Biomedical Engineering Reference
In-Depth Information
Fig. 1
The simulation
system of the neuraminidase
tetramer from the avian
influenza virus H5N1. The
four monomers of the
neuraminidase are colored
blue
,
yellow
,
pink
and
green
,
respectively. Water molecules
are shown as
gray dots
,and
the boundaries of the
simulation box are
highlighted. Figure was
created using structures from
Lawrenz et al. [
78
]
motion and generate the new velocity and position of the atom for the next step. The
100-ns trajectory is obtained by repeating the above calculation 5
10
7
times. We
will discuss these calculations in more detail in Sect.
3
.
Theory and development of MD are deeply rooted in the principles of statistical
mechanics. Although users today normally do not need to write their own MD code,
it is still very helpful to understand these principles, which can be essential to ensure
the proper applications of the method on various complex biomolecular systems. In
this chapter, we will introduce the basic statistical mechanics background of MD,
the various components of a potential energy function, and the algorithm used to
integrate the equations of motion. We will then give some practical examples of
MD, followed by a few tips on how to avoid common pitfalls in the preparation of a
simulation. In the last section, we will briefly introduce some advanced simulation
techniques, such as free energy calculation and enhanced sampling methods. Our
goal is to give an overview of MD, rather than discussing any specific aspect of the
method in great detail. Therefore, we will provide references to important theories
and applications throughout the text for readers to further explore the corresponding
topics.
2
Statistical Mechanics Background
MD simulations can generate a very detailed picture of the system under study, i.e.,
they allow the calculation of microscopic properties, such as positions and velocities
of each individual atom in the system. These microscopic properties, however, are
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