Hierarchical Database Screenings for HIV-1 Reverse Transcriptase Using a Pharmacophore Model, Rigid Docking, Solvation Docking, and MM-PB/SA

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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

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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)

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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 NNRTIs
  (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.

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Method Outline and Evaluation
Database Refined ACD (Available Chemical
Directory) DB of 150,000 compounds

• 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)

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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 NNRTIs
tri-feature pharmacophore model
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Computational MethodsFilter 1 Pharmacophore
Model
wing
head
wing
tail

• 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)

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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

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Computational ResultsFilter 1 Pharmacophore
Model

• Average RMSD of the 19 superimposed NNRTIs
  0.86 angs.
• 40,000 compounds / 150,000 passed this filter
• Hit rate 95
• Enrichment factor 3.56

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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

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Computational ResultsFilter 2 Multiple
Conformation Rigid Docking

• Average RMSD of the 19 superimposed NNRTIs
  0.86 angs.
• 16,000 compounds / 40,000 had atleast 1
  conformation that passed this filter
• Hit rate 76
• Enrichment factor 1.89

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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 lt 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

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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

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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

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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

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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 lt - 6.8 kcal/mol
• 10 / 30 top hits tested yielded MM-PB/SA scores lt
  - 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)

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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