by Warshel and co-workers who have systematically examined performance of
protein dipoles Langevin dipoles (PDLD) and other techniques using phosphorylcholine analogs binding to murine myeloma protein (McPC603) [261].
7 Molecular Recognition and Brownian Dynamics
As earlier discussed diffusional encounter of reacting substrates is the prerequisite
for the binding interaction to happen [153]. Diffusional encounter is basically
controlled by the long-range electrostatic interaction between participating chemical
species [262]. Generally, the timescale of such encounter is from micro- to millisecond, which is tough to achieve with existing hardware technologies using
molecular dynamics even for small- to moderate-sized biomolecules [263].
Therefore, simplified coarse-grained models of biomolecules can be simulated
using Langevin dynamics and Brownian dynamics [262]. Brownian dynamics has
been successfully applied to study ion permeations through ion channels [264] and
enzymatic reactions [265]. However, to gain kinetic insight into receptor–ligand
recognition, BD can be utilized [266–269], but BD being computationally very
expensive is practically challenging [263]. This has called for alternate methods
with simplistic approaches to study recognition process.
Supervised molecular dynamics (SuMD), a tabu-like search algorithm, aims to
predict the pose of the ligand in the binding site of its cognate receptor, monitoring
ligand-binding site distance along a series of short MD simulation has been proposed [225]. SuMD has been successfully applied to study a variety of molecular
recognition processes [270–272]. In particular, Moro and co-workers applied to
study molecular recognition process of four globular receptor–ligand systems and
two transmembrane receptor ligand systems; in all these cases, experimental crystal
structures and binding affinity values (IC 50 , K i or K d ) were already known [271]. In
the study, it is observed that using SuMD, binding from unbound state (where
ligand is placed at >30–50 Å away from binding site) of above ligands to their
cognate receptor can be simulated; moreover, various interaction hot spots (metastable states) during recognition are possible to explore, which may be important in
providing insight into kinetics of the recognition process, hence better designing of
ligand [271]. In another study, the effect of allosteric modulator LUF6000 on
adenosine binding with A 3 adenosine receptor (A 3 AR) was reported. In this study,
recognition of allosteric modulator LUF6000 to A 3 AR and adenosine to A 3 AR in
presence and absence of LUF6000 was studied using SuMD. It is observed that
adenosine visited a metastable site between helices EL3 and EL2, participating in
hydrogen bonds with Val259 and Gln261, and it triggers an orientation change in
adenosine mediated through hydrophobic interactions before occupying the binding
site [270]. In future, such techniques along with Free Energy perturbation method
will provide more accurate estimation of free energy binding of ligands to receptors
which will include the flexibility of both partners.
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
159
protein dipoles Langevin dipoles (PDLD) and other techniques using phosphorylcholine analogs binding to murine myeloma protein (McPC603) [261].
7 Molecular Recognition and Brownian Dynamics
As earlier discussed diffusional encounter of reacting substrates is the prerequisite
for the binding interaction to happen [153]. Diffusional encounter is basically
controlled by the long-range electrostatic interaction between participating chemical
species [262]. Generally, the timescale of such encounter is from micro- to millisecond, which is tough to achieve with existing hardware technologies using
molecular dynamics even for small- to moderate-sized biomolecules [263].
Therefore, simplified coarse-grained models of biomolecules can be simulated
using Langevin dynamics and Brownian dynamics [262]. Brownian dynamics has
been successfully applied to study ion permeations through ion channels [264] and
enzymatic reactions [265]. However, to gain kinetic insight into receptor–ligand
recognition, BD can be utilized [266–269], but BD being computationally very
expensive is practically challenging [263]. This has called for alternate methods
with simplistic approaches to study recognition process.
Supervised molecular dynamics (SuMD), a tabu-like search algorithm, aims to
predict the pose of the ligand in the binding site of its cognate receptor, monitoring
ligand-binding site distance along a series of short MD simulation has been proposed [225]. SuMD has been successfully applied to study a variety of molecular
recognition processes [270–272]. In particular, Moro and co-workers applied to
study molecular recognition process of four globular receptor–ligand systems and
two transmembrane receptor ligand systems; in all these cases, experimental crystal
structures and binding affinity values (IC 50 , K i or K d ) were already known [271]. In
the study, it is observed that using SuMD, binding from unbound state (where
ligand is placed at >30–50 Å away from binding site) of above ligands to their
cognate receptor can be simulated; moreover, various interaction hot spots (metastable states) during recognition are possible to explore, which may be important in
providing insight into kinetics of the recognition process, hence better designing of
ligand [271]. In another study, the effect of allosteric modulator LUF6000 on
adenosine binding with A 3 adenosine receptor (A 3 AR) was reported. In this study,
recognition of allosteric modulator LUF6000 to A 3 AR and adenosine to A 3 AR in
presence and absence of LUF6000 was studied using SuMD. It is observed that
adenosine visited a metastable site between helices EL3 and EL2, participating in
hydrogen bonds with Val259 and Gln261, and it triggers an orientation change in
adenosine mediated through hydrophobic interactions before occupying the binding
site [270]. In future, such techniques along with Free Energy perturbation method
will provide more accurate estimation of free energy binding of ligands to receptors
which will include the flexibility of both partners.
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
159