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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

Genome coverage by comparative protein structure models versus the number of targets with at least one predicted binding site for a small molecule.Pie charts for each of the ten target genomes indicate the percentage of targets with at least one model above and below the accuracy threshold (i.e., MODPIPE quality score 1.0) in the green and red colors, respectively. The total area of each pie chart is proportional to the corresponding genome size.
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pntd-0000418-g002: Genome coverage by comparative protein structure models versus the number of targets with at least one predicted binding site for a small molecule.Pie charts for each of the ten target genomes indicate the percentage of targets with at least one model above and below the accuracy threshold (i.e., MODPIPE quality score 1.0) in the green and red colors, respectively. The total area of each pie chart is proportional to the corresponding genome size.

Mentions: The accuracy of our comparative protein structure models built using MODPIPE was predicted by a variety of criteria, including target-template sequence identity, coverage of the target sequence, fraction of gaps in the alignment, and statistical potential scores. One third of the total models (21,031) were assessed to have sufficient accuracy for predicting the location and type of their binding sites for small compounds (i.e., at least 50% of their Cα atoms are predicted to be within 3.5 Å of their correct positions, corresponding to the correct fold and at least an approximately correct alignment with the template structure). These models covered 11,714 protein targets, corresponding to 17% of all proteins in the ten target genomes (Table 1 and Figure 2). There are an average of ∼2.5 models per protein target, each model potentially based on a different template structure and/or covering a different domain of the modeled sequence. Different genomes presented different levels of difficulty to our modeling procedure: 75% of the models for M. leprae proteins met our accuracy standards, while only approximately 10% of T. gondii models did. These coverage correspond to accurate predictions for 3,070 targets in Trypanosoma cruzi (15.7% of the genome) and 300 targets (3.9% of the genome) for T. gondii (Table 1).


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)

Genome coverage by comparative protein structure models versus the number of targets with at least one predicted binding site for a small molecule.Pie charts for each of the ten target genomes indicate the percentage of targets with at least one model above and below the accuracy threshold (i.e., MODPIPE quality score 1.0) in the green and red colors, respectively. The total area of each pie chart is proportional to the corresponding genome size.
© Copyright Policy
Related In: Results  -  Collection

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

pntd-0000418-g002: Genome coverage by comparative protein structure models versus the number of targets with at least one predicted binding site for a small molecule.Pie charts for each of the ten target genomes indicate the percentage of targets with at least one model above and below the accuracy threshold (i.e., MODPIPE quality score 1.0) in the green and red colors, respectively. The total area of each pie chart is proportional to the corresponding genome size.
Mentions: The accuracy of our comparative protein structure models built using MODPIPE was predicted by a variety of criteria, including target-template sequence identity, coverage of the target sequence, fraction of gaps in the alignment, and statistical potential scores. One third of the total models (21,031) were assessed to have sufficient accuracy for predicting the location and type of their binding sites for small compounds (i.e., at least 50% of their Cα atoms are predicted to be within 3.5 Å of their correct positions, corresponding to the correct fold and at least an approximately correct alignment with the template structure). These models covered 11,714 protein targets, corresponding to 17% of all proteins in the ten target genomes (Table 1 and Figure 2). There are an average of ∼2.5 models per protein target, each model potentially based on a different template structure and/or covering a different domain of the modeled sequence. Different genomes presented different levels of difficulty to our modeling procedure: 75% of the models for M. leprae proteins met our accuracy standards, while only approximately 10% of T. gondii models did. These coverage correspond to accurate predictions for 3,070 targets in Trypanosoma cruzi (15.7% of the genome) and 300 targets (3.9% of the genome) for T. gondii (Table 1).

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