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
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
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
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
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