in-siliico Drug Discovery

Accurate models of receptor-ligand interactions accelerate preclinical drug discovery

Virtual screening of chemical databases to targets of known three-dimensional structure is developing into an increasingly reliable method for finding new lead candidates in drug development. We have developed FlexScreen, forcefield-based virtual screening solution that accounts for flexibility of both the ligand and the receptor binding site in the docking simulation and helps to significantly increase the success rate of structure based drug design. The success rate of in-silico modeling approaches that of much more costly high throughput screening techniques. FlexScreen   can accelerate lead screening, binding mode validation and lead optimization to reduce the cost of the drug discovery process.

Results:

Binding mode prediction with atomic precision

FlexScreen represents both the ligand and the receptor with atomic precision and approximates the binding energy using adaptable biophysical forcefields. Its innovative search technique permits generation and scoring of thousands of possible ligand poses in a user-defined biophysical forcefield.  FlexScreen predicts accurate binding modes, with an average error of less than 1 Å for the 86 compounds of the ASTEX/CDC reference data set.

Fischer, B., Basili, S., Merlitz, H., and Wenzel, W. (2007) Accuracy of binding mode prediction with a cascadic stochastic tunneling method, Proteins-Structure Function and Bioinformatics, 195-204.

FlexScreen’s biophysical approach permits treatment of receptor reorganization and ligand binding in a unified model. As a result, FlexScreen can treat induced-fit effects in the docking process. A recent study demonstrated that FlexScreen’s flexible-sidechain model improves the enrichment rates for 12 pharmaceutically relevant receptors in one-to-one comparison with rigid-receptor screens. Averaged over 12 receptors, four of the top ten molecules turned out to be active compound, demonstrating an unprecedented selectivity in flexible receptor screening.

Receptor specific scoring functions derived from quantum chemical models improve affinity estimates for in-silico drug discovery

The adaptation of forcefield based scoring function to specific receptors remains an important challenge for in-silico drug discovery. Here we compare binding energies of forcefield-based scoring functions with models that are reparameterized on the basis of large-scale quantum calculations of the receptor. We compute binding energies of eleven ligands to the human estrogen receptor subtype $\alpha$ (ER$\alpha)$ and four ligands to the human retinoic acid receptor of isotype $\gamma$ (RAR$\gamma$). We find a high correlation between the classical binding energy obtained in the docking simulation and quantum mechanical binding energies and a good correlation with experimental affinities R=0.81 for ER$\alpha$ and R=0.95 for RAR$\gamma$ using the quantum derived scoring functions. A significant part of this improvement is retained, when only the receptor is treated with quantum-based parameters, while the ligands are parameterized with a purely classical model.

FlexScreen integrates a novel loop-modeling algorithm that permits treatment of receptor backbone flexibility in the vicinity of the binding site. Receptor rearrangement is modeled continuously during ligand insertion to recover the optimal receptor binding energy for each possible ligand and to adapt the size of the binding pocket to each individual ligand. FlexScreen thus permits modeling of allosteric receptor regulation for such important receptor classes as kinases. A loop-reconstruction algorithm permits docking into incomplete receptor-structures.