7. Molecular Docking and Drug Discovery

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7. Molecular Docking and Drug Discovery
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The Docking Problem

• Given receptor binding pocket and ligand.
• Task quickly find correct binding pose.
• Two critical modules
• Search Algorithm
• Scoring Function

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Definitions

• pKd measures tightness of binding
• pKi measures ability to inhibit
• Mechanisms of actionfor instance
• Competitive inhibition (most typical docking
  case)
• Allosteric inhibition (bind to different pocket)
• Allosteric activation

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Challenges

• Search algorithm
• Speed (5M compounds or more)
• Local minima
• High-dimensional search space
• Scoring function
• Strict control of false positives
• Good correlation with pKd
• Multiple terms
• No consensus
• Non-additive effects (solvation, hydrophobic
  interactions)
• Note pKd does not always correspond with
  activity
• ADME concerns

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Examples of Docking Search Algorithms

• Genetic Algorithms
• Incremental Construction
• Fragment Reconstruction
• Gradient Descent
• Simulated Annealing and other MC Variants
• Tiered Scoring Functions
• fast screening functions
• slow accurate functions

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High Dimensionality Flexibility

• Most algorithms handle ligand flexibility but do
  NOT handle receptor flexibility.
• Iterative Docking to find alternate conformations
  of the protein
• Dock flexible ligand
• Minimize receptor holding ligand rigid
• Repeat

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Scoring Function

• Energy of Interaction (pKd)
• Electrostatics
• Van der Waals interactions
• Hydrogen bonds
• Solvation effects
• Loss of entropy
• Active site waters

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ADME

• ADME concerns can be more important than
  bioactivity. Most of these properties are
  difficult to predict.
• Absorption
• Distribution
• Metabolism
• Excretion

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Docking Programs

• Dock (UCSF)
• Autodock (Scripps)
• Glide (Schrodinger)
• ICM (Molsoft)
• FRED (Open Eye)
• Gold, FlexX, etc.

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Evaluation of Docking Programs

• Evaluation of library ranking efficacy in virtual
  screening. J Comput Chem. 2005 Jan
  1526(1)11-22.
• Evaluation of docking performance comparative
  data on docking algorithms. J Med Chem. 2004 Jan
  2947(3)558-65.
• Impact of scoring functions on enrichment in
  docking-based virtual screening an application
  study on renin inhibitors. J Chem Inf Comput Sci.
  2004 May-Jun44(3)1123-9.

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Cluster Based Computing

• Trivially parallelizable
• Divide ligand input files
• Some programs have specific parallel
  implementations (PVM or MPI implementations,)
• Commercial licenses are expensive

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Consensus Scoring

• Combining independent scoring functions and
  docking algorithms can improve results
• Most common method sort using the sum of the
  ranks of component scores
• More sophisticated methods exist
• Consensus scoring criteria for improving
  enrichment in virtual screening. J Chem Inf
  Model. 2005 Jul-Aug45(4)1134-46.

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Adding Chemical Informatics

• Docking results can be improved by using chemical
  information about the hits.
• Chemicals which bind the same protein tend to
  have similar structure.
• Iterating back and forth between docking and
  searching large DB.
• Use other filters and predictive modules (e.g.
  Lipinski rules)
• ALGORITHM
• Dock and rank a chemical database
• Create a bayesian model of the fingerprints of
  the top hits.
• Re-rank the database based on their likelihood
  according to the bayesian model
• Finding More Needles in the Haystack A Simple
  and Efficient Method for Improving
  High-Throughput Docking Results J. Med. Chem.,
  47 (11), 2743 -2749, 2004.

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Visualization

• Viewers must be able to scroll through tens or
  hundreds of small molecule hits
• Accessible viewers designed for this problem
• VIDA from OpenEye (free for academics)
• ViewDock module of Chimera from UCSF (free, open
  source)

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Long-term Goal of Drug Discovery

• LTDD (Low Throughput Drug Design) instead of HTVS
  (High Throughput Virtual Screening)
• Common ground explore virtual space

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Drug DiscoveryCase Study Tuberculosis
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Tuberculosis
Mycobacterium Tuberculosis Very thick, waxy cell
wall
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The Cell Wall Key to Pathogen Survival

• Tuberculosis
• 7th cause of death
• 1 in 3 people have TB
• Leading AIDS death cause
• Multi-drug resistant
• Mycobacterium tuberculosis

gt30 C fatty acid
10 of genome
Sugar
6 different ACCase b subunits, AccD1-6
Acyl-CoA
Homologs of PccB Focus on AccD4-6
Cell wall lipids Important for pathogen
virulence, survival and latency
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Tuberculosis (TB) An old foe
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The White Death
John Keats 1795-1821
Frederic Chopin 1810-1849
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TB still a real threat, because..
Multi-Drug Resistant (Super TB strain)
Its ability to stay alive
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The Cell Wall Key to Pathogen Survival
gt30 C fatty acid

• Tuberculosis
• 7th cause of death worldwide
• 1 in 3 people have TB
• Leading cause AIDS death
• Multi-drug resistant
• Mycobacterium
• tuberculosis

10 of genome
Sugar
6 different ACCase b subunits, AccD1-6
Acyl-CoA
Homologs of PccB Focus on AccD4-6
Cell wall lipids Important for pathogen
virulence, survival and latency
Substrate specificity for AccD4-6?
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AccD5 Protein Structures
AccD4 (3.3 Å)
Solved AccD5 (2.9 Å)
AccD6 (2.7 Å)
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Structure of AccD5
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Structure-Based Drug Design
Enzyme assay
Crystals Crystal structure
3. Combinatorial chemistry
1. High throughput screening
TB ACCase, AccD5
Lead compound
2. Virtual Screening
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The Computational/Experimental Loop
Similarity Search
Docking
Assay
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Docking Results

• Diversity set (1990) from NCI

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NCI 65828 (Lead 1)
NCI 172033 (Lead 2)
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Structure-Based Drug Design Identified AccD5
Inhibitors
KI 4.7 mM, KGI 50 mM
? New TB drug lead
T. Lin, M. Melgar, S. J. Swamidass, J. Purdon, T.
Tseng, G. Gago, D. Kurth, P. Baldi, H. Gramajo,
and S. Tsai. PNAS, 103, 9, 3072-3077, (2006). US
Patent pending.
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Acknowledgements

• Pharmacology
• Daniele Piomelli
• Chemistry
• G. Weiss
• J. S. Nowick
• R. Chamberlin
• S. Tsai
• K. Shea

• Informatics
• Liva Ralaivola
• J. Chen
• S. J. Swamidass
• Yimeng Dou
• Peter Phung
• Jocelyne Bruand
• Chloe Azencott
• Alex Ksikes
• Ryan Allison
• Funding
• NIH
• NSF
• Sun
• IGB

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Two Strategies

• Chemical similarity
• Docking

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AccD5

• Enzyme necessary for mycolic acid biosynthesis in
  M. tuberculosis.