integration. Availability of many conformers of receptors and ligands in solution
suggests the importance of entropy in estimation of binding affinity, but entropy
component of binding free energy directly is not included in such studies. In spite of
unprecedented advancement of computational modeling, faster simulation techniques, accurate solvation models and current best practices, the dependence of
binding affinity on pH, estimation of entropy along with enthalpy in binding
affinity, inclusion of conformational entropy of ligand and receptor, and modulation
of flexibilities during complex formation are important challenges lying ahead.
Therefore, an account of prowess and challenges in structure-based prediction of
binding affinity addressed in present review will provide directions for its appropriate application, understanding its limitations and getting important feedbacks for
its betterment.
Keywords Structure-based drug design Á X-ray crystal structure
Scoring function Á Docking Á Simulation Á Structure validation
MM-PBSA Á Entropy Á Free energy
1 Introduction
The advancement of molecular understanding of the disease processes and their
manifestations, along with computational advancement like in silico studies, aiming
to predict high-affinity molecules/scaffolds binding to the target, grew as a
promising complementary field of study mainly because of its cost-effectiveness
and speed. It facilitated virtual high-throughput screening (vHTS) to narrow down
the search space for further experimental work by making predictions about the
ligand–receptor affinity [1]. Advancements in systems biology along with network
biology helped identifying targets for diseases [2], and crystallography [3] and
nuclear magnetic resonance (NMR) [4] techniques enabled solving structural
models of the target molecules with higher resolution setting foundation of
structure-based drug designing (SBDD). Docking is one of such computational
studies, which aims to search high-affinity molecules from a library of chemicals
and predict relative orientation (pose) of the molecule to the target. It also tries to
rank the set of molecules/poses in a sorted affinity order [5]. Knowledge about the
structure of receptors made binding site identification easier and enabled to screen
the small-molecule libraries against the target seeking complementarity with the
ligand.
Docking and scoring methods due to its promising applicability prospect has
been extensively developed, critically evaluated, and constantly refined with the
time, it has now shaped into a field of research; several software tools have been
developed and are available for academic and industry research [5–11]. Recently,
Taylor et al. [12] have reviewed the broad spectrum of major techniques amenable
to the field of non-covalent docking studies, classifying them into molecular
dynamics, Monte Carlo methods, genetic algorithms, fragment-based methods,
110
S. K. Panday and I. Ghosh
suggests the importance of entropy in estimation of binding affinity, but entropy
component of binding free energy directly is not included in such studies. In spite of
unprecedented advancement of computational modeling, faster simulation techniques, accurate solvation models and current best practices, the dependence of
binding affinity on pH, estimation of entropy along with enthalpy in binding
affinity, inclusion of conformational entropy of ligand and receptor, and modulation
of flexibilities during complex formation are important challenges lying ahead.
Therefore, an account of prowess and challenges in structure-based prediction of
binding affinity addressed in present review will provide directions for its appropriate application, understanding its limitations and getting important feedbacks for
its betterment.
Keywords Structure-based drug design Á X-ray crystal structure
Scoring function Á Docking Á Simulation Á Structure validation
MM-PBSA Á Entropy Á Free energy
1 Introduction
The advancement of molecular understanding of the disease processes and their
manifestations, along with computational advancement like in silico studies, aiming
to predict high-affinity molecules/scaffolds binding to the target, grew as a
promising complementary field of study mainly because of its cost-effectiveness
and speed. It facilitated virtual high-throughput screening (vHTS) to narrow down
the search space for further experimental work by making predictions about the
ligand–receptor affinity [1]. Advancements in systems biology along with network
biology helped identifying targets for diseases [2], and crystallography [3] and
nuclear magnetic resonance (NMR) [4] techniques enabled solving structural
models of the target molecules with higher resolution setting foundation of
structure-based drug designing (SBDD). Docking is one of such computational
studies, which aims to search high-affinity molecules from a library of chemicals
and predict relative orientation (pose) of the molecule to the target. It also tries to
rank the set of molecules/poses in a sorted affinity order [5]. Knowledge about the
structure of receptors made binding site identification easier and enabled to screen
the small-molecule libraries against the target seeking complementarity with the
ligand.
Docking and scoring methods due to its promising applicability prospect has
been extensively developed, critically evaluated, and constantly refined with the
time, it has now shaped into a field of research; several software tools have been
developed and are available for academic and industry research [5–11]. Recently,
Taylor et al. [12] have reviewed the broad spectrum of major techniques amenable
to the field of non-covalent docking studies, classifying them into molecular
dynamics, Monte Carlo methods, genetic algorithms, fragment-based methods,
110
S. K. Panday and I. Ghosh