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sequence but are spatially more distant (~ 30 Å).
These clusters comprise mainly polar residues,
in particular negatively charged.
Considering the clusters containing residues
Leu12, Val14, Val71, Ile107, Ala109 and Leu111,
co-occurrences can be found in four data sets for
some residues, and in three data sets for other
residues. For this reason, for Runs 2 and 3 two
clusters are described. For all data sets, the clusters
are constituted by hydrophobic residues (posi-
tive values of P3) that tend to be close in spatial
proximity. On the other hand, to what relates to
the values of property P1, two distinct patterns
are observed. The residues in each of the clusters
are: (i) far apart in the protein sequence (Run 1;
Run 2, cluster16; Run 3, cluster 16; Run 5), or
(ii) close in the protein sequence (Run 2, cluster
1; Run 3, cluster 14; Run 4).
From these observations, it is clear that the
annotation of the dendrograms, obtained by a
classical hierarchical clustering procedure, with
additional information on protein sequence (P1),
composition (P3), and dynamics (P2) helped in
the discrimination and characterization of well
differentiated clusters.
clusters in several data sets (Table 1). Two main
observations can be made: (i) Phe33 and Lys70
exhibit the same SASA variation profile in all data
sets; and (ii) althoughAla36,Asp39 and Glu42 are
clustered together in all data sets, three different
SASA variation profiles are observed.
Residues Phe33 (β-strand B) and Lys70
(β-strand E) exhibit the same average solvent
exposure across all MD unfolding simulations:
constant solvent exposure around a mean low
value (Figure 3, Panels A and B). In Runs 1, 3 and
4, residue Trp41, located in β-strand C, shows a
SASA variation pattern similar to residues Phe33
and Lys70. However, the relative solvent acces-
sible surface area of this residue is higher than the
former two residues (Figure 3, Panel C).
On the other hand, Ala36 (β-strand B), Asp39
(turn BC) and Glu42 (β-strand C) are identified in
the same clusters across the various data sets, but
two distinct behaviours of solvent exposure are
found. In Runs 1 and 5, throughout most of the
trajectory, these residues favour values of solvent
exposure higher than in the native structure of the
protein, but become buried in the last 2 ns of the
simulation, assuming values of solvent exposure
lower than in the native structure (Figure 3, Panel
F). In Runs 2, 3 and 4, Ala36, Asp39 and Glu42
become buried (Figure 3, Panel D) or less exposed
(Figure 3, Panel E) at different time points and
for different time intervals. In Figure 3, Panel
D, the SASA profile of Ala36 is displayed. Its
SASA value in the native structure is 27.2%, but
it rapidly moves to positions of greater exposure.
Until the 5th nanosecond, the SASA value of
Ala36 varies around 52%. Interestingly, between
the 5th and 8th nanosecond of the simulation, the
residue becomes buried. It then becomes exposed
again in the last 2 ns of the simulation. The pro-
file described can be observed for this group of
residues in Run 2 and Run 3. In Run 4, the SASA
profiles are slightly different as can be observed
in Panel E of Figure 3 for Asp39. During the first
half of the simulation, the residues exhibit SASA
values varying around a mean value lower than
How do the SASA Values of Clustered
Groups of Residues Vary Throughout
the Unfolding Simulations?
To establish if a particular group of residues change
their solvent accessible surface area (SASA) in a
synchronised fashion during the unfolding process
of a protein is an important piece of information,
but the analysis of the magnitude and direction of
these changes is also crucial to the understanding
of the unfolding process. In particular, knowing
which amino-acid residues remain unexposed to
the solvent or which amino-acid residues rapidly
move from positions of low exposure to positions
of high exposure may reveal important clues on
the role those residues play in the stabilization of
the protein. Figure 3 shows the SASA variation
profiles of the residues co-occurring in the same
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