosophila melanogaster
Metazoa
F
16239
Brachydanio rerio
Metazoa
F
15647
Anopheles gambiae
Metazoa
F
13486
Dictyostelium discoideum
Protozoa
F
13017
Rattus norvegicus
Metazoa
F
11987
Yarrowia lipolytica
Fungi
F
6525
Saccharomyces cerevisiae
Fungi
F
5810
Kluyveromyces lactis
Fungi
F
5326
Schizosaccharomyces pombe
Fungi
F
5009
Entamoeba histolytica
Protozoa
P
9772
Giardia duodenalis
Protozoa
P
9646
Trypanosoma brucei
Protozoa
P
9210
Leishmania major
Protozoa
P
8010
Cryptococcus neoformans
Fungi
P
6569
Plasmodium falciparum
Protozoa
P
5283
Theileria parva
Protozoa
P
4071
Cryptosporidium hominis
Protozoa
P
3886
Theileria annulata
Protozoa
P
3790
Encephalitozoon cuniculi
Fungi
P
1909
F, free-living; P, endoparasitic.
Figure 1
Comparative genomics of repeat-containing proteins. Double logarithmic plot of the percentage of highly repetitive (P < 10-10) proteins vs. mean protein length of eukaryotic proteomes. Ag, A. gambiae; At, A. thaliana; Br, B. rerio; Ce, C. elegans; Dd, D. discoideum; Dm, D. melanogaster; Hs, H. sapiens; Kl, K. lactis; Mm, M. musculus; Rn, R. norvegicus; Sc, S. cerevisiae; Sp, S. pombe; Yl, Y. lipolytica; Ch, C. hominis; Cn, C. neoformans; Ec, E. cuniculi; Eh, E. histolytica; Gd, G. duodenalis; Lm, L. major; Pf, P. falciparum; Ta, T. annulata; Tb, T. brucei; Tp, T. parva; rS, Spearman coefficient.
The eukaryote with the largest proportion of highly repetitive proteins, Plasmodium falciparum with 28%, and that with the smallest one, Encephalitozoon cuniculi with 0.42%, were both obligate parasites. The same applied to prokaryotes, where the highest proportions of highly repetitive proteins were exhibited by Mycobacterium bovis (3.0%), M. tuberculosis (2.9%) and Parachlamydia sp. (2.7%), and the lowest ones by Bacillus anthracis (Porton strain, 0.02%) and Streptococcus pyogenes (SSI strain, 0.05%) – however, it must be noted that with bacteria, the available genome sequences are biased towards pathogenic species. The most repetitive protein from eukaryotes was a hypothetical protein from the sleeping sickness parasite T. brucei, followed by the 11-1 gene product from P. falciparum, a known malaria antigen of more than 1 MD size [39]. The most repetitive prokaryotic protein was a predicted cell wall surface anchor family member from Streptococcus pneumoniae, the leading cause of pneumonia. Table 3 summarizes these and other highly repetitive proteins identified from pathogens, emphasizing on sequences with experimentally verified expression. The genome-wide surveys yielded other known virulence factors such as proteophosphoglycans of Leishmania [40] or PGRS (polymorphic GC-rich repetitive sequence) proteins of Mycobacterium, an antituberculosis vaccine candidate [41]. The presence of avirulence proteins from phytopathogenic bacteria among the most repetitive proteins indicates that repeats also serve to specifically trigger host defence responses. Remarkably repetitive are also the ice nucleation proteins of plant pathogens. Table 3 also shows examples of previously undescribed proteins. The complete datasets on repeat-containing proteins from 49 eukaryotes and 193 prokaryotes are accessible on-line in the archive REPository [37].
Table 3
A selection of the most repetitive proteins from pathogens
Name, accession
Sp
L
Repeat
pP
Hypothetical protein, Tb927.1.1740
Tb
7154
132 × LAEESQQHTARSEADIDE
2806
Gene 11-1 protein*, Q8I6U6
Pf
10589
967 × EEV
2457
Conserved protein, LmjF29.0110
Lm
3418
146 × AEEQARR
1080
Proteophosphoglycan-like, LmjF35.0550
Lm
2425
105 × SSSSSAPSA
1052
Putative antigen*, Tb04.29M18.750
Tb
4455
66 × NEQYETLQRTNAA
958
Gb4*, Tb09.160.1200
Tb
8214
35 × VVIIDCRLGSLLIDYKVI
701
Hypothetical protein, Chro.50162
Ch
1589
84 × KKDAP
407
Hypothetical protein, Q8I455
Pf
2349
67 × LKEEER
389
Interspersed repeat antigen*, Q8I486
Pf
1720
67 × QEPVT
313
Putative antigen 332*, Q8IHN3
Pf
5507
144 × EEI
274
Cell wall surface anchor family, Q97P71
Spn
4776
1074 × SAS
3418
Cell surface SD repeat protein, Q88XB6
Lpl
3360
796 × DS
1619
Hypothetical protein, Q8E473
Sag
1310
106 × TSAS
447
Putative peptidoglycan-bound, Q8Y697
Lmo
903
78 × ADADA
403
Avirulence protein, Q5GYF3
Xor
1790
20 × ETVQRLLPVLCQDHGLTP
401
Serine/threonine-rich antigen, Q99QY4
Sau
2271
163 × STS
391
PE-PGRS family, PG54_MYCTU
Mt
1901
136 × GAG
326
Structural toxin RtxA, Q5X7A6
Lpn
7679
29 × RFEDDGPVV
247
Ice nucleation protein, Q8PD38
Xca
1333
52 × GYGST
242
PPE family protein, Q6MX44
Mtu
3300
95 × NTG
184
Eukaryotic proteins (top) whose expression is confirmed by the presence of expressed sequence tags (EST) in GenBank are marked with an asterisk. L, length; pP, negative logarithm of the P-value; Sp, species (Ch, C. hominis; Lm, L. major; Pf, P. falciparum; Tb, T. brucei; Lmo, Listeria monocytogenes; Lpl, Lactobacillus plantarum; Lpn, Legionella pneumophila; Mtu, M. tuberculosis; Sau, S. aureus; Spn, S. pneumoniae; Sag, Streptococcus agalactiae; Xca, Xanthomonas campestris; Xor, X. oryzae).
Amino acid composition of the repeats
To further characterize the repeats, we investigated which amino acids are over- or underrepresented in repeats of P < 10-10 compared to the rest of the respective proteome. Overall, the amino acid composition of the repeats was more biased in eukaryotes than in bacteria (Figure 2). Small amino acids occurred more frequently in the repeats than large ones in both eukaryotes and prokaryotes. Hydrophobic residues were underrepresented in the repeats, with the exception of leucine, which in bacterial repeats was even overrepresented (p < 0.0001, two-tailed Wilcoxon signed rank test). Strongly overrepresented in the repeats were alanine (p < 0.0001) in bacteria and serine (p = 0.0001) in eukaryotes (Figure 2). Thus "cheap" amino acids seem to be preferred over energetically expensive ones. Interestingly, asparagine was overrepresented in the repeats from eukaryotes (p = 0.057) but not from bacteria (Figure 2), suggesting that asparagines might be preferentially glycosylated in repeats. Contrary to expectation though, the probability of an asparagine to be in N-glycosylation consensus was significantly lower in repeats than in non-repetitive sequences (Figure 3). This was the case for free-living eukaryotes (p = 0.004) as well as for parasites (p = 0.027). The only exception was T. brucei, where the likelihood of an asparagine to be in N-glycosylation consensus was three-fold higher in repetitive than in non-repetitive sequences (Figure 3).
Figure 2
Amino acid composition of the repeats. For each amino acid, the frequency in the repeats of P < 10-10 is plotted vs. its frequency in the remainder of the proteome (rS, Spearman coefficient). Data are pooled for bacteria (n = 193) and eukaryotes (n = 49). The small diamonds at 0.05 mark the expected frequency for random distribution, the diagonal represents equal frequency in the repeats as in the remainder of the respective proteome. Complete datatables including standard deviation are provided as a supplementary file [Additional file 1].
Figure 3
Potential N-glycosylation sites in the repeats. The percentage of asparagines that are in glycosylation consensus (Asn-not Pro-Ser/Thr) is plotted for repeats of P < 10-10 and for the remainders of the respective proteomes. Bars indicate the median. The organism with 30% of asparagines in the repeats in N-glycosylation consensus is T. brucei.
Prediction of repetitive surface antigens
In order to predict which of the repeat-containing proteins are at the cell surface, Reptile was combined with Phobius [42], a program for prediction of transmembrane domains and N-terminal export signals, and GPI-SOM [43], a program that predicts C-terminal GPI (glycosylphosphatidyl-inositol) anchor attachment sites. The three programs were run over all available proteomes predicted from completely sequenced genomes. The identified repeats were scanned for potential N-glycosylation sites. The combined output was stored in a relational database called Dora, the d atabase o f r epetitive a ntigens, as outlined in Figure 4. At present, Dora contains data on 1,123,238 proteins from 242 different proteomes (among which 49 eukaryotic). A www interface [44] allows user-defined Boolean searches (Figure 4). With Dora, genome-wide prediction of potential surface antigens and virulence factors is straightforward. A search for repetitive membrane proteins in P. falciparum or T. brucei (Table 4) indeed returned important surface antigens and virulence factors: circumsporozoite protein (CSP), merozoite surface proteins (MSP), erythrocyte membrane proteins (EMP), glycophorin-binding proteins (GBP), apical membrane/erythrocyte binding antigen (MAEBL), ring-infected erythrocyte surface antigen (RESA), mature parasite-infected erythrocyte surface antigen (MESA) for malaria and for T. brucei the procyclins, cysteine-rich acidic membrane protein (CRAM), invariant surface glycoproteins (ISG) and even the variable surface glycoproteins (VSG), which contain a significant number of dipeptide repeats (mostly AA; to our knowledge the repetitive nature of VSG was not previously recognized). In addition to these known proteins there was a large number of uncharacterized ones, particularly from P. falciparum which possesses hundreds of extremely repetitive transmembrane proteins (not shown; please refer to Dora).
Table 4
Repetitive membrane proteins of P. falciparum (top) and T. brucei (bottom)
Name, accession
Topology
Repeat
pP
Hypothetical protein, Q8IJ50
GPI
16 × EESHNFYNPTH
184
Circumsporozoite protein, Q7K740
GPI
38 × ANPN
145
Merozoite surface protein 8, Q8I476
GPI
32 × NN
29
Liver stage antigen, Q8IJ44
1 TM
45 × AKEKLQEQQSDLEQER
839
Erythrocyte membrane protein 3, O96124
1 TM
61 × QQNTGLKNTP
665
Trophozoite antigen, Q8IFL9
1 TM
60 × NHKSD
287
Glycophorin-binding protein, Q8I6U8
1 TM
10 × DPEGQIMREYAADPEYRKHL
213
MAEBL, Q8IHP3
1 TM
19 × EEKKKADELKK
213
PF70 exoantigen, Q8IK15
3 TM
8 × TKKPSKYTMNLDSPLLKGSS
165
MESA, Q8I492
1 TM
94 × KE
97
PfEMP1, Q8I519
1 TM
16 × GGGGGS
77
RESA, Q8IHN1
1 TM
33 × EEN
63
Hypothetical protein, Tb11.02.2360
GPI
11 × TAVTDVNDNNSANTSNEDE
229
Hypothetical protein, Tb11.1550
GPI
12 × IIAHYC
68
Procyclin (EP-type), Tb10.6k15.0020
GPI
29 × PE
46
Hypothetical protein, Tb927.7.360
GPI
3 × DKEKTERTEVEEVPKKDPEG
45
Procyclin (GPEET-type), Tb927.6.510
GPI
6 × EETGP
24
VSG, Tb10.v4.0209
GPI
19 × AA
13
CRAM, Tb10.6k15.3510
1 TM
80 × ITGDCNETDDC
1050
Hypothetical protein, Tb927.3.5530
2 TM
49 × RLRAEEE
337
Hypothetical protein, Tb10.61.0660
3 TM
12 × NEEVPAGVSARRGGVAMSF
241
Procyclic surface glycoprotein, Tb10.26.0790
2 TM
5 × YGQPPPPQ
31
Invariant surface glycoprotein, Tb927.5.350
1 TM
18 × EA
12
TM, transmembrane domain; GPI, glycosylphosphatidyl-inositol anchor; pP, negative logarithm of the P-value. See text for full protein names.
Figure 4
Flowchart to Dora, database of repetitive antigens. Reptile, Phobius [20], and GPI-SOM [43] are integrated into an automated pipeline for the classification of proteins (top). The data are stored in a database that is accessible on-line [44] via the depicted interface (bottom). This allows user-defined Boolean queries for repeat-containing surface proteins.
New specific and robust tests are urgently needed for the diagnosis of sleeping sickness, malaria, tuberculosis, and other neglected diseases [45, 46]. PCR not being applicable in the field, serology (i.e. the detection of parasite-specific antibodies) remains the principal method of detection for many tropical diseases. Dora provides a convenient portal for identification of candidate antigens for serological tests. In addition, it can be helpful for the selection of vaccine candidates. Dora returns the hits in Fasta format, which is suitable for subsequent bioinformatic analyses.
Conclusion
Reptile's simple algorithm allows large-scale and quantitative description of perfect amino acid repeats. Originally designed to scan parasite proteomes for potential antigens and virulence factors, Reptile detects any protein of repetitive nature and thereby complements existing tools which work by self-alignment. Parasite proteomes vary considerably regarding the proportion of repetitive proteins, in contrast to those of free-living eukaryotes which all contain around 3% highly repetitive (P < 10-10) proteins. Furthermore, the proportion of highly repetitive proteins correlates with mean protein length in parasites but not in the proteomes of free-living eukaryotes, illustrating the importance of amino acid repeats for parasites.
Scanning the predicted proteomes of parasites for amino acid repeats returned a large number of interesting proteins. Particularly useful was the combination of Reptile with prediction of glycosylation sites, export signals, transmembrane domains and GPI-anchor attachment sites, carried out on more than one million proteins from 242 different organisms. All data are accessible on-line via Dora, database of repetitive antigens. The approach was validated against T. brucei and P. falciparum, where a Dora search returned the known surface antigens, virulence factors, and vaccine candidates plus many new, so far uncharacterized proteins.
Methods
Proteome files
Predicted proteome files were obtained from the Integr8 database of the European Bioinformatics Institute [47]. The download was automated with a Python script that periodically checks for newly available proteomes, respectively for updates to previous proteome files.
Statistics
Statistical tests were performed with Prism 4.0 (GraphPad Software). Since the percentages of repeats in proteomes as well as the frequencies of amino acids were not normally distributed, non-parametric tests were used: Mann-Whitney test [48], Wilcoxon signed rank test [49], and Spearman correlation [50].
Reptile
The repeat detection algorithm is described under Results. The program is written in C++ and the web-interface in Perl-CGI. Reptile uses sreformat from the HMMer package [51] to convert different input formats (Fasta, GenBank, EMBL, Swiss-Prot, PIR, GCG) to Fasta. Reptile runs on a vmware (virtual infrastructure) server. Availability and requirements:
A Python script periodically runs Reptile, GPI-SOM, and Phobius over all new or updated proteome files of Integr8. The results are stored in a MySQL database. For sake of simplicity, for each protein only the repeat with the lowest P-value is stored. A Perl script is used to interconvert Fasta format and SQL. The web interface of Dora is written in PhP. The database and all the programs run on the vmware server of the Informatics Services of the University of Bern.
Declarations
Acknowledgements
We wish to thank the Informatikdienste of the University of Bern for resources and support. This work was supported by the Swiss National Science Foundation, the Roche Research Foundation, and Biomedizin-Naturwissenschaft-Forschung Bern (TN).
Authors’ Affiliations
(1)
Institute of Cell Biology, University of Bern
(2)
Pädagogische Hochschule Bern
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