Potential and limits of in silico target discovery—Case study of the search for new antimalarial chemotherapeutic targets

Saïdani, Nadia; Grando, Delphine; Valadié, Hélène; Bastien, Olivier; Maréchal, Éric

doi:10.1016/j.meegid.2008.01.001

Cited by 13 publications

(6 citation statements)

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“…falciparum proteins of prokaryotic origin might be designed based on differences between bacterial and malarial proteins. In particular, large amino acid insertions (often predicted to be disordered stretches) are often detected in the sequences of malaria proteins. − Molecular modeling of Plasmodium DNA gyrase B has thus revealed extensive fold conservation with Escherichia coli counterpart but also large disordered insertions in close proximity to the sites of action . Future progress might benefit of the presence of such domains to design antibiotic analogues binding specifically to P.…”

Section: Targeting the Apicoplast Dna Replication Transcription And R...mentioning

confidence: 99%

Plasmodium falciparum Apicoplast Drugs: Targets or Off-Targets?

et al. 2011

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Section: Targeting the Apicoplast Dna Replication Transcription And R...mentioning

confidence: 99%

Plasmodium falciparum Apicoplast Drugs: Targets or Off-Targets?

et al. 2011

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“…Even with this knowledge, experimentally assessing the selected potential targets through wet lab experiments is far from straightforward since such experiments are costly in time and resources [7]. Fortunately, owing to their integrative power and low cost compared to wet lab experiments, in silico approaches appear as valuable tools in improving the efficiency of target identification [8][9][10][11][12][13][14][15][16][17][18][19], as demonstrated through several works using various computational methods [20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

An in silico target identification using Boolean network attractors: Avoiding pathological phenotypes

Poret

Boissel²

2014

Comptes Rendus. Biologies

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Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.

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“…The recent identification of the proteins involved in the export machinery in P. falciparum beautifully illustrates how appropriate data mining, combined with experimental validation, allows major breakthrough in the understanding of basic mechanisms of the parasite biology (de Koning-Ward et al, 2009). nevertheless, such lists highly depend on the accuracy of the scientific question, the quality of the experimental data and also, the quality of the annotations associated with these data (Saidani et al, 2009).…”

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Bioinformatic strategies to provide functional clues to the unknown genes inPlasmodium falciparumgenome

et al. 2010

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Summary:The fight against Plasmodium falciparum, the species responsible for 90 % of the lethal forms of human malaria, took a new direction with the publication of its genome in 2002. However, the hopes that the genome should help bringing to the foreground the expected new "vaccines candidates" or "targets of new medicines" were disappointed by the low number of genes that could be functionally annotated -less than 40 % upon the genome publication, just over 50 % eight years later. This 10 % gain of knowledge was made possible by the efforts of the entire scientific community in many directions which include: the production of transcriptomic and proteomic profiles at various stages of the parasite development and in response to drug or stress treatments; the proteomic study of subcellular compartments; the sequencing of numerous Plasmodium related species (allowing whole genome comparisons) and the sequencing of numerous P. falciparum strains (allowing investigations of gene polymorphism). In parallel with this production of experimental biological data, the development of original mining tools adapted to the P. falciparum specificities quickly appeared as a priority, as the performances of "classical" bioinformatic tools, used successfully for other genomes, had limited efficacy. This was the aim of the PlasmoExplore project launched in 2007. This brief review does not cover all efforts made by the international community to decipher the P. falciparum genome but focuses on improvements and novel mining methods investigated by the PlasmoExplore consortium, and some of the lessons we could learn from these efforts.

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Potential and limits of in silico target discovery—Case study of the search for new antimalarial chemotherapeutic targets

Cited by 13 publications

References 40 publications

Plasmodium falciparum Apicoplast Drugs: Targets or Off-Targets?

Plasmodium falciparum Apicoplast Drugs: Targets or Off-Targets?

An in silico target identification using Boolean network attractors: Avoiding pathological phenotypes

Bioinformatic strategies to provide functional clues to the unknown genes inPlasmodium falciparumgenome

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