included in some of the scoring functions, like Glide XP [42, 43]. However, a more
direct approach is to use information from the X-ray structures and especially MD
simulations with explicit solvent molecules. One example of these approaches is the
already mentioned work by Lee et al., which also considered water networks within
the binding site [16]. Protein cavities are not always fully solvated, and dewetted
regions can substantially affect the binding affinity of ligands and inhibitors. An
example of this is demonstrated by Asquith et al. [7, 44] in two studies where water
networks within GAK and EGFR kinases. In both of these studies, the WaterMap
method was used to identify the effect of individual water molecules, and the pure
docking score was not able to rationally explain the structure-activity relationships.
One must recognize that solvent effects are not independent of equally fundamental ligand ionization properties. An excellent example of how these are
connected to each other is the study by Heider et al., which showed that
pyridinylimidazole as GSK3β inhibitors were strongly affected by both preferred
tautomer and solvent-related binding effects [45]. Naturally, one cannot reach these
conclusions without a proper quantum mechanical evaluation of ligand behavior in
solvent phase. This procedure, unfortunately, requires substantial computational
resources and is thus not an option for a traditional virtual screening. It should
therefore be limited to those cases where more traditional approaches are not
satisfactory.
Protein ionization is typically kept fixed during all the modeling studies. This
assumption has recently been challenged, as it is well known that protein side chain
ionization does affect ligand binding, and protein dynamics and ionization are
affected by the protein 3D environment, solvent, and ions nearby. In addition,
since several side chains have their pK a values near the physiological pH, the
initially assigned protonation state might not be the one which is relevant for the
phenomenon under examination. To solve this issue, Brooks et al. developed a
method which combines classical MD simulation with explicit solvent for accurate
molecular interactions, generalized Born implicit-solvent model for estimating the
free energy of protein solvation, and a pH-based replica exchange scheme to
significantly enhance both protonation and conformational state sampling
[46, 47]. The method, named as hybrid-solvent continuous constant pH molecular
dynamics with pH replica exchange (CpHMD), was used by Shen et al. to study, for
example, how the c-Src kinase DFG domain flip (DFG-in vs. DFG-out) is affected
by the protonation of Asp [48]. The authors showed that protonated DFG-aspartate is
compatible only with DFG-out conformation, while unprotonated aspartate is possible with both DFG forms. This clearly underlines that ionization of all relevant
residues must be properly assigned before MD simulation or any structure-based
drug design method is used. They also used CpHMD to identify catalytically active
but nucleophilic (neutral) lysine residues which can be targeted by covalent inhibitors (the interested reader should look at the very comprehensive Chap. 30 by
Gehringer). In addition, within the same study, the authors were able to identify
charged cysteine residues within kinases which existed even at a physiological
pH [49].
Molecular Modeling of Protein Kinases: Current Status and Challenges
33
direct approach is to use information from the X-ray structures and especially MD
simulations with explicit solvent molecules. One example of these approaches is the
already mentioned work by Lee et al., which also considered water networks within
the binding site [16]. Protein cavities are not always fully solvated, and dewetted
regions can substantially affect the binding affinity of ligands and inhibitors. An
example of this is demonstrated by Asquith et al. [7, 44] in two studies where water
networks within GAK and EGFR kinases. In both of these studies, the WaterMap
method was used to identify the effect of individual water molecules, and the pure
docking score was not able to rationally explain the structure-activity relationships.
One must recognize that solvent effects are not independent of equally fundamental ligand ionization properties. An excellent example of how these are
connected to each other is the study by Heider et al., which showed that
pyridinylimidazole as GSK3β inhibitors were strongly affected by both preferred
tautomer and solvent-related binding effects [45]. Naturally, one cannot reach these
conclusions without a proper quantum mechanical evaluation of ligand behavior in
solvent phase. This procedure, unfortunately, requires substantial computational
resources and is thus not an option for a traditional virtual screening. It should
therefore be limited to those cases where more traditional approaches are not
satisfactory.
Protein ionization is typically kept fixed during all the modeling studies. This
assumption has recently been challenged, as it is well known that protein side chain
ionization does affect ligand binding, and protein dynamics and ionization are
affected by the protein 3D environment, solvent, and ions nearby. In addition,
since several side chains have their pK a values near the physiological pH, the
initially assigned protonation state might not be the one which is relevant for the
phenomenon under examination. To solve this issue, Brooks et al. developed a
method which combines classical MD simulation with explicit solvent for accurate
molecular interactions, generalized Born implicit-solvent model for estimating the
free energy of protein solvation, and a pH-based replica exchange scheme to
significantly enhance both protonation and conformational state sampling
[46, 47]. The method, named as hybrid-solvent continuous constant pH molecular
dynamics with pH replica exchange (CpHMD), was used by Shen et al. to study, for
example, how the c-Src kinase DFG domain flip (DFG-in vs. DFG-out) is affected
by the protonation of Asp [48]. The authors showed that protonated DFG-aspartate is
compatible only with DFG-out conformation, while unprotonated aspartate is possible with both DFG forms. This clearly underlines that ionization of all relevant
residues must be properly assigned before MD simulation or any structure-based
drug design method is used. They also used CpHMD to identify catalytically active
but nucleophilic (neutral) lysine residues which can be targeted by covalent inhibitors (the interested reader should look at the very comprehensive Chap. 30 by
Gehringer). In addition, within the same study, the authors were able to identify
charged cysteine residues within kinases which existed even at a physiological
pH [49].
Molecular Modeling of Protein Kinases: Current Status and Challenges
33