Thus, in silico calculated affinity of receptor–ligand binding contains information
about thermodynamic parameters and does not include kinetic parameters. All the
methods aiming to measure/predict binding affinity would miss the kinetic aspect of
the reaction. The kinetic aspect of the process is related to diffusion of the solute
molecules under influence of the entropy of the system. Collision of the receptor
molecule with the ligand is the requisite for the process to happen. Bigger solute
molecules collide with small water molecules and undergo random Brownian
motion, and their encounter allows reaction to happen [153].
The dissociation constant K d represents the ligand concentration in which half of
the protein binding pockets are occupied and relate to Gibbs free energy [154] by
DG ¼ RT ln K d
ð Þ. Gibbs free energy is a state function and does not depend on the
thermodynamic path followed during reaction; it only depends on the initial/final
chemical potential of the reactants/products [154]. Association and dissociation
rates k on and k off depend on transition states encountered on the pathway during the
chemical reaction. Specifically, they depend on highest free energy barrier for the
transition state that separates bound and unbound states [154].
Even if the reasonable accuracy in predicting affinity is achieved, it is not
sufficient to characterize the protein–ligand-binding process completely [154].
Kinetic aspect of the process can be modeled by mimicking the protein–ligand
diffusional encounter in the solvent under thermal fluctuation, which will be discussed later [155].
4 Estimation of Interactions
Scoring functions aim to predict the interaction energy between the receptor and the
ligand in a given conformational pose, by summation of weighted interaction
features. Scoring functions required to rank chemicals implemented in various
docking tools use different assumptions to evaluate modeled complexes [8].
Simplification is achieved at the cost of neglecting full domain flexibility, entropic
effects, and solvation effect [8].
4.1 Different Types of Scoring Functions
In the literature, wide choice of scoring function is available which can be classified
as force-field-based scoring functions, empirical scoring functions, knowledge-based
scoring functions, and descriptor-based scoring functions [156]. Force-field-driven
scoring functions are based on the molecular mechanics and utilize atomic properties
like atomic charge and vdW forces which are already parameterized such as AMBER
[157] or CHARMM [158]. Dock6 [159], AutoDock [160], G-score [161], and GOLD
[110] are a few popular ones in this class. In scoring functions, only intermolecular
interactions are modeled, vdW interactions are expressed using Lennard-Jones
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S. K. Panday and I. Ghosh
about thermodynamic parameters and does not include kinetic parameters. All the
methods aiming to measure/predict binding affinity would miss the kinetic aspect of
the reaction. The kinetic aspect of the process is related to diffusion of the solute
molecules under influence of the entropy of the system. Collision of the receptor
molecule with the ligand is the requisite for the process to happen. Bigger solute
molecules collide with small water molecules and undergo random Brownian
motion, and their encounter allows reaction to happen [153].
The dissociation constant K d represents the ligand concentration in which half of
the protein binding pockets are occupied and relate to Gibbs free energy [154] by
DG ¼ RT ln K d
ð Þ. Gibbs free energy is a state function and does not depend on the
thermodynamic path followed during reaction; it only depends on the initial/final
chemical potential of the reactants/products [154]. Association and dissociation
rates k on and k off depend on transition states encountered on the pathway during the
chemical reaction. Specifically, they depend on highest free energy barrier for the
transition state that separates bound and unbound states [154].
Even if the reasonable accuracy in predicting affinity is achieved, it is not
sufficient to characterize the protein–ligand-binding process completely [154].
Kinetic aspect of the process can be modeled by mimicking the protein–ligand
diffusional encounter in the solvent under thermal fluctuation, which will be discussed later [155].
4 Estimation of Interactions
Scoring functions aim to predict the interaction energy between the receptor and the
ligand in a given conformational pose, by summation of weighted interaction
features. Scoring functions required to rank chemicals implemented in various
docking tools use different assumptions to evaluate modeled complexes [8].
Simplification is achieved at the cost of neglecting full domain flexibility, entropic
effects, and solvation effect [8].
4.1 Different Types of Scoring Functions
In the literature, wide choice of scoring function is available which can be classified
as force-field-based scoring functions, empirical scoring functions, knowledge-based
scoring functions, and descriptor-based scoring functions [156]. Force-field-driven
scoring functions are based on the molecular mechanics and utilize atomic properties
like atomic charge and vdW forces which are already parameterized such as AMBER
[157] or CHARMM [158]. Dock6 [159], AutoDock [160], G-score [161], and GOLD
[110] are a few popular ones in this class. In scoring functions, only intermolecular
interactions are modeled, vdW interactions are expressed using Lennard-Jones
138
S. K. Panday and I. Ghosh