1. Prediction of inhibitory activities of Hsp90 inhibitors
Paolo Swuec and David J. Barlow
Golan Nadav

2. Hsp90
Hsp90
Hsp90
The 90 kDa heat shock proteins (Hsp90s) are a widespread family of highly conserved molecular chaperones.
Hsp90 is involved in many aspects of cell life and death.
The main Hsp90 isoforms : Hsp90a, Hsp90b.
Recent studies have also shown that Hsp90 may provide a target for treatment of cardiovascular conditions and neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease.

3. 7-10 fold increase of Hsp90 level in some types of tumor cells
Hsp90 inhibitors in the treatment of solid tumours and haematologicalmalignancies

4. Hsp90 conformations and substrate binding
The N-terminal region of Hsp90 maintains the ability to bind and hydrolyze ATP.
ADP-bound
Open
ATP-bound

5. Hsp90 conformations and substrate binding
NTD – ATP binding domain
MD – client binding
CTD - dimerization

6. A novel methodology to aid in design of Hsp90 inhibitors, using molecular docking combined with artificial neural network (ANN) modelling

7. Artificial neural network (ANN) modelling
ANN for use in predicting the pIC50s of Hsp90 inhibitors

8. Methods
molecular docking combined with artificial neural network (ANN) modelling.
N-terminal domain of Hsp90 from Dictyostelium discoideum in complex with Geldanamycin.

9. Methods
The ANN was trained using the data obtained for a total of 32 inhibitors for which crystal structures were available for their complexes with Human Hsp90a.(A Windows based version of the Pulmonary Drug DeliveryLearning engine (PUDDLE)42
The molecular descriptors of each Hsp90 inhibitor used in training and cross validation of the ANN10 parameters were selected as inputs.
The examined inhibitors were generated by means of docking using the software MolegroVirtual Docker (MVD)

10. Methods
The actual and the predicted pIC50 of each inhibitor used to train and cross validation of the network. The final ANN configuration adopted is (10-9-1)

11. Results
The actual and the predicted pIC50 of each inhibitor of the objective test sets.

12. Results
Correlation between the observed pIC50s and the predicted pIC50s for the training, cross validation and objective test data sets.

13. Results

14. Conclusions
Considering the pIC50 predictions made for the training and cross validation data sets using the crystallographic data as inputs it is clear that the ANN was successfully trained.
The pIC50 predictions made for the same set of 32 ligands, using ANN inputs furnished through MVD re-dockings, gave equally encouraging results.

15. Conclusions
Inhibitor 3FT8 gave a predicted pIC50 error of (0.06) when the prediction was made using crystallographic data, and a predicted pIC50 error of (0.10) when the best re-docked pose’s thermodynamic data were used as input.
The model here presented may provide a useful tool for investigating the potential Hsp90 inhibitory activities of novel compounds—of chemotypes represented.