Basic principles in pharmacology pharmacokinetics - pharmacology
Exploiting Edinburgh's Guide to PHARMACOLOGY database as a source of protein design information for...
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Introduction Moving beyond bacterial repressors and operators How can the Guide to PHARMACOLOGY database help? Christopher Southan, Helen E. Benson, Elena Faccenda, Joanna L. Sharman, Adam J. Pawson & Jamie A. Davies Centre for Integrative Physiology & Synthsys, The University of Edinburgh, Edinburgh, EH8 9XD, UK. Contact: [email protected] Exploiting Edinburgh's Guide to PHARMACOLOGY database as a source of protein design information for synthetic biology. Which protein types are most suited to drug control? To assess which functional protein classes are most likely to be amenable to modulation by drugs, the graph below divides 1236 targets in GtoPdb by functional class (by the terms in the Gene Ontology, a standard classification used across bioinformatics). Is the GtoPdb optimised for synthetic biologists? In a word, ‘no’: this application is a new idea not covered by GtoPdb’s existing funding. If there is interest, we can try to raise funding to create specific interfaces for synthetic biological protein engineering. In the meantime, we encourage anyone interested in controlling protein activity to explore the database and to contact the group for specific help. We have skilled pharmacologists and cheminformaticians who can help to identify protein modules that may be transferrable to engineered proteins, and who can navigate wider chemogenomic spaces to find candidate new potential drug/target module matches. While our data are predominantly human, we have direct affinity results for many rodent proteins and homology “walking” out to other species can be explored. If you are interested in this approach, please come and talk to us! The above graph treats all drug/target interactions equally. If we restrict the data to very high affinity (e.g. K i <0.1nM) interactions, receptors dominate (this probably reflects the historical effort into making very good drugs to modulate receptors for clinical applications). The four graphs below show (to the same key as above) the relative contribution of targets of different classes, if we include only affinities <0.1, <1.0, <10 and <100nM respectively. In research applications, there is no reason not to accept a K i of <100nM as a strong interaction. This aim of this poster is to encourage connections between the Scottish synthetic biology community and a potentially very useful local resource. Synthetic biological systems have two broad requirements: 1)Robust internal mechanisms to produce the required output (e.g. a biomolecule, a fuel, a morphogenetic event). 2)Methods to control these mechanisms from outside, during both testing and ‘production’ phases. This poster addresses the requirements for improved external control. The ‘traditional’ way of imposing external control on synthetic systems has been to use bacterial transcription factors that are modulated by binding specific nutrients or antibiotics: examples include the lac and tet operons. These systems are well-tried, but they are limited in number and they operate slowly, response times being limited by transcription and translational delays and, in the case of the on-to-off transition, by decay kinetics of already-made mRNA and protein. Given that most outputs of synthetic systems are achieved by proteins, it would make more sense to regulate the activity of the proteins directly. The history of pharmacology has consisted of developing drugs to modulate specific natural proteins. Synthetic biologists can now run pharmacology in reverse, to add modules to their proteins to make these proteins controllable by specific existing drugs. The Guide to PHARMACOLOGY database (‘GtoPdb’) is database of ligand (‘drug’)/target interactions, run in Edinburgh for the International Union of Basic and Clinical Pharmacology and the British Pharmacological Society. http://www.guidetopharmacology.org/ The entire ligand section of GtoPdb can be considered as a compendium of modulation reagents for protein function, searchable in a variety of ways, with much detailed quantitative information. Targets can be searched by classes (e.g. kinases, metabolic enzymes, channels, nuclear hormone receptors) and, within a class, each example can be examined for agonist and antagonist drugs. There are links to original literature for more detailed information on the particular drug-responsive domains. Th Supported by: <0.1nM <1.0nM <10nM <100nM
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Presented by Jamie Davies at the SULSA Synthetic Biology Meeting, Edinburgh, 10 June 2014 http://www.eventbrite.co.uk/e/sulsa-synthetic-biology-meeting-registration-11251454403?aff=eorg Abstract: Synthetic creation of new biological systems typically incorporates pathways and signaling modules from known protein building blocks. Testing the models underpinning the synthetic engineering thus needs the experimental manipulation of individual proteins, for example, ablating a specific enzyme activity via RNAi, SNP mutation, or knockout. However, the option of small-molecule inhibition as the system perturbation has the advantages of 1) rapid onset 2) dose-response 3) analog testing for structure-activity relationships, 4) exploring mixtures for combinatorial effects 5) pulsing and reversal by wash-out. 6) accurate measurements of added substances and 7) a vast precedent of published results in natural systems from medicinal chemistry, pharmacology, and chemical biology. For the synthetic biologists the GToPdb1 can thus be considered as compendium of the latter. It encompasses an interaction matrix between ~4000 small molecules and ~1000 human proteins with a focus on drugs, clinical candidates, research compounds and peptide ligands These not only have ~ 10,000 mapped binding constants but also the spectrum of documented modulation extends across enzymes, receptors, channels and transporters. It thus becomes an increasingly plausible option to choose a “Lego protein” from GToPdb as a synthetic system component that can have experimentally useable activity probes available from chemical vendors. Even if it does not currently have a suitable target-probe pair, as knowledge base (and expertise resource via the curation team who populate it) GToPdb is an ideal starting point from which to walk out to wider chemogenomic spaces. For example, while an approved drug and its target might seem a logical choice, analogs from the lead series or different chemotypes from which the drug was optimized, or even failed in development, can have superior probe-like properties for in vitro experiments (e.g. be more potent, specific and soluble). The GToPdb facilitates access to such compound data via curated papers and patents. References 1. Pawson AJ, Sharman JL, Benson HE, Faccenda E, Alexander SP, Buneman OP, Davenport AP, McGrath JC, Peters JA, Southan C, Spedding M, Yu W, Harmar AJ; NC-IUPHAR. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 2014 Jan 1;42(1)
Transcript of Exploiting Edinburgh's Guide to PHARMACOLOGY database as a source of protein design information for...
Introduction
Moving beyond bacterial repressors and operators
How can the Guide to PHARMACOLOGY database help?
Christopher Southan, Helen E. Benson, Elena Faccenda, Joanna L. Sharman, Adam J. Pawson & Jamie A. DaviesCentre for Integrative Physiology & Synthsys, The University of Edinburgh, Edinburgh, EH8 9XD, UK.
Contact: [email protected]
Exploiting Edinburgh's Guide to PHARMACOLOGY database as a source of protein design information for
synthetic biology.
Which protein types are most suited to drug control?
To assess which functional protein classes are most likely to be amenable to modulation by drugs, the graph below divides 1236 targets in GtoPdb by functional class (by the terms in the Gene Ontology, a standard classification used across bioinformatics).
Is the GtoPdb optimised for synthetic biologists?
In a word, ‘no’: this application is a new idea not covered by GtoPdb’s existing funding. If there is interest, we can try to raise funding to create specific interfaces for synthetic biological protein engineering. In the meantime, we encourage anyone interested in controlling protein activity to explore the database and to contact the group for specific help. We have skilled pharmacologists and cheminformaticians who can help to identify protein modules that may be transferrable to engineered proteins, and who can navigate wider chemogenomic spaces to find candidate new potential drug/target module matches. While our data are predominantly human, we have direct affinity results for many rodent proteins and homology “walking” out to other species can be explored.
If you are interested in this approach, please come and talk to us!
The above graph treats all drug/target interactions equally. If we restrict the data to very high affinity (e.g. Ki <0.1nM) interactions, receptors dominate (this probably reflects the historical effort into making very good drugs to modulate receptors for clinical applications). The four graphs below show (to the same key as above) the relative contribution of targets of different classes, if we include only affinities <0.1, <1.0, <10 and <100nM respectively. In research applications, there is no reason not to accept a Ki of <100nM as a strong interaction.
This aim of this poster is to encourage connections between the Scottish synthetic biology community and a potentially very useful local resource.
Synthetic biological systems have two broad requirements:1) Robust internal mechanisms to produce the required output
(e.g. a biomolecule, a fuel, a morphogenetic event).2) Methods to control these mechanisms from outside, during
both testing and ‘production’ phases.
This poster addresses the requirements for improved external control.
The ‘traditional’ way of imposing external control on synthetic systems has been to use bacterial transcription factors that are modulated by binding specific nutrients or antibiotics: examples include the lac and tet operons.
These systems are well-tried, but they are limited in number and they operate slowly, response times being limited by transcription and translational delays and, in the case of the on-to-off transition, by decay kinetics of already-made mRNA and protein. Given that most outputs of synthetic systems are achieved by proteins, it would make more sense to regulate the activity of the proteins directly.
The history of pharmacology has consisted of developing drugs to modulate specific natural proteins. Synthetic biologists can now run pharmacology in reverse, to add modules to their proteins to make these proteins controllable by specific existing drugs.
The Guide to PHARMACOLOGY database (‘GtoPdb’) is database of ligand (‘drug’)/target interactions, run in Edinburgh for the International Union of Basic and Clinical Pharmacology and the British Pharmacological Society.
http://www.guidetopharmacology.org/
The entire ligand section of GtoPdb can be considered as a compendium of modulation reagents for protein function, searchable in a variety of ways, with much detailed quantitative information. Targets can be searched by classes (e.g. kinases, metabolic enzymes, channels, nuclear hormone receptors) and, within a class, each example can be examined for agonist and antagonist drugs. There are links to original literature for more detailed information on the particular drug-responsive domains.
Th
Supported by:
<0.1nM <1.0nM
<10nM <100nM
