Chemoinformatics in Molecular Docking and Drug Discovery

2

The Docking Problem

• Given: receptor binding pocket and ligand.• Task: quickly find correct binding pose.

Two critical modules:1. Search Algorithm2. Scoring Function

3

Definitions

• pKd = measures tightness of binding• pKi = measures ability to inhibit• Mechanisms of action—for instance:

– Competitive inhibition (most typical docking case)– Allosteric inhibition (bind to different pocket)– Allosteric activation

4

Challenges• Search algorithm

– Speed (5M compounds or more)– Local minima– High-dimensional search space

• Scoring function – Strict control of false positives– Good correlation with pKd– Multiple terms– No consensus– Non-additive effects (solvation, hydrophobic interactions)

• Note: pKd does not always correspond with activity• ADME concerns

5

Examples of Docking Search Algorithms

– Genetic Algorithms– Incremental Construction– Fragment Reconstruction– Gradient Descent– Simulated Annealing and

other MC Variants– Tiered Scoring Functions

• fast screening functions• slow accurate functions

6

High Dimensionality: Flexibility

• Most algorithms handle ligand flexibility but do NOT handle receptor flexibility.

• Iterative Docking to find alternate conformations of the protein– Dock flexible ligand– Minimize receptor holding ligand rigid– Repeat

7

Scoring Function

• Energy of Interaction (pKd)

• Electrostatics• Van der Waal’s interactions• Hydrogen bonds• Solvation effects• Loss of entropy• Active site waters

8

ADME

ADME concerns can be more important than bioactivity. Most of these properties are difficult to predict.

• Absorption• Distribution• Metabolism• Excretion

9

Docking Programs• Dock (UCSF) • Autodock (Scripps)• Glide (Schrodinger)• ICM (Molsoft)• FRED (Open Eye)• Gold, FlexX, etc.

10

Evaluation of Docking Programs

• Evaluation of library ranking efficacy in virtual screening. J Comput Chem. 2005 Jan 15;26(1):11-22.

• Evaluation of docking performance: comparative data on docking algorithms. J Med Chem. 2004 Jan 29;47(3):558-65.

• Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors. J Chem Inf Comput Sci. 2004 May-Jun;44(3):1123-9.

11

Cluster Based Computing

• Trivially parallelizable– Divide ligand input files– Some programs have

specific parallel implementations (PVM or MPI implementations,…)

• Commercial licenses are expensive

12

Consensus Scoring

• Combining independent scoring functions and docking algorithms can improve results

• Most common method: sort using the sum of the ranks of component scores

• More sophisticated methods exist Consensus scoring criteria for improving enrichment in virtual screening. J

Chem Inf Model. 2005 Jul-Aug;45(4):1134-46.

13

Adding Chemical InformaticsDocking results can be improved by using chemical information

about the hits.Chemicals which bind the same protein tend to have similar

structure.Iterating back and forth between docking and searching large DB.Use other filters and predictive modules (e.g. Lipinski rules)

ALGORITHM:1. Dock and rank a chemical database2. Create a bayesian model of the fingerprints of the top hits.3. Re-rank the database based on their likelihood according to the

bayesian model

Finding More Needles in the Haystack: A Simple and Efficient Method for Improving High-Throughput Docking Results J. Med. Chem., 47 (11), 2743 -2749, 2004.

14

Visualization

• Viewers must be able to scroll through tens or hundreds of small molecule hits

• Accessible viewers designed for this problem:– VIDA from OpenEye (free for

academics)– ViewDock module of Chimera from

UCSF (free, open source)

15

Long-term Goal of Drug Discovery

• LTDD (Low Throughput Drug Design) instead of HTVS (High Throughput Virtual Screening)

• Common ground: explore virtual space

Drug DiscoveryCase Study: Tuberculosis

17

Mycobacterium TuberculosisVery thick, waxy cell wall

Tuberculosis

18

Cell wall lipids: Important for pathogen virulence, survival and latency

6 different ACCase

b subunits,AccD1-6

Acyl-CoA

Tuberculosis• 7th cause of death• 1 in 3 people have TB• Leading AIDS death cause• Multi-drug resistant• Mycobacterium tuberculosis

Homologs of PccBFocus on AccD4-6

>30 C fatty acid

The Cell Wall: Key to Pathogen Survival

Sugar

10% of genome

19

Tuberculosis (TB): An old foe

20

The White Death

Frederic Chopin1810-1849

John Keats1795-1821

21

TB: still a real threat, because…..

Its ability to stay alive Multi-Drug Resistant

(Super TB strain)

22

Cell wall lipids: Important for pathogen virulence, survival and latency

6 different ACCase

b subunits,AccD1-6

Acyl-CoA

Substrate specificity for AccD4-6?

Tuberculosis• 7th cause of death worldwide• 1 in 3 people have TB• Leading cause AIDS death• Multi-drug resistant• Mycobacterium tuberculosis

Homologs of PccBFocus on AccD4-6

>30 C fatty acid

The Cell Wall: Key to Pathogen Survival

Sugar

10% of genome

23

AccD5 Protein Structures

AccD4 (3.3 Å) Solved AccD5 (2.9 Å) AccD6 (2.7 Å)

24

Structure of AccD5

25

Structure-Based Drug Design

Crystals & Crystal structure

1. High throughput screening

Lead compound2. Virtual Screening

3. Combinatorial chemistry

Enzyme assay

-0.006 -0.004 -0.002 0 0.002 0.004 0.006 0.008 0.010 0.012 0.014-1

0

1

2

3

4

5

6

7AccD5-NCI65828

1/[Malonyl-CoA]um-1

1/V

o (m

in-1

)[I] = 0.00[I] = 2.50[I] = 5.00[I] =10.00

TB ACCase, AccD5

26

The Computational/Experimental Loop

-0.006 -0.004 -0.002 0 0.002 0.004 0.006 0.008 0.010 0.012 0.014-1

0

1

2

3

4

5

6

7AccD5-NCI65828

1/[Malonyl-CoA]um-1

1/V

o (m

in-1

)

[I] = 0.00[I] = 2.50[I] = 5.00[I] =10.00

Assay Docking

Similarity Search

27

Docking Results• Diversity set (1990) from NCI

NH2OMe

NN

H2NO2S

NCI 65537

HO3SNH2

NN

OH

NCI 65828

N

CO2H

O

OOH

Cl ClClOH

Cl

OHClCl

HOCl

Cl

NCI 172033

P

NHN

N

NH2N

Br

NCI 211736

HN

COCH2Cl

OMeOMe

NCI 105348

NCI 143444

N

CHBrBrHCCO

O2N

NCI 150289

N N

O2N N

N NO2Me

NCI 294153

H2NO2S

NH2

NN

NCI 299210

NN

Me

NH

NNH

N

OH

3HCl

NCI 322921

28

Cl

HO OH

OH

O

HO

O

Cl

NCI 622444 (IN1)

SO3H

HO

Cl

CH3

CH3Cl

OH

NCI 65079 (IN2)

N

N NH2

HO3S

NCI 4901 (IN3)

N

SO3H

N

NN

OH

O

NCI 65538 (IN4)

N

SO3H

N

NN

O

OH

HO3S

NCI 65553 (IN5)

N

SO3H

N

NN

O

HO SO3H

NCI 65554 (IN6)

N

SO3H

N

NN

O

HO

SO3H

NCI 65555 (IN7)

NH2

SO3H

HO3S

NCI 37050 (IN8)

HO3S

OH N

NH2

N

NCI 45188 (IN9)

H2N

HO3S

N

N N

N NH2

SO3HNCI 45618 (IN10)

HO3S

NH2

N

N

OH

OH

Cl Cl

Cl

OH

Cl

OH

ClCl

HO

Cl

Cl

NCI 65828 (Lead 1)

NCI 172033 (Lead 2)

300uM

50uM 300uM 50-100uM

29

Structure-Based Drug Design Identified AccD5 Inhibitors

New TB drug lead

KI = 4.7 mM, KGI = ~50 mM

T. Lin, M. Melgar, S. J. Swamidass, J. Purdon, T. Tseng, G. Gago, D. Kurth,P. Baldi, H. Gramajo, and S. Tsai. PNAS, 103, 9, 3072-3077, (2006). US Patent pending.

-0.006 -0.004 -0.002 0 0.002 0.004 0.006 0.008 0.010 0.012 0.014-1

0

1

2

3

4

5

6

7AccD5-NCI65828

1/[Malonyl-CoA]um-1

1/V

o (m

in-1

)

[I] = 0.00[I] = 2.50[I] = 5.00[I] =10.00

30

• Chemical similarity:

• Docking:

Two Strategies

31

AccD5

• Enzyme necessary for mycolic acid biosynthesis in M. tuberculosis.