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Can we trust docking results?
Sept 2010 IBM Systems and Technology Group releases a white paper with eHiTS and Cell
Oct 2008
EPA's ToxCastTM project will use SimBioSys' eHiTS as docking engine
Nov, 2007
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[Events]
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| 243rd ACS
Mar 25-29, 2012
San Diego, CA
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Toronto, Canada Jul 18-19, 2008
A novel scoring function in eHiTS and
LASSO
Z. Zsoldos
SimBioSys Inc., 135 Queen's Plate Dr, Unit 520, Toronto, ON M9W
6V1, Canada
Abstract:
The primary goal of most virtual screening experiments is to find new
lead compounds as starting point of the drug discovery pipeline. There
are two typical approaches that are sometimes combined to a screening
funnel: ligand-based (2D similarity, 3D pharmacophore, fingerprint,
surface or other QSAR descriptor) and structure based flexible ligand
docking and scoring. The later is often considered too slow for large
scale screening (databases of millions of structures), while the former
does not provide 3D coordinates or estimated binding energies.
The fragment based exhaustive flexible ligand docking engine of eHiTS
has been published previously [1]. Now we would like to focus on the
scoring function of eHiTS, which departs from the traditional atom
based interaction scoring (typical to most empirical, force field based
and statistical scoring methods as well) and introduces a novel concept
of scoring interactions based on Interacting Surface Points (ISP) that
are represented by 3D position, normal vector and chemical feature type
(23 types including H-bond donor/acceptor, aromatic Pi electron,
hydrophobic etc.). A statistically derived empirical scoring function
is constructed using 4-parameter geometric description of the
relationship between ISP pairs. The geometry parameters include
distance between the pair of ISP, angles between the normal vectors and
the direction of the interaction and a dihedral angle between the
normals. The energy associated with each possibly ISP pair is deduced
from the statistics based on reverse application of the Boltzmann
equation. During the statistics collection, the temperature factors
were considered with the corresponding Gaussian functions applied to
the atom positions to account for the variable uncertainty of the atom
positions in PDB X-ray structures. More accurate geometric statistics
have been collected from the CrystalEye and recently incorporated into the PDB
data. Certain atoms (e.g. Nitrogen in the imidazol ring) may
participate in very different type of interactions at the same time
(e.g. H-bonding and aromatic Pi-stacking). The ISP representation can
capture these interactions better than the atom based approach by
having multiple ISP associated with the same atom in different
directions.
The advantage of the statistically driven ISP scoring is demonstrated
on a case study using the Acethylcholine Binding Protein (AChBP) which
has a key cation-Pi interaction observed crystallographically for
several substrates (e.g. CCE, Nicotine, Lobeline, Epibatidine).
Empirical and force field based scoring functions fail to rank the
correct binding pose highest even when using DFT-6-31**B3LYP charges.
In contrast, eHiTS produces the correct pose with the best score even
when using the default statistical table and weighting scheme for which
no example from this protein family was included. When the automated
training script is run to include the family in the knowledge base, the
energy separation between the correct pose and other generated poses
improves, providing very cleanly distinguished clusters. Furthermore,
the eHiTS score gives a good correlation with the experimentally
measured log(Kd) values for the series, correctly rank ordering the
actives.
A simple count of the various ISP types present on a ligand provides a
very compact descriptor for the ligand's interaction activity profile.
We have used these descriptors via a machine learning technique to
create a very rapid ligand based VHTS filter - called LASSO (Ligand
Activity in Surface Similarity Order) [3]. The descriptor is
independent of 3D conformation and is focused on the interaction
properties rather than connectivity or structural similarity, therefore
it is capable of scaffold hopping, i.e. retrieving active ligands with
different underlying structure. LASSO is demonstrated to achieve high
enrichment rates for all families included in the DUD benchmark set
[4]. LASSO offers an extremely rapid filtering in excess of a million
ligands per minute on a single CPU.
The eHiTS flexible docking has proved to be among the most accurate
pose prediction tools [5] and combined with the LASSO ligand based
filter it provides one of the highest enrichment factors based on
comparative evaluation studies [6]. While LASSO can rapidly and
efficiently reduce the number of candidates to be docked to a few
percent of the total database, the accurate flexible docking with eHiTS
used to take several minutes of CPU time per ligand on traditional
hardware architectures. The algorithm has been recently redesigned and
coded to take advantage of the Cell BE accelerator architecture
providing 30-100 fold speed-up [7] bringing the run-time down to a few
seconds per ligand on a PS3 or an IBM Cell Blade for the most accurate
flexible docking.
The revolutionary hardware technology requires new computation methods,
replacing approximate precomputed grids with proximity look-up and
explicit pair-wise interaction computation. As a result, the
calculation is not only orders of magnitude faster, but it also
provides more accurate energy predictions. The emerging technologies
presented could also be applied to speed-up other molecular modeling
related problems, e.g. QM or MD simulations and protein folding, by
multiple orders of magnitude.
References:
[1] Z. Zsoldos, D. Reid, A. Simon, S.B. Sadjad, A.P. Johnson: eHiTS a
new fast, exhaustive flexible ligand docking system; Journal of
Molecular Graphics and Modeling. Volume 26, Issue 1, July 2007, Pages
198-212; doi:10.1016/j.jmgm.2006.06.002
[2] S.B. Hansen, G. Sulzenbacher, T. Huxfold, P. Marchot, P. Taylor, Y.
Bourne: Structures of Aplysia AChBP complexes with nicotinic agonists
and antagonists reveal distinctive binding interfaces and
conformations. The EMBO Journal (2005) 24, 3635-3646.
doi:10.1038/sj.emboj.7600828
[3] D. Reid, B.S. Sadjad, Z. Zsoldos, A. Simon: LASSO - ligand activity
by surface similarity order: a new tool for ligand based virtual
screening.
Journal of Computer-Aided Molecular Design,
http://dx.doi.org/10.1007/s10822-007-9164-5,
doi: 10.1007/s10822-007-9164-5
[4] N. Huang, B.K. Shoichet, J.J. Irwin: Benchmarking sets for
molecular docking.
J. Med. Chem. 49(23): 6789-801
[5] Kontoyianni, M.; McClellan, L. M.; Sokol, G. S.:
Evaluation of
Docking Performance: Comparative Data on Docking
Algorithms, J. Med. Chem., 2004;
47(3); 558-565.
http://www.simbiosys.com/ehits/ehits_validation.html
[6] G.B. McGaughey, R.P. Sheridan, C.I. Bayly, C. Culberson,
C. Kreatsoulas, S. Lindsley, V. Maiorov, J. Truchon and W.D. Cornell:
Comparison of Topological, Shape, and Docking Methods in Virtual
Screening
J. Chem. Inf. Model. 2007; 47(4), pp 1504 - 19 DOI:
10.1021/ci700052x
http://www.simbiosys.com/ehits/ehits_enrichment.html
[7] Bio-IT World article (http://www.bio-itworld.com/inside-it/2008/05/gta4-and-life-sciences.html)
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