Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

A Comparative Chemogenomics Strategy to Predict Potential Drug Targets in the Metazoan Pathogen, Schistosoma mansoni

  • Conor R. Caffrey,

    Affiliation Sandler Center for Basic Research in Parasitic Diseases, California Institute for Quantitative Biosciences, University of California San Francisco, San Francisco, California, United States of America

  • Andreas Rohwer,

    Affiliation Intervet Innovation GmbH, BioChemInformatics, Schwabenheim, Germany

  • Frank Oellien,

    Affiliation Intervet Innovation GmbH, BioChemInformatics, Schwabenheim, Germany

  • Richard J. Marhöfer,

    Affiliation Intervet Innovation GmbH, BioChemInformatics, Schwabenheim, Germany

  • Simon Braschi,

    Affiliation Sandler Center for Basic Research in Parasitic Diseases, California Institute for Quantitative Biosciences, University of California San Francisco, San Francisco, California, United States of America

  • Guilherme Oliveira,

    Affiliation Laboratory of Cellular and Molecular Parasitology, Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, Brazil

  • James H. McKerrow,

    Affiliation Sandler Center for Basic Research in Parasitic Diseases, California Institute for Quantitative Biosciences, University of California San Francisco, San Francisco, California, United States of America

  • Paul M. Selzer

    Paul.Selzer@sp.intervet.com

    Affiliation Intervet Innovation GmbH, BioChemInformatics, Schwabenheim, Germany

A Comparative Chemogenomics Strategy to Predict Potential Drug Targets in the Metazoan Pathogen, Schistosoma mansoni

  • Conor R. Caffrey, 
  • Andreas Rohwer, 
  • Frank Oellien, 
  • Richard J. Marhöfer, 
  • Simon Braschi, 
  • Guilherme Oliveira, 
  • James H. McKerrow, 
  • Paul M. Selzer
PLOS
x

Abstract

Schistosomiasis is a prevalent and chronic helmintic disease in tropical regions. Treatment and control relies on chemotherapy with just one drug, praziquantel and this reliance is of concern should clinically relevant drug resistance emerge and spread. Therefore, to identify potential target proteins for new avenues of drug discovery we have taken a comparative chemogenomics approach utilizing the putative proteome of Schistosoma mansoni compared to the proteomes of two model organisms, the nematode, Caenorhabditis elegans and the fruitfly, Drosophila melanogaster. Using the genome comparison software Genlight, two separate in silico workflows were implemented to derive a set of parasite proteins for which gene disruption of the orthologs in both the model organisms yielded deleterious phenotypes (e.g., lethal, impairment of motility), i.e., are essential genes/proteins. Of the 67 and 68 sequences generated for each workflow, 63 were identical in both sets, leading to a final set of 72 parasite proteins. All but one of these were expressed in the relevant developmental stages of the parasite infecting humans. Subsequent in depth manual curation of the combined workflow output revealed 57 candidate proteins. Scrutiny of these for ‘druggable’ protein homologs in the literature identified 35 S. mansoni sequences, 18 of which were homologous to proteins with 3D structures including co-crystallized ligands that will allow further structure-based drug design studies. The comparative chemogenomics strategy presented generates a tractable set of S. mansoni proteins for experimental validation as drug targets against this insidious human pathogen.

Introduction

Schistosomiasis is a parasitic disease infecting over 200 million people [1]. Considered a ‘neglected tropical disease (NTD)’ [2] for which, traditionally, there has been little in the way of a concerted drug discovery program, three major species of the flatworm parasite are responsible for disease in sub-Saharan Africa (Schistosoma mansoni, S. haematobium), South America (S. mansoni) and parts of China and South-East Asia (S. japonicum) [1]. Pathology associated with schistosomiasis mansoni and japonica results primarily from the accumulation of parasite eggs over the course of years and even decades giving rise initially to hepatomegaly that may be superseded by extensive liver fibrosis and possibly sequelae such as occlusion of the hepatic portal vein, portal hypertension, and gastrointestinal varices [3]. Furthermore, chronic schistosomiasis haematobia is a risk factor for squamous cell carcinoma of the bladder [4]. The disease is also known for its more subtle, and indeed underestimated [5] morbid effects, particularly in school-aged children. These include physical and cognitive under-performance, anemia and abdominal discomfort.

Despite ongoing attempts to produce a molecular vaccine [6], [7], present treatment and control of schistosomiasis relies on chemotherapy [8]. Just one drug, praziquantel (PZQ), for which the detailed mode of action is still unclear [9], [10], is widely available. Since its introduction in the late 1970's, PZQ has become the sole, WHO-recommended treatment, being safe, effective and affordable [11]. PZQ's success as a drug has contributed to a lack of urgency and investment in identifying new therapies, either in terms of chemical entities or molecular targets. This over-reliance on a single therapy to treat large populations is a serious concern regarding the potential for drug resistance [12], [13]. Resistance to PZQ has been bred on more than one occasion in the laboratory [14] and foci of transient drug resistance have been reported in the literature [15]. Thus, with the WHO-backed goal to more widely disseminate PZQ (the Schistosomiasis Control Initiative [16]), it may just be a matter of time before clinically relevant drug resistance emerges [8].

The tenuousness of therapeutic options for schistosomiasis, together with better knowledge of the molecular and biochemical idiosyncrasies of the parasite, and improved genome sequence information, are spurring increased investment in target discovery and validation [8], [17]. Further, a completely annotated Schistosoma mansoni genome should, in the future, provide a rich source of information for both academia and non-profit interests to identify, prioritize and prosecute drug and vaccine targets. In advance of this milestone, sufficient characterization and annotation of the genome has already taken place [18] so that in the latest Version 4 of Schistosoma mansoni GeneDB [19] the prediction of genes, open reading frames and translation products has been accomplished.

Given the wealth of organized data to hand, therefore, we felt it timely to put this information to work in an in silico comparative genomics strategy to identify a subset of schistosome genes/proteins that have potential value as drug targets in order to jump-start focused discovery efforts. Our approach was to mine the proteomes of the model organisms Drosophila melanogaster and Caenorhabditis elegans for proteins with clear sequence similarities to those in the parasite in order to identify those experimentally proven as essential, i.e., targeted gene disruption produces deleterious phenotypes (e.g. lethal, paralyzed, impaired of motility) in both model organisms. Precedence has shown that even for parasite proteins that share significant sequence similarity with vertebrate proteins, anti-parasite drugs can, nevertheless, be developed (e.g. β-tubulin, the target protein of benzimidazoles) [20]. Accordingly, the 13,283 predicted gene products of S. mansoni were compared in a semi-automatic process to the proteomes and phenotypic databases of D. melanogaster and C. elegans using the software Genlight [21], [22]. The output of 72 potential target proteins was manually curated leading to the identification of 35 S. mansoni proteins with druggable characteristics. Of these, 18 belong to protein families for which extensive 3D structural information is available, including bound small molecule ligands and drugs. Such structural data makes these proteins particularly suitable for prioritization of structure-based drug design strategies.

Results

Semi-automatic in silico workflows identify 72 candidate S. mansoni gene products

For the first in silico workflow, orthologs shared between the predicted proteome of S. mansoni [19], and the proteomes of C. elegans and D. melanogaster available at Wormbase [23] and Flybase [24], respectively, were determined (Figure 1A). For S. mansoni and C. elegans, and S. mansoni and D. melanogaster, 1778 and 1927 orthologs were identified, respectively. A subsequent comparison of both outputs demonstrated that 1258 sequences were identical. By reciprocal blastp [25], [26], this set was then compared with the phenotype databases of C. elegans (Caltech server) [23] and D. melanogaster [24], and those orthologs displaying the appropriate, pre-set, mutant phenotypes identified. The S. mansoni sequences of both ortholog sets were pooled and only those proteins with a 100% sequence identity were considered as potential target proteins. Altogether, 68 S. mansoni sequences with orthologs in C. elegans and D. melanogaster were identified.

thumbnail
Figure 1. In silico workflows to identify putative drug target proteins in S. mansoni based on sequence and phenotype comparisons.

A and B, representations of two independent workflows leading to a similar number of potential targets. C, the combination of workflows A and B generating a final number of 72 sequences (octagon) of which 63 were identical. Numbers of sequences used in each step are indicated within the respective circles. Depending on the intersection, the numbers within represent either sequence orthologs or S. mansoni proteins for which a deleterious phenotype is recorded in either Wormbase or Flybase. Blue, red and yellow circles display sequences from C. elegans (Ce), D. melanogaster (Dm), and S. mansoni (Sm), respectively. Details of the workflows are described in the text.

https://doi.org/10.1371/journal.pone.0004413.g001

To evaluate the performance and results of the first in silico approach, a second workflow was generated (Figure 1B). First, orthologs shared between C. elegans and D. melanogaster were identified. The different numbers of orthologs (2933 in C. elegans compared to 3789 in D. melanogaster) are due to the high occurrence of identical gene copies with different identifiers in Flybase. Thus, certain C. elegans genes generate multiple hits in D. melanogaster. The orthologs from both model organisms were then separately compared with the S. mansoni putative proteome resulting in 1502 orthologs shared between C. elegans and S. mansoni, and 1520 orthologs between D. melanogaster and S. mansoni. A redundancy check of these individual outputs established that 1302 sequences were identical. These 1302 sequences were then pooled with the extra non-redundant sequences (1502−1302 = 200 and 1520−1302 = 218 from the C. elegans and D. melanogaster comparisons, respectively) to give a total of 1720 S. mansoni sequences. By reciprocal blastp and using the pre-set, mutant phenotype criteria, orthologs of these sequences in the phenotype databases of C. elegans and D. melanogaster were determined. Comparison of these outputs identified 67 potential S. mansoni target proteins that have orthologs in both model organisms. Finally, the sequence outputs from both in silico workflows (68 and 67 sequences, respectively) were compared in order to determine the extent to which they were identical (Figure 1C). Sixty-three proteins were shared, thus demonstrating the reliability of the software and the compatibility of the workflows. Overall, by adding the 63 shared proteins to the 5 and 4 extra sequences exclusive to the first and second workflows, respectively, 72 S. mansoni proteins were considered for further manual filtering in order to delineate potential drug targets.

Manual curation identifies 35 potential drug targets in S. mansoni

Manual scrutiny of the 72 sequences generated by the semi-automatic workflows was considered essential in order to remove possible redundant information and improve overall confidence in the results (Figure 1, Table 1). Each curation step is represented by a separate worksheet in Table S1.

First, duplicates of four sequence entries (Smp_062300 ↔ Smp_062300.2, Smp_103470.3 ↔ Smp_103470.4, Smp_120700.1 ↔ Smp_120700.2, and Smp_138970.1 ↔ Smp_138970.4) were removed to leave 68 sequences. Subsequently, three sequences (Smp_028990.1, Smp_124240, and Smp_138970.1), not confirmed as the definitive schistosome orthologs after reciprocal blastp with both Wormbase and Flybase, were removed to produce 65 sequences. Next, seven of these sequences for which deleterious phenotypes (e.g. lethal, paralyzed, movement abnormal, etc.) could not be confirmed for the respective orthologs or for which the relevant orthologous allele or phenotype information was simply unavailable were removed. Upon scrutiny of the EST evidence (see footnote to Table S1), one additional sequence (Smp_154270) was also removed because it is only expressed in the miracidium – a developmental stage not found within the human host. The remaining 57 sequences are expressed in the relevant parasite life-stages that persist in humans, namely, the immature schistosomulum form, either prepared in vitro or removed from a mammalian host (in vivo), adult (male and/or female) and egg.

Next, each of the 57 sequences was assessed for druggability that is defined as the likelihood of being able to modulate a target's activity with a small-molecule drug [27], [28]. We mined a number of biological and literature databases to document whether orthologous proteins or proteins of the same family have been reported to be manipulated by ligands, inhibitors or even targeted by known drugs. For 35 of the S. mansoni proteins we found unambiguous evidence that homologous proteins are druggable (Table 1, Table S1). Accordingly, these might be prioritized as high value drug targets for new treatments of schistosomiasis.

Finally, we searched among the 35 S. mansoni proteins for homologous proteins that have a 3D structure and/or a 3D structure complexed with a ligand, inhibitor or drug. Such structural information would enhance the druggability value by facilitating a structure-based drug design strategy, including homology modeling, docking, virtual screening or pharmacophore-based screening [29], [30]. Eighteen of the 35 S. mansoni proteins fulfilled these conditions (Tables 1 and 2) and another 8 had at least partial 3D structure information available (Table S1).

thumbnail
Table 2. Druggable target proteins belonging to protein families with 3D structural information, including co-crystallized ligands

https://doi.org/10.1371/journal.pone.0004413.t002

Discussion

The comparative chemogenomics strategy described herein provides a prioritized and testable list of potential target proteins for S. mansoni, a metazoan pathogen causing chronic and debilitating disease in humans. The intent was first to mine the S. mansoni genome and identify putative essential genes based on similarity to experimentally-determined essential genes/proteins in two model metazoans and then define a subset of potential drug targets for which structural information of known target proteins, including bound ligands, exist. Both the strategy and outputs are in keeping with the recent establishment of a TDR Drug Targets Prioritization Database [31] by the World Health Organization's Special Programme for Research and Training in Tropical Diseases that facilitates the identification and prioritization of target genes in a number of pathogenic organisms, including those responsible for NTDs.

A number of factors were considered during the development and execution of the workflows in order to provide both confidence in the data generated and a solid platform from which to predict the druggability of individual S. mansoni proteins. First, rather than comparison with one metazoan proteome we incorporated two into the analysis, particularly as both C. elegans and D. melanogaster are phylogenetically remote from Schistosoma. Secondly, we only considered those orthologous genes/proteins for which phenotypes were generated via targeted gene mutagenesis. We discounted orthologs with phenotypes arising from RNAi (RNA interference) due to the potential for non-specific, off-target effects and false positive results including with Drosophila cells [32], [33], [34]. Nevertheless, the present protocol can be adapted to include RNAi phenotypes should one wish to cast the net wider. As a final stricture in the analysis, we selected for severely deleterious phenotypes such as death or those involving motility disorders with the aim of producing both a short list of testable targets and enriching for phenotypes that should be obvious when targeting the respective schistosome genes by chemical and/or genetic means (see below). We would state that our strategy focuses on potential targets based on loss-of-function and that gain-of-function targets such as those involved in drug agonism e.g., ion channels, will be missed. As many current anthelmintics, including PZQ, are agonists, it would be worthwhile developing systems for gain-of-function mutants in model organisms (for example using targeted overexpression banks).

The present strategy differs from previous comparative genomic screens for infectious diseases and for which the goal was to identify potential drug targets unique to the pathogen based on a user-prescribed similarity cut-off value in order to decrease the potential for toxicity of any new chemotherapeutic [35], [36]. Though this ‘exclusion’ approach is sound, nevertheless, examples abound of pathogen proteins that possess considerable similarity to human proteins and yet are valid drug targets e.g., parasite cysteine proteases [37], [38], [39], and the remarkable example of the highly conserved β-tubulin protein, the target of benzimidazoles [20], even though they might have failed a user-prescribed similarity cut-off as part of an exclusion genomics screen [35]. In addition, it is often the case that drug selectivity for a pathogen arises due to idiosyncrasies in pathogen physiology such as slower turnover of the target protein allowing for more pronounced drug action, e.g., ornithine decarboxylase in Trypanosoma brucei, the causative agent of African Sleeping Sickness [40], differences in protein regulation, e.g., dihydrofolate reductase in Plasmodium falciparum [41] or a lack of functional redundancy compared to the host [42]. Thus, we consider that the present similarity-based strategy to mine the S. mansoni genome represents a useful contribution to the consideration of new anti-schistosomal therapeutic targets.

An important aspect of developing drugs to NTDs such as schistosomiasis and for which profits are nil or marginal at best, is the need to keep costs of drug development to a minimum. Experience has shown that drug development programs for pathogen-specific targets of often unknown function, although a valid scientifically, are, in many cases, more expensive. Our strategy, therefore, to identify homologous rather than pathogen-specific potential targets is intended to reduce costs by leveraging the biochemical, chemical and structural data and tools already available for therapeutic targets of proven value in other clinical contexts, i.e., ‘piggy-back’ drug discovery [43], [44], [45]. The finding that 35 of the in silico-identified 72 sequences are indeed homologous to known druggable targets supports our strategy. Moreover, 18 of these belong to protein families for which 3D structural information including ligands or even drugs is available (e.g., N-(4-Methoxybenzyl)-N′-(5-Nitro-1,3-Thiazol-2-YI)Urea and Carbidopa). This opens the route not only for classical biochemical studies but also for structure-based drug discovery approaches [29], [46], [47]. For instance, among the 18 candidate proteins, small molecule scaffolds exist for methionine aminopeptidase targeting cancer [48] and malaria [49], N-myristoyl transferase against fungal infections [50] and Rac-GTPase against cancer [51]. The availability of specific small molecules adds incentive to prioritizing the experimental validation of the orthologous schistosome proteins.

Unlike for both model organisms, standardized functional genomic tools, such as targeted gene disruption or gene ‘knock-in’ technology, are not yet established for schistosomes. This represents a stumbling block for interrogating gene function with confidence. However, transient RNAi with double stranded RNAi or small interfering (si)RNA has gained a foothold as a useful technique for gene knock down (if not knock out), including in those developmental stages relevant to infection and pathology in humans (somules, adults and eggs) [52], [53]. We would assume, therefore, that transient RNAi will be an important tool to experimentally establish gene essentiality until more rigorous reverse genetic techniques become established. Where available, a complementary strategy to RNAi would be to employ a chemical genetics approach using protein-selective small molecules (see examples above). Such an approach has already been validated regarding schistosome cysteine proteases [37] and enzymes involved in redox metabolism [54].

We have mined and compared the predicted proteome of the metazoan pathogen, S. mansoni,with those of two well-studied model organisms in order to identify potential drug targets. The chemogenomics strategy has produced a tractable list of prioritized genes for further investigation, one or more of which might contribute to badly-needed drug discovery programs for this prevalent human disease.

Methods

Datasets

All protein datasets are available on Flybase (version FB2006_01) [24], Wormbase release 195 [23], and Schistosoma mansoni GeneDB v4.0 [19]. The D. melanogaster phenotype database was generated using the Flybase QueryBuilder. All proteins with the mutant phenotypes “lethal” and/or “neurophysiology defective” were downloaded. Similarly, the C. elegans phenotype database was generated using the Caltech server based on Wormbase release 177 [23]. All proteins with the mutant phenotypes “lethal”, “paralyzed”, “movement abnormal” and/or “muscle system physiology abnormal” were downloaded. However, only data from knock-out mutants (alleles) were used; RNAi phenotypes were excluded due to the potential for non-specific effects [32], [33], [34].

Comparative Genomics using Genlight

All genome comparisons performed were based on translated genomes using the software Genlight [21], for which a public WWW-server is available at the University of Bielefeld, Germany [22]. Genlight is a client/server based program suite developed for large scale sequence analyses and comparative genomics calculations. A key functionality of Genlight is the determination of orthologs via reversed or reciprocal blast searches. Sequences from organism A are compared with those from organism B and vice versa using the respective blast program [25], [26]. Orthologous sequences are then defined as those best hit sequences that find each other in such bidirectional blast searches. The sequence alignment overlap and the E-value cut off can be preset in accordance with the goal of the experiment [21], [29]. Within Genlight, we defined orthologs as best reciprocal blastp hits with a minimum of 70% sequence alignment overlap and an E-value of 0.01 or smaller.

A useful feature of the Genlight software is its user friendliness in the post-processing of results including the employment of predefined filters and operations. For example, result sets can be re-used directly to exclude duplicates in order to generate non-redundant datasets. This feature has been employed as described in the two in silico workflows (Figure 1) and resulted in the identification of 72 potential target proteins in the S. mansoni predicted proteome (Table S1) for which deleterious phenotypes were identified for the respective orthologs in both C. elegans and D. melanogaster.

Manual curation and filtering for potential target proteins

After executing two separate electronic workflows (see Figure 1), we manually curated the schistosome protein sequence output for consistency with our goal of identifying potential drug targets. First, redundant sequences were removed from the dataset. Further curation included removal of sequences (i) not confirmed as orthologs by reciprocal blastp searches with both Wormbase and Flybase, (ii) for which targeted gene disruption of the appropriate ortholog does not yield a deleterious phenotype, and (iii) which, based on the available EST evidence, are not expressed in the relevant parasite developmental stages infecting humans (i.e., schistosomulum (immature worm) either prepared in vitro or removed from a mammalian host (in vivo), adult (male and/or female) and egg). Consideration was also given to the potential druggability of the parasite protein by manually mining biological and literature databases on the internet including DrugBank [55], [56], UniProt [57], [58], Prosite [59], [60], OMIM [61], [62], InterPro [63], [64] and Pfam [65], [66]. Druggability was defined as the likelihood of being able to modulate the activity of the protein target with a small-molecule drug [27], [28]. Finally, the PDB database [67] was searched for 3D structural information of proteins homologous to each parasite protein, including co-crystallized ligands. The 72 sequences, together with the subsequent manual curation steps, are presented in Table S1.

Supporting Information

Table S1.

This table shows the output and manual curation of potential S. mansoni drug targets generated by comparative genomics with Caenorhabditis elegans and Drosophila melanogaster. The table comprises 7 worksheets each with a different level of information for the respective sequence lists. Leftmost in the table, the first worksheet contains the 72 sequences generated from the two separate electronic workflows utilizing the genome comparison software, Genlight. Each worksheet thereafter represents a subsequent manual curation step involving the removal of sequences (i) that are redundant (remaining 68 non-redundant entries), (ii) that are not confirmed as orthologs by reciprocal blastp searches with both Wormbase and Flybase (remaining 65 confirmed orthologs), (iii) for which targeted gene disruption of the appropriate ortholog does not yield a deleterious phenotype as declared in both Wormbase and Flybase (remaining 58 confirmed phenotypes), (iv) that are not expressed in the relevant developmental stages of the parasite infecting humans (remaining 57 expressed in the relevant life stage; for an explanation of how this was performed see below), (v) that are not druggable as indicated by manual mining of the biological and literature databases available on the internet (remaining 35 druggable targets), and (vi) for which homologous proteins with 3D structural information (including co-crystallized ligands) was not found (remaining 18 proteins with both a 3D structure and co-crystallized ligand). To determine in which developmental stage a putative protein is expressed, the following procedure was performed. In the S. mansoni genome browser [68], the sequence name (e.g., Smp_000040) is imputed into the text field, “landmark or region”. On the returned search page, the graphic produced by the latest GeneDB working model and displaying the organization of the gene is clicked to reveal a variety of information, including exon/intron junctions, physicochemical characteristics of the putative protein and gene ontology. Activating the hyperlink “DNA” reveals the unspliced DNA, spliced DNA and amino acid sequences. Next, a blastn search at NCBI of the spliced sequence is performed via the hyperlink “Send to BLAST at NCBI”. The analysis is constrained using the search set ‘non-human, non-mouse ESTs (EST_others)’, the organism ID number of 6183 for S. mansoni (taxid:6183) and the program selection set to ‘somewhat similar sequences’. On the EST list returned, each accession is scrutinized for its life-stage origin by activating the relevant link. The lowest maximum score accepted for consideration was 250. Lower scores tended to be too short to be reliably ascribed to the gene under study.

https://doi.org/10.1371/journal.pone.0004413.s001

(0.26 MB XLS)

Author Contributions

Conceived and designed the experiments: CC PMS. Performed the experiments: AR FO RJM PMS. Analyzed the data: CC AR FO RJM SB GO PMS. Contributed reagents/materials/analysis tools: SB GO. Wrote the paper: CC AR JHM PMS.

References

  1. 1. Steinmann P, Keiser J, Bos R, Tanner M, Utzinger J (2006) Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk. Lancet Infect Dis 6: 411–425.P. SteinmannJ. KeiserR. BosM. TannerJ. Utzinger2006Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk.Lancet Infect Dis6411425
  2. 2. Hotez PJ, Molyneux DH, Fenwick A, Kumaresan J, Sachs SE, et al. (2007) Control of neglected tropical diseases. N Engl J Med 357: 1018–1027.PJ HotezDH MolyneuxA. FenwickJ. KumaresanSE Sachs2007Control of neglected tropical diseases.N Engl J Med35710181027
  3. 3. Gryseels B, Polman K, Clerinx J, Kestens L (2006) Human schistosomiasis. Lancet 368: 1106–1118.B. GryseelsK. PolmanJ. ClerinxL. Kestens2006Human schistosomiasis.Lancet36811061118
  4. 4. Herrera LA, Benitez-Bribiesca L, Mohar A, Ostrosky-Wegman P (2005) Role of infectious diseases in human carcinogenesis. Environ Mol Mutagen 45: 284–303.LA HerreraL. Benitez-BribiescaA. MoharP. Ostrosky-Wegman2005Role of infectious diseases in human carcinogenesis.Environ Mol Mutagen45284303
  5. 5. King CH, Dickman K, Tisch DJ (2005) Reassessment of the cost of chronic helmintic infection: a meta-analysis of disability-related outcomes in endemic schistosomiasis. Lancet 365: 1561–1569.CH KingK. DickmanDJ Tisch2005Reassessment of the cost of chronic helmintic infection: a meta-analysis of disability-related outcomes in endemic schistosomiasis.Lancet36515611569
  6. 6. Loukas A, Tran M, Pearson MS (2007) Schistosome membrane proteins as vaccines. Int J Parasitol 37: 257–263.A. LoukasM. TranMS Pearson2007Schistosome membrane proteins as vaccines.Int J Parasitol37257263
  7. 7. Wilson RA, Coulson PS (2006) Schistosome vaccines: a critical appraisal. Mem Inst Oswaldo Cruz 101: Suppl 113–20.RA WilsonPS Coulson2006Schistosome vaccines: a critical appraisal.Mem Inst Oswaldo Cruz101Suppl 11320
  8. 8. Caffrey CR (2007) Chemotherapy of schistosomiasis: present and future. Current Opinion in Chemical Biology 11: 433–439.CR Caffrey2007Chemotherapy of schistosomiasis: present and future.Current Opinion in Chemical Biology11433439
  9. 9. Angelucci F, Basso A, Bellelli A, Brunori M, Pica Mattoccia L, et al. (2007) The anti-schistosomal drug praziquantel is an adenosine antagonist. Parasitology 134: 1215–1221.F. AngelucciA. BassoA. BellelliM. BrunoriL. Pica Mattoccia2007The anti-schistosomal drug praziquantel is an adenosine antagonist.Parasitology13412151221
  10. 10. Greenberg RM (2005) Ca2+ signalling, voltage-gated Ca2+ channels and praziquantel in flatworm neuromusculature. Parasitology 131: SupplS97–108.RM Greenberg2005Ca2+ signalling, voltage-gated Ca2+ channels and praziquantel in flatworm neuromusculature.Parasitology131SupplS97108
  11. 11. Doenhoff MJ, Pica-Mattoccia L (2006) Praziquantel for the treatment of schistosomiasis: its use for control in areas with endemic disease and prospects for drug resistance. Expert Rev Anti Infect Ther 4: 199–210.MJ DoenhoffL. Pica-Mattoccia2006Praziquantel for the treatment of schistosomiasis: its use for control in areas with endemic disease and prospects for drug resistance.Expert Rev Anti Infect Ther4199210
  12. 12. Cioli D, Pica-Mattoccia L (2003) Praziquantel. Parasitol Res 90: Supp 1S3–9.D. CioliL. Pica-Mattoccia2003Praziquantel.Parasitol Res90Supp 1S39
  13. 13. Utzinger J, Xiao SH, Tanner M, Keiser J (2007) Artemisinins for schistosomiasis and beyond. Curr Opin Investig Drugs 8: 105–116.J. UtzingerSH XiaoM. TannerJ. Keiser2007Artemisinins for schistosomiasis and beyond.Curr Opin Investig Drugs8105116
  14. 14. Fallon PG, Doenhoff MJ (1994) Drug-resistant schistosomiasis: resistance to praziquantel and oxamniquine induced in Schistosoma mansoni in mice is drug specific. Am J Trop Med Hyg 51: 83–88.PG FallonMJ Doenhoff1994Drug-resistant schistosomiasis: resistance to praziquantel and oxamniquine induced in Schistosoma mansoni in mice is drug specific.Am J Trop Med Hyg518388
  15. 15. Botros S, Bennett JL (2007) Praziquantel resistance. Expert Opinion in Drug Discovery 2: S35–S40.S. BotrosJL Bennett2007Praziquantel resistance.Expert Opinion in Drug Discovery2S35S40
  16. 16. http://www.schisto.org/ Schistosomiasis Control Initiative. http://www.schisto.org/ Schistosomiasis Control Initiative.
  17. 17. Keiser J, Utzinger J (2007) Advances in the discovery and development of trematocidal drugs. Expert Opinion in Drug Discovery 2: S9–S23.J. KeiserJ. Utzinger2007Advances in the discovery and development of trematocidal drugs.Expert Opinion in Drug Discovery2S9S23
  18. 18. Haas BJ, Berriman M, Hirai H, Cerqueira GG, Loverde PT, et al. (2007) Schistosoma mansoni genome: closing in on a final gene set. Exp Parasitol 117: 225–228.BJ HaasM. BerrimanH. HiraiGG CerqueiraPT Loverde2007Schistosoma mansoni genome: closing in on a final gene set.Exp Parasitol117225228
  19. 19. http://www.genedb.org/genedb/smansoni/ Schistosoma mansoni GeneDB. http://www.genedb.org/genedb/smansoni/ Schistosoma mansoni GeneDB.
  20. 20. Robinson MW, McFerran N, Trudgett A, Hoey L, Fairweather I (2004) A possible model of benzimidazole binding to beta-tubulin disclosed by invoking an inter-domain movement. J Mol Graph Model 23: 275–284.MW RobinsonN. McFerranA. TrudgettL. HoeyI. Fairweather2004A possible model of benzimidazole binding to beta-tubulin disclosed by invoking an inter-domain movement.J Mol Graph Model23275284
  21. 21. Beckstette M, Mailänder JT, Marhöfer RJ, Sczyrba A, Ohlebusch E, Giegerich R, Selzer PM (2004) Genlight: interactive high-throughput sequence analysis and comparative genomics. Journal of Integrative Bioinformatics 1: M. BeckstetteJT MailänderRJ MarhöferA. SczyrbaE. OhlebuschR. GiegerichPM Selzer2004Genlight: interactive high-throughput sequence analysis and comparative genomics.Journal of Integrative Bioinformatics1
  22. 22. http://piranha.techfak.uni-bielefeld.de/ Genlight. http://piranha.techfak.uni-bielefeld.de/ Genlight.
  23. 23. http://www.wormbase.org/ Wormbase. http://www.wormbase.org/ Wormbase.
  24. 24. http://flybase.bio.indiana.edu/ Flybase: A Database of Drosophila Genes&Genomes. http://flybase.bio.indiana.edu/ Flybase: A Database of Drosophila Genes&Genomes.
  25. 25. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215: 403–410.SF AltschulW. GishW. MillerEW MyersDJ Lipman1990Basic local alignment search tool.J Mol Biol215403410
  26. 26. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389–3402.SF AltschulTL MaddenAA SchafferJ. ZhangZ. Zhang1997Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.Nucleic Acids Res2533893402
  27. 27. Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, et al. (2007) Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol 25: 71–75.AC ChengRG ColemanKT SmythQ. CaoP. Soulard2007Structure-based maximal affinity model predicts small-molecule druggability.Nat Biotechnol257175
  28. 28. Keller TH, Pichota A, Yin Z (2006) A practical view of ‘druggability’. Curr Opin Chem Biol 10: 357–361.TH KellerA. PichotaZ. Yin2006A practical view of ‘druggability’.Curr Opin Chem Biol10357361
  29. 29. Krasky A, Rohwer A, Schroeder J, Selzer PM (2007) A combined bioinformatics and chemoinformatics approach for the development of new antiparasitic drugs. Genomics 89: 36–43.A. KraskyA. RohwerJ. SchroederPM Selzer2007A combined bioinformatics and chemoinformatics approach for the development of new antiparasitic drugs.Genomics893643
  30. 30. Oellien F, Cramer J, Beyer C, Ihlenfeldt WD, Selzer PM (2006) The impact of tautomer forms on pharmacophore-based virtual screening. J Chem Inf Model 46: 2342–2354.F. OellienJ. CramerC. BeyerWD IhlenfeldtPM Selzer2006The impact of tautomer forms on pharmacophore-based virtual screening.J Chem Inf Model4623422354
  31. 31. http://tdrtargets.org/ TDR Drug Targets Prioritization Database. http://tdrtargets.org/ TDR Drug Targets Prioritization Database.
  32. 32. Echeverri CJ, Perrimon N (2006) High-throughput RNAi screening in cultured cells: a user's guide. Nat Rev Genet 7: 373–384.CJ EcheverriN. Perrimon2006High-throughput RNAi screening in cultured cells: a user's guide.Nat Rev Genet7373384
  33. 33. Jackson AL, Bartz SR, Schelter J, Kobayashi SV, Burchard J, et al. (2003) Expression profiling reveals off-target gene regulation by RNAi. Nat Biotechnol 21: 635–637.AL JacksonSR BartzJ. SchelterSV KobayashiJ. Burchard2003Expression profiling reveals off-target gene regulation by RNAi.Nat Biotechnol21635637
  34. 34. Moffat J, Reiling JH, Sabatini DM (2007) Off-target effects associated with long dsRNAs in Drosophila RNAi screens. Trends Pharmacol Sci 28: 149–151.J. MoffatJH ReilingDM Sabatini2007Off-target effects associated with long dsRNAs in Drosophila RNAi screens.Trends Pharmacol Sci28149151
  35. 35. Luscher A, de Koning HP, Maser P (2007) Chemotherapeutic strategies against Trypanosoma brucei: drug targets vs. drug targeting. Curr Pharm Des 13: 555–567.A. LuscherHP de KoningP. Maser2007Chemotherapeutic strategies against Trypanosoma brucei: drug targets vs. drug targeting.Curr Pharm Des13555567
  36. 36. Odds FC (2005) Genomics, molecular targets and the discovery of antifungal drugs. Rev Iberoam Micol 22: 229–237.FC Odds2005Genomics, molecular targets and the discovery of antifungal drugs.Rev Iberoam Micol22229237
  37. 37. Abdulla MH, Lim KC, Sajid M, McKerrow JH, Caffrey CR (2007) Schistosomiasis mansoni: novel chemotherapy using a cysteine protease inhibitor. PLoS Med 4: e14.MH AbdullaKC LimM. SajidJH McKerrowCR Caffrey2007Schistosomiasis mansoni: novel chemotherapy using a cysteine protease inhibitor.PLoS Med4e14
  38. 38. Engel JC, Doyle PS, Hsieh I, McKerrow JH (1998) Cysteine protease inhibitors cure an experimental Trypanosoma cruzi infection. J Exp Med 188: 725–734.JC EngelPS DoyleI. HsiehJH McKerrow1998Cysteine protease inhibitors cure an experimental Trypanosoma cruzi infection.J Exp Med188725734
  39. 39. Selzer PM, Pingel S, Hsieh I, Ugele B, Chan VJ, et al. (1999) Cysteine protease inhibitors as chemotherapy: lessons from a parasite target. Proc Natl Acad Sci U S A 96: 11015–11022.PM SelzerS. PingelI. HsiehB. UgeleVJ Chan1999Cysteine protease inhibitors as chemotherapy: lessons from a parasite target.Proc Natl Acad Sci U S A961101511022
  40. 40. Iten M, Mett H, Evans A, Enyaru JC, Brun R, et al. (1997) Alterations in ornithine decarboxylase characteristics account for tolerance of Trypanosoma brucei rhodesiense to D,L-alpha-difluoromethylornithine. Antimicrob Agents Chemother 41: 1922–1925.M. ItenH. MettA. EvansJC EnyaruR. Brun1997Alterations in ornithine decarboxylase characteristics account for tolerance of Trypanosoma brucei rhodesiense to D,L-alpha-difluoromethylornithine.Antimicrob Agents Chemother4119221925
  41. 41. Zhang K, Rathod PK (2002) Divergent regulation of dihydrofolate reductase between malaria parasite and human host. Science 296: 545–547.K. ZhangPK Rathod2002Divergent regulation of dihydrofolate reductase between malaria parasite and human host.Science296545547
  42. 42. Renslo AR, McKerrow JH (2006) Drug discovery and development for neglected parasitic diseases. Nat Chem Biol 2: 701–710.AR RensloJH McKerrow2006Drug discovery and development for neglected parasitic diseases.Nat Chem Biol2701710
  43. 43. Gelb MH, Van Voorhis WC, Buckner FS, Yokoyama K, Eastman R, et al. (2003) Protein farnesyl and N-myristoyl transferases: piggy-back medicinal chemistry targets for the development of antitrypanosomatid and antimalarial therapeutics. Mol Biochem Parasitol 126: 155–163.MH GelbWC Van VoorhisFS BucknerK. YokoyamaR. Eastman2003Protein farnesyl and N-myristoyl transferases: piggy-back medicinal chemistry targets for the development of antitrypanosomatid and antimalarial therapeutics.Mol Biochem Parasitol126155163
  44. 44. Nwaka S, Hudson A (2006) Innovative lead discovery strategies for tropical diseases. Nat Rev Drug Discov 5: 941–955.S. NwakaA. Hudson2006Innovative lead discovery strategies for tropical diseases.Nat Rev Drug Discov5941955
  45. 45. Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5: 993–996.JP OveringtonB. Al-LazikaniAL Hopkins2006How many drug targets are there?Nat Rev Drug Discov5993996
  46. 46. Keil M, Marhöfer RJ, Rohwer A, Selzer PM, Brickmann J, et al. (2008) Molecular visualization in the rational drug design process. Frontiers in Bioscience in press. M. KeilRJ MarhöferA. RohwerPM SelzerJ. Brickmann2008Molecular visualization in the rational drug design processFrontiers in Bioscience in press
  47. 47. Selzer PM, Chen X, Chan VJ, Cheng M, Kenyon GL, et al. (1997) Leishmania major: molecular modeling of cysteine proteases and prediction of new nonpeptide inhibitors. Exp Parasitol 87: 212–221.PM SelzerX. ChenVJ ChanM. ChengGL Kenyon1997Leishmania major: molecular modeling of cysteine proteases and prediction of new nonpeptide inhibitors.Exp Parasitol87212221
  48. 48. Zhong H, Bowen JP (2006) Antiangiogenesis drug design: multiple pathways targeting tumor vasculature. Curr Med Chem 13: 849–862.H. ZhongJP Bowen2006Antiangiogenesis drug design: multiple pathways targeting tumor vasculature.Curr Med Chem13849862
  49. 49. Chen X, Chong CR, Shi L, Yoshimoto T, Sullivan DJ Jr, et al. (2006) Inhibitors of Plasmodium falciparum methionine aminopeptidase 1b possess antimalarial activity. Proc Natl Acad Sci U S A 103: 14548–14553.X. ChenCR ChongL. ShiT. YoshimotoDJ Sullivan Jr2006Inhibitors of Plasmodium falciparum methionine aminopeptidase 1b possess antimalarial activity.Proc Natl Acad Sci U S A1031454814553
  50. 50. Lodge JK, Jackson-Machelski E, Higgins M, McWherter CA, Sikorski JA, et al. (1998) Genetic and biochemical studies establish that the fungicidal effect of a fully depeptidized inhibitor of Cryptococcus neoformans myristoyl-CoA:protein N-myristoyltransferase (Nmt) is Nmt-dependent. J Biol Chem 273: 12482–12491.JK LodgeE. Jackson-MachelskiM. HigginsCA McWherterJA Sikorski1998Genetic and biochemical studies establish that the fungicidal effect of a fully depeptidized inhibitor of Cryptococcus neoformans myristoyl-CoA:protein N-myristoyltransferase (Nmt) is Nmt-dependent.J Biol Chem2731248212491
  51. 51. Nassar N, Cancelas J, Zheng J, Williams DA, Zheng Y (2006) Structure-function based design of small molecule inhibitors targeting Rho family GTPases. Curr Top Med Chem 6: 1109–1116.N. NassarJ. CancelasJ. ZhengDA WilliamsY. Zheng2006Structure-function based design of small molecule inhibitors targeting Rho family GTPases.Curr Top Med Chem611091116
  52. 52. Delcroix M, Sajid M, Caffrey CR, Lim KC, Dvorak J, et al. (2006) A multienzyme network functions in intestinal protein digestion by a platyhelminth parasite. J Biol Chem 281: 39316–39329.M. DelcroixM. SajidCR CaffreyKC LimJ. Dvorak2006A multienzyme network functions in intestinal protein digestion by a platyhelminth parasite.J Biol Chem2813931639329
  53. 53. Skelly PJ, Da'dara A, Harn DA (2003) Suppression of cathepsin B expression in Schistosoma mansoni by RNA interference. Int J Parasitol 33: 363–369.PJ SkellyA. Da'daraDA Harn2003Suppression of cathepsin B expression in Schistosoma mansoni by RNA interference.Int J Parasitol33363369
  54. 54. Sayed AA, Simeonov A, Thomas CJ, Inglese J, Austin CP, et al. (2008) Identification of oxadiazoles as new drug leads for the control of schistosomiasis. Nat Med 14: 407–412.AA SayedA. SimeonovCJ ThomasJ. IngleseCP Austin2008Identification of oxadiazoles as new drug leads for the control of schistosomiasis.Nat Med14407412
  55. 55. http://redpoll.pharmacy.ualberta.ca/drugbank/ DrugBank. http://redpoll.pharmacy.ualberta.ca/drugbank/ DrugBank.
  56. 56. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, et al. (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34: D668–672.DS WishartC. KnoxAC GuoS. ShrivastavaM. Hassanali2006DrugBank: a comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res34D668672
  57. 57. http://www.pir.uniprot.org/ Uniprot: the universal protein resource. http://www.pir.uniprot.org/ Uniprot: the universal protein resource.
  58. 58. Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, et al. (2006) The Universal Protein Resource (UniProt): an expanding universe of protein information. Nucleic Acids Res 34: D187–191.CH WuR. ApweilerA. BairochDA NataleWC Barker2006The Universal Protein Resource (UniProt): an expanding universe of protein information.Nucleic Acids Res34D187191
  59. 59. http://www.expasy.org/prosite/ Prosite: database of protein domains, familes, and functional sites. http://www.expasy.org/prosite/ Prosite: database of protein domains, familes, and functional sites.
  60. 60. Hulo N, Bairoch A, Bulliard V, Cerutti L, De Castro E, et al. (2006) The PROSITE database. Nucleic Acids Res 34: D227–230.N. HuloA. BairochV. BulliardL. CeruttiE. De Castro2006The PROSITE database.Nucleic Acids Res34D227230
  61. 61. http://www.ncbi.nlm.nih.gov/sites/entrez?dbomim OMIM: Online Mendelian Inheritance in Man. http://www.ncbi.nlm.nih.gov/sites/entrez?dbomim OMIM: Online Mendelian Inheritance in Man.
  62. 62. Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, et al. (2007) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res.DL WheelerT. BarrettDA BensonSH BryantK. Canese2007Database resources of the National Center for Biotechnology Information. Nucleic Acids Res.
  63. 63. http://www.ebi.ac.uk/interpro/ InterPro. http://www.ebi.ac.uk/interpro/ InterPro.
  64. 64. Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, et al. (2007) New developments in the InterPro database. Nucleic Acids Res 35: D224–228.NJ MulderR. ApweilerTK AttwoodA. BairochA. Bateman2007New developments in the InterPro database.Nucleic Acids Res35D224228
  65. 65. Finn RD, Tate J, Mistry J, Coggill PC, Sammut SJ, et al. (2008) The Pfam protein families database. Nucleic Acids Res 36: D281–288.RD FinnJ. TateJ. MistryPC CoggillSJ Sammut2008The Pfam protein families database.Nucleic Acids Res36D281288
  66. 66. http://pfam.sanger.ac.uk/ Pfam. http://pfam.sanger.ac.uk/ Pfam.
  67. 67. http://www.rcsb.org/pdb/home/home.do PDB: Protein Data Bank. http://www.rcsb.org/pdb/home/home.do PDB: Protein Data Bank.
  68. 68. http://www.genedb.org/perl-gb/gbrowse/S.mansoni/ Schistosoma mansoni genome browser. http://www.genedb.org/perl-gb/gbrowse/S.mansoni/ Schistosoma mansoni genome browser.