select all mutations wherein drug binding is hampered and substrate binding is
either improved or [26].
In case of free energy calculations, molecular dynamics (MD) simulations are the
most commonly used technique to generate conformational ensembles. Hence, it is
rightly called as one of the main toolkits for theoretically studying biological
molecules (Hansson et al. [27], Binder et al. [28]. MD calculates the time-dependent
behaviour of particles or atoms, by numerical integration of Newton’s second law of
motion and predicts the future positions and momenta. MD simulations have provided detailed information on the fluctuations and conformational changes of proteins and nucleic acids upon drug/substrate binding. As a result, it is now routinely
used to investigate the structure, dynamics and thermodynamics of biological
molecules and their complexes. MD simulations have an advantage in that, starting
from an X-ray or NMR solved structure, it can provide insights into the dynamic
nature of biomolecules that are inaccessible to experiments. To accurately simulate
the behaviour of molecules, one must be able to account for the thermal fluctuations
and the environment-mediated interactions arising in diverse and complex systems
(e.g., a protein-binding site or bulk solution). This depends on how accurately the
force fields represent the atoms and treats the non-bonded interactions. A complete
account of force fields can be found in the review by Pissurlenkar et al. [29].
However, most of the biological events occur at timescales that are not routinely
reachable by classical MD simulations, for example, protein folding occurs in the
timescale of few seconds, whereas drug binding and unbinding occur in the timescale of few microseconds to milliseconds. The routine timescale that is feasible
using high-end servers equipped with graphic processing units [30–32] and distributed grid computing [33, 34], is few tens of microseconds, that is nearly 1/100th
of the timescale required to study protein folding. Conventional MD suffers from the
severe limitation that it is extremely difficult to sample high-energy regions and
surmount energy barriers, leading to inaccuracies in free energy calculations.
The limitations of classical MD simulations have motivated the development of
new conformational sampling algorithms that facilitate the sampling of conformational space that is inaccessible to classical MD simulation. The simplest way to
encourage the system to sample the high-energy regions on the phase space is to
increase the target temperature [35]. This leads to increased kinetic energy of the
system that enables it to surmount these barriers. However, it has been argued by
many, that such elevated temperatures (*400 K and above) lead to physiologically
unrealistic states that may severely distort the results; however, such methods have
been found to be advantageous in improving the sampling efficiency during MD
simulations. Another method that uses elevated temperature to enhance the sampling is the replica-exchange molecular dynamics (parallel tempering, [36, 37]). In
this approach, several replicas are simulated in parallel at different temperatures. At
appropriate intervals, the replicas switch temperatures with the nearest replica, and
this exchange is governed by the Metropolis acceptance criteria. However, all these
methods do not prohibit the system from revisiting the same conformational space.
This problem was resolved by adding the memory concept in molecular dynamics
Free Energy-Based Methods to Understand Drug Resistance Mutations
5
either improved or [26].
In case of free energy calculations, molecular dynamics (MD) simulations are the
most commonly used technique to generate conformational ensembles. Hence, it is
rightly called as one of the main toolkits for theoretically studying biological
molecules (Hansson et al. [27], Binder et al. [28]. MD calculates the time-dependent
behaviour of particles or atoms, by numerical integration of Newton’s second law of
motion and predicts the future positions and momenta. MD simulations have provided detailed information on the fluctuations and conformational changes of proteins and nucleic acids upon drug/substrate binding. As a result, it is now routinely
used to investigate the structure, dynamics and thermodynamics of biological
molecules and their complexes. MD simulations have an advantage in that, starting
from an X-ray or NMR solved structure, it can provide insights into the dynamic
nature of biomolecules that are inaccessible to experiments. To accurately simulate
the behaviour of molecules, one must be able to account for the thermal fluctuations
and the environment-mediated interactions arising in diverse and complex systems
(e.g., a protein-binding site or bulk solution). This depends on how accurately the
force fields represent the atoms and treats the non-bonded interactions. A complete
account of force fields can be found in the review by Pissurlenkar et al. [29].
However, most of the biological events occur at timescales that are not routinely
reachable by classical MD simulations, for example, protein folding occurs in the
timescale of few seconds, whereas drug binding and unbinding occur in the timescale of few microseconds to milliseconds. The routine timescale that is feasible
using high-end servers equipped with graphic processing units [30–32] and distributed grid computing [33, 34], is few tens of microseconds, that is nearly 1/100th
of the timescale required to study protein folding. Conventional MD suffers from the
severe limitation that it is extremely difficult to sample high-energy regions and
surmount energy barriers, leading to inaccuracies in free energy calculations.
The limitations of classical MD simulations have motivated the development of
new conformational sampling algorithms that facilitate the sampling of conformational space that is inaccessible to classical MD simulation. The simplest way to
encourage the system to sample the high-energy regions on the phase space is to
increase the target temperature [35]. This leads to increased kinetic energy of the
system that enables it to surmount these barriers. However, it has been argued by
many, that such elevated temperatures (*400 K and above) lead to physiologically
unrealistic states that may severely distort the results; however, such methods have
been found to be advantageous in improving the sampling efficiency during MD
simulations. Another method that uses elevated temperature to enhance the sampling is the replica-exchange molecular dynamics (parallel tempering, [36, 37]). In
this approach, several replicas are simulated in parallel at different temperatures. At
appropriate intervals, the replicas switch temperatures with the nearest replica, and
this exchange is governed by the Metropolis acceptance criteria. However, all these
methods do not prohibit the system from revisiting the same conformational space.
This problem was resolved by adding the memory concept in molecular dynamics
Free Energy-Based Methods to Understand Drug Resistance Mutations
5