A computational high-throughput screening approach of iNKT-agonists: a novel tool to find optimized iNKT cell ligands

Depending on the environment and the activating glycolipids, iNKT cells are known to induce T-helper 1 and/or T-helper 2 cytokines. This highly versatile nature makes these innate-like cells very interesting targets for immunomodulation. As many pathologies as well as physiological ageing are associated with altered immune responses, iNKT cells could play a role in new therapies. Many analogs of the glycolipid alpha-galactosylceramide (a-GalCer) are known to activate iNKT-cells through their interaction with CD1d-expressing antigen-presenting cells, inducing the release of Th1 and/or Th2 cytokines. The design of iNKT cell ligands with selective Th1 and Th2 properties requires refined structural insights. Therefore, the chemical space of 333 currently known iNKT activators, including several newly tested analogs, was visualized by more than 3000 chemical descriptors which were calculated for each individual analog. The immunological responses consisted of four different cytokines in five different test-systems. With these two information-sets, structure-activity models were developed using a system biology computational approach. We present highly sensitive and specific predictive models that can be further exploited as high-throughput instruments to in-silico screen potential glycolipids, thereby reducing the attrition rate.

Publication year:2013

Accessibility:Open