1 Copyright © 2006 Chemical Computing Group Inc. All Rights Reserved. Chemical Computing Group Inc. Docking and Multi Fragment Search.

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Copyright © 2006 Chemical Computing Group Inc.All Rights Reserved.

ChemicalComputingGroup Inc.

Docking andMulti Fragment Search

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Outline

Part A: Docking

Docking is based on a more complete and quantitative description and analysis of ligand-protein interactions compared to Ph4 queries.

• Examines the ensemble of all actual and potential interactions between ligands and their binding sites while optimizing the geometry of the complex.

• Typically involves forcefield minimization and eventually further terms to predict binding poses.

Part B: Multi-Fragment Search• Attempts to predict poses of smaller functional groups rather than

whole molecules. • The accuracy of those predictions increases while the search space

is less constrained to a given set of molecules.

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Docking

Objective

MOE-Dock identifies favorable poses of flexible ligands in rigid binding sites of macromolecules, typically proteins. MOE-Dock consists of a “toolbox” which offers different routines for conformational sampling, placement and scoring. This enables the user to optimize his workflow for a given target.

Note: The MOE-Dock algorithm was completely rewritten in MOE 2005.06.

Torsion RulesTorsion Rules

Receptor 3D ligand

ScoringScoring

PH4 FilterPH4 Filter

AnnotationAnnotation

PlacementPlacement 3D conformations

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Placement Methods

• The Alpha PMI method generates poses by aligning ligand conformations' principal moments of inertia to a randomly chosen subset of alpha sphere dummies in the receptor site. This method is preferred for tight pockets.

• The Alpha Triangle placement method (default) derives poses by random superposition of ligand atom triplets and alpha sphere dummies in the receptor site.

• The Triangle Matcher method generates poses by aligning ligand triplets of atoms on triplets of alpha spheres in a more systematic way than the Alpha Triangle method. (most complete screen of poses)

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Scoring Methods

• The Affinity dG scoring function estimates the enthalpic contribution to the free energy of binding using a linear function of hydrophobic, ionic, hydrogen bond and metal ligation terms.

• The DephtHB scoring function is a linear combination of two terms:*)

- the burying of the ligand and - the hydrogen bond effects.

• The London scoring function (default) estimates the free energy of ligand binding from a given pose. The functional form is a sum of energy term (ligand flexibility, H-bonds, desolvation) and geometric imperfections.

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Affinity dG Scoring Function

ΔG is the sum of terms (fit from ~100 pKi complexes)

• H-bonds are between donorand acceptor heavy atoms

• Metal ligations are betweentransition metal and –O, –N and –S

• Ionic contacts are betweenfunctional groups (not just ions)

• Contacts are between heavy atoms of receptor and ligand

Functions f decrease with distance

• fI and fB functions have7.5 Å cutoff

• Close contacts not penalized

jicontacts

ijBBjiionic

ijIjiIjiligmetal

ijMMjihbonds

ijHH rfCrfqqCrfCrfCG::::

)()()()(

C(+1)

O(-1)

O(-1)

N(+1)

O(-1/2)

O(-1/2)

(+1)P

O(-1)

O(-1)

(+2)S

O(-1)

O(-1)

N(+1/2)

N(+1/2)

N(+1/2)

N(+1/2)

N(+1/2)

N(+1)Na+1

Zn+2

0

0,2

0,4

0,6

0,8

1

1,2

1 1,25 1,5 1,75 2 2,25 2,5 2,75 3 3,25 3,5 3,75 4 4,25 4,5 4,75 5

fH fM fI fB

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http://webpages.ull.es/users/bioquibi/temascompletos/InteraccionesNC/inicial/radio_de_van_der_waals.htm

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London dG Scoring Function

• Sum of terms intended to estimate ΔG of binding

- c is average entropy loss/gain due to rotational/translational motion

- Eflex is entropy loss due to conformational flexibility (ligand topology only)

• H-bond fHB measures geometric imperfections

- cHB is H-bond maximum energy

- Distance, in-plane and out-of-plane angles into account- Functional forms and distributions taken from MOE contact statistics

• Metal ligation fM measures geometric imperfections

- cM is metal ligation maximum energy

- Landis’s VALBOND sd3 hybridization potential for angles

• ΔDi estimates desolvation energy of each atom i

- HB and M terms recover desolvation energy for hydrophilic atoms- Desolvation energy is the main component of the model- No ionic effects are part of the model (difficult to incorporate consistently)

iatoms

iligm

MMbondsh

HBHBflex DfcfcEcG

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London dG Desolvation Model

For atom i with radius Ri estimate desolvation energy by integrating London dispersion forces over solvent space

Implementation:

• Radii are OPLS-AA van der Waals radii plus 0.5 Å

• Coefficients ci are assigned for each of ~12 atom types (e.g., Csp3/Csp2)

• Integration approximated with GB/VI type integrals over solutes A and B

BuBAu

iii duuduuRcD 663 ||||

+ i i

A

BB

A

ΔDi

solvent

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Molecular Docking Updates

Placement• AlphaTriangle and TriangleMatcher

placement methods updated for speed

Scoring• New London dG scoring function

for binding affinity estimation• New DepthHB scoring function

for ranking poses (no affinity estimate)

Ligand strain energy no longer used in pose selection• Strain energy rarely helps and often hurts pose selection

Structural binding affinity database

• A new database of 500+ complexes and experimental pKi data ($MOE/sample/mol/complex.mdb)

• Ligands verified for correctness, receptors verified for close contacts

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Exercise: MOE-Dock – Binding Site Preparation

Perform a docking study with a kinase receptor and a ligand using alpha sphere dummy atoms defining the binding site.

1. Open 1ke6_rec.moe(MOE | File | Open)

2. Add hydrogens before docking (MOE | Edit | Hydrogens | Add Hydrogens)and calculate the partial charges(MOE | Compute | Partial Charges)with Amber99 forcefield*).

In addition, MOE-Dock requires definition of adocking site in the receptor. This can be done in various ways:

• Select the ligand (it will be ignored during docking),

• Select the residues of the pocket,

• Select dummy atoms generated by MOE’s Site Finder

3. Open the Alpha Site Finder (MOE | Compute | Site Finder) to isolate theactive site. Identify and select the relevant site and create Dummies.

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MOE-Dock Panel

MOE-Dock can be launched with (MOE | Compute | Simulations | Dock).

Choose between various options for receptor, binding site and ligand atoms definitions

Ph4 query can be loaded.

Ph4 query can be loaded.

Specifies which atoms in MOE are used as receptor.

Specifies which atoms in MOE are used as receptor.

Specifies the placement and scoring method

Specifies the placement and scoring method

Specifies which atoms in MOE (or mdb-file) are used as ligand(s).

Specifies which atoms in MOE (or mdb-file) are used as ligand(s).

Specifies which atoms in MOE are used as docking site.

Specifies which atoms in MOE are used as docking site.

Verification of the selected atoms.

Verification of the selected atoms.

Visualize backbone, binding site residues

Visualize backbone, binding site residues

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Exercise: MOE-Dock I

4. Launch MOE-Dock (MOE | Compute | Simulations | Dock).

5. Specify an output database file name (e.g. dock_tm_lon.mdb).

6. Ensure that Receptor is set to "Receptor Atoms".

7. Ensure that Site is set to “Dummy Atoms".

8. Use the Render button to isolate thedocking site.

9. Switch Ligand to “MDB File” and select the conformation database of the ligand (conf_out_1ke6.mdb); disable Conformational Search.

10. Select Placement and Scoring Methods: Triangle Matcher and London dG

11. Click “OK”.

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Exercise: MOE-Dock II

12. Examine the results in the output database (dock_tm_lon.mdb) :

The poses of each ligand (mseq) are already ranked according to the “best” score, i.e. lowest S value at the top.

Compare the position of the original, co-crystallized ligand with the docked conformations of the database.

13. Load the original ligand (copy the structure from the crystal field in 1ke6_crystal.mdb into the MOE window), select it, and color the ligand orange to better distinguish the crystal ligand from the docking poses.

Scoring function [kcal/mol]

Scoring function [kcal/mol]

Distance based scoring function

Distance based scoring function

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Exercise: MOE-Dock III

14. Use the Browser in the Database Viewer (DBV | File | Browser) to step through the output database, one by one.

15. Select the first pose in the MOE window, and render it by (MOE | Render | Stick).1)

16. Evaluate the position of the best scored pose (lowest S) and the pose with the lowest RMSD2) with the co-crystallized ligand (orange).

Best scored pose:S = -10.26 kcal/mol RMSD = 1.6 Å

Best scored pose:S = -10.26 kcal/mol RMSD = 1.6 Å

Lowest RMSD (rank 88):S = -8.1 kcal/mol RMSD = 0.69 Å

Lowest RMSD (rank 88):S = -8.1 kcal/mol RMSD = 0.69 Å

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Validation of the Docking Scores I

Placement methodologies in MOE may be compared by calculating the RMSDs of the docked poses and the X-ray structure of the ligand.

Ideally, the correlation plot of the ASE score vs. the RMSD should show a clear trend from lower left to upper right.

The figure shows three independent docking runs withthe Alpha PMI placement methodology. Smallest RMSDs(most similar to the crystal pose)correlate with the highest scores.As search space is not coveredsystematically results may differ slightly from one simulation to another. Red diamonds: 1st run; green dots: 2nd run;

blue squares: 3rd run

3 Docking Simulations with the Alpha Triangle Placement Methodology

-9

-8

-7

-6

-5

-4

-3

-2

0 1 2 3 4 5 6 7 8 9 10

RMSD [A]

AS

E s

co

re

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Validation of the Docking Scores II

Successful scoring functions will rank lowest RMSD placements at the top with about 80 % accuracy. The plot compares lowest E versus lowest RMSD placements for three runs using different placement strategies. In this case lowest RMSD solutions are ranked quite low (11-30). Since the implementation of docking routines in MOE is in the form of a toolbox derive an optimal combination of tools for each project.

Docking Methods

Alpha PMITriangle Matcher

Alpha PMI

Triangle Matcher

Alpha Triangle

AlphaTriangle

-5

-4

-3

-2

-1

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

RMSD [A]

En

erg

y [

kc

al/m

ol]

sorted by lowest E sorted by lowset RMSD (Rank indicated in brackets)

(25)(30)

(11)

(1)

(1)

(1)

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Validation of the Docking Scores III

To compare the performance of different placement strategies 5 different CDK2 ligands are cross-docked into the 5 different pockets to give an idea about the variability of results obtained with different methods. This also shows that some pockets tend to perform better than others thus also validate the pockets before starting a large scale screening.

As a rule of thumb alpha PMI tends to provide best results for small pockets.

Alpha Triangle

0

1

2

3

4

5

6

7

8

1ke6 pocket 1ke7 pocket 1ke8 pocket 1oiy pocket 1oit pocket

protein pockets

RM

SD

1ke6

1ke7

1ke8

1oiy

1oit

Triangle Matcher

00.5

11.5

22.5

33.5

44.5

5


protein pockets

RM

SD

1ke6

1ke7

1ke8

1oiy

1oit

Alpha PMI

0

2

4

6

8

10


protein pockets

RM

SD

1ke6

1ke7

1ke8

1oiy

1oit

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Multi-Fragment Search I

Objective

Multi-fragment search (MFS) attempts to determine preferred positions and interactions for certain functional groups in a given receptor. Docking small fragments of limited flexibility can be achieved at higher accuracies than docking entire molecules. Results may be used as input to a “de novo” design procedure or to derive a pharmacophore.

Miranker, A., Karplus, M. Functionality Maps of Binding Sites: A Multiple Copy Simultaneous Search Method. Proteins: Structure, Function, and Genetics, 1991, 11, 29-34

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Multi-Fragment Search II

Methodology

The active site of a receptor is randomly populated with many copies of a fragment and then energy minimized (fragments do not interact).

• Removes duplicates and calculates interaction energies with or without solvation effects

• Fragment output is sent to amolecular database for analysis

• All or part of a receptor can beheld fixed during minimization

Save System

Place Fragments

Save Fragments

Fix Atoms

Minimize Classes

Minimize Receptor

Delete Duplicates

Save System

Save Fragments

Save Receptor

Input system mfss_orgsys.moe

mfss_orgcop.moe

mfss_output.mdb

mfss_mincop.moe

mfss_minrec.moe

convergence

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Exercise: Multi-Fragment Search I

First, prepare the receptor for Multi-Fragment Search:

1. Open the receptor file 1ke6_rec.pdb(MOE | File | Open)

2. Add hydrogens

(MOE | Edit | Hydrogens | Add Hydrogens)

and calculate the partial charges

(MOE | Compute | Partial Charges)

with the Amber99 forcefield. After calculation

use the Potential Setup panel (MOE | Window | Potential Setup) to choose an appropriate forcefield for receptor-ligand interactions (e.g. MMFF94x).*)

3. Select the atoms of the active site to guide placement of fragments. Be careful to pick only the solvent exposed atoms of the active site (to improve the speed of fragment placement). Use Site Finder dummies in absence of any other ligand information.

Select the ligand atoms/ Site Finder dummies and extend the selection to 4.5Å

(MOE | Selection | Extend | Nearby (4.5A))

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Exercise: Multi-Fragment Search II

4. Delete the chain of the ligand/alpha spheres in the SE (without deselecting the pocket*).

5. Choose (Compute | Simulations | MultiFragment Search) in the MOE window to open the MultiFragment Search panel.

6. Select the fragments that should be included:

E.g. acetate ion and phenol.

7. Click “Next”.

Fragment list contained in the fragment database.

Fragment list contained in the fragment database.

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Exercise: Multi-Fragment Search III

8. The next window contains parameters for minimization (stay with defaults).

Click “Next”.

9. Before starting the MF-search choose several output file names (stay with defaults).

10. Click “Start”.

The selected fragments will be flooded into the pocket and minimized. This may take some time.

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Exercise: Multi-Fragment Search IV

11. Examine the results:

Open the *_orgcop.moe into the MOE window.This is the molecular system after initial placement of the fragments together with the receptor but prior to energy minimization (the original copies).

Open the *_mincop.moe into the MOE window.These are all the resulting unique fragments and the receptor after energy minimization (the minimized copies).

The same picture will result if all entries from the *_output.mdb are copied into the MOE window.

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Exercise: Multi-Fragment Search V

12. Open the *_output.mdbThis fragment database contains 58 entries of the acetate and 74 entries of the phenol fragment after minimization including interaction energy information.

Sort the entries according to different criteria: best binding, lowest potential, best binding in fragment class, or lowest potential in fragment class.*) (All values are in kcal/mol.)

13. Open the receptor again (please use the prepared pocket.moe file for examination; otherwise the surface etc. has the begenerated).

Copy all entries of one fragment into the receptor pocket to get a first impression if there are some preferred regions (clusters) of the fragment. In this phenol example there are two main clusters – one inside and one more exterior to the pocket.

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Exercise: Multi-Fragment Search VI

14. Examine each placement individually using the Browser in the Database Viewer (BDV | File | Browser) and step through the output fragments. Each fragment will be placed in the receptor context. (delete the fragments of point 13 before.)

15. If a placement may be a suitable starting point for “de novo” design, keep the entry in the MOE window by clicking Keep in the Browser panel.

16. Compare also the positions of the fragments with the original ligand position.

With knowledge of preferred fragment positions it may now be possible to join the fragments to new ligands, build combinatorial libraries, or derive pharmacophores.

1 Copyright © 2006 Chemical Computing Group Inc. All Rights Reserved. Chemical Computing Group Inc. Docking and Multi Fragment Search.

Documents

chemical computing group

binding poses

analysis of ligand

ligand triplets of atoms

free energy of ligand

alpha triangle method

scoring function g

alpha pmi method