Four years ago we highlighted a paper in which researchers
performed a fragment screen against ion channels. There have been other
occasional reports, but for the most part this has been a quiet area. A new
open-access paper in Neuropharmacology
by Andrew Thompson and collaborators at Cambridge
University, University
of Bern, VU University Amsterdam, and Washington State University
provides another case study.
The researchers were interested in the P2X1 purinergic
receptor, which allows calcium ions to pass into cells when ATP binds. An
antagonist could be a safe anti-clotting agent as well as a potential male
contraceptive. However, the only reported inhibitors are freakish molecules
like suramin.
The paper is heavily focused on assay development and
validation, in this case using cells stably transfected with P2X1. These were
loaded with a voltage-sensitive fluorescent dye: when the channel opens,
fluorescence increases. (Control cells not expressing P2X1 do not behave this
way.) By adding potential ligands first and then adding ATP, both agonists and
antagonists could be identified.
The researchers screened 1443 fragments (from IOTA) at 300
µM each. Cell-based fragment screens are rare but not unprecedented. In this
case, 46 hits were obtained, and these were retested at multiple concentrations;
39 hits showed dose responses. These were both agonists and antagonists, with
EC50 values ranging from low micromolar to above 1 millimolar.
For confirmation, the researchers used a fluorescently
labeled analog of ATP that binds to the P2X1 on transfected cells but not to
cells that don’t express P2X1; the increased fluorescence of the cells could be
visualized using confocal microscopy. Most of the fragment hits reduced the
fluorescent signal, suggesting that they block ATP binding.
1 comment:
The lack of structures provides an excellent illustration of why it is an error to equate Open Access with Open Science. It is possible to find examples of this sort of thing in the cheminformatics field where Open Access articles tell us about the models (especially how well they perform) without actually saying what the models are.
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