[254], the configurational entropy was estimated using MD dataset of *5 ls,
adding enthalpy, Free energy was calculated. It indicates that largely omitted
entropic contributions can play important role and even deciding factor in case of
small ligands binding to the flexible proteins (Fig. 14).
As shown in Fig. 14, combining enthalpy ðDHÞ and configurational entropy
ÀTDS config
À
Á
of binding for chosen inhibitors, the binding free energy ðDGÞ for
best binder PVB is highest. However, binding free energy does not discriminate
between OLM and INR, where experimentally OLM is weakest binder. Lower DG
for INR (−18.5 kcal/mol) in comparison to OLM (−20.0 kcal/mol) may be
attributed to the role of solvation free energy which is not accounted rigorously in
MM-PBSA methods. Variations in configurational entropy of binding from
21.8 kcal/mol to mere 2.9 kcal/mol suggest that different ligands modulate and
influence receptor flexibility in their own different way while forming complex,
highlighting importance of receptor flexibility in binding affinity prediction studies;
recently more attentions are attracted in this field.
6.3 Thermodynamic Methods
Relative binding free energy for a ligand formed by a chemical group substitution
relative to parent compound can be computed using free energy perturbation
molecular dynamics simulation [255]. This technique requires constructing a path
from parent ligand L 1 to analog ligand L 2 , which binds to a common receptor R, in
two steps as follows. First, by carrying out a sequence of simulations in solvent and
mutating L 1 to L 2 through several intermediate points and adding up the free energy
changes along hypothetical intermediate points to yield free energy (say A s ) of
mutating L 1 to L 2 in solvent, then similarily, mutating the ligand L 1 to L 2 in the
Fig. 14 Enthalpy, configurational entropy and free energy of binding of chosen inhibitors is
shown in kcal/mol. Inhibitors are arranged in increasing experimental affinity (RTln(IC50)) order
from left to right. Enthalpy is calculated using MM-PBSA method as discussed earlier. Here,
temperature is taken to be 300 K
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
157
adding enthalpy, Free energy was calculated. It indicates that largely omitted
entropic contributions can play important role and even deciding factor in case of
small ligands binding to the flexible proteins (Fig. 14).
As shown in Fig. 14, combining enthalpy ðDHÞ and configurational entropy
ÀTDS config
À
Á
of binding for chosen inhibitors, the binding free energy ðDGÞ for
best binder PVB is highest. However, binding free energy does not discriminate
between OLM and INR, where experimentally OLM is weakest binder. Lower DG
for INR (−18.5 kcal/mol) in comparison to OLM (−20.0 kcal/mol) may be
attributed to the role of solvation free energy which is not accounted rigorously in
MM-PBSA methods. Variations in configurational entropy of binding from
21.8 kcal/mol to mere 2.9 kcal/mol suggest that different ligands modulate and
influence receptor flexibility in their own different way while forming complex,
highlighting importance of receptor flexibility in binding affinity prediction studies;
recently more attentions are attracted in this field.
6.3 Thermodynamic Methods
Relative binding free energy for a ligand formed by a chemical group substitution
relative to parent compound can be computed using free energy perturbation
molecular dynamics simulation [255]. This technique requires constructing a path
from parent ligand L 1 to analog ligand L 2 , which binds to a common receptor R, in
two steps as follows. First, by carrying out a sequence of simulations in solvent and
mutating L 1 to L 2 through several intermediate points and adding up the free energy
changes along hypothetical intermediate points to yield free energy (say A s ) of
mutating L 1 to L 2 in solvent, then similarily, mutating the ligand L 1 to L 2 in the
Fig. 14 Enthalpy, configurational entropy and free energy of binding of chosen inhibitors is
shown in kcal/mol. Inhibitors are arranged in increasing experimental affinity (RTln(IC50)) order
from left to right. Enthalpy is calculated using MM-PBSA method as discussed earlier. Here,
temperature is taken to be 300 K
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
157