Limits...
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.

Show MeSH

Related in: MedlinePlus

Experimental validation of two predicted target-ligand pairs.(A) P. falciparum thymidylate kinase (UniProt identifier Q8I4S1) interactions with dTMP, ATM and Zidovudine. (B) M. leprae nucleoside diphosphate kinase (UniProt identifier Q9CBZ0) interactions with GDP, cAMP and Fludarabine. Structures colored as in Figure 2. Each NMR spectrum shows a detail of the aromatic region for the interacting molecules, the bottom spectra corresponding to the reference 1D 1H experiment (black line). In this experimental setting, a non-interacting compound results in negative resonances in the Water-LOGSY experiment and no signals in the STD spectrum. In contrast, protein-ligand interactions in the Water-LOGSY (magenta line) are characterized by positive signals or by a reduction in the negative signals obtained in the absence of the protein (reference spectrum, grey line). In the STD experiment, a positive interaction is recognized by the presence of positive signals (green line). Signals marked with an asterisk arise from exchangeable protons, and although positive, do not indicate an interaction between the protein and the ligand, as they also show the same behavior in the absence of protein.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2667270&req=5

pntd-0000418-g004: Experimental validation of two predicted target-ligand pairs.(A) P. falciparum thymidylate kinase (UniProt identifier Q8I4S1) interactions with dTMP, ATM and Zidovudine. (B) M. leprae nucleoside diphosphate kinase (UniProt identifier Q9CBZ0) interactions with GDP, cAMP and Fludarabine. Structures colored as in Figure 2. Each NMR spectrum shows a detail of the aromatic region for the interacting molecules, the bottom spectra corresponding to the reference 1D 1H experiment (black line). In this experimental setting, a non-interacting compound results in negative resonances in the Water-LOGSY experiment and no signals in the STD spectrum. In contrast, protein-ligand interactions in the Water-LOGSY (magenta line) are characterized by positive signals or by a reduction in the negative signals obtained in the absence of the protein (reference spectrum, grey line). In the STD experiment, a positive interaction is recognized by the presence of positive signals (green line). Signals marked with an asterisk arise from exchangeable protons, and although positive, do not indicate an interaction between the protein and the ligand, as they also show the same behavior in the absence of protein.

Mentions: Using NMR Water-LOGSY and STD experiments, we have tested the binding capacity of both ATM and Zidovudine to the surface of P. falciparum TMPK. In the Water-LOGSY experiments, the large bulk water magnetization is partially transferred via the protein-ligand complex to the free ligand in a selective manner. As a consequence, the resonances of the ligand have a sign opposite to that of non-interacting compounds; their signal also appears stronger. To test the applicability of the Water-LOGSY experiment to P. falciparum TMPK, we tested glucose as a negative control (i.e., non-interacting ligand) and dTMP as a positive control (i.e., a known ligand for TMPK), resulting in the expected negative and positive interacting signals, respectively (Figure 4A). With this validation in hand, similar experiments were performed with ATM and Zidovudine. Both ATM and Zidovudine result in positive Water-LOGSY signals, confirming their predicted interaction with P. falciparum TMPK. The results were further validated by the positive signals in the STD spectra that are better suited for detecting interactions between strong binders and proteins.


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)

Experimental validation of two predicted target-ligand pairs.(A) P. falciparum thymidylate kinase (UniProt identifier Q8I4S1) interactions with dTMP, ATM and Zidovudine. (B) M. leprae nucleoside diphosphate kinase (UniProt identifier Q9CBZ0) interactions with GDP, cAMP and Fludarabine. Structures colored as in Figure 2. Each NMR spectrum shows a detail of the aromatic region for the interacting molecules, the bottom spectra corresponding to the reference 1D 1H experiment (black line). In this experimental setting, a non-interacting compound results in negative resonances in the Water-LOGSY experiment and no signals in the STD spectrum. In contrast, protein-ligand interactions in the Water-LOGSY (magenta line) are characterized by positive signals or by a reduction in the negative signals obtained in the absence of the protein (reference spectrum, grey line). In the STD experiment, a positive interaction is recognized by the presence of positive signals (green line). Signals marked with an asterisk arise from exchangeable protons, and although positive, do not indicate an interaction between the protein and the ligand, as they also show the same behavior in the absence of protein.
© Copyright Policy
Related In: Results  -  Collection

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

pntd-0000418-g004: Experimental validation of two predicted target-ligand pairs.(A) P. falciparum thymidylate kinase (UniProt identifier Q8I4S1) interactions with dTMP, ATM and Zidovudine. (B) M. leprae nucleoside diphosphate kinase (UniProt identifier Q9CBZ0) interactions with GDP, cAMP and Fludarabine. Structures colored as in Figure 2. Each NMR spectrum shows a detail of the aromatic region for the interacting molecules, the bottom spectra corresponding to the reference 1D 1H experiment (black line). In this experimental setting, a non-interacting compound results in negative resonances in the Water-LOGSY experiment and no signals in the STD spectrum. In contrast, protein-ligand interactions in the Water-LOGSY (magenta line) are characterized by positive signals or by a reduction in the negative signals obtained in the absence of the protein (reference spectrum, grey line). In the STD experiment, a positive interaction is recognized by the presence of positive signals (green line). Signals marked with an asterisk arise from exchangeable protons, and although positive, do not indicate an interaction between the protein and the ligand, as they also show the same behavior in the absence of protein.
Mentions: Using NMR Water-LOGSY and STD experiments, we have tested the binding capacity of both ATM and Zidovudine to the surface of P. falciparum TMPK. In the Water-LOGSY experiments, the large bulk water magnetization is partially transferred via the protein-ligand complex to the free ligand in a selective manner. As a consequence, the resonances of the ligand have a sign opposite to that of non-interacting compounds; their signal also appears stronger. To test the applicability of the Water-LOGSY experiment to P. falciparum TMPK, we tested glucose as a negative control (i.e., non-interacting ligand) and dTMP as a positive control (i.e., a known ligand for TMPK), resulting in the expected negative and positive interacting signals, respectively (Figure 4A). With this validation in hand, similar experiments were performed with ATM and Zidovudine. Both ATM and Zidovudine result in positive Water-LOGSY signals, confirming their predicted interaction with P. falciparum TMPK. The results were further validated by the positive signals in the STD spectra that are better suited for detecting interactions between strong binders and proteins.

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