Hierarchical Database Screenings for HIV-1 Reverse Transcriptase Using a Pharmacophore Model, Rigid Docking, Solvation D

1. Hierarchical Database Screenings for HIV-1 Reverse Transcriptase Using a Pharmacophore Model, Rigid Docking, Solvation Docking, and MM-PB/SA Junmei Wang, Xinshan Kang, Irwin D.Kuntz, and Peter A. Kollman Encysive Pharmaceuticals Inc. University of California, San Francisco Presentation by Susan Tang CS 379A

2. Background • There are two approaches to identifying drug leads • De novo design • Aimed to design novel compounds that have electrostatic and hydrophobic properties complementary to target • Requires 3D structures of drug targets • Database screening • Applies filters to identify potential drug leads from databases • Can be divided into query-based and scoring-function-based methods • Only scoring-function-based methods requires 3D structures of drug targets • Query-based screenings - Search queries such as MW, #H-bond donors/acceptors, and pharmacophore models are applied to database - Computationally efficient since 3D structures are not used - Wrong query fields may produce too high/too low # of hits 2) Scoring-function based approaches - Apply target functions (typically free-energy calculations of inhibitor binding to target) to obtain hits - The most rigorous and accurate methods of free energy calculation are FEP (Free energy perturbation) and TI (Thermodynamic integration)  but they are too computationally intensive and thus not appropriate for DB screening - There are several alternative methods as well (such as MM-PB/SA)

3. Purpose • Purpose: To develop a method for the identification of HIV-1 RT drug leads using hierarchical database screening • Sequential Methods Used 1) Pharmacophore model 2) Multiple-conformation rigid docking 3) Solvation docking 4) MM-PB/SA (Molecular Mechanics-Poisson-Boltzman/surface area) • Significance of HIV-1 Reverse Transcriptase • Important target in AIDS-related drug design • Biological role is to transcribe viral RNA into dsDNA, which is necessary for viral replication • Recently, many crystal structures of NNRTI’s (non-nucleoside reverse transcriptase inhibitors) with HIV-1 RT have been solved • Since 3-D structures are available, HIV-1 RT poses as a good target for drug lead development/screening By showing that their methodology is accurate for HIV-1 RT, the authors hope to demonstrate that the method can be widely applied to other systems where target 3D structures are available.

4. Evaluation Criteria for Database Screening Performance Hit rate = known inhibitors that passed filter(s) total number of known inhibitors in database Enrichment factor = (Hit rate) x total number of compounds in database total number of hits that passed filter(s) Method Outline and Evaluation Database = Refined ACD (Available Chemical Directory) DB of 150,000 compounds

5. Computational MethodsFilter 1: Pharmacophore Model What is a pharmacophore model? Defined as the three-dimensional arrangement of atoms - or groups of atoms – responsible for the biological activity of a drug molecule. 19 crystal structures of HIV-1 RT in complex with NNRTI’s tri-feature pharmacophore model

6. Computational MethodsFilter 1: Pharmacophore Model wing head • 19 HIV-1RT/NNRTI crystal structures were superimposed on PDB structure 1uwb (HIV-1 RT/TBO) • Spheres indicate where inhibitor atoms reside • Overall shape of bound inhibitors is like a butterfly (allosteric binding site of enzyme) wing tail

7. Computational MethodsFilter 1: Pharmacophore Model • Tri-featured pharmacophore model designed from the “butterfly” shape • X1 : represents a 5 or 6 membered aromatic ring • X2 : represents a 5 to 7 membered ring • X3 : represents nitrogen, oxygen, or sulfur • Distinct distance patterns were also identified

8. Computational ResultsFilter 1: Pharmacophore Model • Average RMSD of the 19 superimposed NNRTI’s = 0.86 angs. • 40,000 compounds / 150,000 passed this filter • Hit rate = 95 % • Enrichment factor = 3.56

9. Computational MethodsFilter 2: Multiple-Conformation Rigid Docking • Spheres, where inhibitor atoms could potentially be, were highlighted on HIV-1 RT/TBO reference structure • Cluster analysis selected one cluster consisting of 30-40 spheres around the binding site and chose this as a center for docking • Conformational searches for the hits having passed Filter 1 • Average Number of searched conformations for each molecule = 30 • Rigid Docking was performed for all conformations • Crucial docking parameters: • Maximum orientations = 1000 • Minimum matching nodes = 4 • Maximum matching nodes = 15 • No intramolecular score • Dielectric constant = 4.0

10. Computational ResultsFilter 2: Multiple Conformation Rigid Docking • Average RMSD of the 19 superimposed NNRTI’s = 0.86 angs. • 16,000 compounds / 40,000 had atleast 1 conformation that passed this filter • Hit rate = 76 % • Enrichment factor = 1.89

11. Computational MethodsFilter 3: Solvation Docking • Solvation docking parameters in the binding free energy formula could easily vary from system to system • To derive solvation docking model specific for HIV-1 RT, a training set of 12 known HIV-1 RT/NNR-TI crystal structures were used • Each molecule in training set had an RMSD < 3.0 angstroms between the docked and crystal structure • Parameters (alpha, beta, gamma) in formula I were optimized to reproduce experimental binding free energies Formula I: • Solvation docking was performed for molecules having passed filter II using a solvation docking program • Program outputs the following terms: 1)VDW energy (hydrophobic interaction) 2)Screened electrostatic energy 3)Polar and non-polar accessible surface areas • Using derived solvation docking model, binding free energies were calculated

12. Computational ResultsFilter 3: Solvation Docking • The solvation docking model with the following coefficients was produced ( Alpha = 0.1736, beta = 0.1709, gamma = 0.0049 ) • Solvation docking model achieved average unsigned and rms errors of 1.03 and 1.16 kcal/mol between deltaG(calc) and deltaG(expt) for the training set

13. Computational ResultsFilter 3: Solvation Docking • 3360 compounds / 16,000 passed this filter with a threshold of –8.8 kcal/mol • Hit rate = 79 % • Enrichment factor = 3.74

14. Computational MethodsFilter 4: MM-PB/SA • First 3 filters: only ligand flexibility was taken into account • Current filter: application of MD simulations to sample conformational space of BOTH inhibitor and receptor • For each molecule, MD simulations were done at 300 K with 2.0 fs time step • MD simulations carried out using this formula: • The inhibitor, water molecules, and receptor residues that are within 20 angs. Of inhibitor mass center were allowed to move during the simulations • Equilibration for 50 ps  20 snapshots were collected • For each snapshot: MM-PB/SA analysis was performed to calculate binding free energy

15. Computational ResultsFilter 4: MM-PB/SA • Because this is the most time/resource demanding step, MM-PB/SA was only done on the 22 molecules in the control set & 30 top hits that passed Filter 3 • 16 / 22 control hits from Filter 3 yielded MM-PB/SA scorese < - 6.8 kcal/mol • 10 / 30 top hits tested yielded MM-PB/SA scores < - 6.8 kcal/mol • Best hit had a binding free energy of – 17.7 kcal/mol (likely to be a real HIV-1 RT inhibitor)

16. Summary Results • Overall, 16/37 known NNRTs survived all filters • Overall hit rate = 41 % • Hit rate (first 3 filters) = 56 % • Enrichment rate (first 3 filters) = 25 Translates to: the probability of finding a real inhibitor randomly from the hits of the first 3 filters is 25 fold higher than from the whole database Conclusion The hierarchical multiple-filter database searching strategy attained both high efficiency and high reliability, making it a viable option for drug lead discovery. Future Development • Making the time/resource limiting step, MM-PB/SA, more efficient • Run MD simulations using implicit (rather than explicit) water models such as GB/SA and PB/SA • Development of new algorithm to calculate entropy accurately and efficiently