Transcript ISMB2006-Docking7

7. Molecular Docking and Drug
Discovery
R
M
ChemDB
Filters
RChemDB
NM
Experiments
2
The Docking Problem
•
•
Given: receptor binding pocket and ligand.
Task: quickly find correct binding pose.
Two critical modules:
1. Search Algorithm
2. 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 dockingbased virtual screening: an application study on renin
inhibitors. J Chem Inf Comput Sci. 2004 MayJun;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 Informatics
Docking 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 database
2. 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 HighThroughput 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
16
Drug Discovery
Case Study: Tuberculosis
Tuberculosis
Mycobacterium Tuberculosis
Very thick, waxy cell wall
18
The Cell Wall: Key to Pathogen
Survival
Tuberculosis
•
7th cause of death
•
1 in 3 people have TB
•
Leading AIDS death cause
•
Multi-drug resistant
•
Mycobacterium tuberculosis
>30 C fatty acid
10% of
genome
Sugar
Acyl-CoA
6 different
ACCase
b subunits,
AccD1-6
Homologs of PccB
Focus on AccD4-6
Cell wall lipids: Important for pathogen
virulence, survival and latency
19
Tuberculosis (TB): An old foe
20
The White Death
Frederic Chopin
1810-1849
John Keats
1795-1821
21
TB: still a real threat, because…..
Its ability to stay alive
Multi-Drug Resistant
(Super TB strain)
22
The Cell Wall: Key to Pathogen Survival
>30 C fatty acid
Tuberculosis
•
•
•
•
•
7th cause of death worldwide
1 in 3 people have TB
Leading cause AIDS death
Multi-drug resistant
Mycobacterium
tuberculosis
Acyl-CoA
10% of
genome
Sugar
6 different
ACCase
b subunits,
AccD1-6
Homologs of PccB
Focus on AccD4-6
Cell wall lipids: Important for pathogen
virulence, survival and latency
Substrate specificity for AccD4-6?
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
Enzyme assay
AccD5-NCI65828
7
Crystals &
Crystal structure
6
[I] = 0.00
[I] = 2.50
[I] = 5.00
[I] =10.00
1/Vo (min-1)
5
3. Combinatorial chemistry
4
3
2
1
0
-1
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
0.008
0.010
0.012
0.014
1/[Malonyl-CoA]um-1
TB ACCase, AccD5
1. High throughput screening
2. Virtual Screening
Lead compound
26
The Computational/Experimental
Loop
Similarity Search
AccD5-NCI65828
7
6
[I] = 0.00
[I] = 2.50
[I] = 5.00
[I] =10.00
1/Vo (min-1)
5
4
3
2
1
0
-1
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
1/[Malonyl-CoA]um-1
Assay
0.008
0.010
0.012
0.014
Docking
27
Docking Results
• Diversity set (1990) from NCI
NCI 65828
NCI 65537
NCI 294153
NCI 105348
NCI 150289
NH2
NH2
OMe
OMe
H
N
OMe
N
N
N
HO3S
N
N
COCH2
Cl
N
O2N
Me
N
BrHC CHBr
N
CO
NO2
N
H2NO2S
O2N
OH
NCI 172033
NCI 143444
NCI 211736
H2N
OH
Cl
O
N
Cl
Cl
Cl
CO2H
HN
OH
O
NCI 299210
N
N
N
Cl
OH
N
N
NH2
N
N
P
Cl
Cl
Me
H2NO2S
Cl
HO
NCI 322921
N
3HCl
N
NH
N
H
Br
OH
28
NCI 65079 (IN2)
NCI 622444 (IN1)
NCI 65828 (Lead 1)
O
300uM
SO3H
O
NCI 4901 (IN3)
HO
NH2
HO
OH
N
CH3
Cl
HO3S
HO
Cl
OH
Cl
HO3S
NH2
N
CH3
Cl
OH
N
N
NCI 65538 (IN4)
NCI 65553 (IN5)
SO3H
SO3H
N
N
NCI 65554 (IN6)
NCI 65555 (IN7)
SO3H
SO3H
50uM
N
300uM
N
N
N
50-100uM
N
N
OH
N
N
N
N
O
N
N
N
NCI 172033 (Lead 2)
O
O
O
N
HO
SO3H
OH
HO
OH
SO3H
HO3S
Cl
Cl
Cl
OH
OH
NCI 45188 (IN9)
NCI 37050 (IN8)
Cl
SO3H
NCI 45618 (IN10)
Cl
NH2
HO3S
HO
SO3H
Cl
Cl
OH
N
NH2
Cl
HO3S
OH
N
N
N
H2N
NH2
N
N
HO3S
29
Structure-Based Drug Design
Identified AccD5 Inhibitors
AccD5-NCI65828
7
6
[I] = 0.00
[I] = 2.50
[I] = 5.00
[I] =10.00
1/Vo (min-1)
5
4
3
2
1
0
-1
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
0.008
0.010
0.012
0.014
1/[Malonyl-CoA]um-1
KI = 4.7 mM, KGI = ~50 mM
 New TB drug lead
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.
30
Acknowledgements
•
Informatics
–
–
–
–
–
–
–
–
–
•
Liva Ralaivola
J. Chen
S. J. Swamidass
Yimeng Dou
Peter Phung
Jocelyne Bruand
Chloe Azencott
Alex Ksikes
Ryan Allison
Funding
–
–
–
–
• Pharmacology
– Daniele Piomelli
• Chemistry
–
–
–
–
–
G. Weiss
J. S. Nowick
R. Chamberlin
S. Tsai
K. Shea
NIH
NSF
Sun
IGB
31
Two Strategies
• Chemical similarity:
• Docking:
32
AccD5
• Enzyme necessary for mycolic acid
biosynthesis in M. tuberculosis.
33