Using Data Mining Techniques to Probe the Role of Hydrophobic Residues in Protein Folding and Unfolding Simulations - Evolving Application Domains of Data Warehousing and Mining - page 259

IntroductIon processes under study, while others may be more

sensitive to later stages. Looking at a wide range
of properties and experimental conditions further
increases the amount of data generated by such
simulation models. Analyzing and interpreting
these data requires automated methods such as
data mining. These issues have been addressed
before by Kazmirski et al (1999) and Brito et al
(2004). While Kazmirski et al (1999) presented
several methods based on structure and property
data to compare different MD trajectories, Brito
et al (2004) discussed the usefulness of data
mining techniques, which include machine learn-
ing, artificial intelligence, and visualization, to
address the data analysis problem arising from
multiple computational simulations, including
protein folding and unfolding simulations. Figure
1 depicts a general overview of this process, from
the initial system under study to the interpreta-
tion of the results using data mining tools. The
researcher begins by performing multiple MD
simulations, starting from the same experimental
structure (same atom coordinates) but different
initial atom velocities. For each simulation, a set
of varying atom coordinates and velocities over
time (a trajectory) is obtained. At the end of each
simulation, a collection of molecular properties
may be calculated to characterize the structural
variation of the protein during the process. Finally,
the molecular property variation profiles may be
subjected to analysis using data mining tools.
The solvent accessible surface area (SASA)
is one of the molecular properties that might be
calculated for each MD trajectory. SASA reports
on an important parameter from the protein
conformational stability point of view: solvent
exposure and protein compactness. Its value may
be calculated for the entire protein, but also for
subsets of amino-acid residues, accounting for
example for the polar or non-polar contributions.
Furthermore, the study of the SASA variation of
each individual amino-acid residue provides a
greater level of detail on the individual contri-
butions for the folding or unfolding processes.

Molecular dynamics (MD) is one of the most realis-
tic simulation techniques available to study protein
folding in silico . In MD simulations, the structural
fluctuations of a single protein can be tracked over
time by numerically solving Newton's equations
of motion (Adcock & McCammon, 2006). When
using molecular dynamics simulations to study
protein folding and unfolding processes, multiple
simulations need to be considered to probe the
large conformational space and multidimensional
potential energy surface available to the poly-
peptide chain, and obtain significant statistical
mechanical averages of the system properties
(Brito, 2004; Kazmirski, 1999; Scheraga, 2007).
Even though the computational power available
keeps increasing, it is still a major challenge to
simulate protein folding or unfolding processes
in its real time scale (hundreds of µs to seconds
or more). However, it has been suggested that
performing multiple short simulations (usually 5
to 10) provides better sampling of the conforma-
tional space than having a single long simulation
(Caves, 1998). Thus, performing multiple simula-
tions on the 10 to 100 ns time scale is becoming
routine, which generates huge amounts of data to
be analysed and compared. Furthermore, a large
set of structural and physical properties (such as
root mean square deviation, radius of gyration,
secondary structure content, native contacts, and
solvent accessible surface area) is usually calcu-
lated from the MD trajectories to characterize the
conformational space explored.
Most of the structural and physical properties
calculated from the MD trajectories are easy to
extract. However, the next challenge for data
analysis in multiple MD simulations is to identify,
among the properties, those that are essential in
describing the protein unfolding or folding pro-
cesses. Additionally, it is important to define the
relative importance of each property along the
folding/unfolding pathway. It is expected that some
of the properties best describe initial stages of the