502
S. von Grafenstein et al.
a local energy minimum. As proteins may undergo major conformational rearrangements in solution even at room temperature, information accessible from static protein structures is inherently limited [1].
Hence, also the paradigm of protein–ligand recognition via a lock-and-key
model [2] had to be adapted several times over the last years. Koshland proposed
an induced fit of a single protein conformation to adapt to the ligand or substrate
upon complex formation [3]. This idea has been expanded by Tsai et al. to include
the whole conformational ensemble thermally accessible to a protein [4, 5]. They
propose a mechanism of conformational selection picking a protein conformation
favorable to bind the ligand from a pre-existing conformational thermodynamic
equilibrium, therefore covering the whole free energy landscape. Consequently,
higher energy conformations may contribute to ligand binding although not available from a static view of a protein [6].
These novel findings of protein chemistry are currently being incorporated
into structure-based drug design [7]. Structural ensembles available from various
sources are included in the computer-aided design process of novel therapeutics.
Protein–ligand docking may be extended from static docking to a single X-ray
structure to multiple X-ray structures, an NMR ensemble or an in silico generated
conformational ensemble [8]. Besides covering a more realistic view on protein–
ligand recognition, these novel approaches including conformational plasticity of
the receptor have been recognized as important step to treat these macromolecular
binding events with higher accuracy [9, 10]. The acceptance of flexibility as an
important issue in molecular recognition is also reflected in attempts to include
backbone-flexibility in protein-protein docking [11].
15.1.2 Molecular Dynamics Simulations
Molecular dynamics (MD) simulations are a computational technique giving access to a conformational ensemble for, e.g., a receptor protein [12]. The evolution
of an atomic system is simulated via a numerical solution of Newton’s law of motion over time. The potential energy is evaluated at every time step by a molecular
mechanics force field (e.g., AMBER force field FF99SB-ILDN [13]). The starting
configuration of the system is generated from an X-ray or NMR structure of the
macromolecule and a surrounding water shell is added. A time interval according to
the fastest movements in the system (hydrogen positions)—typically in sub-femtosecond scale—is chosen allowing for a numerical integration over the differential
equation for the particle movements. Hence, after every time step new positions,
new forces and new velocities are calculated for each atom of the system. After a
plethora of repeats a so-called trajectory of the system is successfully generated
containing an in silico image of biomolecular movements in solution. Nowadays,
typical trajectories realistically capture protein dynamics at atomistic level of detail
at nano to microsecond time scale [14]. The latter has been facilitated by the broad
S. von Grafenstein et al.
a local energy minimum. As proteins may undergo major conformational rearrangements in solution even at room temperature, information accessible from static protein structures is inherently limited [1].
Hence, also the paradigm of protein–ligand recognition via a lock-and-key
model [2] had to be adapted several times over the last years. Koshland proposed
an induced fit of a single protein conformation to adapt to the ligand or substrate
upon complex formation [3]. This idea has been expanded by Tsai et al. to include
the whole conformational ensemble thermally accessible to a protein [4, 5]. They
propose a mechanism of conformational selection picking a protein conformation
favorable to bind the ligand from a pre-existing conformational thermodynamic
equilibrium, therefore covering the whole free energy landscape. Consequently,
higher energy conformations may contribute to ligand binding although not available from a static view of a protein [6].
These novel findings of protein chemistry are currently being incorporated
into structure-based drug design [7]. Structural ensembles available from various
sources are included in the computer-aided design process of novel therapeutics.
Protein–ligand docking may be extended from static docking to a single X-ray
structure to multiple X-ray structures, an NMR ensemble or an in silico generated
conformational ensemble [8]. Besides covering a more realistic view on protein–
ligand recognition, these novel approaches including conformational plasticity of
the receptor have been recognized as important step to treat these macromolecular
binding events with higher accuracy [9, 10]. The acceptance of flexibility as an
important issue in molecular recognition is also reflected in attempts to include
backbone-flexibility in protein-protein docking [11].
15.1.2 Molecular Dynamics Simulations
Molecular dynamics (MD) simulations are a computational technique giving access to a conformational ensemble for, e.g., a receptor protein [12]. The evolution
of an atomic system is simulated via a numerical solution of Newton’s law of motion over time. The potential energy is evaluated at every time step by a molecular
mechanics force field (e.g., AMBER force field FF99SB-ILDN [13]). The starting
configuration of the system is generated from an X-ray or NMR structure of the
macromolecule and a surrounding water shell is added. A time interval according to
the fastest movements in the system (hydrogen positions)—typically in sub-femtosecond scale—is chosen allowing for a numerical integration over the differential
equation for the particle movements. Hence, after every time step new positions,
new forces and new velocities are calculated for each atom of the system. After a
plethora of repeats a so-called trajectory of the system is successfully generated
containing an in silico image of biomolecular movements in solution. Nowadays,
typical trajectories realistically capture protein dynamics at atomistic level of detail
at nano to microsecond time scale [14]. The latter has been facilitated by the broad