shows 5.2 pM K i , with −20.2 kcal/mol enthalpy, but merely 4.7 kcal/mol entropy
to result −15.5 kcal/mol binding free energy [34].
The second strategy has been utilized for optimizing HIV-1 protease inhibitors.
After the FDA approval of Indinavir in 1995, which binds only because of
−14.2 kcal/mol entropy despite 1.8 kcal/mol unfavorable enthalpy with binding free
energy −12.4 kcal/mol, the process of affinity optimization started. The constant
optimization of inhibitors for efficacy leads to Darunavir which binds with only
−2.3 kcal/mol favorable entropy; however, −12.7 kcal/mol favorable enthalpy
yielded binding free energy −15.0 kcal/mol. The free energy gain of −2.6 kcal/mol
was reported where every −1.4 kcal/mol results ten times better binder [36, 37].
Another such example involves cholesterol-lowering drug statins to HMG-CoA
reductase, and Fluvastatin binds only due to −9.0 kcal/mol favorable entropy despite
zero contribution from enthalpy. However, newer drug Rosuvastatin binding has
only −3.0 kcal/mol entropy contributions, but additional −9.3 kcal/mol enthalpy
gain results −12.3 kcal/mol binding free energy, −3.3 kcal/mol better than
Fluvastatin [38].
The third strategy is more tedious and challenging mainly because of enthalpy
entropy compensation, more often enthalpy can be increased by introducing new
hydrogen bonding groups as a strong hydrogen bond which provides *4–5 kcal/mol
enthalpy; however, introduction of hydrogen bond decreases favorable solvation and
entropy by structuring regions involved in hydrogen bonding. Alternatively, in theory,
introducing multiple hydrogen bonds targeting same structural regions of receptor has
been suggested to mitigate the extent of enthalpy entropy compensation [33].
1.4 Flexibility and Adaptability of Target
Initially, the protein–ligand docking was modeled as a lock-and-key, where protein
was treated as “lock” containing a binding site as “key-hole” which can host a
complementary ligand or “key.” However, later it was realized that lock-and-key
model is not sufficient to characterize all binding events; thus, advanced models
were proposed which can be put broadly in three groups: (i) lock-and-key (ii) induced fit (IF), and (iii) conformational selection (CS) [39]. The IF and CS models
introduced to account for the receptor flexibility during the binding with ligands
will be discussed in detail later. Although these models represent receptor–ligand
binding in better way, still estimate only enthalpy of the interaction and the entropy
component of the binding free energy remains to be estimated. It has been reported
in the literature that entropic component of binding can be important in many
interactions. A recent experimental and computational study of a human heat-shock
protein 90 (HSP90) highlighted important alterations in binding properties of target
on complex formation with small-molecule inhibitors [40]. Surprisingly, they found
that compounds binding to helical conformation have increased target flexibility
and gained entropy preference over compounds binding to loop conformation
which was less flexible on complex formation [40].
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
115
to result −15.5 kcal/mol binding free energy [34].
The second strategy has been utilized for optimizing HIV-1 protease inhibitors.
After the FDA approval of Indinavir in 1995, which binds only because of
−14.2 kcal/mol entropy despite 1.8 kcal/mol unfavorable enthalpy with binding free
energy −12.4 kcal/mol, the process of affinity optimization started. The constant
optimization of inhibitors for efficacy leads to Darunavir which binds with only
−2.3 kcal/mol favorable entropy; however, −12.7 kcal/mol favorable enthalpy
yielded binding free energy −15.0 kcal/mol. The free energy gain of −2.6 kcal/mol
was reported where every −1.4 kcal/mol results ten times better binder [36, 37].
Another such example involves cholesterol-lowering drug statins to HMG-CoA
reductase, and Fluvastatin binds only due to −9.0 kcal/mol favorable entropy despite
zero contribution from enthalpy. However, newer drug Rosuvastatin binding has
only −3.0 kcal/mol entropy contributions, but additional −9.3 kcal/mol enthalpy
gain results −12.3 kcal/mol binding free energy, −3.3 kcal/mol better than
Fluvastatin [38].
The third strategy is more tedious and challenging mainly because of enthalpy
entropy compensation, more often enthalpy can be increased by introducing new
hydrogen bonding groups as a strong hydrogen bond which provides *4–5 kcal/mol
enthalpy; however, introduction of hydrogen bond decreases favorable solvation and
entropy by structuring regions involved in hydrogen bonding. Alternatively, in theory,
introducing multiple hydrogen bonds targeting same structural regions of receptor has
been suggested to mitigate the extent of enthalpy entropy compensation [33].
1.4 Flexibility and Adaptability of Target
Initially, the protein–ligand docking was modeled as a lock-and-key, where protein
was treated as “lock” containing a binding site as “key-hole” which can host a
complementary ligand or “key.” However, later it was realized that lock-and-key
model is not sufficient to characterize all binding events; thus, advanced models
were proposed which can be put broadly in three groups: (i) lock-and-key (ii) induced fit (IF), and (iii) conformational selection (CS) [39]. The IF and CS models
introduced to account for the receptor flexibility during the binding with ligands
will be discussed in detail later. Although these models represent receptor–ligand
binding in better way, still estimate only enthalpy of the interaction and the entropy
component of the binding free energy remains to be estimated. It has been reported
in the literature that entropic component of binding can be important in many
interactions. A recent experimental and computational study of a human heat-shock
protein 90 (HSP90) highlighted important alterations in binding properties of target
on complex formation with small-molecule inhibitors [40]. Surprisingly, they found
that compounds binding to helical conformation have increased target flexibility
and gained entropy preference over compounds binding to loop conformation
which was less flexible on complex formation [40].
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
115