Download Immersive Projection for Biochemical Pattern Matching

IMMERSIVE PROJECTION FOR BIOCHEMICAL PATTERN MATCHING
T. Nadan1§, V.N. Alexandrov1, K.A. Watson2
1
2
ACET, School of Systems Engineering
Structural Biology Unit, School of Biological Sciences
§
[email protected]
University of Reading, UK
ABSTRACT
In all biological processes, protein molecules and other
small molecules interact to function and form transient
macromolecular complexes. This interaction of two or
more molecules can be described by a docking event.
Docking is an important phase for structure-based drug
design strategies, as it can be used as a method to
simulate protein-ligand interactions. Various docking
programs exist that allow automated docking, but most
of them have limited visualization and user interaction.
It would be advantageous if scientists could visualize
the molecules participating in the docking process,
manipulate their structures and manually dock them
before submitting the new conformations to an
automated docking process in an immersive
environment, which can help stimulate the
design/docking process. This also could greatly reduce
docking time and resources. To achieve this, we
propose a new virtual modelling/docking program,
whereby the advantages of virtual modelling programs
and the efficiency of the algorithms in existing docking
programs will be merged.
1. INTRODUCTION
Large protein molecules, called receptors, and other
small molecules, called ligands, interact together to
form complex molecules and/or perform complex
biological functions. This process of protein-protein or
protein-ligand recognition can be described as docking.
The docking procedure is an important phase for most
drug design strategies, particularly those employing a
structure-based approach. There are existing docking
programs that perform automated docking, but these
are desktop-based and have limited visualization and
user interaction during the docking phase itself. To
stimulate the molecular design of new ligands
(potential drugs), it would be helpful if scientists could
visualize the molecules participating in the docking
process, manipulate their structures and manually dock
them before submitting the new conformation to the
automated docking process. Additionally, this could
result in a significant decrease in automated docking
time and computational resources used. Virtual Reality
(VR) is a field of computer simulation having
beneficial aspects that can be used for better
visualization and understanding of the different
receptor and ligand interactions.
This paper presents an overview of the current
state-of-the-art VR, protein information files, and
docking algorithms. An analysis of existing nonimmersive desktop docking programs and immersive
visualization and docking programs is also included for
the comparative purpose. A novel immersive program
is proposed and future work is outlined.
1.1.
Virtual reality
Virtual reality is a computer simulation of a real or
imaginary system that allows a user to perform
operations on the virtual (or simulated) system and the
effects are shown in real time. VR offers immersion,
imagination and interaction. There are three main
types of virtual reality systems, namely: nonimmersive, semi-immersive and fully immersive.
These vary in the level of immersion and degree of
presence, which is a subjective feeling of being
immersed in the virtual world.
In non-immersive systems the user is not
surrounded by the system (that is, the user is not
immersed into the environment). Semi-immersive
systems use high performance graphics computers and
usually project the simulated system on large monitors
or projectors. This provides a larger field of view
which subsequently increases presence. Fullyimmersive VR systems on the other hand completely
obscures the reality, such that the user is fully
immersed into the virtual environment. The two most
common ways of achieving full immersion are either
by wearing a Head Mounted Display (HMD) [1] or by
being in a Cave Automatic Virtual Environment
(CAVE) [2].
The HMD is a device that can partially or fully
exclude the user’s view of the real world, thus
enhancing the field of view of the virtual image. For
amino group
carboxylic group
peptide bond
(b)
(a)
Figure 1. (a) Structure of amino acid; (b) Condensation of two amino acids to form peptide bond.
realistic immersion, some have a motion tracker, which
tracks the user’s head position and orientation, and
updates the virtual image to the current view. However,
HMDs can be heavy and cumbersome, and a CAVE is
thus preferred.
The CAVE is a room-size, high resolution
environment with semi-transparent walls, onto which
stereo images are projected. Shutter glasses are worn to
provide the illusion of depth. These are synchronised
with the projectors and rapidly alternate between the
right and left eye, and allows the wearer to perceive
depth. The CAVE also has trackers, which serve the
same purpose as a tracker in a HMD. These can be
head or motion trackers. Within a CAVE, navigation
can be achieved by using a hand-held wand. The latter
is a small hardware device with a pressure-sensitive
joystick and trigger buttons. The wand allows
navigation within the environment and interaction with
the virtual objects themselves. Gloves can be used for
interaction but can also be extended to simulate a sense
of touch.
Since the CAVE optimally provides full immersion
and a large field of view, it is appropriate for the
development of an application for visualization and
manipulation of large complex biological molecules.
1.2.
Proteins
Proteins are large organic compounds made up of
amino acids. The latter are small molecules which have
a central alpha carbon (Cα) linked to an amino group
(NH2), carboxyl group (COOH), a hydrogen atom and
a side chain (R group), as shown in Figure 1 (a). The
naturally occurring 20 different amino acids vary in
their side chain, which confers specific chemical
properties upon the individual amino acids.
A peptide bond is formed when the carboxyl group
of one amino acid forms a covalent bond with the
amino group of another amino acid, releasing a water
molecule. Figure 1 (b) illustrates the condensation
process of two amino acids. Proteins are formed when
amino acids sequentially bond together giving rise to a
long polymer to form the primary structure, which in
turn stabilises to form the secondary structure (alpha
helixes or beta sheets). This stabilised structure then
folds to form the tertiary structure, the fully folded,
stable state of the protein. The interactions involved in
stabilising this folded state of the protein primarily
consist of weak van der Waal’s forces, disulfide bonds,
electrostatic interactions and hydrogen bonds. The
quaternary structure of a protein arises from binding of
two or more similar or different polypeptide chains.
1.2.1. Protein files
The three-dimensional (3D) structure of protein
molecules can be determined experimentally using
techniques such as Nuclear Magnetic Resonance
(NMR) spectroscopy and X-ray crystallography. The
3D molecular information is ultimately stored as
Cartesian coordinates (x, y, z) in files. There are
different file formats to store these data; the most
common are Protein Data Bank (PDB) [3],
Crystallographic Information File (CIF) [4] and
Macromolecular CIF Dictionary (mmCIF) [5]. Of
these different file types, the PDB format is the
simplest and most commonly used by biologists. The
PDB files contain important molecular information
such as the number of atoms, types of amino acid
residues, types of atoms, and spatial coordinates in
addition to text annotation referring to methodology
and publications. There are different atom related
records in a PDB files denoted by ATOM and
HETATM, which give details corresponding to atoms
of specific amino acids and any ligands (metals, small
molecules) respectively, that may be present in the
structure.
In the past there were different organisations
dealing with the annotation and public deposition of
PDB files, therefore PDB files often had different
formats. However, these organisations have now
adopted a common PDB format. Also, major changes
were brought about in the format of PDB files to cater
for the increasing number of files deposited. These
changes are elaborated in [6]. Due to the changes that
took place in the PDB format, inconsistencies in PDB
files are very common, such as mismatches in protein
Stick
CPK
Wireframe
Ball And Stick
Pipes And Planks
Ribbon
Figure 2. Molecular Representations.
chain identifiers. Furthermore, structural details which
cannot be observed experimentally result in missing
information (that is, breaks in the polypeptide chain or
missing amino acids in R side chains). If missing atoms
occur in backbones, there can be discrepancies in the
visualization of these molecules. Bhat et al. [7] and
Westbrook et al. [8] provide an in-depth study of PDB
inconsistencies.
1.2.2. Molecular representations
While visualizing and analysing protein structures,
scientists use different molecular representations to
emphasise different aspects and level of detail of the
molecules.
The
most
common
molecular
representations are: Wireframe, Stick, Ball-and-Stick,
Space Filling / Corey-Pauling-Koltun (CPK), Pipesand-Planks and Ribbon, these representations are
illustrated in Figure 2. The Ball-and-Stick is the most
detailed molecular representation and is therefore
mostly used to represent the individual atomic details
of proteins; while Pipes-and-Planks and Ribbons focus
more on the secondary and tertiary structures,
respectively.
1.3.
Drug design
Drug design can be performed in the traditional way
whereby researchers carry out high throughput in vitro
screening and many times obtain their results by trial
and error or after several years of research. However,
there is another way of finding new drugs, by making
use of computational tools, mainly docking
mechanisms. Drug designers use special tools with
appropriate docking mechanisms to find new drugs,
which fit the active site of a specific receptor. Docking
is used as an important concept in being able to better
understand molecular reactions and interactions as well
as for designing new drugs. The main steps involved in
structure based drug design are: 3D structure
determination, site representation, site identification /
characterization, ligand generation (either by docking
or de novo design) and scoring [9].
When a ligand is already known prior to the
docking process, several low energy ligand
conformations are searched and the best pose is
accepted. The best pose is defined as the conformation
which fits the binding site both geometrically and
energetically. However, when the ligand is not known
beforehand, huge chemical databases of molecules are
searched during the process. Alternatively, ligands may
be found through novel design and generation, a
process referred to as de novo construction. This can be
achieved either by a linking method, whereby
individual fragments are placed independently in the
active site and then linked together or by a growing
method, whereby a seed fragment is placed in the
active site to which fragments are added sequentially.
1.4.
Docking
Docking is the process of binding a ligand into the
cavity (also known as pocket or active site) of a
receptor to form a complex which is energetically and
geometrically stable. Figure 3 (a) shows a ligand and
receptor when undocked and the ligand in the
receptor’s cavity when docked. Different types of
docking interactions exist, ranging from simple to more
complex interactions, such as: protein-protein, proteinDNA, protein-metal and protein-ligand.
1.4.1. Types of docking
There are three main types of docking: rigid, semiflexible and flexible. In rigid docking, both the ligand
and the protein are considered to be rigid, as shown in
(a)
(b)
(c)
Figure 3. Docking; (a) Receptor and ligand docking (undocked and docked position); (b) Rigid docking; (c) Semiflexible docking. Arrows indicate degrees of allowed rotational freedom.
Figure 3 (b). Semi-flexible docking, on the other hand,
considers a flexible ligand and a rigid receptor, as
shown in Figure 3 (c). However, this is not how
docking naturally occurs. In reality, docking occurs as
a dynamic, fully flexible event, but due to the
complexities of the process, most automated docking
programs provide only non-flexible or semi-flexible
docking. Additionally, fully flexible docking requires
massive computational resources.
1.4.2. Docking algorithms
The docking process comprises two main steps: search
and evaluation. The search procedure scans for the best
configurational and conformational space.
For
effective docking results, the search algorithm must
cover 3D conformational spaces very quickly while the
scoring function must evaluate the different
conformations quickly among huge population of poses
(individual docked results) generated.
The most common search algorithms include
systematic search, stochastic search and simulation
methods. Systematic search algorithms include
incremental search and conformational search. These
types of algorithms scan for all possible degrees of
freedom and are therefore exhaustive. Stochastic search
algorithms include Monte Carlo [10], Genetic
Algorithm (GA) and Tabu search. These algorithms are
cyclic and have repetitive steps to improve the chance
of success. Simulation methods typically use a
combined energy minimization and molecular
dynamics algorithm.
The evaluation step makes use of a scoring function
which is used to discriminate between correct and
incorrect docking solutions. Scoring functions methods
are categorised as Knowledge-based [11], Empiricalregression [12] and Force-field. Knowledge-based
functions use simple atomic interactions to model
receptor-ligand complexes; while empirical methods
use the individual correlated terms to approximate
binding energies [13]. Force field functions on the
other hand use the energies of the receptor-ligand
interactions and may also include the ligand’s internal
energy.
2. EXISTING TECHNOLOGIES
There are many different docking programs which
provide both visualization and docking of molecules.
Most programs are run on desktop systems, are nonimmersive and thus have limited visual capabilities.
Visualization is an important aspect that helps
stimulate the design process.
2.1.
Non-immersive docking programs
Some common examples of non-immersive docking
programs are: DOCK [14], AutoDock [15, 16], FlexX
[17], Surflex [18] and Glide [19]. These programs have
different search algorithms and scoring functions.
DOCK is based on a sphere matching procedure
with a fixed ligand conformation. Early versions of
DOCK considered both the ligand and the receptor to
be rigid. Later on, partial ligand flexibility was
introduced by dividing the ligand into a few fragments,
which were then docked independently and then fused..
However, only the position and rotation of the ligand
were flexible and could be changed while the protein
part remained rigid. Recent versions of DOCK have
added features, such as force-field scoring enhanced by
solvation, receptor flexibility and better optimisation.
The DOCK scoring function is a simplification of the
Assisted Model Building and Energy Refinement
(AMBER) force field [20].
AutoDock is a commonly used docking program
among the scientific community. It is mainly used for
automated docking of flexible ligands to
macromolecules. It caters for more flexibility than
DOCK. AutoDock comprises two modules: AutoGrid
and AutoDock. AutoGrid pre-calculates a set of grids
for the receptor and the ligand while AutoDock
performs the docking of the ligand to these set of grids.
Early versions of AutoDock combined a rapid gridbased method for energy evaluation to calculate the
interaction energies. This was then used for Monte
Carlo simulated annealing to explore the
conformational space. However, the Monte Carlo
simulated annealing search performs well only with
ligands of eight or less rotatable bonds. Later versions
of AutoDock have incorporated a modified genetic
algorithm, known as the Lamarckian Genetic
Algorithm (LGA).
FlexX is an incremental construction approach
consisting of 3 main stages. The base fragments are
first selected followed by a placement of these base
fragments into the active pocket. The base fragments
are placed independently of the rest of the ligand. The
last step involves the incremental reconstruction of the
ligand inside the active site of the receptor. This
process is based on a simple greedy strategy.
Surflex is a fully automatic flexible molecular
docking algorithm which has an incremental
construction search approach. The ligand is first
fragmented into 1 – 10 fragments, with each having
some rotatable bonds. The search algorithm then scans
for surface-based molecular similarity to rapidly
generate putative conformations of ligand fragments. A
scoring function is then applied to the poses generated.
Glide is a docking program which makes an
approximation of a complete systematic search of the
conformational, orientational, and positional space of
the ligand. The search space is narrowed by having an
initial rough position and scoring phase. This step is
followed by a flexible energy optimization. Monte
Carlo sampling is then used to refine the best
candidates from the optimisation process. The scoring
function uses empirical and force-field based
expressions. Glide can be ‘more than twice as accurate
as FlexX for ligands having up to 20 rotatable bonds’
[19].
Different docking programs have been compared
for their efficiency and the main criteria to differentiate
between the various docking methods, which are, the
descriptions of the receptor site, type of ligand,
docking algorithm involved and scoring function have
been described [21]. All docking applications must
identify and prepare the binding site, dock the ligand
and evaluate the poses through the use of a scoring
function. According to an experiment performed by
Warren et al. [22], where ten docking programs were
tested against eight proteins, it was found that all of the
docking programs could correctly identify the ligand
conformation and were similar to those found in the
equivalent crystallographically determined proteinligand complexes. However, the scoring functions in
each case were less successful due primarily to the fact
that the scoring functions being evaluated were unable
to differentiate between closely related active and
inactive compounds.
2.2.
Immersive docking programs
In addition to desktop docking programs there are also
immersive modelling/docking programs, such as, Vibe
[23], Stalk [24], Applying Virtual Reality Visualization
to Protein Docking (VRDD) [25], Pauling World and
Distributed Pauling World [26] and AMMP-Vis [27].
Vibe is among the first molecular modelling
programs which have made use of a CAVE to provide
an immersive sensory experience to the user. The Vibe
program consists of simulation and display sub
modules. The simulation program calculates molecular
dynamics based on CHARMM (Chemistry at HARvard
Macromolecular Mechanics) [28] force field and a
parameter set, which uses bonded and non-bonded
interactions in the energy function. Once the molecular
dynamics are calculated, the simulation program sends
relevant information to the display program which
provides an immersive and interactive environment in
the CAVE. Any change in position of the ligand is sent
back to the simulation program which then calculates
the molecular dynamics for the new Cartesian points.
Stalk comprises two main parts: simulation and
visualization. The simulation program runs a robust
genetic algorithm. It calculates the lowest energy
conformation and assigns a value to each individual
(referred to as a ‘string’) in the population. After any
iteration the simulation program interacts with the
visualization program by sending the string
corresponding to the lowest energy conformation. The
visualization program uses the string parameter to
compute and display the lowest energy conformation in
a CAVE. The user of Stalk can interrupt the GA and
translate or rotate the molecule. The new position and
orientation values are sent back to the GA and an
energy evaluation can be done. However, it was found
that when the GA is interrupted it gives worst results
than when it runs uninterrupted. Stalk has another
limitation which is the visualization program has a
sphere representation for the molecules. This is a timeconsuming mode, therefore creating a time lag between
the time the user moves a molecule and the time the
molecule is redrawn in the CAVE. It is also not the
most helpful representation for detailed molecular
analysis and further molecular design. Stalk can also be
confusing sometimes for the user as molecules usually
have many atoms and the sphere representation of all
these atoms in the CAVE disorientates the user in the
immersive environment.
The main objective of VRDD is to address the
drawbacks of Stalk. VRDD is based on a Monte Carlo
algorithm, which explores the current ligand
orientation for a number of steps defined by the user.
For each step, a translation and/or rotational
perturbation is applied to the ligand orientation. Only
new orientations having lower free energies are
accepted. The Monte Carlo local search removes
clashes involved in van der Waal’s and electrostatic
energies. One tremendous advantage of VRDD is that
it uses a Solvent Accessible Surface (SAS)
representation which is faster than the sphere
representation in Stalk.
Pauling World (PW) is a virtual application for
simulation and visualization as well as interaction and
manipulation of molecular structures. Distributed
Pauling World is a distributed version of PW. The
virtual world is shared among users who are located at
different places. The main user has control of the menu
and the other users are represented as avatars and
control can be passed on among the users. Distributed
Pauling World makes use of a distributed database of
models to maintain a distributed virtual environment.
Each user has a local copy of all the models. Thus,
there is no need for a high bandwidth, as only state
changes are communicated to the users. The main
drawback of distributed PW is that it is currently
limited to two users only and problems of bandwidth
can arise for real time interaction. Also, Distributed
PW makes use of distributed virtual environment
which can be costly.
AMMP-vis is a collaborative visualization and
modelling program consisting of a molecular dynamics
server (AMMP), an
orientation server, a
communication server and any number of clients. The
AMMP server has the current state of the molecule and
writes and receives molecular updates from each client.
Each user in the virtual environment can define an area
of interest by defining a bounding box. Objects outside
the bounding box are thus de-emphasised. If there is an
overlapping of the area of interest, the bounding box
area is allocated on a first-come-first-serve basis.
AMMP-vis is very flexible in that users can be
equipped with a HMD or simply use a desktop monitor
with cheap gaming gloves. However, AMMP-vis has a
scalability problem. When there is a change in
molecular orientation, the information is broadcasted to
all the clients. If there are too many clients,
performance may be affected. Another limitation of
AMMP-vis is the way feedback information is sent,
which is currently done via text.
3. CURRENT WORK
As mentioned earlier, it is beneficial if scientists can
visualize the receptor and the ligand in order to
manipulate and manually dock the ligand prior to
submission to an automated docking program. To
achieve this, they should be able to properly visualize
the binding pocket/cavities. Existing non-immersive
automated docking programs are limited on visual
information. A better visualization and understanding
of molecules in 3D are achieved if the molecules are
projected in a virtual system such as the fullyimmersive CAVE. Although there are a limited
number of virtual modelling/docking programs, these
have been shown to have disadvantages, which may
limit their use in stimulating novel drug design. The
aim of our work is to exploit the advantages of each
method and produce an alternative method which has
ultimate functionality and flexibility in both
visualization and modelling/docking.
Flexible visualization of the molecular information
obtained from a PDB file format has already been
achieved in the CAVE. Figure 4 shows a user
visualizing a CPK representation of a molecule
structure in the CAVE.
Figure 4. Visualization of CPK structure in CAVE.
Current work is now focused on the
modelling/docking methods in the CAVE to develop a
user-friendly interface for molecular design and
interaction. This is being achieved by a set of menus
and sub-menus that allow complete manipulation of the
visualized receptor and ligand. The menus/sub-menus
provide operations such as loading different files,
manipulation and modelling molecules (rotation,
translation, bond making and bond breaking), fragment
manipulation and data handling of multiple PDB files.
These operations are performed using a combination of
wireless technologies; wand, glove and tracking.
4. FUTURE WORK
Future work will focus on the docking process. Initial
work will emphasise on the use of existing docking
programs to make use of their robust algorithms.
Another possibility would be to use the GA as utilised
by Stalk coupled with the SAS representation of
VRDD. This could then be used as a stepping stone for
a distributed collaborative system. The idea of a local
database of models at a client’s side also may be
adopted from Distributed Pauling World, along with
the bounding box concept proposed in AMMP-vis.
Furthermore, it may be advantageous to develop a new
docking algorithm based on a Monte Carlo approach,
specific to the CAVE environment and exploiting
distributed computing.
5. CONCLUSION
High quality visualization in a 3D environment is
significant for a more thorough understanding and
analysis of the 3D structure of molecules, particularly
with respect to finding cavities and active sites in
receptor molecules and trying to optimally dock
ligands into those cavities. The CAVE provides an
ideal environment to achieve such quality
visualization. In this paper, molecular details such as
amino acids, proteins and protein information files
have been outlined to provide contextual understanding
of the problem. Details of existing docking methods
have been explained to provide background rationale
toward the development of a more fully functional and
flexible 3D environment that will greatly enhance the
design of potential new drug molecules.
6. REFERENCES
[1] I.E. Sutherland, ‘A head-mounted threedimensional display’, in Proceedings of the Fall
Joint Computer Conference, AFIPS Conference
Proceedings, AFIPS, Arlington, VA, 1968, vol.
33, pp 757-764.
[2] C. Neira, D. Sandin and T. DeFanti, ‘The CAVE:
audio visual experience automatic virtual
environment’, Communications of the ACM, 1992,
vol. 35, no. 6, pp 64-72.
[3] F.C. Bernstein, T.F. Koetzle, G.J.B. Williams,
E.F. Meyer Jr., M.D. Brice, J.R. Rodgers,
O.Kennard, T. Shimanouchi and M. Tasumi, ‘The
Protein Data Bank: A computer-based archival
file for macromolecular structures’, Journal of
Molecular Biology, vol. 112, no. 3, pp 534-542,
1977.
[4] S.R. Hall, F.H. Allen and I.D. Brown ‘The
Crystallographic Information File (CIF): A new
standard archive file for crystallography”, Acta
Crystallographica, vol. 47, pp 655-685, 1991.
[5] P.E. Bourne, H.M. Berman, B. McMahon, K.
Watenpaugh, J. Westbrook and P.M.D. Fitzgerald,
‘The
Macromolecular
Crystallographic
Information File (mmCIF)”, Methods in
Enzymology, vol. 277, pp 571-590, 1997.
[6] J. Callaway, M. Cummings, B. Deroski, P.
Esposito, A. Forman, P. Langdon, M. Libeson, J.
McCarthy, J. Sikora, D. Xue, E. Abola, F.
Bernstein, N. Manning, R. Shea, D. Stampf and J.
Sussman, ‘Protein Data Bank contents guide:
atomic coordinate entry format description’
Brookhaven National Laboratory, 1996 .
[7] T.N. Bhat, P. Bourne, Z. Feng, G. Gilliland, S.
Jain, V. Ravichandran, B. Schneider, K.
Schneider, N. Thanki, H. Weissig, J. Westbrook
and H.M. Berman, ‘The PDB data uniformity
project’, Nucleic Acid Research, vol. 29, no. 1, pp
214-218, 2001.
[8] J. Westbrook, Z. Feng, S. Jain, T.N. Bhat, N,
Thanki, V. Ravichandran, G.L. Gilliland, W.F.
Bluhm, H. Wessig, D.S. Greer, P.E. Bourne and
H.M. Berman, ‘The Protein Data Bank: unifying
the archive’, Nucleic Acids Research, vol. 30,
no.1, pp 245-248, 2002.
[9] P.E.
Bourne,
H.
Weissig
Bioinformatics’, Wiley-Liss, 2003.
‘Structural
[10] R.A. Abagyan and M.M. Totrov, ‘Biased
probability Monte Carlo conformation searches
and electrostatic calculations and peptides and
proteins’, Journal of Molecular Biology’, vol.
235, no. 3, pp 983-1002, 1994.
[11] H. Gohlke, M.Hendlich and G. Klebe, ‘
Knowledge-based scoring function to predict
protein-ligand interactions’, Journal of Molecular
Biology, vol. 295, pp. 337-356, 2000.
[12] H.J. Bohm, ‘ The development of a simple
empirical scoring function to estimate the binding
constant for a protein-ligand complex of known
three-dimensional
structure’,
Journal
of
Computer-Aided Molecular Design, vol. 8, no. 3,
pp. 243-256. 1994.
[13] D. Kitchen, H. Decornex, J. Furr and J. Bajorath
‘Docking and scoring in virtual screening for drug
discovery: methods and applications’, Nature
Reviews Drug Discovery, vol. 3, pp. 935-949,
2004.
[14] I.D. Kuntz, J.M. Blaney, S.J. Oatley, R. Langridge
and T.E. Ferrin, ‘A geometric approach to
macromolecule-ligand interaction’, Journal of
Molecular Biology, vol. 161, no. 2, pp 269-288,
1982.
[15] D.S. Goodsell and
docking of substrates
annealing’, Proteins:
Genetics, vol. 8, no. 3,
A.J. Olson, ‘Automated
to proteins by simulated
Structure, Function and
pp 195-202, 1990.
[16] G.M. Morris, D.S. Goodsell, R.S. Halliday, R.
Huey, W.E. Hart, R.K. Belew and A.J Olson,
‘Automated docking using a Lamarckian genetic
algorithm and an empirical binding free energy
function’, Journal of Computational Chemistry,
vol. 19, no.14, pp 1639-1662, 1998.
[17] M. Rarey, B. Kramer, T. Lengauer and G. Klebe,
‘A fast flexible docking method using an
incremental construction algorithm’, Journal of
Molecular Biology, vol. 261, no. 3, pp 470-489,
1996.
[18] A.N. Jain, “Surflex: fully automatic flexible
molecular docking using a molecular similaritybased search engine’, Journal of Medicinal
Chemistry, vol. 46, no. 4, pp 499-511, 2003.
[19] R.A. Friesner, J.L. Banks, R.B. Murphy, T.A.
Halgren, J.J. Klicic, D.T. Mainz, M.P. Repasky,
E.H. Knoll, M. Shelley, J.K. Perry, D.E. Shaw, P.
Francis and P.S. Shenkin, ‘Glide: a new approach
for rapid, accurate docking and scoring. 1. Method
and assessment of docking accuracy”, Journal of
Medicinal Chemistry, vol. 47,no. 7, pp 1739-1749,
2004.
[23] C. Cruz-Neira, R. Langley and P.A. Bash ‘VIBE:
a virtual biomolecular environment for interactive
molecular modeling’, Computers and Chemistry,
vol. 20, no. 4, pp 469-477, 1996.
[24] D. Levine, M. Facello, P. Hallstrom, G. Reeder,
B. Walenz and F. Stevens, ‘Stalk: an interactive
system for virtual molecular docking’, IEEE
Computational Science and Engineering, vol. 4,
no.2, pp 55–65, 1997.
[25] A. Anderson and Z. Weng, ‘VRDD: applying
virtual reality visualization to protein docking and
design’, Journal of Molecular Graphics and
Modelling, vol. 17, no. 3, pp 180-186, 1999.
[26] S. Su, B. Loftin, D. Chen, Y.C. Fang and C.Y.
Lin,
‘Distributed
collaborative
virtual
environment : Pauling World’, In Proceedings of
the Tenth International Conference on Artificial
Reality and Tele-existence, Taipei Taiwan, 25-27
Oct 2000, pp. 112-116.
[20] W.D. Cornell, P. Cieplak, C.I. Bayly, I.R. Gould,
K.M. Merz Jr., D.M. Ferguson, D.C. Spellmeyer,
T. Fox, J.W. Caldwell and P.A. Kollman, ‘A
second generation force field for the simulation of
proteins, nucleic acids and organic molecules’
Journal of American Chemical Society, vol. 117,
pg 5179-5177, 1995.
[27] J. Chastine, Y. Zhu, J. Brooks, S. Owen and R.
Harrison, ‘A collaborative multi-view virtual
environment for molecular visualization and
modeling’, In Proceedings of the Third
International Conference on Coordinated &
Multiple Views in Exploratory Visualization
(CMV), London, UK, IEEE, 2005, pp. 77-84.
[21] S.E. Harding and B.Z. Chowdhry, ‘Protein-ligand
interactions:
structure
and
spectroscopy
(Practical Approach)’, Oxford University Press,
2001.
[28] B.R. Brooks, R.E. Bruccoleri, B.D.Olason, D.J.
States, S. Swaminathan and M. Karplus,
‘CHARMM: a program for macromolecular
energy, minimization, and dynamics calculations’,
Journal of Computational Chemistry, vol. 4, no. 2,
pp. 187-217, 1983.
[22] G. Warren, W. Andrews, A.M. Capelli, B. Clarke,
J. LaLonde, M.H. Lambert, M. Lindvall, N.
Neveins, S.F. Semus, S. Senger, G. Tedesco, I.D.
Wall, J.M. Woolven, C.E. Peishoff and M.S.
Head, ‘A critical assessment of docking programs
and scoring functions’, Journal of Medicinal
Chemistry, vol. 49, no. 20, pp 5912-32, 2006.