1. Ugur Sezerman Sabanci University
Molecular Docking
Ugur Sezerman
Sabanci University

2. What is docking?
Docking is finding the binding geometry of two interacting molecules
with known structures
The two molecules (“Receptor” and “Ligand”) can be:
- two proteins
- a protein and a drug
- a nucleic acid and a drug
Two types of docking:
- local docking: the binding site in the receptor is known,
and docking refers to finding the position
of the ligand in that binding site
- global docking: the binding site is unknown. The search
for the binding site and the position of the
ligand in the binding site can then
be performed sequentially or simulaneously

3. What Are Docking & Scoring?
To place a ligand (small molecule) into the binding site of a receptor in the manners appropriate for optimal interactions with a receptor.
To evaluate the ligand-receptor interactions in a way that may discriminate the experimentally observed mode from others and estimate the binding affinity.
complex
ligand
docking
scoring
receptor
X-ray structure
& DG
… etc

4. Why Do We Do Docking?
Drug discovery costs are too high: ~$800 millions, 8~14 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, selectivity, and drugeability.

5. Docking Applications
Determine the lowest free energy structures for the receptor-ligand complex
Search database and rank hits for lead generation
Calculate the differential binding of a ligand to two different macromolecular receptors
Study the geometry of a particular complex
Propose modification of a lead molecules to optimize potency or other properties
de novo design for lead generation
Library design

6. Key aspects of docking Scoring Functions Search Methods
What are they?
Which Scoring Functions are feasible?
Search Methods
How do they work?
Which search method should I use?
Which program should I use?

7. Docking Challenge
Both molecules are flexible and may alter each other’s structure as they interact:
Hundreds to thousands of degrees of freedom
Total possible conformations are astronomical

8. Formulation of Docking Problem
A scoring function that can discriminate correct (experimentally observed) docking complex structure from incorrect ones
A search algorithm that finds the docking complex structure measured by the scoring function

9. Formulation of Docking Problem
Factors Affecting ∆G0
Intramolecular Forces(covalent)
• Bond lengths
• Bond angles
• Dihedral angles
Intermolecular Forces (noncovalent)
• Electrostatics
• Dipolar interactions
• Hydrogen bonding
• Hydrophobicity
• Van der Waals

10. Types of Docking Problems
Bound docking : the goal is to reproduce a known complex
Unbound docking : complex structure not known
Protein-Small Molecule Docking
Rigid receptor, rigid ligand
Rigid receptor, flexible ligand
Flexible receptor, flexible ligand

11. Types of Docking Problems

12. Docking strategies require:
Protein representation
A search method
Final refinement and scoring

13. 1. Protein Structure
A 3-D structure of the target protein at atomic resolution must be available
Crystal and solution structures (PDB)
Homology models
Pseudoreceptor models
Ideally, the atomic resolution of crystal structures should be below 2.5 A
Even small changes in structure can drastically alter the outcome

14. Receptor Structures & Binding Site Descriptions
PDB (Protein Data Bank, containing proteins or enzymes:
X-ray crystal: >60,000 structures,~10 % have ≤ 1.5 Å, ~80% between Å
NMR:, ensemble accuracy of Å in the backbone region, 1.5 Å in average side chain position (Billeter 1992; Clore et al. 1993)
(and high quality homology models built from highly similar sequences)
Limitation of experimental structures (Davis et al. 2003):
Locations of hydrogen atoms, water molecules, and metal ions
Identities and locations of some heavy atoms (e.g., ~1/6 of N/O of Asn & Gln, and N/C of His incorrectly assigned in PDB; up to 0.5 Å uncertainty in position)
Conformational flexibility of proteins
Binding site descriptions: atomic coordinates, surface,
volume, points & distances, bond vectors, grid and
various properties such as electrostatic potential,
hydrophobic moment, polar, nonpolar, atom types, etc.
DOCK

15. Drug, Chemical & Structural Space
Drug-like: MDDR (MDL Drug Data Report) >147,000 entries, CMC (Comprehensive Medicinal Chemistry) >8,600 entries
Non-drug-like: ACD (Available Chemicals Directory) ~3 million entries
Literatures and databases, Beilstein (>8 million compounds), CAS & SciFinder
CSD (Cambridge Structural Database, ~3 million X-ray crystal structures for >264,000 different compounds and >128,00 organic structures
Available compounds
Available without exclusivity: various vendors (& ACD)
Available with limited exclusivity: Maybridge, Array, ChemDiv, WuXi Pharma, ChemExplorer, etc.
Corporate databases: a few millions in large pharma companies

16. 3D Structural Information & Ligand Descriptions
2D->3D software: CORINA, OMEGA, CONCORD, MM2/3, WIZARD, COBRA. (reviewed by Robertson et al. 2001)
CSD: <0.1 Å for small molecules, but may not be the bound conformation in the receptor
PDB: ligand-bound protein structures ~6000 entries
Atoms associated with inter-atom distances, physical and chemical properties, types, charges, pharmacophore, etc
Flexibility: conformation ensemble, fragment-based

17. Scoring Functions A fast and simplified estimation of binding energies
scores <-> DGbinding
X-ray
structure
-scores ?
configurations of the complex

18. 3. Scoring Functions Factors Affecting ∆G0
Intramolecular Forces(covalent)
• Bond lengths
• Bond angles
• Dihedral angles
Intermolecular Forces
• Electrostatics
• Dipolar interactions
• Hydrogen bonding
• Hydrophobicity
• Van der Waals

19. 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

20. 3. Scoring Functions
Force Field Based • CHARMM [Brooks83] • AMBER [Cornell95] Empirical methods: • ChemScore [Eldridge97] • GlideScore [Friesner04] • AutoDock [Morris98] • AutoDock Vina[Trott09] Knowledge-based methods • PMF [Muegge99] • Bleep [Mitchell99] • DrugScore [Gohlke00]

21. Force Field Based Scoring Functions
e.g. AMBER FF in DOCK
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

22. Molecular mechanics force fields
Usually quantify the sum of two energies
the receptor–ligand interaction energy
internal ligand energy (such as steric strain induced by binding)
Interactions between ligand and receptor are most often described by using van der Waals and electrostatic energy terms.
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

23. Molecular mechanics force fields
CHARMM
[Brooks83]

24. Molecular mechanics force fields
AMBER:
[Cornell95]

25. FF Scoring: Implementations
AMBER FF: DOCK, FLOG, AutoDOCK
CHARMm FF: CDOCK, MC-approach (Caflisch et al. 1997)
Potential Grid: rigid receptor structure upon docking. The grid-based score interpolates from eight surrounding grid points only. 100-fold speed up. Examples: DOCK, CDOCK, and many other docking programs.
Soften VDW: A soft-core vdw potential is needed for the kinetic accessibility of the binding site (Vieth et al. 1998). FLOG: 6-9 Lennard-Jones function; GOLD: 4-8 vdw + H-bond, and intraligand energy.
Solvent Effect on Electrostatic: often approximated by rescaling the in vacuo coulomb interactions by 1/D, where D = 1-80 or = n*r, n = 1-4, r = distance.
Solvation and Entropy Terms: Solvation terms decomposed into nonpolar and electrostatic contributions (e.g., DOCK):

26. Empirical Scoring Functions
LUDI & FlexX
(Boehm 1994)
Goals: reproduce the experimental values of binding energies and with its global minimum directed to the X-ray crystal structure
Advantages: fast & direct estimation of binding affinity
Disadvantages
Only a few complexes with both accurate structures & binding energies known
Discrepancy in the binding affinities measured from different labs
Heavy dependence on the placement of hydrogen atoms
Heavy dependence of transferability on the training set
No effective penalty term for bad structures

27. Empirical Scoring: Implementations
Mostly differ by what training set and how many parameters are used
Cerius2/Insight2000: LUDI, ChemScore, PLP, LigScore
SYBYL: FlexX, F-Score
Hammerhead: 17 parameters for hydrophobic, polar complementary, entropy, solvation. sLOO = 1.0 logK for 34 complexes
VALIDATE: 8 parameters for VDW and Coulomb interactions, surface complementarity, lipophilicity, conformational entropy and enthalpy, lipophilic and hydrophilic complementarity between receptor and ligand surfaces
PRO_LEADS: 5 coefficients for lipophilic, metal-binding, H-bond, and a flexibility penalty term. sLOO = 2 kcal/mol for 82 complexes
SCORE (Tao & Lai, 2001); ChemScore (GOLD)

28. 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
The Boltzmann hypothesis converts the frequencies of finding atom A of the ligand at a distance r from atom B of the receptor into an effective interaction energy between A and B as a function of r
Advantages
Similar to empirical, but more general (much more distance data than binding energy data)
Disadvantages
The Boltzmann hypothesis originates from the statistics of a spatially uniform liquid, while receptor-ligand complex is a two-component non-uniform medium
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

29. PMF: Implementations
Verkhivker et al.(1995): 12 atom pairs, 30 complexes (HIV-1 and simian immunodeficiency virus). Test on 7 other HIV-1 protease complexes
Wallqvist et al. (1995): 38 complexes, 21 atom types (10 C, 5 O, 5 N, 1 S). Test on 8 complexes sd=1.5 kcal/mol, and 20 complexes rmsd=1.0 A.
Muegge et al. (1999): 697 complexes, 16 atom types from receptor & 34 from ligand, 282 statistically significant PMF interactions. Test on 77 diverse compounds: sd=1.8 log Ki. The PMF was combined with a vdw term to account for short-range interactions for DOCK4 docking:
DrugScore (Gohlke et al, 2000), FlexX, BLEEP
where

30. Two Kinds of Search
Systematic ✽ Exhaustive ✽ Deterministic ✽ Outcome is dependent on granularity of sampling ✽ Feasible only for low dimensional problems ✽ e.g. DOT (6D)
Stochastic
✽ Random
✽ Outcome varies
✽ Must repeat the search to improve chances of
success
✽ Feasible for bigger
problems
✽ e.g. AutoDock

31. Searching Algorithms Systematic search Molecular dynamics
Monte Carlo Simulations
Simulated annealing
Genetic algorithms
Lamarckian Genetic Algorithm
Incremental construction

32. Systematic Search Uniform sampling of search space
Relative position (3)
Relative orientation (3)
Rotatable bonds in ligand (n)
Rotatable bonds in protein (m)
FRED [Yang04]

33. Systematic Search Uniform sampling of search space
• Exhaustive, deterministic
• Quality dependent on granularity of sampling
• Feasible only for low-dimensional problems
Example: search all rotations
FRED [Yang04]

34. Molecular Mechanics Energy minimization:
• Start from a random or specific state (position, orientation, conformation)
• Move in direction indicated by derivatives of energy function
• Stop when reach local minimum

35. Monte Carlo Simulations
Tries to dock the ligand inside the receptor site through many random positions and rotations
In ICM and MCDOCK, this method is used to make random moves of the ligand inside a receptor binding site.
After each random move, a force-field based energy minimization is applied.
To avoid trapping in local minima, Monte Carlo combine this procedure with other search methods, such as Simulated Annealing, Genetic Algorithm and Lamarckian GA

36. Simulated Annealing
Global optimization technique based on the Monte Carlo method :
• Start from a random or specific state
(position, orientation, conformation)
• Make random state changes, accepting up-hill moves with probability dictated by “temperature”
• Reduce temperature after each move
• Stop after temperature gets very small

37. Genetic Algorithm (GA)
Genetic search of parameter space:
• Start with a random population of states
• Perform random crossovers and mutations to make children
• Select children with highest scores to populate next generation
• Repeat for a number of iterations
Gold [Jones95], AutoDock [Morris98]

38. Lamarckian Genetic Algorithm
LGA finds lowest fitness function (energy) values first, then maps these values to their respective genotypes
• Each new child is allowed to create a new generation • Genetic algorithm plus Solis and Wets local search Better performance than either simulated annealing or genetic algorithm alone

39. Incremental Extension
Used in DOCK, FLEXX, FLOG and Surflex
Greedy fragment-based construction:
• Partition ligand into fragments

40. Incremental Extension
Greedy fragment-based construction:
• Partition ligand into fragments
• Place base fragment (e.g., with geometric hashing)

41. Incremental Extension
Greedy fragment-based construction:
• Partition ligand into fragments
• Place base fragment (e.g., with geometric hashing)
• Incrementally extend ligand by attaching fragments

42. Descriptor Matching Methods: DOCK
Distance-compatibility graph in DOCK (Ewing and Kuntz 1997): distances between sphere centers and distances between ligand heavy atoms

43. Descriptor Matching Methods
Distance-compatibility graph in DOCK (Ewing and Kuntz 1997): distances between sphere centers and distances between ligand heavy atoms
Interaction site matching in LUDI (Boehm 1992): HBA<->HBD, HYP<->HYP
Pose clustering and triplet matching in FlexX (Rarey et al. 1996): HBA<->HBD, HYP<->HYP
Shape-matching in FRED (Openeye
Vector matching in CAVEAT (Lauri and Bartlett 1994)
Steric effects-matching in CLIX (Lawrence and Davis 1992)
Shape chemical complementarity in SANDOCK (Burkhard et al. 1998)
Surface complementarity in LIGIN: (Sobolev et al. 1996)
H-bond matching in ADAM (Mizutani et al. 1994)

44. Fragment-based Methods
Flexibility and/or de novo design
Identification and placement of the base/anchor fragment are very important
Energy optimization (during or post-docking) is important
Examples
Incremental construction in FlexX with triplet matching and pose clustering to maximize the number of favorable interactions
Growing and/or joining in LUDI from pre-built fragment and linker libraries and maximize H-bond and hydrophobic interactions
Anchor-based fragment joining in DOCK

45. 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
Grid for energy calculation. Larger steps or multiple starting poses are often used for speed and sampling coverage in MD:
Di Nola et al. 1994; Mangoni et al. 1999; Pak & Wang 2000; CDOCKER by Wu et al

46. MC-based Docking
where T is reduced based on a so-called cooling schedule, and grid can be used for energy calculation.
An advantage of the MC technique compared with gradient-based methods (e.g. MD) is that a simple energy function can be used which does not require derivative information, and able to step over energy barrier.
AutoDOCK (Goodsell & Olson 1990). MCDOCK (Liu & Wang 1999), PRODOCK (Trosset & Scheraga 1999), ICM (Abagyan et al. 1994).
Simulated annealing is used in DockVision (Hart & Read 1992) and Affinity (Accelrys Inc., San Diego, CA)
Energy minimization is used in QXP (McMartin & Bohacek 1997).

47. Genetic Algorithm Docking
A fitness function is used to decide which individuals (configurations) survive and produce offspring for the next iteration of optimization. Degrees of freedom are encoded into genes or binary strings.
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.
Requires the generation of an initial population where conventional MC and MD require a single starting structure in their standard implementation.
GOLD (Jones et al. 1997); AutoDock 3.0 (Morris et al. 1998); DIVALI (Clark & Ajay 1995).

48. DOCK (Kuntz, UCSF) Receptor Structure
X-ray crystal
NMR
homology
Binding Mode Analysis for Lead Optimization: binding orientations and scores for each ligands
Virtual Screening for MTS/HTS and Library Design: ligands in the order of their best scores
Binding Site
Scoring Orientations
1. Energy scoring (vdw and electrostatic)
2. Contact scoring (shape complementarity)
3. Chemical scoring
4. Solvation terms
Molecular Surface
of Binding Site
Filters
Ligands
3D structure
atomic charges
potentials
labeling
Spheres describing the
shape of binding site and
favorable locations of
potential ligand atoms
Matching heavy atoms of
ligands to centers of
spheres to generate thousands
of binding orientations

49. FlexX (Tripos/SYBYL)
Fragment-based, descriptor matching, empirical scoring (Rarey et al. 1996)
Procedures:
Select a small set of base fragment suitable for placement using a simple scoring function.
Place base fragments with the pose clustering algorithm: rigid, triplet matching of H-bond & hydrophobic interactions, Bohm's scoring function
Build up the remainder of the ligand incrementally from other fragments
Ligand conformations
MIMUMBA model with CSD derived low energy torsional angles for each rotatable bond and ring from CORINA.
Multiple conformations for each fragment in the ligand building steps
Other works: Explicit waters are placed into binding site during the docking procedure using pre-computed water positions(Rarey et al. 1999). Receptor flexibility using discrete alternative protein conformations (Claussen et al. 2001; Claussen & Hindle 2003)

50. GOLD GA method, H-bond matching, FF scoring (Jones et al. 1997)
A configuration is represented by two bit strings:
The conformation of the ligand and the protein defined by the torsions;
A mapping between H-bond partners in the protein and the ligand.
For fitness evaluation, a 3D structure is created from the chromosome representation. The H-bond atoms are then superimposed to H-bond site points in the receptor site.
Fitness (scoring) function: H-bond, the ligand internal energy, the protein-ligand van der Waals energy
Rotational flexibility for selected receptor hydrogens along with full ligand flexibility
Highlights:
Validation test set: 100 complexes, 66 with rmsd<2A.
The structure generation is biased towards inter-molecular H-bonds.
Hydrophobic fitting points was added (GOLD 1.2, CCDC, Cambridge, UK 2001).

51. LUDI: Matching polar and hydrophobic groups
Calculate protein and ligand interaction sites (H-bond or hydrophobic), which are defined by centers and surface, from
non-bonded contact distributions based on a search through the CSD,
a set of geometric rules,
the output from the program GRID (Goodford 1985) which calculates binding energies for a given probe with a receptor molecule.
Fit fragments onto the interaction sites.
distance between interaction sites on the receptor
an RMSD superposition algorithm,
A hashing scheme to access and match surface triangles onto a triangle query of a ligand interaction center.
A list-merging algorithm creates all triangles based on lists of fitting triangle edges for two of the three query triangle edges.
Join/grow fragments using the databases of fragments and the same fitting algorithm.

52. GLIDE (www.schrodinger.com)
Funnel: site point search -> diameter test -> subset test -> greedy score -> refinement -> grid-based energy optimization -> GlideScore.
Approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand.
Hierarchical filters, including a rough scoring function that recognizes hydrophobic and polar contacts, dramatically narrow the search space
Torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses.
The very best candidates are further refined via a MC sampling of pose conformation.
A modified ChemScore (Eldridge et al. 1997) that combines empirical and force-field-based terms.
Validation: 282 complexes, new ligand conformation, the top-ranked pose: 50%<1 A, ~33% >2 A.

53. Matrix of Accuracy & Success
Drug <- Quality Novel Lead <- Active
Reproduce binding mode (X-ray crystal structures)
Predict binding affinity (free energies)
Rank diverse set of compounds (by binding affinity)
Enhance hit rate for database mining
Reduce false positive (Nselected-Nhits) and false negative (Nall_hits-Nhits)
Fast enough for iterative SBDD
expt.
pred.

54. Accuracy of Docking Reality Boundary Current
Experimental errors: kcal/mol (18-53%) with MSR (maximum significant ratio) as much as 3 fold (0.65 kcal/mol)
Free energy calculation accuracy: ~1 kcal/mol (5.4 fold) starting with an accurate geometric model & fully sampling
Entropy and solvation estimation need a sufficiently long simulation run with an accurate force field, an ensemble of explicit of water molecules, and fully sampling
Current
Reproduce X-ray structure with rmsd<2A: 50-90% achievable
Binding affinity: 1.5~2 log unit ( fold, kcal/mol)
Correlation between scores and affinities, r^2<0.3
Enthalpy ranking with minimization: ±5 kcal/mol
Hit rate enhancement : 2~50 fold with hit rate 1-20% (and high false negative rate if 1~5% of total compounds selected)

55. Background & Motivation
Docking = process of starting with a set of coordinates for two distinct molecules and generating a model of the bound complex
Numerous methods which perform protein- protein docking exist today
Fourier correlation approach (Ritchie and Kemp, 2000) enabled the generation of billions of possible docked conformation via defined scoring functions
Problem: Many false-positives (good surface complementarity) that are far from the native complex
Motivation: Need to develop methods to filter and rank the docked conformations such that near-native complexes can be identified
ClusPro: an automated, fast rigid-body docking and discrimination algorithm that:
1) Rapidly filters docked conformations
2) Ranks the conformations using clustering of computed pairwise RMSD values

56. Input and Method Outline
Free Energy
Filtering
CAPRI
Receptor-Ligand
Pairs
2,000 conformations
w/ low desolvation or
electrostatic energies
2,000 docked
conformations for 48
receptor-ligand pairs
Discrimination
Via Clustering
Top 10 Clusters
(Centers)
Compare with
Native Structure
(RMSD)

57. Part I: Free-Energy Filtering
Goal: to identify docked conformations having good surface complementarity by selecting those w/ lowest desolvation and electrostatic energies
Surface complementarity is an important criteria due to the observation that proteins tend to bury large surface areas after complex formation
Electrostatic and desolvation potentials (capturing the free energy of association) are used independently since different binding mechanisms are governed by different ratios of electrostatic/desolvation contributions
500 structures w/ lowest values of desolvation free energy retained
1500 structures w/lowest electrostatic energy retained
Electrostatics more sensitive to small coordinate perturbations  noisy
Cannot combine desolvation and electrostatics due to the noisy behavior of electrostatics potential

58. Part II: Clustering based on Pairwise RMSD
By examining free energy landscapes of partially solvated receptor-ligand complexes: native binding site is expected to be characterized by a local minima having greatest width
In other words, the most probable conformation is expected to be surrounded by lots of other low-energy conformations
Goal: to use a hierarchical clustering method to select and rank docked conformations having the most “neighbors” given a defined cluster radius (in terms of C-alpha RMSD)
Procedure:
1) Need to define fixed molecule (receptor) and flexible molecule (ligand)
Define a set of relevant ligand residues to be within 10 Angs of any atom in receptor
For each docked conformation X, calculate its pairwise ligand RMSD with 1999 other conformations
- Pairwise ligand RMSD = deviations between coordinates of X’s defined set of ligand residues and corresponding coordinates of another conformation
Cluster the set of 2000 docked conformations using a 2000 by 2000 matrix of RMSD values, and a cluster radius constraint of 9 Angs RMSD from the center
Pick largest cluster  rank cluster center  remove conformations within this cluster from matrix
Pick next largest cluster -> rank cluster center  remove conformations within this cluster from matrix  keep iterating until matrix is empty

59. Results
Result I:
Tested the discrimination step of the method on a benchmark set of 48 interacting protein pairs (2000 docked conformations each)
In 31/48 protein pairs, top 10 predictions include at least one near-native complex (average RMSD of 5 angs from native structure)
Result II:
Tested method in the CAPRI (Critical Assessment of Predictions of Interactions) experiment and generated predictions for 9 target complexes
Round 3 (automated server): ClusPro prediction ranked as #3 for Target 8

60. ClusPro Web Server
User Input: PDB files of the 2 protein structures that user would like to analyze in terms complex formation
Output: 10 (default) top predictions of docked conformations closest to native structure
First, docking of the 2 proteins is performed using 2 established FFT-based docking programs (DOT and ZDOCK)
Then, filtering and discrimination is performed
Server allows for customization of parameters:
Clustering radius
Smaller protein  smaller radius maybe more suitable
Relative number of desolvation and electrostatic best hits used during filtering
Number of predictions to generate (1-30)

61. Protein Drug Discovery
Although small molecule drugs are more prevalent therapeutics in current drug discovery, protein drugs is a rapidly growing area in pharmaceuticals
It is true that protein therapeutics can be much more costly (in terms of R&D and synthesis) than small-molecule therapeutics, but protein therapeutics can deliver biological mechanisms that are not possible with small-molecule therapeutics
Multiple blockbuster protein drugs are currently on the market
Conservative estimation: there exist between 3,000 and 10,000 possible drug targets
Many of these new targets offer great opportunities for the development of protein drugs
In 2002, drug companies sold nearly $33 billion in protein drugs
Rising at an average annual growth rate (AAGR) of 12.2%, this market is expected to reach $71 billion in 2008.
Examples of popular classes of drug targets:
1) G-protein-coupled receptors
Compounds will be screened for their ability to inhibit (antagonist) or stimulate (agonist) the receptor
2) Protein kinases
Compounds will be screened for their ability to inhibit the kinase

62. Application to Protein Drug Discovery
Ideal Drug: demonstrate high specificity and high affinity for the target protein
In order to evaluate the affinity of the potential drug with the target, you must first predict what the binding interface looks like, and the relative positions of the potential drug and target
ClusPro is the first integrated automated server that incorporates both docking and discrimination steps for structural predictions of protein-protein complexes
Using ClusPro, one can generate many relative orientation/conformations of the 2 proteins  filter using desolvation + electrostatics potentials  discriminate via clustering  find the best fit (closest to native structure from x-ray crystallography results) between the 2 proteins
Top ranked predictions of ClusPro  further manual refinement and discrimination using existing biochemical constraints and analysis to eliminate false positives  test binding affinity of promising protein pairs in vitro  lead compounds used as starting points for drug development/optimization
Can use ClusPro to screen databases of various existing, recombinant, or de novo proteins for their interaction to a protein target of interest
ClusPro can be used to predict either:
How a protein drug may bind (either inhibit or stimulate) a receptor
How 2 proteins bind, and based on the structural details of the interaction  design/screen for a drug that can inhibit that interaction

63. 2.1 Rigid Docking Protein and ligand fixed.
Search for the relative orientation of the two molecules with lowest energy
Fastest way to perform an initial screening of a small-molecule database
-> virtual-screening initiative

64. Rotamer Libraries Rigid docking of many conformations:
• Precompute all low-energy conformations
• Dock each precomputed conformations as rigid bodies
Glide [Friesner04]

65. Rigid Docking Methods
All rigid-body docking methods have in common that superposition of point sets is a fundamental sub-problem that has to be solved efficiently:
Geometric hashing
Pose clustering
Clique detection

66. Geometric Hashing
Originates from computer vision technology for recognizing partially occluded objects in camera scenes
Given a picture of a scene and a set of objects within the picture, both represented by points in 2d space, the goal is to recognize some of the models in the scene
Objects with certain geometric features can be accessed very fast through a geometric hashing table

68. Pose-Clustering
Originally developed to detect objects in 2-D scenes with unknown camera location
For each triangle of receptor compute the transformation to each ligand matching triangle.
Cluster transformations.
If a cluster grows large, a location with a high number of matching features is found

69. eg. The FlexX Method
The base fragment (the ligand core) is automatically selected and is placed into the active site using a pattern recognition technique called pose clustering
Next, the remainder of the ligand is built up incrementally from other fragments.

70. Clique-Detection Nodes comprise of matches between protein and ligand
Edges connect distance compatible pairs of nodes
In a clique all pair of nodes are connected

71. Eg. DOCK 6
The rigid body orienting code is written as a direct implementation of the isomorphous subgraph matching method of Crippen and Kuhl
Conceptually, the algorithm matchings the centers of the ligand heavy atom to the centers of the receptor site spheres.

72. DOCK 6 The algorithm follows the steps below:
1) Generate node 2) Label as match if atom and sphere edges are equivalent 3) Extend match by adding more nodes 4) Exhaustively generate set of non-degenerate matches 5) Use matches to create transformation matrices to move the entire molecule
node = pairing of one heavy atom and one sphere center
edge length = Euclidean distance between atom or sphere centers
Once an orientation has been generated, the interaction between the ligand and the receptor can be energetically optimized (ligand is allowed to be flexible in optimization)

73. 2.2. Rigid Receptor, Flexible Ligand
Multiple steps in the receptor – ligand interaction:
Approach
• Desolvation of the ligand and the binding site of a protein
• Penetration into the protein cavity
• Change of the ligand orientation
• Adoption of the correct “active” conformation
• Establishing of new H-bonds, electrostatic and hydrophobic contacts
Free energy function :

74. Challenges Predicting energetics of protein-ligand binding
Searching space of possible poses & conformations
Relative position (3 degrees of freedom)
Relative orientation (3 degrees of freedom)
Rotatable bonds in ligand (n degrees of freedom)
Rotatable bonds in protein (m degrees of freedom)

75. 2.3. Flexible Receptor, Flexible Ligand
Protein flexibility can be introduced through Monte Carlo or Molecular Dynamics
Protein can be divided into rigid and flexible parts
-> only flexible receptor site atoms are free to move
The procedure is still very slow
Leach* developed a docking algorithm that sequentially fixes the degrees of freedom of the protein side-chain atoms
Broughton** reported the use of conformational samples from short protein MD simulation runs+
*Leach AR. Ligand docking to proteins with discrete side-chain flexibility. J Mol Biol1994; 235:345–356
**Broughton HB. A method for including protein flexibility in protein–ligand docking: Improving tools for database mining and virtual screening. J Mol Graph Model 2000;18:247–257

76. AutoDock 4
AMBER FF-based energy grid, flexible ligands, rigid protein as represented in a grid
GA as a global optimizer combined with energy minimization as a local search method
The fitness function:
a Lennard-Jones 12-6 dispersion/repulsion term
a directional hydrogen bond term
a coulombic electrostatic potential
a term proportional to the number of sp3 bonds in the ligand to represent unfavorable entropy of ligand binding
a desolvation term

77. Comparison of Two Recent Versions
Autodock 4
Autodock Vina
Scoring Function is based on AMBER FF
FF includes electrostatic interactions, hydrogen bonds, desolvation energy.
“Torsion Tree” for Ligand Flexibility
Protein Flexibility by side-chain rotations
Too many torsions are problematic
Faster than AutoDock 4
More accurate than AutoDock 4
More User-friendly than AutoDock 4 in case of calculation of grid maps and clusters

78. Our Case: Triacylglyceride Docking into Lipase
Lipase: Geobacillus thermocatenulatus Lipase (BTL2)
Crystal Structure in 2009
2.2 Å Resolution (Carrasco-López C et al, J Biol Chem. 2009, PMID: )
2 Triton X-100 Molecule found in the crystal allows identification of putative binding pockets for the acyl chains (sn-1, sn-2, sn-3) of triglyceride.
Tributyrin (4 carbons in chain)
Tricaprylin (8 carbons in chain)
BTL2 (Apo-enzyme in open-conformation)

79. SER114
Catalytic Triad
ASP318
HIS359
79 /26

80. HB HA HH
sn-1
sn-3
sn-2

81. Work-Flow of Docking Study
Separating bound molecules from active site cleft
Apo-enzyme
Ligand
Definition of flexible/rigid bonds
Autodock 4.2 and Vina
Assesment of Docking Outcomes
Poses, Scores
Selection of Best Binding Modes

82. Preparation of Input Structures: Protein (BTL2)
Open-Lid Conformation displaying catalytic residues for ligand binding
Locating search space (grid-box) for triglyceride binding

83. Preparation of Input Structures: Ligand (tricaprylin)

84. Preparation of Input Structures: Ligand (tributyrin)

85. Results and Evaluation of Poses: Tricaprylin (8C)
The predicted binding affinity is in kcal/mol.
rmsd/lb(c1, c2) = max(rmsd'(c1, c2), rmsd'(c2, c1))
This score matches each atom in one conformation with itself in the other conformation, ignoring any symmetry

86. Results and Evaluation of Poses: Tricaprylin (8C)
S114_OH
TCPN_O
Mode_1
F17

87. Results and Evaluation of Poses: Tricaprylin (8C)
S114_OH
TCPN_O _2 _1
F17

88. Results and Evaluation of Poses: Tricaprylin (8C)
S114_OH
TCPN_O _7
F17

89. Results and Evaluation of Poses: Tricaprylin (8C)
S114_OH
TCPN_O _8
F17
S114_OH
TCPN_O _7
F17

90. Results and Evaluation of Poses: Tributyrin (4C)
_OH
TBTN _O _3
F17 _1

91. Results and Evaluation of Poses: Tributyrin (4C)
_OH
TBTN _O _6
F17 _1

92. Results and Evaluation of Poses: Tributyrin (4C)
_OH
TBTN _O _7
F17 _1

93. VINA Outcome
After 1ns
93 /26

94. 3.1. Force field-based scoring functions
The parameters of the Lennard–Jones potential vary depending on the desired ‘hardness’ of the potential.
D-Score: Higher terms, 12–6 Lennard–Jones potential,result in increasingly repulsive potentials and will be less forgiving of close contacts between receptor and ligand atoms
G-score: Lower terms, 8–4 Lennard–Jones potential, make the potential softer

95. 3.1. Force field-based scoring functions

96. 3.2. Empirical methods
Goals: reproduce the experimental values of binding energies and with its global minimum directed to the X-ray crystal structure
Advantages: fast & direct estimation of binding affinity
Disadvantages
Only a few complexes with both accurate structures & binding energies known
Discrepancy in the binding affinities measured from different labs
Heavy dependence on the placement of hydrogen atoms
Heavy dependence of transferability on the training set
No effective penalty term for bad structures

97. 3.2. Empirical methods
ChemScore:

98. 3.2. Empirical methods
GlideScore:

99. 3.2. Empirical methods
Autodock 4.0

100. 3.2. Empirical methods Autodock Vina:
Combines advantages of empirical methods and knowledge-based potentials
AutoDock Vina can be several orders of magnitude faster than AutoDock 4

101. 3.3. Knowledge-based methods
Designed to reproduce experimental structures rather than binding energies.
Protein–ligand complexes are modelled using relatively simple atomic interaction-pair potentials.
Advantages
Similar to empirical, but more general (much more distance data than binding energy data)
Disadvantages
The Boltzmann hypothesis originates from the statistics of a spatially uniform liquid, while receptor-ligand complex is a two-component non-uniform medium
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

102. 3.3. Knowledge-based methods
Parametrized Pairwise Potential (PMF) score:
Boltzmann
constant
Radial distribution function for a protein atom i and a ligand atom j
Ligand volume
correction factor

103. 3.3. Knowledge-based methods
DrugScore:
[Gohlke00]

104. Multiple Method Approach
systematic search
conformations
rigid DOCK
minimization
MD/SA
(Wang et al. 1999)
filters
initial poses
finer docking
final scoring
(FRED, GLIDE, DOCK)
Similarity-guided MD simulated annealing to improve accuracy (Wu & Vieth 2004).
Shape similarity & clustering to speed up conformational search in docking (Makino & Kuntz 1998).
Better input or constrains for the existing docking engines

105. Computing Scoring Functions
Point-based calculation:
• Sum terms computed at positions of ligand atoms (this will be slow)

106. Computing Scoring Functions
Grid-based calculation:
• Precompute “force field” for each term of scoring function for each conformation of protein (usually only one)
• Sample force fields at positions of ligand atoms
-> Accelerate calculation of scoring function by 100X
[Huey & Morris]

107. Consensus Scoring
Typically evaluate the ranking of binding modes measured with different scoring functions and favor those that rank consistently high in several of them
Reduces false positive rate
Examples
SYBYL Cscore (Tripos) : FlexX, PMF, DOCK energy, GOLD score
C2 (Accelrys) : LigScore2, PLP, PMF, Ludi, Jain
FRED (OpenEye) : ChemScore, PB-SA, ChemGauss, PLP, ScreenScore
DOCK: AMBER FF, PMF, contact scores, ChemScore

108. Flexible ligand-search methods
Random/stochastic • AutoDock (MC) • MOE-Dock (MC,TS) • GOLD (GA) • PRO_LEADS (TS) Systematic • DOCK (incremental)24 • FlexX (incremental)50 • Glide (incremental)134 • Hammerhead (incremental)28 • FLOG (database)
Simulation
• DOCK
• Glide
• MOE-Dock
• AutoDock
• Hammerhead

109. Docking Software: Important Factors
Sensitivity on and transferability of the parameters, including the starting conformation
Adaptability to additional scoring functions, pre- and/or post- docking processing and filters
Ability for iteratively refining docking parameter/protocol based on new results
Design, components, and results of validation studies
Speed, user interface & control, I/O, structural file formats
User learning curve, customer supports, and cost
Code availability and upgrading possibility

110. Docking Softwares DOCK 6.0 (Ewing & Kuntz 1997) de novo design tools
AutoDOCK 4.0 (Morris et al. 1998)
GOLD (Jones et al. 1997)
FlexX: (Rarey et al. 1996)
GLIDE: (Friesner et al. 2004)
ADAM (Mizutani et al. 1994)
CDOCKER (Wu et al. 2003)
CombiDOCK (Sun et al. 1998)
DIVALI (Clark & Ajay 1995)
DockVision (Hart & Read 1992)
FLOG (Miller et al. 1994)
GEMDOCK (Yang & Chen 2004)
Hammerhead (Welch et al. 1996)
LIBDOCK (Diller & Merz 2001)
MCDOCK (Liu & Wang 1999)
SDOCKER (Wu et al. 2004)
de novo design tools
LUDI (Boehm 1992),
BUILDER (Roe & Kuntz 1995)
SMOG (DeWitte et al. 1997)
CONCEPTS (Pearlman & Murcko 1996)
DLD/MCSS (Stultz & Karplus 2000)
Genstar (Rotstein & Murcko 1993)
Group-Build (Rotstein & Murcko 1993)
Grow (Moon & Howe 1991)
HOOK (Eisen et al. 1994)
Legend (Nishibata & Itai 1993)
MCDNLG (Gehlhaar et al. 1995)
SPROUT (Gillet et al. 1993)

111. FRED (OpenEye www.eyesopen.com)
Systematic, nonstochastic, docking
Multiple active site comparisons
Multiple simultaneous scoring functions and hit lists
RMS clustering of hit-lists
Algorithm:
1. Exhaustive Docking
(a) Enumerate all possible poses of the ligand around the active site by rigidly rotating and translating each conformer within the site.
(b) Filter the resulting pose ensemble by rejecting poses that do not fit within the larger of the two volumes specified by the receptor file’s shape potential grid and a contour level.
2. Systematic solid body optimization by Shapegauss, PLP, Chemgauss2, Chemgauss3, CGO, CGT, Chemscore, OEChemscore or Screenscore
3. Rank poses via the Consensus Structure method and discard all but the top ranked poses

112. DOCK 6.4
Generates many possible orientations/conformations of a putative ligand within a user-selected region of a receptor structure
Orientations may be scored using several schemes designed to measure steric and/or chemical complementarity of the receptor-ligand complex
Evaluate likely orientations of a single ligand, or to rank molecules from a database
Search databases for DNA-binding compounds
Examine possible binding orientations of protein-protein and protein-DNA complexes
Design combinatorial libraries

113. GOLD GA method, H-bond matching, FF scoring (Jones et al. 1997)
A configuration is represented by two bit strings:
The conformation of the ligand and the protein defined by the torsions;
A mapping between H-bond partners in the protein and the ligand.
For fitness evaluation, a 3D structure is created from the chromosome representation. The H-bond atoms are then superimposed to H-bond site points in the receptor site.
Fitness (scoring) function: H-bond, the ligand internal energy, the protein-ligand van der Waals energy
Highlights:
Full ligand flexibility
Partial protein flexibility, including protein side chain and backbone flexibility for up to ten user-defined residues
A choice of GoldScore, ChemScore, Astex Statistical Potential (ASP) or Piecewise Linear Potential (PLP) scoring functions
GOLD's genetic algorithm parameters are optimised for virtual screening applications

114. Hammerhead Focus on screening large databases of small molecules
The algorithm is fast enough to allow screening of a library of roughly small organic compounds in a few days
Empirical scoring function
Start with automatic pocket finder
Breaking ligands into fragments, and aligning each of these onto the protein.
At each stage of the fragment alignment computation, gradient-descent pose optimization improves the conformation and alignment of the growing ligand
Relaxing van der waals surface interpenetrations
Improving hydrogen bond and hydrophobic surface contact geometries.

115. LUDI: Matching polar and hydrophobic groups
Calculate protein and ligand interaction sites (H-bond or hydrophobic), which are defined by centers and surface, from
non-bonded contact distributions based on a search through the CSD,
a set of geometric rules,
the output from the program GRID (Goodford 1985) which calculates binding energies for a given probe with a receptor molecule.
Fit fragments onto the interaction sites.
distance between interaction sites on the receptor
an RMSD superposition algorithm,
A hashing scheme to access and match surface triangles onto a triangle query of a ligand interaction center.
A list-merging algorithm creates all triangles based on lists of fitting triangle edges for two of the three query triangle edges.
Join/grow fragments using the databases of fragments and the same fitting algorithm.

116. GLIDE (www.schrodinger.com)
Funnel: site point search -> diameter test -> subset test -> greedy score -> refinement -> grid-based energy optimization -> GlideScore.
Approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand.
Hierarchical filters, including a rough scoring function that recognizes hydrophobic and polar contacts, dramatically narrow the search space
Torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses.
The very best candidates are further refined via a MC sampling of pose conformation.
A modified ChemScore (Eldridge et al. 1997) that combines empirical and force-field-based terms.
Validation: 282 complexes, new ligand conformation, the top-ranked pose: 50%<1 A, ~33% >2 A.

118. GRAMM v1.03 Protein-Protein Docking and Protein-Ligand Docking
exhaustive 6-dimensional search through the relative translations and rotations of the molecules.
Empirical approach to smoothing the intermolecular energy function.
The quality of the prediction depends on the accuracy of the structures.

119. CDOCKER & SDOCKER Randomly generate ligand seeds in the binding site
High temperature MD using a modified version of CHARMM
Locate minima from all of the MD simulations
Fully minimization
Cluster on position and geometry
Rank by energy (interaction + ligand conformation)
SDOCKER: X-ray structure of complex as templates to guide docking
Wu et al. 2003;
Wu et al

120. Docking Webservers
Assessment of CAPRI Predictions 2009

121. ClusPro Webserver Fast rigid-body docking
Ligand-Protein, Protein-Protein Docking
Use FFT-based docking programs (DOT and ZDOCK)
1) Rapidly filters docked conformations
2) Ranks the conformations using clustering of computed pairwise RMSD values
Desolvation and Electrostatic energies are calculated

122. Haddock
Driven by experimental knowledge (e.g., from mutagenesis, mass spectrometry or a variety of NMR experiments)
Protein-Protein Docking server
Supports nucleic acids
Algorithm:
Rigid-body Energy Minimization,
Semi-flexible Refinement In Torsion Angle Space
Final refinement in explicit solvent.
The HADDOCK score : van der Waals, electrostatic, desolvation and restraint violation energies together with buried surface area

123. GRAMM-X Protein-Protein Docking server
Use FFT for the global search of the best rigid body conformations.
Use a smoothed Lennard-Jones potential on a fine grid
Ability to smooth the protein surface to account for possible conformational change
The smoothing of the intermolecular energy landscape is achieved by increasing potential range and lowering the value of the repulsion part
Softened Lennard-Jones potential function:

124. PatchDock and SymmDock Server
Based on a rigid-body geometric hashing algorithm
Aim: Good molecular shape complementarity yield
Algorithm divides the Connolly dot surface representation of the molecules into concave, convex and flat patches.
Then, complementary patches are matched in order to generate candidate transformations.
Each candidate transformation is further evaluated by a scoring function that considers both geometric fit and atomic desolvation energy.

125. PatchDock detects transformations with high shape complementarity
SymmDock Server
SymmDock restricts its search to symmetric cyclic transformations of a given order n.

126. FireDock server Fast rigid-body docking algorithms
Protein-protein docking

127. RosettaDock protein-protein docking server
Computationally intensive approach incorporating models flexibility
Multi-start, multi-scale Monte Carlo based algorithm
Start with 1000 independent structures, and the server returns pictures, coordinate files and detailed scoring information for the 10 top-scoring models
The low-resolution phase:
Random rigid-body perturbations
Scoring : residue–residue contacts and bumps, knowledge-based terms for residue environment and residue–residue pair propensities and for antibody-antigen targets, a score to favor interactions with antibody complementarity determining regions.
The high-resolution (all-atom, including hydrogens) phase
Smaller rigid-body perturbations, sidechain optimization via rotamer packing and continuous minimization, and explicit gradient-based minimization of the rigid-body displacement.
Scoring: the energy is dominated by van der Waals energies , orientation-dependent hydrogen bonding , implicit Gaussian solvation, side-chain rotamer probabilities and a low-weighted electrostatics energy.

128. ZDOCK

129. HexServer In order to address the main limitations of the Cartesian
FFT approaches, we developed the ‘Hex’ spherical polar
Fourier (SPF) approach which uses rotational correlations
(10), and which reduces execution times to a matter of
minutes

131. Bold entries in the first column correspond to programmes that can be run on a web
server.
(a) Refined with SMOOTHDOCK.
(b) Uses DOT or ZDOCK as search methods;
(c) Refined with RDOCK

132. Virtual Screening
Drug discovery costs are too high: ~$800 millions, 8~14 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, selectivity, and drugeability.

133. Drug, Chemical & Structural Space
Drug-like: MDDR (MDL Drug Data Report) >147,000 entries, CMC (Comprehensive Medicinal Chemistry) >8,600 entries
Non-drug-like: ACD (Available Chemicals Directory) ~3 million entries
Literatures and databases, Beilstein (>8 million compounds), CAS & SciFinder
CSD (Cambridge Structural Database, ~3 million X-ray crystal structures for >264,000 different compounds and >128,00 organic structures
Available compounds
Available without exclusivity: various vendors (& ACD)
Available with limited exclusivity: Maybridge, Array, ChemDiv, WuXi Pharma, ChemExplorer, etc.
Corporate databases: a few millions in large pharma companies

135. Docking to Nucleic Acid Targets
RNA and DNA as potential drug targets
Ribosome RNA structures (Agalarov et al. 2000; Ban et al. 2000; Filikov et al. 2000; Nissen et al. 2000; Wimberly et al. 2000)
Highly charged environments, well-defined binding pocket
DOCK identified compounds selectively bind to RNA duplexes or DNA qudraplexes (Chen et al. 1996; Chen et al. 1997). The portions in the DOCK suite that calculate electrostatics, including solvation, partial charges, and scoring function were recently optimized for RNA targets (Downing et al. 2003; Kang et al. 2004).
A MC minimization and an empirical scoring function which accounts for solvation, isomerization free energy, and changes in conformational entropy were used to rank compounds (Hermann & Westhof 1999).