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A kernel for open source drug discovery in tropical diseases.

Ortí L, Carbajo RJ, Pieper U, Eswar N, Maurer SM, Rai AK, Taylor G, Todd MH, Pineda-Lucena A, Sali A, Marti-Renom MA - PLoS Negl Trop Dis (2009)

Bottom Line: Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively.The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate.Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other.

View Article: PubMed Central - PubMed

Affiliation: Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.

ABSTRACT

Background: Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels".

Methodology/principal findings: HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other.

Conclusions/significance: The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.

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Related in: MedlinePlus

AnnoLyze protocol.(A) Prediction of a binding site in a known structure based on its structural alignment to a known binding site in another structure. (B) Prediction of a binding site in a model based on its structural alignment to a known or predicted binding site in the template structure used to construct the model.
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pntd-0000418-g001: AnnoLyze protocol.(A) Prediction of a binding site in a known structure based on its structural alignment to a known binding site in another structure. (B) Prediction of a binding site in a model based on its structural alignment to a known or predicted binding site in the template structure used to construct the model.

Mentions: The AnnoLyze program [9] was used to predict binding sites for small molecules on all well-assessed models. Briefly, AnnoLyze predicts ligand-binding sites on the surface of a model by transferring known ligands in the LigBase database [36] via the target-template alignment. Such predictions are made in a two step process (Figure 1): (i) transfer of a binding site between known structures (i.e. a ligand co-crystallized with a protein structure is transferred to another known structure if at least 75% of the LigBase-defined binding site residues are within 4 Å of the template residues in a global superposition of the two structures and if at least 75% of the binding site residue types are invariant); and (ii) transfer of a binding site to a comparative model using as a reference the alignment to its template (i.e. a ligand predicted in the previous step to bind the template or a ligand co-crystallized with the template is transferred to the comparative model if the binding sites are conserved at the same level as in the previous step). Using these cutoffs, approximately 30% of the selected models had at least one predicted binding site for small molecules (Table 1), which were then mapped to MSDChem entries.


A kernel for open source drug discovery in tropical diseases.

Ortí L, Carbajo RJ, Pieper U, Eswar N, Maurer SM, Rai AK, Taylor G, Todd MH, Pineda-Lucena A, Sali A, Marti-Renom MA - PLoS Negl Trop Dis (2009)

AnnoLyze protocol.(A) Prediction of a binding site in a known structure based on its structural alignment to a known binding site in another structure. (B) Prediction of a binding site in a model based on its structural alignment to a known or predicted binding site in the template structure used to construct the model.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2667270&req=5

pntd-0000418-g001: AnnoLyze protocol.(A) Prediction of a binding site in a known structure based on its structural alignment to a known binding site in another structure. (B) Prediction of a binding site in a model based on its structural alignment to a known or predicted binding site in the template structure used to construct the model.
Mentions: The AnnoLyze program [9] was used to predict binding sites for small molecules on all well-assessed models. Briefly, AnnoLyze predicts ligand-binding sites on the surface of a model by transferring known ligands in the LigBase database [36] via the target-template alignment. Such predictions are made in a two step process (Figure 1): (i) transfer of a binding site between known structures (i.e. a ligand co-crystallized with a protein structure is transferred to another known structure if at least 75% of the LigBase-defined binding site residues are within 4 Å of the template residues in a global superposition of the two structures and if at least 75% of the binding site residue types are invariant); and (ii) transfer of a binding site to a comparative model using as a reference the alignment to its template (i.e. a ligand predicted in the previous step to bind the template or a ligand co-crystallized with the template is transferred to the comparative model if the binding sites are conserved at the same level as in the previous step). Using these cutoffs, approximately 30% of the selected models had at least one predicted binding site for small molecules (Table 1), which were then mapped to MSDChem entries.

Bottom Line: Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively.The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate.Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other.

View Article: PubMed Central - PubMed

Affiliation: Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigación Príncipe Felipe, Valencia, Spain.

ABSTRACT

Background: Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels".

Methodology/principal findings: HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other.

Conclusions/significance: The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.

Show MeSH
Related in: MedlinePlus