MOLECULAR DOCKING

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What is Protein-Ligand Docking?

• Definition:Computationally predict the structures of protein-ligand

complexes from their conformations and orientations. The orientation that maximizes the interaction reveals the most accurate structure of the complex.

• Importance of complexes- structure -> function

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What is Docking?

• Given two molecules find their correct association:

+

=

T

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3-D Representation of a Protein Binding Site

5.24.2-4.7

6.7

4.8

5.1-7.1 Distances betweenbinding groupsin Angstroms and the type of interactionis searchable

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General Protein–Ligand Binding• Ligand

- Molecule that binds with a protein- DNA, drug lead compounds, etc.

• Protein active site(s)- Allosteric binding- Competitive binding

• Function of binding interaction- Natural and artificial

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Issues Involved in Docking

• Protein Structure and Active Site- Assumed knowledge (PDBs, comparative modeling etc.)- PROCAT database: 3d enzyme active site templates

• Ligand Structure- Pharmacophore (base fragment) in potential drug compound

- well known groups

• Rigid vs. Flexible- In solution or in vacum- Structure fixed, partly fixed, modeling of flexibility

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Algorithmic Approaches to Docking

• Qualitative– Geometric– Shape complementarity and fitting

• Quantitative– Energy calculations– Determine global minimum energy– Free energy measure

• Hybrid– Geometric and energy complementarity– 2 phase process: soft and hard docking

It involves:

Finding useful ways of representing the molecules and molecular properties.

Exploration of the configuration spaces available for interaction between ligand and receptor.

Evaluate and rank configurations using a scoring system, in this case the binding energy

However, since it is difficult to evaluate the binding energy because the binding sites may not be easily accessible, the binding energy is modeled as follows:

∆G bind= ∆Gvdw + ∆Ghbond + ∆Gelect + ∆G conform+ ∆G tor + ∆G sol

Docking uses a “search and score” method

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PDBfiles

Surface Representation

Patch Detection

Matching Patches

Scoring & Filtering

Candidatecomplexes

Docking Strategy

Adding angles in Cartesian space

x

y

r

(x,y)

(x’,y’)

x' = |r| cos (= |r|(cos cos sin sin = (|r| cos cos |r| sin sin = xcos y sin

y' = |r| sin (= |r|(sin cos sin cos = (|r| sin cos |r| cos sin = ycos x sin

x = |r|cos y = |r|sin

x'y'

cos sinsin cos

xy

in matrix notation...

converting internal motion to Cartesian motion

rotation matrix

A 3D rotation matrix

Is the product of 2D rotation matrices.

cos sin 0sin cos 00 0 1

cos 0 sin0 1 0

sin 0 cos

coscos sin cossincos cos sinsin

sin 0 cos

Kinds of: search

Exhaustive

Deterministic

Dependent on granularity of sampling

Feasible only for low-dimensional problems

DOF, 6D search

Anchor‐and‐grow (or) incremental construction algorithmIt tries to explore all the degrees of freedom in a molecule, butultimately face the problem of combinatorial explosionSo ligands are often incrementally grown into active sites

DOCK (incremental) FlexX (incremental) Glide (incremental) 

Kinds of search:SYSTEMATIC

Kinds of:::: search

Random

Outcome varies

Repeat to improve chances of success

Feasible for higher-dimensional problems

Simulated Annealing (SA) Evolutionary Algorithms (EA)

Genetic Algorithm (GA) /Tabu Search (TS) Hybrid Global-Local Search/Lamarckian GA (LGA)

Monte Carlo (MC) methods and Evolutionary algorithm It works by making random changes to either a single ligand or a population of ligands Novel ligand is evaluated by pre‐defined probability function In Tabu search, to accept the novel molecule, it calculates RMSD between current moecular coordinates and every molecule’s previously recorded conformation 

AutoDock (MC/SA,GA/LGA) GOLD (GA) 

Kinds of search:STOCHASTIC

Energy Minimization methods and Molecular Dynamics simulations 

Molecular Dynamics simulations are often unable to cross high‐energy barriers within feasible simulation time periods, they might accommodate ligands in local minima of the energy surface so, an attempt is made to simulate different parts of a protein‐ligand system at different temperatures Energy minimization is rarely used as stand‐alone search techniques, as only local energy minima can be reached 

DOCK Glide AutoDock 

Kinds of Search:DETERMINISTIC

Random/stochastic• AutoDock (MC) • MOE-Dock (MC,TS) • GOLD (GA) • PRO_LEADS (TS) Systematic•DOCK (incremental) • FlexX (incremental) • Glide (incremental) • Hammerhead (incremental) Simulation/Deterministic•DOCK• Glide• MOE-Dock• AutoDock• Hammerhead

SCORING FUNCTIONS:FORCE – FIELD BASED SCORINGQuantifying the sum of two energiesReceptor‐ligand interaction energy and internal ligand energy

Most scoring functions consider a single protein conformationto omit the internal protein energy, which simplifies thescoring

Force‐field scoring functions varies based on different forcefield parameter setsFor E.g.:G‐Score Tripos force fieldAutoDock AMBER force field

SCORING FUNCTIONS:EMPIRICAL SCORING

Based on binding energies and/or conformations

It is designed based on idea that binding energies can beapproximated by a sum of individual uncorrelated terms

The coefficients are obtained from regression analysis usingexperimentally determined binding energies and X‐raystructural information

Disadvantage – it depends on the molecular data sets used toperform regression analysis

KNOWLEDGE BASED SCORINGIt reproduce experimental structures rather than binding energies

Protein‐ligand complex is modelled using relatively simple atomicinteraction‐pair potentials

A number of atom‐type interactions are defined depending ontheir molecular environment

The main attraction is computational simplicity which permitsefficient screening of large compound databases

Disadvantage‐derivation is essentially based on informationimplicitly encoded in limited sets of protein‐ligand complex

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Scoring in Ligand-Protein Docking

Potential Energy Description:

Type of Scoring Functions

FORCE FIELD BASEDD-ScoreG-ScoreGold ScoreAutodockDock

KNOWLEDGE BASEDPMFDrug Score

CONSENSUSCSCOREX-ScoreEMPIRICAL

LudiF-ScoreChem Score

AutoDock

Cerius2/LigScore

vdW 6-9

C+pol buried polar surface in attractive protein – ligand complex

Totpol2 square of buried polar surface in attractive – repulsive protein –

ligand complex

Cerius2/PLP

Cerius2/PMF

Cerius2/ LUDI

SYBYL/F-Score

SYBYL/G-Score:

SYBYL/D-Score:

SYBYL/ChemScore:

DrugScore:

X-Score:

Bonded Interactions:  Non‐Bonded Interactions: 

It based on the bonded and non bonded interactions of ligand – binding site 

DOCKING SCORE 

BINDING AFFINITY 

The interaction of most ligands with their binding sites can be characterized in terms of a binding affinity 

The free energy of binding (ΔG) is related to binding affinity by 

The equilibrium equation is : 

Where ΔG is Gibb’s freeenergy, R is gas constant, T is temperatures and K is equilibrium constant E is enzyme and I is inhibitor 

172 Protein – ligand complexes chosen basedon resolution (better than 2.5 Å)

172 100 passes

100 Protein – Ligand complex has: 43 different proteins Molecular weight 122 – 913 KDa Rotatable single bond (ligand) 0 – 20

Conformational sampling procedure

The selection of suitable sample for study

It is done by AutoDock (Genetic Algorithm)

Parameter: For best fitting, Translation, rotation, and torsions are

set to 0.5 Å, 15°, and 15°, respectively

The size of the docking box is 30 Å X 30 Å X 30 Å

Screening parameter: RMSD 0 - 15 Å Distinctive conformational clusters 30 - 70 Docked conformation should close to experimental

conformation (RMSD ≤ 2.0 Å)

ga-num-generations determines the quality of the sample 50 - 200 runs per complex

Force Field based scoring: AutoDock, G-Score, D-Score

Empirical scoring: LigScore, PLP,LUDI, F-Score, ChemScore, X-Score

Knowledge – Based scoring: PMF, DrugScore

Success rates of 11 scoring functions under different rmsd criteria

AUTODOCKSimulated Annealing Based on temperature effects Start with high temperature and global search Lower temperature local search

Genetic Algorithm Charles Darwin’s Theory of Evolution

Genotype Phenotype Lamarckian Algorithm ( Jean –Baptiste de Lamarck)

Phenotype Genotype

Search parameters

Population sizeCrossover rateMutation rateLocal searchenergy evals

Termination criteriaenergy evalsgenerations

Genetic function algorithmStart with a random population (50-200)

Perform Crossover (Sex, two parents -> 2 children) and Mutation (Cosmic rays, one individual gives 1 mutant child)

Compute fitness of each individual Proportional Selection & ElitismNew Generation begins if total energy evals or

maximum generations reached

Dimensionality of molecular docking

Degrees of Freedom (DOF) Position or Translation (x,y,z) = 3

Orientation or Quaternion (qx, qy, qz, qw) = 4

Rotatable Bonds or Torsions (tor1, tor2, … torn) = n

Total DOF, or Dimensionality, D = 3 + 4 + n

AutoDock uses grid-based dockingLigand-protein

interaction energies are pre-calculated and then used as a look-up table during simulation

Grid maps are constructed based on atoms of interest in ligand (here CANOSH)

Docking Preparation – Grid

(SYBYL)

Initial X‐Ray crystallographic positions of protein and ligand

Simulated annealingOne copy of the ligand (Population = 1) Starts from a random or specific

postion/orientation/conformation (=state) Constant temperature annealing cycle

(Accepted & Rejected Moves) Temperature reduced before next cycleStops at maximum cycles

Docking – Simulated Annealing• Runs = 100• Cycles = 50• Initial Temp (RT) = 1,000• Temp reduction factor = .95• Linear temperature reduction• Translation reduction factor = 1• Quaternion reduction factor = 1• Torsional reduction factor = 1• # rotatable bonds = 12• Initial coordinates = Random• Initial quaternion = Random• Initial dihedrals = Random• Translation step = 2.0 Å• Quaternion step = 50 deg• Torsion step = 50 deg

Results: 100 different clusters Energy range: -0.63 to +64,000 Conformation #81: -0.63 Conformation #67: +20.02 Conformation #68: +10.74

Lowest energy conf not close to position but similar to original

Conf #67 closest to position and conformation of original ligand; higher energy

Conf #68 close to position but not conformation of original ligand; not as high energy

Original ligand confSA conformation #67

(SYBYL)

Close‐up of previous

(SYBYL)

Original ligand confBest GA confBest LGA confBest SA confBest LS conf

GOLD (CCDC, Cambridge, UK)www.ccdc.cam.ac.uk/products/life_sciences/gold/

• Flexible docking:– match protein and ligand hydrogen bond “fitting points”– optimizes the poses using a Genetic algorithm– Flexible rings by flipping ring corners

• Locally flexible protein: polar hydrogens allowed to move

• Water switched on and off to maximize interactions

• SF: GOLDfitness score

FRED(OpenEye, Santa Fe, CA, USA)

• Rigid body docking using a shape-based approach: – random generation of poses within active site– use of Gaussian functions to represent atoms– Use of Gaussian docking functions (combines overlap

between ligand with protein atoms and area intersection) and a Quasi-Newton rigid body optimization algorithm to place ligand and select poses

– Uses Rigid protein

SF : ChemScore; Emperical FF

FlexX/FlexE(BioSolveIt, Sankt Augustin, Germany)

• Flexible docking:• incremental construction for the ligand combined with a matching of

ligand groups to protein interaction types• multiple conformations for rings

• Rigid of flexible protein:• all atom representation• composite structures assessed (FlexE) • water considered using the particle concept (waters placed before docking

and only kept during the docking run if favourable interactions are created)

• SF : Flex X SF;Emperical FF

The general schema

Incremental construction

Scoring function

Receptor-ligand interactions

Ligand conformational flexibility

Modeling

AlgorithmBase selection

Base placement

FLEX-X

Scoring function• Estimates the free binding energy in the complex

• The function is additive in the ligand atoms.

match score

contact score

Ligand fragmentation

• Good results are produced if the added fragments are small

• Every fragment, except for the base fragment consist of only one component.

DOCK 6.0(http://dock.compbio.ucsf.edu)(UCSF,CA,USA)

• Rigid body docking using a clique matching algorithm

• Flexible ligand using an incremental construction algorithmcombined with a simplex minimizer

• Flexible protein:-negative image of the active site using spheres -use of precomputed grids based on AMBER intermolecular

energy and GB/SA(Generalised Born/surface Area) solvation energy

-protein flexibility considered using combined grids

• SF: Dockscore; Amber Force Field

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DOCK as an Example

DOCK works in 5 steps:• Step 1 Start with 3D coordinates of target receptor• Step 2 Generate molecular surface for receptor• Step 3 Generate spheres to fill the active site of the

receptor: The spheres become potential locations forligand atoms

• Step 4 Matching: Sphere centers are then matched tothe ligand atoms, to determine possible orientations forthe ligand

• Step 5 Scoring: Find the top scoring orientation

DOCK as an Example

4 5

• Three scoring schemes: Shape scoring, Electrostatic scoring and Force-field scoring

• Image 5 is a comparison of the top scoring orientation of the molecule thioketal with the orientation found in the crystal structure

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The DOCK AlgorithmTwo steps in rigid ligand mode:

Orienting the putative ligand in the siteGuided by matching distances, between pre-defined site points on the target to interatomic distances of the ligand. The RT matrix is used for the transform of the ligand.

Scoring the resulting orientationEach orientation is scored for each quality fit. The process is repeated a user-defined number of orientations or maximum orientations

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Site Points Generation in DOCK

• Program SPHGEN identifies the active site, and other sites of interest.

• Each invagination is characterized by a set of overlapping spheres.

• For receptors, a negative image of the surface invaginations is created;

• For a ligand, the program creates a positive image of the entire molecule.

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The MatchingCan be directed by 2 additional features:

• Chemical matching - labeling the site points such that only particular atom types are allowed to be matched to them.

• Critical cluster - subsets of interest can be defined as critical clusters, so that at least one member of them will be part of any accepted ligand “match”.

Increase in efficiency and speed due to elimination of potentially less promising orientations!

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.. .

.

. .. .

N

NHN

SO

F

.. .

N

NHN

SO

F

.

N

NHN

SO

F

N

NHN

SO

F

1. Define the target binding site points.

2. Match the distances.

3. Calculate the transformation matrix for the orientation.

4. Dock the molecule.

5. Score the fit.

DOCK

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Pharmacophore-Based Docking

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Pharmacophore-based DockingBasic idea:

• Appropriate spatial disposition of a small number of functional groups in a molecule is sufficient for achieving a desired biological effect.

• The ensemble formation will be guided by these functional groups.

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Pharmacophore Fingerprint• Pharmacophore fingerprint - a set of pharmacophore

features and their relative position.• Typical pharmacophore features:

– Hydrogen-bond donors and acceptors– Positive and negative ionizable atoms/groups– Hydrophobes and ring centroids

• Implemented in DOCK 4.0.1– Hydrogen-bond donors– Hydrogen-bond acceptors– Dual hydrogen-bond donor and acceptor – 5 or 6 membered ring centroids

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Pharmacophore DOCK

Prepare target structure

Generate a set ofchemically labeled site

points

Read a 3D pharmacophorefrom the database

Compare distances betweenpharmacophore points andsite points to determine an

orientation matrix

Match?NoYes

Orientationstries >MAX

Orientationstries >MAX

No No

Yes Yes

Use the transformation matrix todock all conformers associated with

the pharmacophore

Score allconformers

Save the best scoringconformer for each molecule

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Advantages of Pharmacophore-based Docking

• Rapid elimination of ligands containing functional groups which would interfere with binding.

• Speed increase over docking of individual molecules.

• More information pertaining to the entire molecule is retained (no rigid portions).

• Chemical matching and critical clusters are encouraged.

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Limitations of Pharmacophore-based Searching

• A limited subset of key interactions (typically 4-6) which must be extracted from the target site with dozens of potential interactions.

• Complex queries are extremely slow.• The majority of the information contained in the target

structure is not considered during the search. There is no scoring function beyond the binary (match/no match). Any steric or electronic constraints imposed by the target, but not defined by the target are ignored.

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Conformational Ensembles DockingObservations:

1. Generating an orientation of a ligand in a binding site may be separated from calculating a conformation of the ligand in that particular orientation.

2. Multiple conformations of a given ligand usually have some portion in common (internally rigid atoms such as ring systems), and therefore, contain redundancies.

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Conformational Ensembles DockingObservations:

1. Generating an orientation of a ligand in a binding site may be separated from calculating a conformation of the ligand in that particular orientation.

2. Multiple conformations of a given ligand usually have some portion in common (internally rigid atoms such as ring systems), and therefore, contain redundancies.

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Overview of the Ligand Ensemble Method

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Disadvantages of Conformational Ensemble Docking

• Loss of information when the orientations are guided only by a subset of the atoms in molecule. Orientations may be missed because potential distance matches from non-rigid portions of the molecule are not considered.

• The ensemble method will fail for ligands that lack internally rigid atoms.

• The use of chemical matching and critical clusters is limited.

CASTP:http://cast.engr.uic.edu/

XSITE:http://www.biochem.ucl.ac.uk/~roman/xsite/manual/man2.html

Voidoo:http://spec.ch.man.ac.uk/prog_man/o-sat/voidoo.html

APROPOS:http://www.csb.yale.edu/userguides/datamanip/apropos/apropos_descrip.html

CANGAROO:http://chem.leeds.ac.uk/ICAMS/eccc/cangaroo.html

Surfnet:http://www.biochem.ucl.ac.uk/~roman/surfnet/surfnet.html

PASS: http://www.delanet.com/~bradygp/pass/

Active site templates for Enzymeshttp://www.biochem.ucl.ac.uk/bsm/PROCAT/PROCAT.html

ACTIVE SITE IDENTIFICATION PROGRAMS

Protein – Ligand Docking Programs

AutoDockhttp://www.scripps.edu/mb/olson/doc/autodock/GOLDhttp://www.ccdc.cam.ac.uk/products/life_sciences/gold/FLEXXhttp://www.biosolveit.de/FlexX/GLIDEhttp://www.schrodinger.com/ICMhttp://www.molsoft.com/docking.htmlDockhttp://www.cmpharm.ucsf.edu/kuntz/dock.html

Protein protein Docking Programs

ZDOCK : http://zlab.bu.edu/zdock/HEX : http://www.csd.abdn.ac.uk/hex/GRAMM : http://vakser.bioinformatics.ku.edu/resources/grammICM : http://www.molsoft.com/docking.htmlCLUSPRO : http://nrc.bu.edu/cluster/clusdoc.htmlKORDO : http://www.bioinfo.de/isb/gcb99/poster/zimmermann/MOLFIT : http://www.weizmann.ac.il/Chemical_Research_Support//molfit/