Bioinformatics-2012-Vlachakis-bioinformatics_bts637.pdf

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Bioinformatics Advance Access published October 25, 2012
Introducing Drugster: a comprehensive and fully integrated drug
design, lead and structure optimization toolkit
Dimitrios Vlachakis
1#
, Dimosthenis Tsagrasoulis
1#
, Vasileios Megalooikonomou
2
and Sophia
Kossida
1
*
1
2
Bioinformatics and Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Athens, Greece
Computer Engineering and Informatics Department, School of Engineering University of Patras, 26500 Patras, Greece
Editor:
Prof Alfonso Valencia
Downloaded from
http://bioinformatics.oxfordjournals.org/
at Uniwertytet Gdanski on June 3, 2014
ABSTRACT
Summary:
Drugster is a fully interactive pipeline designed to break
the command line barrier and introduce a new user-friendly envi-
ronment to perform drug design, lead and structure optimization
experiments through an efficient combination of the PDB2PQR,
Ligbuilder, Gromacs and Dock suites. Our platform features a novel
workflow that guides the user through each logical step of the itera-
tive 3D structural optimization setup and drug design process, by
providing a seamless interface to all incorporated packages.
Availability:
Drugster can be freely downloaded via our dedicated
server system at
http://www.bioacademy.gr/bioinformatics/drugster/.
Contact:
For support, comments and bug reports please contact:
dvlachakis@bioacademy.gr.
2
DESCRIPTION OF DRUGSTER
Drugster’s main-window is a menu-driven interface as well as a
tab step-by-step layout (Fig1.A). It provides the user with a process
window to monitor active calculations in real time as well as with a
command-line equivalent (Fig1.B).
1
INTRODUCTION
Drugster is a fully integrated, Perl/Tcl-Tk based, interactive plat-
form combining in a rational pipeline the algorithms of PDB2PQR
v.1.8 (Dolinsky
et al.,
2004 and Dolinsky
et al.,
2007) Ligbuilder
v.1.2 and v.2.0 (Yuan
et al.,
2011 and Wang
et al.,
2000), Gromacs
v.4.5.5 (Hess
et al.,
2008) and Dock v.6.5 (Lang
et al.,
2008). All
previously mentioned algorithms remain a native set of numerous
UNIX-based modules, lacking a comprehensive and object-
oriented graphical user interface (GUI). Therefore, Drugster was
developed to ease and automate the full task of setting up drug
design, lead and structure optimization experiments. All major 3D
molecular viewers can be used for visualization purposes. In this
study we used Pymol (DeLano, 2002) as molecular viewer. In the
beginning, Drugster addresses all common problems associated
with PDB file formatting and partial charges. Subsequently, the
receptor will be structurally optimized by energy minimization
using a variety of different forcefields as implemented into
Gromacs. Upon structural optimization the Ligbuilder algorithm is
used to generate novel molecules for the given site or to improve
an existing lead compound. Dock is used to verify and evaluate the
potential of each newly designed ligand, as it is used to re-score all
candidate compounds and search for better docking interactions.
Finally, the receptor-ligand complex is energetically minimized, to
reduce any residual geometrical strains, and subsequently subject-
ed to molecular dynamics simulations (MDs) allowing full degrees
of freedom to both the ligand and the receptor. There is an option
for a final energy minimization step after the MDs.
*
#
Fig. 1. A:
The main window of the Drugster platform.
B:
The process
window helps to monitor active calculations in real time and below the
command-line equivalent translator window,
C:
The output trajectory post-
molecular dynamics analysis window
D:
The Help Button.
E:
A snapshot
of the full parameterization potential offered by Drugster.
F:
The incorpo-
rated visualization tool.
To whom correspondence should be addressed.
Equally contributed to this study
A complete drug design and/or lead and structure optimization
experiment using the Drugster toolkit is broken down in five steps:
1) Input preparation.
This is a very crucial step missing from most major suites, where
all common PDB file problems are automatically fixed prior to the
experiment. There are some other platforms that provide tools for
protein preparation but they include modules that are commercially
available even for academic use (Lill
et al.,
2011 and MOE, 2010
and Sybyl, 1994). Missing hydrogens are added, partial charges are
calculated, heteroatoms can be removed and the C’ and N’ termini
of the protein can be neutralized.
2) Receptor optimization.
One of the major drawbacks of structure based drug design algo-
rithms is the lack of conformational optimization of the receptor.
© The Author (2012). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
1
Using the versatile Gromacs suite, the receptor is energetically
minimized prior to the
de novo
drug design experiment. This au-
tomated step addresses many inconsistencies and free energy is-
sues that may derive by removing heteroatoms, without restoring
to the “relaxed” conformation of the receptor PDB file, which will
be used for the generation of new ligands.
3) Ligand building.
At this stage the actual
de novo
structure-based drug design of new
ligand structures takes place. This tab enables the user to fully
parameterize the ligand building process, by offering support to
both Ligbuilder 1.2 and 2.0 versions. Here the process is organized
in three fully user-customizable phases. First a pharmacophore is
prepared, which prepares and summarizes the 3D properties of the
scaffolding, common core structures that will be later generated
and analyzed. Then the user has the choice of either the growing or
the linking algorithms of Ligbuilder. The combination of molecu-
lar fragments starts automatically as soon as the user has complet-
ed the parameters setup section (i.e. molecular weight, number of
donors/acceptors, LogP and other chemical properties). The third
phase is the compound screening function, where the elite mole-
cules are selected for the next step.
4) Ligand optimization and rescoring.
All ligand candidate molecules prepared in the previous step are
subjected to docking simulations using Dock, which are followed
by energy minimizations (EM) within the receptor. The ligand
molecules are then re-scored and re-ranked. Notably at this stage
EM is performed allowing full degrees of freedom for both the
ligand and the receptor. This way a certain degree of receptor flex-
ibility is allowed to the iterative drug design process. The re-
scoring approach is based on free energy perturbation and com-
pound-receptor interaction analysis. The scoring functions includ-
ed in Dock are very fast and versatile offering a reliable set of tools
for scoring and re-ranking our candidate compounds. Finally lig-
and topologies can be either automatically assigned or manually
using freely available dedicated software (Schüttelkopf
et al.,
2004
and Malde
et al.,
2011).
5) Complex optimization.
The final step of the Drugster pipeline is the automatic, error-prone
feeding of the top scoring candidate compounds to the Dock’s
molecular dynamics engine. Here options include a force field
selector, generator for the coordinate and topology files, the setup
of a periodic system, solvation and the actual molecular dynamics
simulation. Basic, but rather informative post MD simulation tra-
jectory analysis has been incorporated in order to aid the user to
speed up the selection of the best candidate compounds (Fig1. C).
Furthermore, an extensive manual for the use of Drugster, will
pop-up through the Help button (Fig1. D), using the system’s de-
fault HTML browser. Notably, through the Drugster interface it is
possible to start a log file, which is vital for keeping track of all
useful information that may prove to be time-consuming and com-
plicated for some users. Moreover, the status tray area provides
information about the logging process plus a process interruption
switch. There are three progress indicators, a progress bar showing
the current step as a fraction of the total steps, a label showing an
estimation of the remaining run time and the percentage (%) of
work completed. A processor, memory and swap file usage gauge
is to be found right next to the logging indicator, providing real
time information of the system’s resources. Calculations and step
counts are done by Drugster. Noteworthy, all input, output and
intermediate files are automatically stored in user pre-defined di-
rectories for further analysis, simulation/experiment resuming,
error tracing and archiving purposes.
3
CONCLUSIONS
In conclusion, the Drugster toolkit provides a novel, user-friendly,
fast and reliable tool for conducting drug design experiments with
the incorporation of a series of elite molecular modelling algo-
rithms in one platform. It is a fast and easy-to-use alternative to
rather expensive commercial suites, whilst being the only modern
and updated tool of its kind that is fully distributed as freeware.
4
AVAILABILITY
Drugster is an open source, cross platform application available
freely to all users under a GNU license basis. The full package,
including installation scripts, figures, a full description, a detailed
manual, complete tutorials as hands-on use cases, software prereq-
uisites and various examples can be downloaded at:
http://www.bioacademy.gr/bioinformatics/drugster/.
Prior to down-
load; check the provided information on the website about soft-
ware prerequisites. Please email comments and bug reports at
dvlachakis@bioacademy.gr.
Downloaded from
http://bioinformatics.oxfordjournals.org/
at Uniwertytet Gdanski on June 3, 2014
ACKNOWLEDGEMENTS
This work was partially supported by: 1) the EDGE (National
Network for Genomic Research) EU and Greek State co-funded
Project (09SYN-13-901 EPAN II Co-operation grant). 2) EU-
funded COST action BM1006, Next Generation Sequencing Data
Analysis Network. 3) European Union (European Social Fund -
ESF) and Greek national funds through the Operational Program
"Education and Lifelong Learning" of the National Strategic Ref-
erence Framework (NSRF) - Research Funding Program: Τhales.
Investing in knowledge society through the European Social Fund.
Finally, the authors would like to thank Dr. Zoe Cournia and her
team for critically reviewing the Drugster suite.
Conflict of Interest:
none declared.
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