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Prediction of Biological Targets of Actives
Communitymalaria research community
SubjectRequest for Help
One of the interesting features of the GSK set of antimalarial compounds that are acting as the starting point for this project is that they are whole-cell actives, meaning that though they are extremely promising hits, we don't know how they work - i.e. what the targets are. To some extent this doesn't matter - praziquantel has been used for over 30 years and nobody knows how it works. However, a combination of factors (ease of regulatory approval, the possibility of some rational drug design, sheer curiosity) means it would be nice to know what these antimalarials are actually doing. How to figure that out?
There are ways. One is to use predictive cheminformatics - to use a correlation of all the known drug vs. drug target matches that are known, and to extrapolate that model to a molecule of interest. This exact part of the open malaria project was in our original grant proposal as something with which the core team had no expertise and so was an area where we were going to have to appeal for help. One of the super nice extra features that such an approach can bring is to predict off-target effects, which can help make a drug more effective (for example in this tremendous paper).
Last week such help arrived. I was talking with John Overington and Iain Wallace from ChEMBL about uploading the data from our project to their database (about which more shortly). It was an extremely interesting conversation. Iain has an interest in target prediction. He'd already taken the most active compound from our last round of biological evaluation and run it through his system to predict the likely biological targets of the drug. The raw data are here. The outcome from this search were these possible targets:
1. Carboxy-terminal domain RNA polymerase II polypeptide A small phosphatase 1
2. Dihydroorotate dehydrogenase (DHODH) - MMV/GSK have run these assays, e.g. here.
3. SUMO-activating enzyme subunit 2
4. SUMO-activating enzyme subunit 1
5. Cyclin-dependent kinase 1
What can we do with this information? We can try to find someone willing to screen this compound against those targets directly, to see if they are really targets. Anyone running these assays?
Iain's method is described in the online lab book, but he says it's this in essence: "Basically, a naive bayes model is built to distinguish compounds that are known to bind a particular target in ChEMBL from all others. We repeat this procedure for ~1,300 targets creating a model for each and score a compound with each model. I then generate the reports for only malaria proteins."
It's important to bear in mind that these are preliminary results, as with everything in an open source project, and should be taken as work in progress. Iain understands this and wants to make sure everyone else does. Iain also points out that similar approaches have been used to successfully to identify novel targets of FDA compounds (see here and here), and the Shoichet lab have a nice webserver that can used interactively.
The other way of doing target prediction is experimental. Iain mentioned a couple of guys that might be perfect for this - Corey Nislow who runs a yeast-based assay for target ID, and Andrew Emili who is developing a proteomic-based assay. They're both at the University of Toronto, along with Gary Bader, whom Iain also suggested we contact. I'll reach out to see if they're interested. Any advice on the best approach gratefully received - chances of success here? Favoured method for target ID?