Plasmobase: a comparative database of predicted domain architectures for Plasmodium genomes
© The Author(s) 2017
Received: 4 October 2016
Accepted: 31 May 2017
Published: 7 June 2017
With the availability of complete genome sequences of both human and non-human Plasmodium parasites, it is now possible to use comparative genomics to look for orthology across Plasmodium species and for species specific genes. This comparative analyses could provide important clues for the development of new strategies to prevent and treat malaria in humans, however, the number of functionally annotated proteins is still low for all Plasmodium species. In the context of genomes that are hard to annotate because of sequence divergence, such as Plasmodium, domain co-occurrence becomes particularly important to trust predictions. In particular, domain architecture prediction can be used to improve the performance of existing annotation methods since homologous proteins might share their architectural context.
Plasmobase is a unique database designed for the comparative study of Plasmodium genomes. Domain architecture reconstruction in Plasmobase relies on DAMA, the state-of-the-art method in architecture prediction, while domain annotation is realised with CLADE, a novel annotation tool based on a multi-source strategy. Plasmobase significantly increases the Pfam domain coverage of all Plasmodium genomes, it proposes new domain architectures as well as new domain families that have never been reported before for these genomes. It proposes a visualization of domain architectures and allows for an easy comparison among architectures within Plasmodium species and with other species, described in UniProt.
Plasmobase is a valuable new resource for domain annotation in Plasmodium genomes. Its graphical presentation of protein sequences, based on domain architectures, will hopefully be of interest for comparative genomic studies. It should help to discover species-specific genes, possibly underlying important phenotypic differences between parasites, and orthologous gene families for deciphering the biology of these complex and important Apicomplexan organisms. In conclusion, Plasmobase is a flexible and rich site where any biologist can find something of his/her own interest.
Plasmobase is accessible at http://genome.lcqb.upmc.fr/plasmobase/.
A large amount of genomic and post-genomic data is now available for the Plasmodium genus. The fully sequenced genomes of 11 species are accessible from PlasmoDB , but despite the availability of these complete genomes a major limitation still remains on their functional annotation. The number of proteins with unknown functions is still high for all Plasmodium species. This could be due (i) to the AT-richness of these genomes, (ii) to the specificity of a number of parasitic functional mechanisms evolved to evade host immune recognition, (iii) to the strong divergence of Plasmodium protein sequences that make homology detection a difficult task. These three reasons might have contributed to the development of new gene functions and changes in the parasite’s genome, occurring through gene acquisition and deletion .
Proteins are composed of one or more “domains”, that is structural motifs that can evolve, function, and exist independently of the rest of the protein chain. Domains might be found in different combinations, and the arrangement of these domains in a protein forms the so called “protein architecture”. By focalizing on domain recognition, genome annotation can be highly improved. Several approaches and databases have been developed to detect and identify functional domains in a large number of proteins, including Pfam , SMART , and PROSITE . Based on these resources, a number of conflicting domain predictions (potential domains) can be resolved and, for each protein to be annotated, a domain architecture can be proposed. There are different methods for identifying a domain architecture and the most successful ones explore domain co-occurrence for controlling the false discovery rate (FDR) associated with the predictions [6–8]. Here, DAMA  (domain annotation by a multi-objective approach), an approach that treats protein domain architecture prediction as a multi-objective optimization problem, is used. DAMA combines a number of criteria to handle multi-(possibly pairwise-) domain co-occurrence and domain overlapping, and it outperforms existing methods. It detects domain architectures with a larger number of domain co-occurrences.
DAMA can improve domain recognition methods such as HMMer [9, 10] used by Pfam, but it is limited by the number of potential domains given as input. Hence, CLADE (closer sequences for annotations directed by evolution) , the new generation of annotation tools based on a “multi-source strategy”, is used to increase the set of potential domains and consequently to improve DAMA performance. CLADE uses several hundred probabilistic profiles to represent each Pfam domain instead of one profile, based on global consensus, as for mono-source strategies. These probabilistic models are originated from different species, spanning the whole phylogenetic tree, and describe alternative evolutionary pathways for a domain. Tested on the Plasmodium falciparum genome, CLADE outperforms the widely used tools based on a mono-source annotation strategy, HMMer and HHblits . The new domain annotation obtained by CLADE for P. falciparum 3D7 has been released with .
Features of Plasmodium genomes
Genome size (Mb)
P. falciparum 3D7
P. falciparum IT
P. knowlesi H
P. reichenowi CDC
P. yoelii 17X
P. yoelii 17XNL
P. yoelii YM
The 11 fully sequenced genomes present in PlasmoDB  and considered here are: three human parasites (P. falciparum 3D7, P. falciparum IT, and Plasmodium vivax), two macaques parasites (Plasmodium knowlesi and Plasmodium cynomolgi), one chimpanzee parasite (Plasmodium reichenowi), and five rodent parasites (Plasmodium chabaudi, Plasmodium berghei, and Plasmodium yoelii 17X, Plasmodium yoelii yoelii 17XNL and Plasmodium yoelii YM). All genomes were extracted from http://PlasmoDB.org, the official repository of the Plasmodium proteins used as a reference database by malaria researchers. For each species, the genome size and the number of proteins are shown in Table 1.
The UniProt database
In order to display the proportion of proteins with similar architecture to a given query protein, all proteins (18,523,877) with known Pfam domain architectures are extracted from UniProtKB  and organized according to their taxon groups. Four taxon groups are considered: Eukaryota, Bacteria, Archaea and “Viruses and others” including metagenome and unclassified sequences. These architectures are obtained by parsing the file swisspfam, available at ftp://ftp.ebi.ac.uk/pub/databases/Pfam/releases/Pfam27.0/swisspfam.gz. Within each taxon group, sequences are grouped according to the name of their phylogenetic clade. A generic clade named “others” collects all clades with less than 2% of domain architectures. A reference list of clades has been extracted from NCBI (http://www.ncbi.nlm.nih.gov/taxonomy) and used for organizing the species where similar architectures are found.
The gene ontology initiative , besides its value as a database of annotations, maintains and develops a controlled vocabulary of gene and gene product attributes. The gene ontology terms (GO terms)  is a machine-readable vocabulary that provides a standard output for functional predictions, avoiding the ambiguity of natural language. GO terms describe three aspects of gene product function: molecular function, biological process, and cellular location. pfam2go , a mapping that associates a specific GO term with a Pfam domain, is used to provide GO terms for Plasmobase domain predictions. In consequence, all proteins containing this domain share the same GO term.
CLADE  and DAMA  were applied to all Plasmodium organisms, CLADE to identify potential domains and DAMA to reconstruct protein domain architectures. Both tools were run with default parameters, corresponding to a FDR smaller than 1%. The two tools are briefly described below. For the FDR estimation and other details refer to the original articles.
CLADE: closer sequences for annotations directed by evolution
CLADE is a computational approach that highly increases the sensitivity of domain prediction. It is a multi-source approach where several hundred probabilistic profiles are used to represent each domain, instead of one as for mono-source strategies, employed by methods like HMMer [9, 10] and HHblits . CLADE predicts domains based on two classes of probabilistic profiles. The first is the profile library available in the Pfam database (version 27). There are 14,831 profiles, one for each domain. These profiles capture the consensus of homologous sequences, and the idea behind them is that homologous proteins should share common physico-chemical and structural features that could be described by consensus on the entire set of homologs.
The second class of profiles is constituted by hundreds of probabilistic models, associated to each Pfam domain. They are constructed starting from homologous sequences spreading a large panel of taxonomic origins, to guarantee that the phylogenetic tree is well represented. For each selected homologous sequence, a specific profile, named “clade-centred model” (CCM), is constructed. To construct a CCM, the selected homologous sequence is used as a query to search for close homologs within the non-redundant protein database (NR) with PSI-BLAST . The CCM is the resulting PSI-BLAST profile. Note that PSI-BLAST constructs the CCM profile from NR sequences detected with an E-value threshold set to 1e−3 by default. CLADE uses this PSI-BLAST default E-value (1e−3) and sets the number of PSI-BLAST iterations to 5. On average 161 CCMs were constructed to represent each Pfam domain. These models span regions of protein sequence space that are not well represented by Pfam consensus models, and they highlight motifs, structural characteristics or physico-chemical properties that are shared by similar homologous sequences. Hence, if the original set of sequences for a domain is made of very divergent homologs, clade-centred models, are expected to describe properties that could be missed by the original Pfam model, representing the global consensus.
The CLADE library, made of Pfam consensus models and clade-centred models, is composed of 2,404,066 profiles. This library is used to identify potential domains in query proteins. Potential domains are then filtered by using support vector machines (SVM) combining various optimisation criteria, ultimately converted into a score, to specifically deal with false positives and discriminate domains. The SVM selects most probable predictions among domain hits displaying a small E-value, a sufficiently long domain hit, the phylogenetic proximity between the taxon of the sequence to be annotated and the reference species generating the probabilistic profile, and the agreement among models leading to the prediction. This filtering step is fundamental in domain prediction. Once domains are filtered, CLADE calls DAMA to find the most probable architecture for a given query sequence. CLADE can be downloaded at http://www.lcqb.upmc.fr/CLADE.
DAMA: domain annotation by a multi-objective approach
Since homologous proteins might share their architectural context, domain architecture prediction are used to improve the performance of CLADE annotation. The problem can be complex when a query sequence matches several probabilistic models, producing a set of conflicting predictions with overlapping domain boundaries. To address this problem, DAMA combines a number of criteria including multi- (possibly pairwise-) domain co-occurrence and domain overlapping. Domain co-occurrence is expected to enhance the level of confidence in a prediction  mainly because (i) the majority of proteins are multi-domain and (ii) fewer combinations than the statistically expected ones are observed. Some overlapping must also be admitted to increase the number of correct domain predictions, as demonstrated in . DAMA encodes domain co-occurrence and hit overlapping criteria into objective functions, and treats protein domain architecture prediction as a multi-objective optimization problem. First, DAMA generates a list of possible architectures, and then maximizes a set of objective functions to select the best architecture. Five functions, designed according to several objectives, are applied in order of importance. The first objective function ensures that higher confidence domains are in the final architecture since domain scores greatly augment the trust on protein annotation. The second and the third function explore the tendency of some domain families to occur preferentially with a few other favourite families. The second function maximizes the number of multi-domain co-occurrences, while the third one maximizes the number of pairwise domain combinations when two architectures present the same number of multi-domain co-occurrences. The fourth function privileges architectures with distinct domains since domain duplication is less likely to change the protein function. Finally, the fifth function selects the architecture whose domains have the highest scores. DAMA can be downloaded at http://www.lcqb.upmc.fr/DAMA.
DAMA was used with default parameters, including the parameter ‘- - review” that adds new domains into an architecture if: (1) they present a significant E-value (<1e−10) and (2) they do not overlap an existing domain in the architecture. By setting this parameter, one can increase the number of predicted domains and takes into account the identification of new domain architectures (see "Discussion").
Plasmobase website provides access to downloadable xls files containing the full list of annotations for the 11 Plasmodium species. Each file contains, for each domain hit, the PlasmoDB accession number of the sequence where the hit is identified, its starting and ending position, the Pfam domain name, the Pfam accession number, the E-value, CLADE SVM probability, the model identifier in the CLADE model library (either a clade-centred model or a Pfam consensus model), the start and the end position of the hit either in the domain sequence used to construct the clade-centred model or in the Pfam consensus model, the clade name (if any) and the organism name of the sequence originating the clade-centred model used for the domain identification. Note that if a protein sequence is annotated by several domains, the file contains several rows, one for each domain annotation.
Plasmobase is a platform for the exploration of protein architectures and their comparison across species. Its features and its multiple ways to analyse a predicted architecture or to explore potential architectures, are presented together with some global statistics on new domain annotations for the 11 Plasmodium genomes.
The Plasmobase platform
The Plasmodium database Plasmobase is a friendly interface allowing users to search by domains (with a Pfam accession number or a Pfam description), proteins (with a protein accession number or a protein annotation) or domain architectures (with a list of Pfam accession numbers) on all Plasmodium species or on a specific one. As a result, it provides the list of corresponding proteins with their accession number, Plasmodium species name, PlasmoDB annotation, a list of domains forming the predicted architecture (where new domains are highlighted), and the accessibility (“Look up” link) to a graphical interface providing comparative information on the predicted architecture. Each protein in the list is linked to its PlasmoDB description, and each domain forming the protein architecture is linked to its Pfam description.
An immediate access to the list of architectures of orthologous and paralogous genes in Plasmodium species is possible by clicking on the “show” link at the bottom of the page (Fig. 1). Each protein accession number is provided together with the species name, the length of the sequence and the PlasmoDB annotation. The display of the associated Plasmobase architectures can be obtained by clicking the “Compare orthologous/paralogous architectures” button.
Figure 2a (left) shows all the Plasmodium species having protein sequences with a similar architecture to PF3D7_1369500 in Plasmobase. The proportion of proteins including the same co-occurring domains is shown by a pie chart. There are 10 orthologous proteins, in 10 Plasmodium species. The only exception is P. cynomolgi where MIF4G_like_2 was not identified. Architectures can be compared in more details by ticking some or all organisms and pressing the button “Compare architecture in Plasmobase”. In this example, P. chabaudi and P. vivax are selected, and the architectures are displayed in Fig. 2a (right), where CLADE domains are shown with the same graphical interface of PF3D7_1369500. Information on the species, functional annotation and GO classification is provided.
Figure 2b shows proteins with similar architecture in UniProt, the information is organized by taxon groups: Eukaryota, Bacteria, Archaea and “Viruses and others”. For PF3D7_1369500, 173 eukaryotic proteins with similar architectures and spread over several clades are found. The main clades (collecting most proteins) are shown in Fig. 2a (left) and the remaining ones are grouped in the checkbox “others” (see "Methods"). Like for Plasmobase, one can explore some or all clades and compare domain architectures for UniProt species within clades (button “Compare architecture in Eukaryota”). The architecture for Fungi proteins are shown in Fig. 2b (right). There are 41 proteins with the same architecture in Fungi and 29 in Viridiplantae. By clicking “Show”, all protein architectures are displayed.
Many brand-new domains and many enriching ones
New domains identified in Plasmobase, possibly by co-occurrence (Cooc)
P. falciparum 3D7
P. falciparum IT
P. knowlesi H
P. reichenowi CDC
P. yoelii 17X
P. yoelii yoelii 17XNL
P. yoelii YM
Improvement over PlasmoDB
Comparison between PlasmoDB and Plasmobase domain predictions
#Prots with no domaina
#Prots with no domaina
P. falciparum 3D7
P. falciparum IT
P. knowlesi H
P. reichenowi CDC
P. yoelii 17X
P. yoelii yoelii 17XNL
P. yoelii YM
Comparison with EuPathDomain
considers 11 Plasmodium species while EuPathDomain considers just 3 organisms,
proposes a visualization of the predicted architecture while EuPathDomain displays the list of predicted domains found within the sequence,
compares architectures in a species with architectures in other Plasmodium or in UniProt species, while EuPathDomain does not. Note that in Plasmobase, the user can explore domain architectures formed by any combination of CLADE domains, belonging to the proposed architecture or simply detected as potential hits of the sequence,
searches proteins by keywords (concerning both domains and protein functions, such as kinases, transcription, AP2 and HMGB, translation, 40S ribosomal) and not only by Pfam identifiers and genome accession numbers, as EuPathDomain,
allows searching for architectures described by multiple domains, while EuPathDomain only considers a single domain,
displays domain architectures for orthologous groups, while EuPathDomain does not.
All information contained in the database can be downloaded in xls files, one for each species, permitting the user to process the information in alternative ways.
Protein annotation plays a major role in the comprehension of the biology of Plasmodium species. Plasmodium clade contains particularly AT rich genomes (Table 1) and this specificity of Plasmodium species makes Plasmobase contribution even more important. Indeed, Plasmobase provides a huge amount of new information concerning protein domain annotation across Plasmodium species. The new protein architectures suggested in Plasmobase can play a crucial role in the functional annotation of Plasmodium proteins, and potentially, on the identification of new functions, possibly rising from new domain combinations.
The reconstruction of the most likely domain architecture for a protein sequence constitutes one of the main steps of all predictive annotation strategies. Indeed, an accurate identification of the domain architecture of a multi-domain protein provides important information for function prediction, comparative genomics and molecular evolution. Here, the latest generation tools available (the new generation annotation method CLADE, employing a multi-source annotation strategy, and the state-of-the-art architecture reconstruction approach DAMA) are used to reconstruct the domain architecture of a large number of sequences presenting no domain annotation in Pfam_27 in order to annotate all Plasmodium complete genomes. The success of CLADE methodology was demonstrated on the P. falciparum genome . Here, CLADE analysis is extended to 10 more species and a web interface simplifies the comparison between annotations by helping researchers to dig more profoundly into the evolution of the species and in the functional characteristics of their proteins. In Plasmobase, this can be done with a direct enquiry on functional keywords, associated to domains or proteins. Clearly, final confirmations are expected to be experimental.
On false discovery rate in Plasmobase Plasmobase provides multiple pieces of information to help users to evaluate a domain prediction: E-value, SVM probability, co-occurrences, and the possibility to compare predictions in orthologous genes. Indeed, one of the purposes of Plasmobase is to highlight several evidences to believe in the proposed domain annotation. Yet, as for any domain prediction tool, there exists the possibility that a domain is falsely predicted by CLADE. In this respect, several statistical tests to estimate CLADE false discovery rate (FDR) have been reported in , where it has been shown that, for the same FDR value, CLADE detects much more domains than its competitors, HMMer and HHblits. Run with default parameters (used for constructing Plasmobase), CLADE presents a FDR of 1e−3, that is, 1 in 1000 predictions are expected to be a false domain.
Plasmobase contribution to new protein annotations In Plasmobase, the user can explore and compare a large number of new domain annotations helping to the identification of a protein function. The protein PF3D7_1369500 illustrated in Fig. 1, for instance, contains three domains MIF4G/MIF4G-like obtained with E-value 1e−21, 1e−19 and 1e−8 and known to co-exist within protein sequences of metazoan, fungi and viridiplantae. In Plasmobase, this information, coming from UniProt, is accessible. The 96 UniProt metazoan sequences can be listed and their UniProt description can be looked up by clicking the protein accession number. By so doing, the user discovers that for these sequences, the architecture suggests the involvement of the protein in RNA metabolism and its role of RNA binding protein, but that its annotation goes from “uncharacterized protein” to “nuclear cap binding protein sub unit 1 (NCBP1)” inferred by homology, by experimental evidence at transcript level (in Mustela pitorius furo et Xenopus laevis) and by experimental evidence at protein level (in Mus musculus and Homo sapiens). This finding is supported by inspecting the 26 UniProt viridiplantae sequences. The majority of them was not studied but the Arabidopsis thaliana Q9SIU2 sequence was also annotated as NCBP1 by experimental evidence at protein level. From this analysis, one can infer that the annotation of NCBP1 is not solely a putative one because a biological validation was carried out in several studies conducted in mammals, Xenopus and Arabidopsis. Waiting for the biological validation in Plasmodium, this annotation could be proposed for the Plasmodium protein. This annotation is missing in PlasmoDB.
Another example is the PBANKA_0110700 protein with unknown function and no identified domain in Pfam. CLADE identified a peptidyl-tRNA hydrolase domain in it, with the extremely low E-value 2e−60.
Also, CLADE is helpful to confirm annotations identified with a low confidence. For instance, protein PF3D7_1452000 has no Pfam nor InterPro annotation but it is annotated as a rhoptry neck protein 2 (RON2) in PlasmoDB. In contrast, CLADE identifies the CLAG domain with an E-value 0 in PF3D7_1452000 based on a “clade-centred model” (in short CCM), that is a CLADE probabilistic model (see "Methods"). Note that homology between CLAG and RON2 has been previously assessed with a blastp search at E-value 0.001 , underlying the difficulty of current methods to identify divergent domains and the contribution of CCMs in raising the confidence.
Plasmobase contribution to new protein architectures The detection of new architectures is fundamental to the understanding of genome evolution. In this respect, DAMA was designed by trying to minimise the effect of prior knowledge on known architectures. This was done in two ways. First, DAMA combines information coming from different known architectures. This allows to identify new architectures with coexisting domains possibly belonging to different known ones. Second, DAMA searches for extra domains (not belonging to known architectures) that have no overlapping with those identified by exploiting known architectures, and that have a sufficient good score compared to the other predicted domains. These extra domains, satisfying the required conditions (overlapping and E-value), are added to the predicted architecture based on prior knowledge and enrich it. These two properties of the DAMA design assure the possibility to identify innovative architectures.
Plasmobase is a valuable new resource for domain annotation in Plasmodium genomes. All Plasmodium species reach a very large improvement in domain annotation, as reported in Table 3. Many Plasmodium sequences are also annotated for the first time. Plasmobase graphical presentation of protein sequences, based on predicted domain architectures, is of easy exploitation for comparative genomic studies. Plasmobase is expected to help to discover species-specific genes, possibly underlying important phenotypic differences between parasites, and orthologous gene families for deciphering the biology of these complex and important Apicomplexan organisms. The interactive nature of the platform allows the user to easily learn plenty of information on a given protein, on its protein family, on its orthologs in other Plasmodium species, and in other eukaryotes. This is especially important for those highly AT-rich proteins whose annotation by comparison to eukaryotic proteins is particularly difficult, due to sequence divergence.
Conceived and designed the database and the web interface: AC JB. Implemented the database and the web interface: JB. Analysed the data: JB AC CV. Wrote the paper: AC JB. All authors read and approved the final manuscript.
Experiments were carried out using Grid’5000 (https://www.grid5000.fr) and the UPMC MESU machine.
The authors declare that they have no competing interests.
Availability of data and materials
Plasmobase and all downloadable files are accessible at http://genome.lcqb.upmc.fr/plasmobase/.
Consent for publication
All authors provide their consent to publish.
This work undertaken (partially) in the framework of CALSIMLAB is supported by the public grant ANR-11-LABX-0037-0 from the “Investissements d’Avenir” programme (ANR-11-IDEX-0004-02). Experiments were carried out using the UPMC MESU machine financed by the project Equip@Meso (ANR-10-EQPX-29-01) of the “Investissements d’Avenir” program. Funds from the Institut Universitaire de France (AC).
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