Docking of small molecules

1
Docking of small molecules using Discovery
Studio Tel-Aviv University
2
Goal of the workshop Provide useful
information required for using Discover Studio
docking algorithms.
3

• Outline
• A brief review on docking algorithms
• LigandFit the workflow
• CDocker the workflow
• Hands-on session
• Visualization tools in Discovery Studio
• Docking with LigandFit
• Docking with Cdocker
• Post-docking analisys

4
The molecular docking problem

• Given two molecules with 3D conformations in
  atomic details
• Do the molecules bind to each other?
• How does the molecule-molecule complex looks
  like?
• How strong is the binding affinity?

5
What do we dock?

• The two molecules might be
• A protein (enzyme, receptor) and a small
  molecule (substrates, ligands)
• A protein and a DNA molecule
• Two proteins

6
Why do we dock?

• Drug discovery costs are too high 800
  millions, 814 years, 10,000 compounds (DiMasi
  et al. 2003 Dickson Gagnon 2004)
• Drugs interact with their receptors in a highly
  specific and complementary manner.
• Core of the target-based structure-based drug
  design (SBDD) for lead generation and
  optimization.
• Lead is a compound that
• shows biological activity,
• is novel, and
• has the potential of being structurally modified
  for improved bioactivity and selectivity

7
Three components of docking
pre- and/or during docking
Representation of receptor binding site and
ligand
during docking
Sampling of configuration space of the
ligand-receptor complex
during docking and scoring
Evaluation of ligand-receptor interactions
8
Basic principles

• The association of molecules is based on
  interactions
• H-bonds
• salt bridges
• hydrophobic contacts
• electrostatic
• very strong repulsive (VdW) interactions on short
  distances
• Ligands are flexible
• Receptors are mostly rigid

9
Sampling of configuration space of the
ligand-receptor complex

• Descriptor-matching using pattern-recognizing
  geometric methods to match ligand and receptor
  site descriptors
• geometric, chemical, pharmacophore properties,
  such as distance pairs, triplet, volume, vector,
  hydrogen-bond, hydrophobic, charged, etc.
• Molecular simulation MD (molecular dynamics), MC
  (Monte Carlo)
• Others GA (genetic algorithm), similarity,
  fragment-based
• No best method

10
Molecular simulation MD MC

• Two major components
• The description of the degrees of freedom
• The energy evaluation
• The local movement of the atoms is performed
• Due to the forces present at each step in MD
  (Molecular Dynamics)
• Randomly in MC (Monte Carlo)
• Usually time consuming
• Search from a starting orientation to low-energy
  configuration
• Several simulations with different starting
  orientation must be performed to get a
  statistically significant result

11
Genetic algorithm docking

• Requires the generation of an initial population
  where conventional MC and MD require a single
  starting structure in their standard
  implementation.
• The collection of genes (chromosome) is assigned
  a fitness based on a scoring function. There are
  three genetic operators
• mutation operator randomly changes the value of a
  gene
• crossover exchanges a set of genes from one
  parent chromosome to another
• migration moves individual genes from one
  sub-population to another.

12
Docking programs

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

13
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.
• And more.

14
CDOCKER
LigandFit
Shape-based docking
CHARMm-based docking/refinement
Methodology
Screening of medium-size libraries in well
defined binding cavities
Screening of small libraries refinement of
docking poses
Usage
Medium
Medium-Slow
Speed
Site definition by ligand or receptor Pose
interactions filters
Binding site sphere definition Forcefield typing
Associated Tools and Utilities
15
LigandFit

• Active-site finding
• Automatic active site location using flood
  filling algorithm
• Flexible docking of ligands
• Searches the ligand conformational space to find
  the best fit into the protein active site
• 1,000 conformations per sec
• Fast ligand scoring
• Initial scoring based on both internal energy of
  ligand and interaction energy between ligand and
  protein
• With DS LigandScore, a variety of scoring
  functions are available for final analysis

16
LigandFit workflow
Define binding site/site partition
Generate ligand conformation
No. Monte Carlo trials
Fail
Ligand/Site Shape Match
Rank the poses
Pass
Position and Orient Ligand to Site
Apply scoring function(s)
Is it better than saved poses? Is it different
from saved poses?
Save pose in Save List
No
Yes Replace the worst pose
17
Prepare your protein

• All hydrogens must be added
• All atom valencies must be satisfied for correct
  atom typing
• Use Tools ? Protein Modeling ? Clean to
• check structural disorder
• fix connectivity and bonds order
• add H at a specified pH
• Use Preferences ? Protein Utilities to set Clean
  tool options

18
Binding site identification

• Before beginning docking calculations
• Where is the binding site?

19
Binding site characteristics

• Liang et al. 1998 found small molecule binding
  sites to be
• Indentations,
• Crevices, or
• Cavities
• And often the largest site is the true binding
  site
• Laskowski et al. 1996 reported an analysis of
  cleft volumes
• Often the ligand is bound in the largest cleft
• Usually the largest cleft is considerably larger
  than the others

HSV-1 thymidine kinase
Abl tyrosine kinase
20
Prepare you protein define the active site using
Site Search Algorithm

• Set up a grid around the protein
• Default resolution is 0.5 Å but can be adjusted
  by the user

• Use a probe to test for Van der Waals clashes at
  each grid point

21
Site Search Algorithm

• Clean free points by an eraser
• Clean free grid points
• Eraser size can be varied

22
Site Search Algorithm

• If the eraser is unable to enter a cavity, all
  grid points inside the cavity are considered as a
  site.

23
Site editing

• A site definition can be modified
• Site Editing links in Binding Site Tool Panel
• Contraction/Expansion
• Site points are objects
• Manually selected and deleted
• Recommended
• manually remove tails
• expand 2-3 times
• Preferences ? Binding sites ? site opening.
  Changes the eraser size (recommended size is 5)

24
If the ligand is smaller than site use partition
site option
25
Site search by protein shape

• Flood-filling algorithm identifies possible
  binding sites
• Fast (a few seconds)
• Will work on any protein shape
• Not sensitive to the orientation of the protein
  in the grid

26
Prepare your protein Interaction Filters

• If you know that certain /residue atom promote
  your ligand-receptor interaction you can define
  an interaction site
• Select protein atom(s) as interaction sites
• Hydrogens for defining a donor on the protein
• Heavy atom (such as O, N) for an acceptor
• Select Carbons for Hydrophobic
• Attributes and type can be edited
• Accessed by Edit Attributes
• menu
• Right-click a selected object
• and select Attributes of

27
Energy grid parameter

• Select a forcefield and partial charge
  calculation method to be used in the evaluation
  of ligand pose-receptor interaction energies
  during docking
• Dreiding - default
• PLP1 good for many (and mainly) hydrophobic
  interactions
• CFF more accurate then Dreiding time-consuming
  though
• Click on the arrow symbol to reveal advanced
  parameters

28
LigandFit conformational search

• Required for flexible fit of the ligand
• Monte Carlo search in torsional space
• Bond lengths and bond angles fixed
• Multiple torsion changes simultaneously
• Rings are not varied
• Upper limit of random dihedral perturbation is
  180
• Lower limit depends on the number of rotating
  atoms

29
Monte Carlo (MC) trials dialog

• Perform Rigid Docking
• A docking mode that treats the ligand as a rigid
  body. The ligand conformation is not changed
  during docking
• Use a fixed number of MC steps
• Specify a fixed number of iterations for the
  Monte Carlo conformer generation which is
  employed for all input ligands

• Use variable number of MC steps from table
• This table allows you to adjust the number of
  iterations and consecutive
• failures based on the number of ligand
  torsion

30
Docking mode

• Docking or Rigid-Body Minimization only
• Docking
• places ligand into the binding site
• shape matching and refinements done
• Rigid-Body Minimization
• position of input ligands specified by the
  starting coordinates
• rigid-body minimization of the ligand-protein
  interaction energy
• No attempt is made to place the ligand into the
  binding site, so the input file should be
  "pre-docked" for meaningful results

31
Evaluating the ligand position

• Once fit is completed
• how good is it?
• Ligand position initially evaluated using
  DockScore
• Energy-based
• Grid-based
• Higher scores indicate better fit
• Choice of forcefields
• Dreiding - default
• PLP1 good for many (and mainly hydrophobic
  intercations)
• CFF is more accurate then Dreiding
  time-consuming though

32
Protein-ligand interaction filters

• Features may be used as a filter for docked
  poses
• Does not affect how a ligand is positioned or
  optimized
• Once a ligand is docked, its pose is examined to
  find how many features are matched between the
  receptor and the docked pose
• The number of matched features influences whether
  the pose will be saved to the Save List

33
Scoring functions

• Used for final evaluation of positions after the
  DockScore is computed
• Used during LigandFit Docking Protocol
• Or evaluated for a completed run in Score Ligand
  Poses Protocol
• Choice of Scores
• LigScore1
• LigScore2
• PLP1
• PLP2
• Jain
• PMF
• Ludi

34
Types of scoring functions

• Force field based nonbonded interaction terms as
  the score, sometimes in combination with
  solvation terms
• Empirical multivariate regression methods to fit
  coefficients of physically motivated structural
  functions by using a training set of
  ligand-receptor complexes with measured binding
  affinity
• Knowledge-based statistical atom pair potentials
  derived from structural databases as the score
• Other scores and/or filters based on chemical
  properties, pharmacophore, contact, shape
  complementary
• Consensus scoring functions approach

35
Force field based scoring functions
e.g. CharmM in CDocker

• Advantages
• FF terms are well studied and have some physical
  basis
• Transferable, and fast when used on a
  pre-computed grid
• Disadvantages
• Only parts of the relevant energies, i.e.,
  potential energies sometimes enhanced by
  solvation or entropy terms
• Electrostatics often overestimated, leading to
  systematic problems in ranking complexes

36
Empirical scoring functions
LUDI PLP LigScore Jain

• Counts the number of interactions and assign a
  score based on the number of occurrences
• H-bonds, ionic interactions (easy to quantify)
• Hydrophobic interactions (more difficult to
  assess and quantify)
• Number of rotatable bonds frozen (link to
  entropic cost of binding, quite difficult to
  estimate)
• Advantages fast direct estimation of binding
  affinity

37
Knowledge-based potentials of mean force scoring
functions (PMF)

• Assumptions
• An observed crystallographic complex represents
  the optimum placement of the ligand atoms
  relative to the receptor atoms
• Advantages
• Similar to empirical, but more general (much more
  distance data than binding energy data)
• Disadvantages
• PMF are typically pair-wise, while the
  probability to find atoms A and B at a distance r
  is non-pairwise and depends also on surrounding
  atoms

38
Consensus Scoring

• Combination of several scoring functions
• The common top rankers get a higher consensus
  rank than single outliers
• False positives can be detected easier than one
  singular scoring function
• Advisable to use 2-4 well-suited scoring
  functions for the consensus score

39
Take home message

• There is no best method!
• Try different methods, force-fields, scoring
  functions
• Refer to your results as a suggestion
• Use the experimental data

40
CDOCKER
LigandFit
Shape-based docking
CHARMm-based docking/refinement
Methodology
Screening of medium-size libraries in well
defined binding cavities
Screening of small libraries refinement of
docking poses
Usage
Medium
Medium-Slow
Speed
Site definition by ligand or receptor Pose
interactions filters
Binding site sphere definition Forcefield typing
Associated Tools and Utilities
41
CDOCKER

• CDOCKER is a CHARMm-based docking algorithm

Generate Ligand Conformations Through High
Temperature Molecular Dynamics
Random (rigid-body) rotation Grid-based
Simulated Annealing
Full Minimization
Output of Refined Ligand Poses
42
CDOCKER

• CHARMm-based docking/refinement algorithm
• Uses soft-core potentials and an optional grid
  representation to dock ligands into the receptor
  active site
• High temperature MD to generate (10) starting
  conformations
• Take each conformation and perform random rigid
  body rotations (10)
• Minimise resulting structures (lt50)

43
Prepare your protein

• All hydrogens must be added
• All atom valencies must be satisfied for correct
  atom typing
• Use Tools ? Protein Modeling ? Clean to
• check structural disorder
• fix connectivity and bonds order
• add H at a specified pH
• Use Preferences ? Protein Utilities to set Clean
  tool options

44
Prepare your protein define your binding site

• If you know the residues involved in the
  interaction with your ligand you
• can define your binding site
• Enlarge your site using attributes of the
  site-sphere

45
Advanced parameters

• Advanced parameters for
• Forcefield
• CHARMm
• cff
• Use Full Potential
• Grid extension
• Ligand partial charge method (MMFF/CHARMm)
• Final minimization
• Grid-Based
• Full potential

46
Post docking tools

• Score your poses
• Consensus score
• Analyze your poses