pathways undergo mutations to improve the survival rate of the organism by either
improving the protein function or catalytic efficiency and stability to escape the
inhibitory action of the drug. In the latter case, the motive for modifying the drug
target is to ensure that drug binding is weakened. Moreover, the mutations are such
that substrate binding is unaffected or minimally affected. Most of the computational methods employed to study the mechanism of drug resistance, attempt to
understand the differences in the binding patterns of the substrate and the drug
molecule, i.e. understanding the “substrate-envelope hypothesis”. Here, we present an overview of those computational methods that employ free energy of
binding as a tool to gauge the differences in the binding of the substrate and the
drug molecule before and after mutation.
In the Sect. 1, we discuss the driving force for resistant mutations and throw
some light on the different mechanisms by which drug resistance can occur. In
Sect. 2, we present a brief overview of molecular dynamics, thermodynamics of
protein–ligand binding, and various methods for computing the free energy of
binding. The last section, Sect. 3, has a detailed discussion on various free
energy-based methods used to understand and predict the target site mutations
leading to loss in drug binding.
1.1 Overview of the Mechanisms of Drug Resistance
The drug-induced selection pressure [1–4] is the major driving force for infectious
organisms to try to evade the effects of drugs. One of the primary moves that any
organism will adopt is to disrupt the action of drug molecules by one or more
possible mechanisms. To show its effect, the drug must enter the cells and find its
target protein. As a primary defence mechanism against drugs, the organism may
down regulate the expression of influx channels that enable the entry of the drug,
resulting in a decreased concentration build-up within the cell. Another strategy that
hinders the build-up of the drug inside the cell is the upregulation of the expression
of efflux channels/pumps that facilitate the egress of the drug molecules. These
strategies are often very difficult to understand owing to the complicated pathways
involved in the upregulation or downregulation of various proteins associated in the
regulation of traffic to and from the cell. This attribute is difficult to study using
computational techniques that use free energy-based methods. Target site mutations
[5–8] that lead to disruption in the drug binding without significant loss of the
protein function [9, 10] is another mechanism of drug resistance. Such mutations
can be studied using computer simulations that enable us to estimate the free energy
difference between the drug binding to the mutant and the wild-type protein. An
essential factor to consider while understanding target site mutation is the fitness
cost associated with the mutational change. This can be estimated by the change in
the free energy of binding of the natural ligands/substrates; for example, a drop in
their binding energy indicates that substrate binding is impeded, which this leads to
increased fitness cost. This means the enzyme now must expend more energy to
2
E. A. F. Martis and E. C. Coutinho
improving the protein function or catalytic efficiency and stability to escape the
inhibitory action of the drug. In the latter case, the motive for modifying the drug
target is to ensure that drug binding is weakened. Moreover, the mutations are such
that substrate binding is unaffected or minimally affected. Most of the computational methods employed to study the mechanism of drug resistance, attempt to
understand the differences in the binding patterns of the substrate and the drug
molecule, i.e. understanding the “substrate-envelope hypothesis”. Here, we present an overview of those computational methods that employ free energy of
binding as a tool to gauge the differences in the binding of the substrate and the
drug molecule before and after mutation.
In the Sect. 1, we discuss the driving force for resistant mutations and throw
some light on the different mechanisms by which drug resistance can occur. In
Sect. 2, we present a brief overview of molecular dynamics, thermodynamics of
protein–ligand binding, and various methods for computing the free energy of
binding. The last section, Sect. 3, has a detailed discussion on various free
energy-based methods used to understand and predict the target site mutations
leading to loss in drug binding.
1.1 Overview of the Mechanisms of Drug Resistance
The drug-induced selection pressure [1–4] is the major driving force for infectious
organisms to try to evade the effects of drugs. One of the primary moves that any
organism will adopt is to disrupt the action of drug molecules by one or more
possible mechanisms. To show its effect, the drug must enter the cells and find its
target protein. As a primary defence mechanism against drugs, the organism may
down regulate the expression of influx channels that enable the entry of the drug,
resulting in a decreased concentration build-up within the cell. Another strategy that
hinders the build-up of the drug inside the cell is the upregulation of the expression
of efflux channels/pumps that facilitate the egress of the drug molecules. These
strategies are often very difficult to understand owing to the complicated pathways
involved in the upregulation or downregulation of various proteins associated in the
regulation of traffic to and from the cell. This attribute is difficult to study using
computational techniques that use free energy-based methods. Target site mutations
[5–8] that lead to disruption in the drug binding without significant loss of the
protein function [9, 10] is another mechanism of drug resistance. Such mutations
can be studied using computer simulations that enable us to estimate the free energy
difference between the drug binding to the mutant and the wild-type protein. An
essential factor to consider while understanding target site mutation is the fitness
cost associated with the mutational change. This can be estimated by the change in
the free energy of binding of the natural ligands/substrates; for example, a drop in
their binding energy indicates that substrate binding is impeded, which this leads to
increased fitness cost. This means the enzyme now must expend more energy to
2
E. A. F. Martis and E. C. Coutinho