Molecular

dockingASSOC PROF. KIATTAWEE CHOOWONGKOMON

What is docking ?

•Enzymes Substrates

•Receptors Signal inducing

ligands

•Antibodies Antigens

a central phenomenon in biology

What is molecular docking?

4

Molecular docking

Basic Principles

The association of molecules is based on interactions• H-bonds, salt bridges, hydrophobic contacts, electrostatic

• Very strong repulsive (VdW) interactions on short distances.

Association interactions are weak and short ranged.• Strong binding implies surface complementarity.

Most molecules are flexible.

What molecular docking can do ?

Find potential drugs

Find active site of enzyme

Find potential inhibitor binding site

Find conformation of ligands in binding state

Predict change of conformation upon binding

Only can flexible the side chain but can’t change backbone conformation

What molecular docking cannot do ?

Sources of protein-ligand

structures

Sources of protein-ligand

structures

Stamos, J., Sliwkowski, M.X., Eigenbrot, C. Structure of the epidermal growth factor receptor kinase domain alone and in

complex with a 4-anilinoquinazoline inhibitor. J.Biol.Chem. v277 pp.46265-46272 , 2002

Erlotinib

1M17

8

Molecular Docking

Docking algorithms

are able to generate a large

number of possible structures

Scoring function

is able to rank the right structure

from others

12

Molecular docking

Scoring Function

(Ideally) Lowest value when the ligand is naturally docked.

Higher value everywhere else

Should be able to distinguish between correctly and incorrectly docked structures.

Should be fast! to compute.

Docking Target

Accuracy

Resource

Accuracy

Resource

Docking Target

Accuracy

Resource

unlimited resource

Docking Target

How do we know the hit ?

Empirical scoring

rotrot NGGG ´D+D=D0

( )-

DDD+bondsHneutral

hb RfG.

, a

( )-

DDD+.

,intionic

io RfG a

( ) DDD+intarom

arom RfG.

, a

( ) DDD+..

,contlipo

lipo RfG a

Loss of entropy during binding

Hydrogen-bonding

Ionic interactions

Aromatic interactions

Hydrophobic interactions

12

Self-docking

Docking Software

DOCK: (Kuntz et al. 1982)DOCK 4.0 (Ewing & Kuntz 1997)AutoDOCK (Goodsell & Olson 1990)AutoDOCK 3.0 (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)PRO_LEADS (Baxter et al. 1998)

SDOCKER (Wu et al. 2004)QXP (McMartin & Bohacek 1997)Validate (Head et al. 1996)

➢ 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)

Other Docking programs

AutoDock

AutoDock was designed to dock flexible

ligands into receptor binding sites

The strongest feature of AutoDock is the range

of powerful optimization algorithms available

GOLD

Genetic Optimization for Ligand Docking

Using Genetic algorithm

two scoring functions: GoldScore or

ChemScore

AutoDock

A program for the automated docking of flexible ligands to macromolecules

Containing the following programs:

addsol: adding solvation parameters to macromolecule “pdbq” file

atmtobnd: converting “.atm” to “bnd” bond file;

autodock3: automated docking of small molecules to proteins;

autogrid3: calculating atomic affinity and electrostatic potential grid maps for use in AutoDock;

autotors: Interactively defining rotatable bonds in the ligand and creating a ligand pdbq file for autodock;

makelaunch: creating scripts to launch concurrent dockings, group results, and creating a clustering docking parameters;

protonate: adding polar hydrogens to proteins.

(AutoDockTools)

Grid maps

Autodock requires pre-calculated grid maps, one for each atom type present in the ligand.

3-D lattice of regularly spaced points, surrounding (either entirely or partly) and centered on some region of interest of the macromolecule

This is done by autogrid

Typical grid point spacing varied from 0.2 to 1.0 A, default value is 0.375A (1/4 of C-C bond).

Each point within the grid map stores the potential energy of a “probe” atom or functional group that is due to all the atoms in the macro

Grid maps (contd)

GOLD

’Genetic Optimization for Ligand Docking’

Input:

Exact protein and ligand configurations in order to

get good results

Demand for other programs specialized in

molecular visualization

Genetic algorithm search method used to search

the different binding modes of the ligands

The binding mode has geometric and chemical components

Docked ligands ranked by fitness score

➢ Fitness functions

Fitness functions

For determining the rank between possible

geometries

Many choices: GoldScore or ChemScore

Different parameters used

Calculations based on chemical and physical

theories

Geometrical properties

Bonding affinities

Genetic algorithm

Initially a population of conformations is generated

Scoring algorithm evaluates the fitness of each conformation

conformation=chromosome

Genetic operations occur

Crossing-over

Fit members of the population crossover and replace the worst member of the population

Migration

Mutation

XK-263 to HIV-1 Protease

Sialic acid-Hemagglutinin

Benzamidine binding to beta-Trypsin

1stp-btn-1.mpeg

Biotin binding to Streptavidin

LAB 4Gold Docking

ASSOC. PROF. KIATTAWEE CHOOWONGKOMON, PH.D.

DEPARTMENT OF BIOCHEMISTRY

FACULTY OF SCIENCE

KASETSART UNIVERSITY

EMAIL: KIATTAWEE.C@KU.TH

MOBILE PHONE: 085-555-1480

GOLD docking

Self-docking

1M17

Docking with new ligand

Download ligand source

Pubchem

Draw new ligand

Discovery Studio

How are drugs discovered ?

By serendipity (Chlordiazepoxyde, Aspartam, etc...)

by structure-activity relationships (most)

from natural products (digitalin, taxol)

by rational design (since the 80‘s)

by systematic screening (since the 90‘s)

Protein-Based Drug Design

Bringing a New Drug to Market

Review and approval by Food & Drug Administration

Phase III: Confirms effectiveness and monitors adverse reactions from long-term use in 1,000 to5,000 patient volunteers.

Phase II: Assesses effectiveness and looks for side effects in 100 to 500 patient volunteers.

Phase I: Evaluates safety and dosagein 20 to 100 healthy human volunteers.

5 compounds enter clinical trials

Discovery and preclininal testing:Compounds are identified and evaluated in laboratory and animal studies for safety, biological activity, and formulation.

5,000 compounds evaluated

0 2 4 6 8 10 12 14 Years 16

Source: Tufts Center for the Study of Drug Development

1 compound approved

Lead Finding: Experimental vs. Virtual Screening

HTS Screening

20,000 molecules

5 hits

1 mM lead

expensive

selected targets

industrial environment

Comb. Lib.

ligand selectivity

Virtual Screening

210,000 molecules

100 proposals

10 tested

1 mM lead

cheap

any target (3D !!)

any environment

3D Comb. Lib.

ligand selectivity ?

Structure-Based Virtual Screening

Protein-Ligand Docking

Aims to predict 3D structures when a molecule “docks” to a protein

Need a way to explore the space of possible protein-ligand geometries (poses)

Need to score or rank the poses

Problem: many degrees of freedom (rotation, conformation, solvent effects)

Ligand databaseTarget Protein

Molecular

docking

Ligand docked into protein’s

active site

Schematic of Computer-aided drug design

Protein Structureof Protein Target

Virtual Screening

Ligand Design Library Synthesis

Biochemical Screening

Structure Determination

Of ligand-protein target complex

Lead Compound

Case Study 1: Molecular Docking

and Virtual Screening of tyrosine kinase

of EGFR with NCI database

ASSOC. PROF. KIATTAWEE CHOOWONGKOMON, PH.D.

Epidermal growth factor receptor (EGFR)

◼ It is a member of

the ErbB family

receptors, a

subfamily of four

closely related

receptor tyrosine

kinase

EGFR Overview

Activation Mechanism

http://faculty.plattsburgh.edu/donald.slish/tyrosinekinase/TK1.html

EGFR signaling pathways

http://cgap.nci.nih.gov/Pathways/BioCarta/egfPathway

Cell proliferation

Cell differentiation

Ephithelial organogenesis

EGFR Misregulation

Cancer Polycystic kidney disease

Ogiso et. al., (2002) Cell, 110, 775-787Garrett et. al., (2002) Cell, 110, 763,773

Ferguson et.al. (2003) Mol. Cell., 11, 507

Stomos et.al. (2002), JBC

1990s

2005

2002

2002

Choowongkomon et.al. (2005), JBC

Drug Name Target Class Developer PhaseGefitinib (Iressa) EGFR inhibitor AstraZeneca Approved & Phase III (lung)

Erlotinib (Tarceva) EGFR inhibitorRoche, Genentech,

OSI PharmaceuticalsPhase III (lung)

Lapatinib (GW-572016) EGFR, ErbB2 inhibitor GlaxoSmithKline Phase III (breast, kidney)

Zactima (ZD6474) EGFR, ErbB2 inhibitor AstraZeneca Phase III (NSC-lung)

Cl-1033 EGFR, ErbB2 inhibitor Pfizer Phase II (NSC-lung, lung)

EKB-569 EGFR inhibitor Wyeth Phase II (lung)

AEE788 EGFR, ErbB2 inhibitor Novartis Phase I (solid tumor)

Cetuximab

(erbitux)EGFR Anti-EGFR MoAB

ImClone Systems,

Bristol-Myers Squibb

Phase III (lung,

head&neck)

ABW-EGF EGFR Anti-EGFR MoAB Abgenix & Amgen Phase II (lung)

Pertuzumab (Omnitarg) ErbB2

Anti-ErbB2 MoABGenentech, Roche

Phase II (lung)

Trastuzumab (Herceptin) ErbB2

Anti-ErbB2 MoABGenentech, NCI

Approved & Phase III

(breast)

SAI-EGF EGFR Anti EGFR Vaccine Cancer Vax Phase I (lung)

APC 8024 ErbB2HER2 antagonist

Dendreon

CorporationPhase I (breast)

Drug Against EGFR

52

Stamos, J. et al. J. Biol. Chem. 2002;277:46265-46272

Kinase domain from different proteins

Substrate specificity

Virtual Screening (VS)

Lead compounds

Databases of substance compounds

Molecular Docking

Scoring function

Chemiebase database

NCI database

Zinc database

Autodock

Dock

Gold

Surflex dock

Fred program

Method

11

Autodock 3.0.5

“DRUG-LIKENESS” AND

COMPOUND FILTERS

Which features of drug molecules confer

biological activity?

Substructure filters to eliminate molecules known to have problems

For a specific target, may have to modify or extend

the filters

Analyze the values of simple properties (MW, logP,

No. of rotatable bonds)

Lipinski Rule of Five

Poor absorption or permeation is

more likely when:

MW > 500

LogP >5

More than 5 H-bond donors (sum of

OH and NH groups)

More than 10 H-bond acceptors (sum

of N and O atoms)

60

http://dtp.nci.nih.gov/branches/dscb/diversity_explanation.html

61

NCI structural diversity set I (1,990 compounds)

This diversity set was assessed for anti-cancer

activity in several solid tumor line e.g. leukemia,

NSCLC, colon, melanoma and ovarian cell lines.

the Office of the Associated Director of the

Developmental Therapeutics Program, Division of

Cancer Treatment and Diagnosis, National Cancer

Institute

http://dtp.nci.nih.gov/branches/dscb/diversity_explanation.html

62

1,990

63(Choowongkomon et al., 2010)

Receptor-Based Virtual Screening

Based on

a Molecular Docking Technique

Ligands

Preparation

Step 2

NCI compounds

NSC351123

NSC130831

NSC299137

NSC135371

NSC48283

NSC306698

NSC125910

From: Choowongkomon at al (2010)

230

compounds

Receptor-Based Virtual Screening

Based on

a Molecular Docking Technique

Assessment

Step 4

Compounds MW. ChemScore ∆G H-bond lipo DE Clash

NSC294928 1035.00 65.07 -71.69 2.19 613.92 6.06

NSC294926 999.00 62.82 -70.49 1.81 595.22 1.83

NSC294927 951.00 61.23 -67.00 2.01 569.73 3.98

NSC294924 847.00 57.75 -69.37 1.82 585.85 11.60

NSC297168 625.00 49.26 -51.83 1.63 232.86 1.81

NSC41838 625.00 47.34 -51.95 1.63 258.00 3.56NSC7804 578.61 47.25 -51.57 1.63 251.10 1.90

NSC295442 663.00 46.56 -54.34 2.73 307.81 6.33

NSC295305 681.00 46.10 -50.86 1.61 274.96 3.67

NSC7795 625.00 46.04 -49.52 1.63 251.36 1.03

NSC265450 729.78 45.71 -46.89 2.62 310.32 0.99

NSC364365 588.00 44.39 -46.18 1.70 144.65 1.70

NSC116555 788.00 43.56 -52.35 3.57 410.13 4.07

NSC41817 597.00 42.81 -45.81 1.65 215.72 1.56

NSC28093 547.00 41.75 -48.45 2.04 412.03 5.56

NSC366218 474.00 41.04 -44.32 1.00 199.54 3.06

NSC115360 432.00 40.50 -44.03 1.00 189.89 3.33

NSC167429 394.00 40.37 -42.71 1.00 191.27 2.06

NSC643046 505.00 40.00 -43.86 1.26 355.60 2.59

NSC2053 489.00 39.65 -43.58 1.43 356.83 2.28

NSC514050 519.00 39.65 -41.46 2.13 341.76 0.80

NSC128355 380.00 39.17 -40.76 2.21 299.63 0.36

NSC133940 386.00 39.02 -43.78 1.44 358.83 2.43

NSC115372 432.00 38.09 -41.66 1.00 187.32 3.36

NSC84255 395.00 37.36 -39.74 2.27 302.29 1.42

NSC48283 440.00 37.18 -38.54 1.00 304.49 0.26

NSC130831 481.00 36.33 -37.31 1.19 213.47 0.16

NSC30883 517.00 36.05 -39.11 1.84 327.60 0.40

NSC143544 379.00 36.00 -40.18 1.59 304.66 1.72

NSC88839 423.00 35.87 -36.29 1.33 292.42 0.29

NSC88841 423.00 34.64 -38.42 1.33 263.99 1.05

31 hit compounds

Order for test

IC50 Half maximal inhibitory concentration

NCI compounds EGFR-TK (nM)A549 Cell line

(µM)Gefitinib 5.7 64.53

NSC116555 31.56 6.32NSC125910 38.06 96.27NSC351123 75.34 >100NSC130831 76.78 >100NSC299137 90.87 >100NSC48283 296.7 >100

Case Study 2: Integration of computer modeling and in vitro studies for identifying antiviral

substance against Yellow head virus in Penaeidshrimp from NCI

67

Assist. Prof. Kiattawee Choowongkomon, Ph.D.

Shrimp farming and diseases68

69Yellow head disease

Genomic RNA

Nucleoprotein (p20)

Gp64Gp116

Order Nidovirales

Family Ronivirus

Genus Okavirus

YHV virions are enveloped, rod-shaped particles

Comprise three structural proteins and a ~26 kb (+) ssRNA genome

Yellow head disease

70

+ SS-RNA

RdRp

Translation of viral proteins

- SS-RNA

Capsid proteins

EncapsidationSurface proteins

+ SS-RNA

Nucleolus?

??

+ SS-RNA

Protease

Proposed YHV replication cascade

71

RdRp

Protease

+ SS-RNA

Translation of viral proteins

- SS-RNA

Capsid proteins

Encapsidation

+ SS-RNA

Nucleolus?

??

+ SS-RNA

72

Proposed YHV replication cascade

73Table 1 Sequence identity (similarity) and Ca-atom RMSD between

the YHV protease with templates

PDB

Template

% Identity

(Similarity)

Ca-RMSD

(Å)

1LVM 16 (34) 2.88

1CQQ 20 (30) 2.56

1HAV 14 (30) 2.8

1AGJ 14 (30) 2.4

1ARB 14 (37) 2.75

5PTP 21 (26) 1.94

1DLE 17 (37) 2.49

1A7S 16 (27) 1.84

74Structural comparison of YHV

protease to eight template

proteases.

Secondary structure

assignment is based on the

sequence alignment

-strand and α-helical

structures are shown in colors

The catalytic triad residues are

indicated in yellow rectangles

75Ribbon diagram for the YHV protease

Virtual Ligand Screening

Modeling of Protein–Ligand Complexes

In Silico Optimization of the Hits

AutoDock 4.0

SiMMap server

77

Protease structure with first 32 hits of NCI compounds

78

79Relative percent inhibition

LAB 5Virtual Screening

ASSOC. PROF. KIATTAWEE CHOOWONGKOMON, PH.D.

DEPARTMENT OF BIOCHEMISTRY

FACULTY OF SCIENCE

KASETSART UNIVERSITY

EMAIL: KIATTAWEE.C@KU.TH

MOBILE PHONE: 085-555-1480

Xanthone Derivatives from

G. succifolia Kurz.

82