Molecular modeling, Interactions in Biological Systems II. · Molecular modeling, Interactions in Biological Systems II. 3 The success of a docking algorithm in predicting a ligand

Molecular modeling, Interactions in Biological Systems II.

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INTERREG IIIA Community Initiative Program

Szegedi Tudományegyetem Prirodno-matematički fakultet, Univerzitet u Novom Sadu

„Computer-aided Modelling and Simulation in Natural Sciences“

University of Szeged,

Project No. HUSER0602/066

Molecular modeling, Interactions in Biological

Systems II.

Balázs Jójárt Content: I. Theoretical background II. Practical guide for docking small ligands to the enzyme active site Appendix A. – Tutorial files


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I. Theoretical background

I.1. Introduction

As biological research has become increasingly data intensive, biomedical projects require

informatics tools.

In drug discovery research, high-through-put screening often requires the screening of

millions of compounds for a particular protein target. Important tools that can enhance such

screens are molecular docking and database mining.

Molecular docking can be defined as the prediction of the structure of receptor-ligand

complexes, where the receptor is usually a protein or a protein oligomer and the ligand is

either a small molecule or another protein.

There are two key parts to any docking program, namely a search of the configurational and

conformational degrees of freedom and the scoring or evaluation function. The search

algorithm must search the potential energy landscape in enough detail to find the global

energy minimum. In rigid docking this means that the search algorithm explores different

positions for the ligand in the receptor active site using the translational and rotational degrees

of freedom. Flexible ligand docking adds exploration of torsional degrees of freedom of the

ligand to this process.

The most cited and used programs are summarized in Table 1.

Software Algorithm References

DOCK Geometric alignment, incremental ligand building

[1]

FlexX Geometric alignment, incremental ligand building

[2, 3]

SLIDE Geometric alignment, multiconformer ligand dictionary

[4]

AutoDock Genetic algorithm [5] ICM Monte Carlo minimization [6] QXP Monte Carlo minimization [7] MDD Molecular Dynamics [8] Glide Systematic Search [9] GOLD Genetic algorithm [10] PRO_LEADS Tabu Search [11] MOE-Dock Tabu Search / Simuleted annelaing [12] FRED Systematic search, multiconformer

ligand dictionary [13,14]

FLOG Systematic search [15] Table 1 The most cited and used docking programs.


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The success of a docking algorithm in predicting a ligand binding pose is normally measured

in terms of the root-mean-square deviation (RMSD) between the experimentally observed

heavy-atom positions of the ligands and the one(s) predicted by the algorithm.

AutoDock uses the so called Lamarckian genetic algorithm to predict binding modes of

ligands in proteins and nucleic acids. The genetic algorithm uses the language of the

evolution, where the genes are the state variables (translation, orientation and conformation),

and the atomic coordinates (3D structure) correspond to the phenotype.

The steps of docking calculations are as follows [The abbreviations in brackets will be

referred to Dokcing Parameter File (dpf) and Grid Paramater File (gpf).]:

1. The rapid energy evaluation is achieved by precalculated atomic affinity potentials for

each atom type in the substrate molecule. In the AutoGrid procedure the protein is

embedded in a three-dimensional grid and a probe atom is placed at each grid point.

The energy of interaction of this single atom with the protein is assigned to the grid

point. An affinity grid is calculated for each type of atom in the substrate, typically

carbon, oxygen, nitrogen and hydrogen, as well as a grid of electrostatic potential,

either using a point charge of +1 as the probe. The time to perform an energy

calculation using the grids is proportional only to the number of atoms in the substrate,

and is independent of the number of atoms in the protein.

2. Generating a random population, where the ga_pop_size (dpf) determines the number

of the individuals. The initial population can be visualized only if the output level is

set to 4 in the dpf.

3. Assinging random values for each gene:

a. 3 values for the translational genes – x, y, z, which determines the position of

the ligand in the binding cavity. The binding cavity is determined by setting up

the grid box, the x, y, z values are between the minimum and maximum

extents of the grid box (gpf)

b. 4 values for the orientation gene of the ligand in the binding cavity.

c. N values for the torsional gene, where N is the number of the rotatable bonds.

4. Translation of the genotype to the phenotype (x, y, z coordinates of the ligand) by

means of MAPPING.

5. In the next step the fitness of the individuals will be determined, which is the sum of

the intermolecular interaction energy between the target molecule and the ligand, and

the intramolecular energy of the ligand. (Every time if the fitness is evaluated the

number of the energy evaluations (ga_num_evals in dpf) is increased.)


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6. SELECTION: In this step the program decides which individuals will reproduce.

Thus, individuals that have better- than-average fitness receive proportionally more

offspring.

7. CROSSOVER and MUTATION:

a. Two-point crossover: ABC×abc � AbC and aBc, and the parents are replaced

by this individuals.

b. Mutation: add a random real number to the real variable (gene): ABC � Abc.

c. The crossover and mutation rate is controlled via the ga_mutation_rate

ga_crossover_rate keywords in the dpf file.

8. ELITISM: determines how many of the top individuals survive into the next

generation.

9. AutoDock has also a local search implementation: the proportion of the population set

by the ls_search_freq parameter will undergo local searches.

The scoring function has to be realistic enough to assign the most favourable scores to the

experimentally determined complex. Estimating binding free energies accurately is a time-

consuming process. State-of-the-art efforts are represented by the free energy

perturbation/thermodynamic integration methodology. Although the MM-PBSA and explicit

solvent/implicit solvent (ES/IS) methods can achieve similar accuracy at a smaller

computational cost, these methodologies cannot currently be used in screening large numbers

of ligands against a protein target.

In AutoDock3 version the authors applied a molecular mechanics approach to evaluate

enthalpic contributions such as dispersion/repulsion and hydrogen bonding and an empirical

approach to evaluate the entropic contribution of changes in solvation and conformational

mobility. Empirical weights were applied to each of the components based on calibration

against a set of known binding constants (30 protein-ligand complex). The final

semiempirical force field is designed to yield an estimate of the binding constant. In the

following version, in AutoDock4, the semiempirical scoring function was calibrated on 188

complexes and tested on 100 complexes. In this scoring function a new thermodynamic model

was applied describing the binding process, and a full desolvation term was included. The free

energy of binding is estimated to be equal to the difference between (1) the energy of the

ligand and the protein in a separated unbound state and (2) the energy of the ligand–protein

complex.

The question can be arisen, why choose we the AutoDock for detailed description, learning

and studying. The first answer is: it is free (of course for academic users). The second one


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(which is more important): it can be applied in several field of the computer aided drug

design: (1) investigation of various receptor-ligand interactions (saccharides [16],

cytochromes [17], 3D-QSAR (structure-based alignment) [18], alcohol dehydrogenase [19],

CADD [20], HIV [21]); (2) ‘blind docking’ [22,23]; (3) it can be applied in virtual screening

[24].


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II. Practical guide for docking small ligands to the enzyme

active site

During the practice session we are going to study the interaction between the cyclooxygenase

(COX) enzyme and a selective inhibitor, SC-588 (Figure 1, PDB ID: 6COX).

Figure 1 The structure of SC-588.

II.1. Obtaining the enzyme – ligand complex structure &

visualization using Visual Molecular Dynamics

Open a browser and write the following address: www.rcsb.org, which is the homepage of the

greatest database of protein, nucleic acid structures (and its complexes, Figure 2).


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Figure 2 The homepage of the greatest 3D structure database.

What kind of information can we obtain from this page (Figure 3)?

6COX


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Figure 3 Selected and important information about the molecule.

By clicking on the download button ( ) you save the file.

On the console go to 6COX directory and type vmd, the following screens appear (Figure 4).

TM NAME, LIGAND NAME

IMPORTANT, IF THE

RESOLUTION = 0.00 �� NMR

VIEW THE ASCII FILE


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Figure 4 The graphical user intareface (GUI) of VMD.

In the VMD Main window click: File/New Molecule …/; and in the appearing window write

6COX in the filename box and press the Load button. VMD downloads the appropriate file.

VMD Main/Graphics/Representation. On the Graphical Representation window click the

‘Create Representation’ button.

Click on the first representation and write the following in the Selected Atoms box:

protein and chain A, in the Coloring Method choose Structure and in the Drawing Method

NewCartoon.

Click on the second representation and write the following in the Selected Atoms:

resname S58 and chain A, in the Coloring Method choose Name and in the Drawing

Method Licorice.

Change the background color as follows: main window: Graphics/Colors/Display (in

Categories)/Background (in Names)/8 white (in colors).

MAIN WINDOW

COMMAND LINE

INTERFACE


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We can save our work as follows: VMD Main/File/Save State � 6cox.vmd. (you can load

your work via VMD Main/File/Load State).

If you would like to make a high quality picture do the following (Figure 5):

VMD Main/File/Render and use from the Render using the Tachyon program, and press

the Start Rendering button.

Figure 5 6COX enzyme in NewCartoon representation complex with SC-588 in Licorice representation.

We can save our work:

VMD Main/Save coordinates/Selected atoms – protein and chain A; filetype: pdb and press

the Save button � 6COX_prot.pdb

VMD Main/Save coordinates/Selected atoms – resname S58 and chain A; filetype: pdb and

press the Save button � 6COX_ligand.pdb.

II.2. Preparing the input files for docking calculation

You obtain the minimized structures (which were prepared via AMBER9), in this section we

are focusing on the input file preparation with the GUI of AutoDockTools1.5 (Figure 6) and

we show also the python scripts.


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Figure 6 The GUI of AutoDockTools1.5.

Launch the AutoDockTools by clicking on the appropriate icon.


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II.2.1. Ligand file preparation (6COX_lig_min.pdb)

PMV/Ligand/Input/Open and choose

6COX_lig_min.pdb.

A summary window appears with important

information:

• charges were assigned;

• non-polar hydrogens were merged

• the program found 15 aromatic hydrogens

• 5 rotatable bounds were detected �

torsional degree of freedom was set to 5.


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PMV/Ligand/Torsion Tree/Detect Root

• the root atom will be depicted with a

green sphere


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PMV/Ligand/Torsion

Tree/Choose Torsions

Change the ligand representation to

lines, otherwise you can not see the

rotatable bonds!

• Green bonds are flexible

during the calculation

• Red bonds are rigid during

the calculations.


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PMV/Ligand/Output/Save as PDBQT ��

6COX_lig_min.pdbqt

II.2.2. Receptor file and GRID parameter file preparation

(6COX_prot_min.pdbqt, 6COX.gpf)

PMV/Grid/Macromolecule/Open and choose 6COX_prot_min.pdb.

Save the molecule as 6COX_prot_min.pdbqt.

The summary window appears with relevant information:

• how many non-polar hydrogens were found

• non-polar hydrogens were merged.

Press the ‘N’ button in order to normalize the location of the molecules.


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In the next steps the necessary parameters for grid parameter files are set up.

PMV/Grid/Set Map

Types/Choose Ligand ...

and choose the

6COX_lig_min.pdbqt.

PMV/Grid/Grid Box … and


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Grid Options/Center/Center On Ligand

Grid Options/File/Close saving current we

can save our work by this action.


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We can save the gpf:

PMV/Grid/Output/Save GPF

… �� 6COX.gpf

The size of the grid box is increased in the output gpf file using a simple text editor,

change the line:

npts 40 40 40 # num.grid points in xyz

to

npts 62 46 60 # num.grid points in xyz.

II.2.3. DOCKING parameter file preparation (6COX.dpf)

PMV/Docking/Macromolecule/Set Rigid Filename and choose 6COX_prot_min.pdbqt.


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PMV/Docking/Ligand/Choose 6COX_lig_min.pdbqt.

PMV/Docking/Search Parameters/Genetic algorithm and set the following values:

• Number of GA runs: 50

• Population size: 150

• Maximum Number of evals:

5.000.000, and click Accept button.


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PMV/Docking/Docking

parameters…

In the window Set Docking Run Options the:

‘for the step size parameters’

are changed as follows:

Translation 0.5; Quaternation

5.0 and Torsion 5.0.

and

‘RMS cluster tolerance’: 1.0


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PMV/Docking/Output/Lamarckian GA �� 6COX.dpf

II.3. Performing the grid and docking calculations

In the first step using the autogrid4 command we can calculate the grid maps by executing:

autogrid4 –p 6COX.gpf –l 6COX.glg

After the grid maps calculation, we can perform the docking calculations also as follows:

autodock4 –p 6COX.dpf –l 6COX.dlg

II.4. Evaluation of the results

The evaluation of the results can be performed via the GUI of AutoDockTools1.5 or by

python scripts. Here we perform evaluation via the GUI; about the scripts you can find a good

description on this site: http://autodock.scripps.edu/faqs-help/faq/where-can-i-find-the-

python-scripts-for-preparing-and-analysing-autodock-dockings.

Read the docking file:

PMV/Analyze/Dockings/Open/6COX.dlg

The molecule appears on the screen in the initial conformation/location. Press the ‘N’ and

after that he ‘C’ buttons in order to normalize and centre the view of the ligand.

Mouse buttons:

MIDDLE � ROTATE (push) & SCALING (rolling)

RIGHT � TRANSLATE

If you also like to visualize the macromolecule:

PMV/Analyze/Choose … and localize the macromolecule filename and press the ‘N’ button.

Here in PMV you can also use different colouring and visualization schemes.


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Figure 7 The easiest way to change the representation of the molecules on the GUI of AutoDockTools1.5

To change the background colour:

PMV/3D Graphics/SetBackGroundColor/Edit/Add new Color and change the parameters

in the small boxes as follows: R:0.3, G:0.3; B:0.3 and press ‘Add to custom’ and click the

new colour button in the panel and after all click DISMISS.

Change the colouring method of 6COX_origin_lig to Mol, in this case the ligand is coloured

by blue. (We have to keep in

mind that this structure is still

the initial structure of the

ligand!!!!) You can visualize

the docking posses by green

spheres in the binding pocket as

follows:

PMV/Analyze/Dockings/Show as Spheres �� and select 6COX_origin_test1.dlg. You can

set up the radii of the spheres, set it to 0.23, as well.


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Now we can see the results of the clustering. In order to see the difference between the crystal

and docked structire, we have to load the crystal structure as well:

PMV/File/Read Molecule… and select 6COX_lig_min.pdbqt and select the following

representation for this molecule: S&B and Atom (colouring method).

PMV/Analyze/Clustering/Show


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As you can see in the appearing window the cluster analysis revealed in two different

populations, click on the largest one and a new window appears:

Click on the icon in the window � in Set Play Options check the Show Info box

�Conformation_1_1 Info


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In case of this conformation we were able to reproduce the binding structure of the ligand

with a 0.54 Ǻ RMS (refRMS = ∑=

n

i

id1

2 ). The inhibition constant is also calculated according

to the following equation: ∆Gbinding=RTlnKi. (We have to keep in mind that the unit of

∆Gbinding is kcal×mol-1 and that of R is J×mol-1×K-1, therefore you have to convert it!!!!) Here

we have to mention, that the torsional free energy term was calculated as follows:

∆Gtors=Ntors×ctors=5×0.274 kcal×mol-1=1.37 kcal×mol-1, where Ntors is the number of the

rotatable bonds, and ctors is the torsional coefficient.

The -10.8 kcal×mol-1 value of the ∆Gbinding is very important if you plan to do virtual

screening on this enzyme, and you would like to use this compound as positive control. This

energy, plus the standard deviation in the predicted ∆Gbind of the AutoDock 4 force field, 2.62

kcal/mol, forms the threshold above which you will be looking for “hits”, molecules with

better ∆Gbind than the positive control’s ∆Gbind.

If you click on the other cluster, you obtain the lowest energy structure, and you can see, that

the ligand recognizes the binding pocket (it binds exactly to the same site), but two aromatic

ringst of the ligand is changed. Nevertheless the refRMS is also significantly higher (4.86 Ǻ)

and the ∆Gbind (-9.57 kcal×mol-1) is also higher (which means lower binding affinity).

Click on the first cluster again, and select 6COX_origin_lig in the PMV window

Now you can save only the docked structure: PMV/File/Save/Write PDB �

6COX_origin_lig.pdb.


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Appendix A. – Tutorial files

01_input

− 6COX.pdb – 3D coordinates of COX – S58, downloaded from PDB databank.

− 6COX_lig.pdb – S58 coordinates, retrieved from 6COX.pdb

− 6COX_lig_min.pdb – minimized ligand structure

− 6COX_prot.pdb– enzyme coordinates, retrieved from 6COX.pdb

− 6COX_prot_min.pdb – minimized enzyme structure

02_GUI

− 6COX.dpf – docking parameter file

− 6COX.gpf – grid parameter file

− 6COX_lig_min.pdbqt – ligand coordinate file in AutoDock4.01 format

− 6COX_prot_min.pdbqt – enzyme coordinate file in AutoDock4.01 format

03_autogrid

− 6COX.glg – grid log file

− *.map – atom affinity files

04_autodock

− 6COX.dlg – docking log file

− 6COX_origin_lig.pdb – lowest energy structure of the ligand from the highest

populated cluster


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Molecular modeling, Interactions in Biological Systems II. · Molecular modeling, Interactions in Biological Systems II. 3 The success of a docking algorithm in predicting a ligand