104
P. Gong et al.
and cancer) tends to be less well understood and often encompasses multiple mechanisms and pathways to adverse outcome. Consequently, the success of system-level
modeling or simulation of dynamic biological processes leading to toxicity is mostly
limited to “fit-for-purpose” [8, 33] due to incomplete knowledge of complex biological systems. Hence, for the purpose of accurately predicting chemical toxicity, it
is currently unrealistic and unnecessary to capture and reconstruct all the cascading
processes leading from an MIE to an adverse outcome.
6.1.4 Molecular Docking for Virtual Chemical Screening:
A Green Toxicology Approach
The pharmaceutical and pesticide/herbicide industries have a long history of using
molecular docking as a key tool in computer-assisted virtual chemical design and efficacy screening of candidate compounds. A wide variety of ligand–protein docking
methods have been developed to predict the predominant conformation and orientation [i.e., pose(s) or binding mode(s)] of a ligand within a targeted binding site of a
biomacromolecule (e.g., a protein) with a known 3D structure [34–36]. These methods can model the interaction between a small molecule (chemical) and a biomacromolecule at the atomic level. This allows us to characterize the behavior of small
molecules in the binding site of target biomacromolecules and elucidate fundamental
biochemical processes [37].
In essence, this virtual screening approach falls within the scope of an emerging
discipline called green toxicology. Similar to the green chemistry movement, it moves
the toxicity and risk or safety assessment schemes to the beginning of the production
cycle of a chemical or a product, i.e., to the molecular design [38]. Green toxicology uses predictive toxicology tools for the design of less harmful substances, tests
early in the development process to prioritize less dangerous chemicals, and reduces
exposures—thereby “designing out” undesirable human health and environmental
risks, reducing animal testing demands, and increasing the likelihood of launching
a successful, sustainable product [21].
However, molecular docking-based virtual screening has not been applied to
quantitative assessment of long-term chemical toxicity. Its application to qualitative mechanistic studies is also limited, largely due to the historical unavailability of
3D macromolecular structures of many toxicity targets and the extremely high computational expenses associated with allowing conformational flexibility of both the
ligand and the protein. Recently, advances in structural biology (e.g., high-throughput
protein purification, crystallography and nuclear magnetic resonance spectroscopy
techniques [35]), rapid progress in algorithm development, and great advances in
supercomputing resources (e.g., high-performance computing technology) [39] have
paved the way for pursuing this approach and its potential to scale-up for quickly
screening thousands of critical toxicity targets. For instance, the molecular modeling
database (MMDB) [40], which is based on the Protein Data Bank (PDB) [41, 42]
P. Gong et al.
and cancer) tends to be less well understood and often encompasses multiple mechanisms and pathways to adverse outcome. Consequently, the success of system-level
modeling or simulation of dynamic biological processes leading to toxicity is mostly
limited to “fit-for-purpose” [8, 33] due to incomplete knowledge of complex biological systems. Hence, for the purpose of accurately predicting chemical toxicity, it
is currently unrealistic and unnecessary to capture and reconstruct all the cascading
processes leading from an MIE to an adverse outcome.
6.1.4 Molecular Docking for Virtual Chemical Screening:
A Green Toxicology Approach
The pharmaceutical and pesticide/herbicide industries have a long history of using
molecular docking as a key tool in computer-assisted virtual chemical design and efficacy screening of candidate compounds. A wide variety of ligand–protein docking
methods have been developed to predict the predominant conformation and orientation [i.e., pose(s) or binding mode(s)] of a ligand within a targeted binding site of a
biomacromolecule (e.g., a protein) with a known 3D structure [34–36]. These methods can model the interaction between a small molecule (chemical) and a biomacromolecule at the atomic level. This allows us to characterize the behavior of small
molecules in the binding site of target biomacromolecules and elucidate fundamental
biochemical processes [37].
In essence, this virtual screening approach falls within the scope of an emerging
discipline called green toxicology. Similar to the green chemistry movement, it moves
the toxicity and risk or safety assessment schemes to the beginning of the production
cycle of a chemical or a product, i.e., to the molecular design [38]. Green toxicology uses predictive toxicology tools for the design of less harmful substances, tests
early in the development process to prioritize less dangerous chemicals, and reduces
exposures—thereby “designing out” undesirable human health and environmental
risks, reducing animal testing demands, and increasing the likelihood of launching
a successful, sustainable product [21].
However, molecular docking-based virtual screening has not been applied to
quantitative assessment of long-term chemical toxicity. Its application to qualitative mechanistic studies is also limited, largely due to the historical unavailability of
3D macromolecular structures of many toxicity targets and the extremely high computational expenses associated with allowing conformational flexibility of both the
ligand and the protein. Recently, advances in structural biology (e.g., high-throughput
protein purification, crystallography and nuclear magnetic resonance spectroscopy
techniques [35]), rapid progress in algorithm development, and great advances in
supercomputing resources (e.g., high-performance computing technology) [39] have
paved the way for pursuing this approach and its potential to scale-up for quickly
screening thousands of critical toxicity targets. For instance, the molecular modeling
database (MMDB) [40], which is based on the Protein Data Bank (PDB) [41, 42]