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: jamie.davies@ed.ac.uk

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