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working_example_poster
- 1. Huikun Zhang1, Spencer S. Ericksen2, Scott A. Wildman2, Anthony Gitter3,4, F. Michael Hoffmann2, and Michael A. Newton1,3,5
1Department of Statistics, UW-Madison, WI; 2UW School of Medicine and Public Health, UW Carbone Cancer Center, Small Molecule Screening Facility, Madison, WI; 3Department of Biostatistics and Medical Informatics, UW-Madison, WI;
4Morridge Institute of Research, UW-Madison, Madison, WI; 5Stochastic Modeling Laboratory, Center for Predictive Computational Phenotyping, UW-Madison, WI.
INTRODUCTION
Structure-based Virtual Screening (VS) has been broadly applied in
early-stage drug discovery. Given one protein target, the process of
screening involves docking through millions of ligands and
computationally prioritize the ligands that are most likely to truly
interact with the target.
A docking software will report an optimized interaction score for each
ligand on a certain target.
METHODS
Meta-docking, as a Bayesian mixture model, accounts for
score distributional differences observed between actives
and decoys arise from both program and ligand effect.
Compared to standard consensus docking schemes
(average, minimum and maximum), results of meta-docking
on DUD-E: 14 targets x 8 programs x 271,145 ligands show
small but consistent improvements in ranking ligands: more
active ligands are detected among top ranked ligands.
Future work will include investigation on inter-ligand features
from multiple docking programs, which might lead to further
improvement on ranking ligands. On the other hand, more
docking programs and ligands can be investigated using
meta-docking model.
Of the millions of commercially-available, drug-like compounds
that could potentially interact with a disease-linked protein, only
a tiny fraction may have strong, specific effects against a novel
target. Docking algorithms, which approximate how the three-
dimensional ligand and protein structures interact, enable us to
prioritize compounds for primary drug screening, and thus to
save on experimental time, effort, and money. However
available docking algorithms show low accuracy for compound
ranking and large variation in performance both within and
across targets. We develop a stochastic model of multiple
docking scores for a ligand/target pair and with this model
derive a novel consensus scoring method – meta-docking. The
model leverages score distributional differences observed
between actives and decoys from 8 docking programs applied
using HT Condor against 14 protein targets and 271,451
ligands. Meta-docking shows small but consistent
improvements in ligand ranking compared to standard
consensus schemes.
ABSTRACT
CONTACT
Huikun Zhang:
huikun.zhang@wisc.edu
Michael A. Newton:
newton@biostat.wisc.edu
A library of 271,451 ligands from Directory of Useful Decoys,
Enhanced (DUD-E) [5] are docked by 8 different programs to 14
different protein targets using HT Condor.
Meta-docking leverages this heterogeneity between distributions of
actives and decoys to achieve improvement on ranking ligands.
[1] Pictures and description are provided by Spencer S. Ericksen, UW Carbone
Cancer Center, Small Molecule Screening Facility.
[2] Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S.
and Olson, A. J. "Autodock4 and AutoDockTools4: automated docking with selective
receptor flexibility." J. Computational Chemistry 2009, 16: 2785-91.
[3] Allen WJ1, Balius TE, Mukherjee S, Brozell SR, Moustakas DT, Lang PT, Case
DA, Kuntz ID, Rizzo RC. "DOCK 6: Impact of new features and current docking
performance." J Comput Chem. 2015 Jun 5;36(15):1132-56. doi: 10.1002/jcc.23905.
[4] David Ryan Koes* Matthew P. Baumgartner Carlos J. Camacho. "Lessons
Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking
Exercise." J. Chem. Inf. Model., 2013, 53 (8), pp 1893–1904.
[5] Michael M. Mysinger, Michael Carchia, John. J. Irwin, and Brian K. Shoichet.
"Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for
Better Benchmarking." J. Med. Chem., 2012, 55 (14), pp 6582–6594. DOI: 10.1021/
jm300687e.
[6] Cleves AE, Jain AN. (2015) "Knowledge-guided docking: accurate prospective
prediction of bound configurations of novel ligands using Surflex-Dock." J Comput
Aided Mol Des. 2015 Jun;29(6):485-509. doi: 10.1007/s10822-015-9846-3.
[7] M. McGann, "FRED and HYBRID docking performance on standardized datasets",
J. Comp.-Aid. Mol. Design, 2012.
[8] Korb O, Stützle T, Exner TE. "Empirical Scoring Functions for Advanced Protein-
Ligand Docking with PLANTS" J.Chem.Inf.Model. 2009, 49, 84-96.
[9] Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, et
al. "rDock: A Fast, Versatile and Open Source Program for Docking Ligands to
Proteins and Nucleic Acids." PLoS Comput Biol, 2014, 10(4): e1003571. doi:10.1371/
journal.pcbi.1003571.
[10] Henderson, Nicholas C., and Michael A. Newton. "Making the cut: improved
ranking and selection for large‐scale inference." Journal of the Royal Statistical
Society: Series B (Statistical Methodology) (2015).
REFERENCES
SUMMARY & FUTURE WORK
Figure 1
Figure 1 A docking (green, red, yellow, and blue atoms) are docked to
a protein target of interest (grey surface) using docking software
AutoDock Smina v1.1.2 (smina). [1]
ENRICHMENT RESULTS
First, we estimate other parameters using maximum likelihood
estimation by integrating out θ. Then, θ of each ligand is
estimated by maximum a posteriori estimation:
Let Y = (y1,..., ym ) be a vector of docking scores for a ligand, θ be ligand
effect and
!
φ be program effect. Then distribution of Y is given by
p(Y)= p(Y |θ)p(θ)∫ dθ, where
Y|θ is Nm (
!
µθ,Σθ ) distributed,
!
µθ =
!
µ0 −θξ
!
φ, Σθ =σθ
2
!
1
!
1t
+
ξ2
η2
I −
!
φ
!
φt
( )
⎧
⎨
⎩
⎫
⎬
⎭
,
and σθ
2
is an increasing function of θ.
ˆθligand = arg max
θ
p(Y|θ)p(θ), where p(θ) is prior distribution of θ.
Meta-docking is applied on 14 targets × 8 programs × 271,451
ligands. Results from meta-docking are compared to standard
consensus schemes (Figure 5).
Meta-docking to improve protein-ligand interaction predictions from multiple docking scores
Figure 3 DUD-E: 8 programs, 14
targets and 271,451 ligands; 112
points in this plot: each is one
combination of a program and a
target; scaling scheme has been
applied on raw program scores
before calculating mean and
variance.
Figure 3
Figure 4
Figure 4 A subsample (actives
+ 5000 random decoys) for
target protein ACE is shown in
2-score (ad4 × dock6) space.
Background color shows the
magnitude of likelihood in the 2-
dimension space. Green
ellipses show different value of
ligand effect θ.
ACKNOWLEDGEMENTS
This work has been carried out
with the support of the NIH BD2K
grant: U54 AI117924 and CPCP
Stochastic Modeling Lab.
One of the key features seen in actives (ligands of high binding affinity
for a target) is the increased variation from ligand to ligand and decrease
in docking scores compared to decoys (ligands of low binding affinity for a
target) (indicated in Figure 2 and Figure 3).
Table 1: Data Summary
Target ace, adrb1, braf, cdk2, drd3, esr1, fa10, fabp4, gria2, hdac8,
mmp13, pde5a, ptn1, src
Software
AutoDock v4.2.6 (ad4), Dock 6.7 (dock6), AutoDock
Smina v1.1.2 (smina), Surflex v3.040[6] (surf), FRED
v3.0.1 (fred) [7] and Hybrid v3.0.1[7] (hybrid), PLANTS
v1.2[8] (plants), rDock v2013.1[9] (rdock)
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−4 −3 −2 −1 0
1234567
mean difference v.s. variance ratio between actives and decoys
[mean active] − [mean decoy]
[varactive]/[vardecoy]
−18 −16 −14 −12 −10 −8 −6
−16−14−12−10−8−6
ACE(2'd)
ad4
dock6
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Figure 5
Figure 2 Left: docking scores from 8 programs against target ACE; For
simplicity, actives(red) and 5000 decoys(grey) are shown in the plot.
Right: density histogram of docking score ad4 against target ACE.
Figure 2
−18 −10
−18−10
ad4
−18 −10 −18 −10 −18 −10 −18 −10 −18 −10 −18 −10 −18 −10
−18−10
dock6
−18−10
smina
−18−10
surf
−18−10
fred
−18−10
hybrid
−18−10
plants
−18−10
−18 −10
−18−10
rdock
ACE
Virtual Screening & Docking
A wide variety of docking software , such as AutoDock v4.2.6[2],
Dock 6.7 [3], and AutoDock Smina v1.1.2 [4], are available. We use
HT Condor to score all the ligands. However, none of these
programs have fully explored the interactions between ligand
molecules and target proteins. And multiple docking software varies
from each other.
Our focused task is to find a consensus docking method, that
attempts to combine results from multiple docking programs.
Docking Scores
Histogram(ACE)
ad4
Density
−20 −15 −10 −5
0.000.050.100.150.200.25
decoys
actives
Meta-docking
Multiple docking scores of a ligand against one target are modeled
as multivariate Gaussian distribution. We allow a parameter θ for
each ligand to distinguish distribution difference between actives
and decoys.
0 10000 20000 30000 40000 50000
0100200300400500
ace
0 10000 20000 30000 40000 50000
0100200300400500
adrb1
0 10000 20000 30000 40000 50000
0100200300400500
braf
0 10000 20000 30000 40000 50000
0100200300400500
cdk2
0 10000 20000 30000 40000 50000
0100200300400500
drd3
0 10000 20000 30000 40000 50000
0100200300400500
esr1
0 10000 20000 30000 40000 50000
0100200300400500
fa10
0 10000 20000 30000 40000 50000
0100200300400500
hdac8
Figure 5 Number of active ligands among number of top ranked ligands
by average (black), minimum (green), maximum (magenta), and meta-
docking (gold). DUD-E: 14 targets × 8 programs × 271,451 ligands.
0 10000 20000 30000 40000 50000
0100200300400500
fabp4
0 10000 20000 30000 40000 50000
0100200300400500
gria2
0 10000 20000 30000 40000 50000
0100200300400500
mmp13
0 10000 20000 30000 40000 50000
0100200300400500
pde5a
0 10000 20000 30000 40000 50000
0100200300400500
ptn1
0 10000 20000 30000 40000 50000
0100200300400500
src
average
minimum
maximum
meta−docking
An alternative to estimate θ is RVALUE[10].