As binding energy (and affinity) is related to the chemical potential of the proteinligand system in solution, one must consider all the factors which are involved in the
chemical potential. Thus, one must study how the solvent interacts with the protein/
ligand and the protein-ligand complex. In addition, one must consider all the
configurations (protein-ligand poses with different conformations) of the proteinligand complex which are relevant for binding. This is not done by two very popular
methods, namely, docking and QSAR, since in docking every individual pose is
scored independently, and QSAR does usually not consider anything else than
ligand 2D or 3D structural descriptors. Both of these methods have been successfully
used for quite a long time, QSAR since early 1960 [8] and docking from the early
1990s [9]. As one can easily understand, those methods were developed to be fast
and easily available, thus not requiring substantial computational power. This was
only possible by making those major simplifications which, at the time, were
acceptable but should be reconsidered in the current world.
Thanks to the current massive GPU and classical supercomputer environments, it
is now possible to study a full protein-solvent-ligand ensemble in a dynamical
fashion. Without going into details, it can be stated that molecular dynamics
(MD) approaches are the natural answer to the problem presented in Eq. (1).
Unfortunately, usage of MD simulations means that the computational burden is
much higher than with classical molecular docking or QSAR. This is not the only
issue, since results from MD simulations are quite complicated. Both, docking and
QSAR, are popular methods, partially, because they deliver simple numerical results
(scoring or predictions), easy to understand, and be compared. Even the most
“complicated” QSAR method, CoMFA [3], returns a clear (and often misleading)
3D image indicating those regions around the ligand structure which should be
modified to gain better binding interactions. The results from MD simulations are in
the form of molecular trajectories, describing atomistic movement and
corresponding kinetic and potential energies. One must use a substantial amount
of time and, paradoxically, computing power to analyze large MD trajectories before
results can be used to guide medicinal chemistry work. At the same time, there is no
easy and general procedure how to analyze MD trajectories quantitatively. Analytical procedure strongly depends on the research question. Thus it may be very timeconsuming just to find what to search for from the trajectory data.
Besides understanding atomic motion, one must use an appropriate protein
conformation for kinase modeling. Kinase inhibitors are classified as types I, 1½,
and II–VI [10]. The consensus is that type I inhibitors target catalytically active,
DFG-in conformation, and thus compete with ATP, while type II inhibitors target
inactive DFG-out conformation which lacks the ATP. Type 1½ inhibitors have high
affinity toward both DFG-in-like and DFG-out conformations, while types III and IV
are used for allosteric inhibitors. The last two types, V (bivalent inhibitor) and VI
(covalent) are not commonly used. Since this classification is based on the kinase
conformation, as seen in the corresponding inhibitor-kinase complex, one can easily
understand that protein kinase conformation does actually matter. Modeling must be
based on the protein structure matching the requirements of an inhibitor. Thus, if one
is modeling a classical type II inhibitor but the target protein conformation is a
28
A. Poso
chemical potential. Thus, one must study how the solvent interacts with the protein/
ligand and the protein-ligand complex. In addition, one must consider all the
configurations (protein-ligand poses with different conformations) of the proteinligand complex which are relevant for binding. This is not done by two very popular
methods, namely, docking and QSAR, since in docking every individual pose is
scored independently, and QSAR does usually not consider anything else than
ligand 2D or 3D structural descriptors. Both of these methods have been successfully
used for quite a long time, QSAR since early 1960 [8] and docking from the early
1990s [9]. As one can easily understand, those methods were developed to be fast
and easily available, thus not requiring substantial computational power. This was
only possible by making those major simplifications which, at the time, were
acceptable but should be reconsidered in the current world.
Thanks to the current massive GPU and classical supercomputer environments, it
is now possible to study a full protein-solvent-ligand ensemble in a dynamical
fashion. Without going into details, it can be stated that molecular dynamics
(MD) approaches are the natural answer to the problem presented in Eq. (1).
Unfortunately, usage of MD simulations means that the computational burden is
much higher than with classical molecular docking or QSAR. This is not the only
issue, since results from MD simulations are quite complicated. Both, docking and
QSAR, are popular methods, partially, because they deliver simple numerical results
(scoring or predictions), easy to understand, and be compared. Even the most
“complicated” QSAR method, CoMFA [3], returns a clear (and often misleading)
3D image indicating those regions around the ligand structure which should be
modified to gain better binding interactions. The results from MD simulations are in
the form of molecular trajectories, describing atomistic movement and
corresponding kinetic and potential energies. One must use a substantial amount
of time and, paradoxically, computing power to analyze large MD trajectories before
results can be used to guide medicinal chemistry work. At the same time, there is no
easy and general procedure how to analyze MD trajectories quantitatively. Analytical procedure strongly depends on the research question. Thus it may be very timeconsuming just to find what to search for from the trajectory data.
Besides understanding atomic motion, one must use an appropriate protein
conformation for kinase modeling. Kinase inhibitors are classified as types I, 1½,
and II–VI [10]. The consensus is that type I inhibitors target catalytically active,
DFG-in conformation, and thus compete with ATP, while type II inhibitors target
inactive DFG-out conformation which lacks the ATP. Type 1½ inhibitors have high
affinity toward both DFG-in-like and DFG-out conformations, while types III and IV
are used for allosteric inhibitors. The last two types, V (bivalent inhibitor) and VI
(covalent) are not commonly used. Since this classification is based on the kinase
conformation, as seen in the corresponding inhibitor-kinase complex, one can easily
understand that protein kinase conformation does actually matter. Modeling must be
based on the protein structure matching the requirements of an inhibitor. Thus, if one
is modeling a classical type II inhibitor but the target protein conformation is a
28
A. Poso