Antibody-Free ADP Detection Assay
Although there are many commercial kinase assays, there remains high demand for a single assay that is applicable to both a single kinase target and all kinase targets. “Having a single assay that meets criteria for HTS and MOA will simplify and standardize assay development for kinase drug discovery. Not only will it reduce development time and costs (one assay vs. two or more), it will also make data comparison more straightforward. For example, the potency of SAR compounds can be directly compared with HTS single-shot or dose-response data,” says Hu Li, Ph.D., manager, molecular discovery research, GlaxoSmithKline.
Dr. Li’s research group recently evaluated the ADP-Glo™ assay developed by Promega as part of a beta-testing effort. This is a homogenous signal-increase assay that measures ADP production from a kinase reaction via conversion to ATP; then quantifies ATP using luciferase in the presence of luciferin. The assay provides high sensitivity at ATP concentrations from low µM to high mM because the unused ATP in the reaction is depleted prior to ADP-ATP conversion. It is antibody-free and label-free and applicable to all kinases.
“Compared with phosphopeptide detection, the ADP detection method is universal because it is applicable to both serine/theonine and tyrosine kinases and can accommodate broad substrates,” explains Dr. Li.
Since it is antibody-free, the assay’s advantages include cost-effectiveness and tolerance of high ATP concentration. It includes a step that eliminates unused ATP in the reaction before quantifying ADP produced in the kinase reaction. This significantly reduces background and increases sensitivity, further helping to improve assay robustness at lower ATP conversion while enabling initial rate determination.
“ADP-Glo may prove to meet the challenges we are facing in working with protein kinases (substrate specificity, different catalytic efficiency, different Km toward ATP) due to its uniqueness,” summarizes Dr. Li.
Researchers at the Dana-Farber Cancer Institute are using an integrated approach to better understand cancer initiators and drivers. “Most of our functional genomic screens have focused on kinases because it’s a trackable number of genes for proof of principal, and kinases are important in cancer,” says William Hahn, Ph.D., associate professor of medicine, Harvard Medical School.
Dr. Hahn explains that they use either RNAi libraries to suppress or overexpress genes and then combine the resulting data to look for either mutated or amplified genes. “One of the biggest challenges with functional screening is the high false positive and negative rates. The first is really the one that challenges most people because you get a long list of genes that is laborious to go through and test.”
This is the main reason why they have adopted an integrated approach, Dr. Hahn says, in addition to making their own reagents and developing better informatics tools to study data sets in a more statistically rigorous manner. They are also working with a HT facility to optimize screening; they currently spend an average of two to eight months optimizing conditions.
His group is also focused on identifying cancer drivers because many mutated genes do not contribute to the cancer phenotype. “The key in applying these comprehensive approaches is to discover cancer genes that are really important.”
This approach has proven successful— his group was able to identify a gene (CDK8) that plays a key role in colon cancer. “We had two screens going in parallel, and when we got a list of genes that scored in both of those it was only a total of nine genes. We were able to work with our collaborators to see that CDK8 was amplified in colon cancer.”
He adds that current efforts are focusing on two main areas: expanding the scale of the screens to interrogate the entire genome and generating larger data sets to look for gene co-dependencies (a mutation sets up a co-dependency on another gene). “The reason we’re trying to use this in cancer is because there are many oncogenes that are considered un-druggable. But, if you can find partners that were only acquired in cells within the mutation, it would allow you to develop co-targeting methods.”