Supplementary:
1
Details of Gene design and assembly
2
Two new restriction sites for endonucleases were introduced in the gene sequence. To substitute lysine 45 with
3
cysteine, AAA codon (coding sequence of lysine) was substituted with TGC codon (coding sequence of
4
cysteine) in the fifth gene constructed oligonucleotides. By adjusting the melting temperature (Tm) of each
5
overlapping region to about 60 °C, the gene assembly and synthesis of mutant SmtA gene were accomplished by
6
simplified gene synthesis (SGS) approach (wu et al., 2006). The procedure for PCR was as follows: 1µl of the
7
oligo mixture (final concentration of 100 µl) was used as a template with the oligonucleotides Sm1 and Sm6 as
8
primers. The PCR conditions were: initial denaturation step of DNA at 94 °C for 1 min, followed by 30 cycles
9
of denaturation at 94 °C for 30s, primer annealing at 52 °C for 30 s and primer elongation at 72 °C for 1 min.
10
The cycling was followed by a final extension step at 72 °C for 4 min.
11
Simulation details:
12
MD simulations were carried out with periodic boundary conditions. Van der Waals forces were treated with a
13
cut-off of 12 Å. The Particle-Mesh Ewald method was used with a 14 Å cut-off (Darden, York & Pedersen,
14
1993).The protein was solvated by a layer of water molecules with a thickness of 1.3 nm in all directions. Then
15
the size of simulation box was 7.15×7.15×5.05 nm. The frequency to update the neighbor list was 10. The
16
protonation state of Gromacs package was used to calculate the total charge of SmtA. The ionic strength of the
17
simulation box was set at about 140 mM. This ionic strength was chosen because it is close to biological ionic
18
strength (Brenner et al., 1982., Arnold,James,& Minou, 1979).This makes ion absorption in a simulated
19
biological system possible. MD simulation was accomplished in four steps. In the first step, the entire system
20
was minimized using the steepest descent followed by conjugate gradient algorithms. In the second step or
21
equilibration step, heavy atoms were restrained using a force constant of 1000 kJ/Mol nm and the solvent and
22
Na+ and Cl- ions were allowed to evolve. This was done through minimization and molecular dynamics in the
23
NVT ensemble for 500 ps and in the NPT ensemble for 1000 ps. Then in order to obtain equilibrium geometry
24
at 300 K and 1 atm, the temperature of the system was increased and the velocities at each step were reassigned
25
according to the Maxwell-Boltzmann distribution at that temperature and equilibrated for 200 ps. Temperature
26
coupling was set to 0.1 ps and pressure coupling to 0.5 ps. The Berendsen algorithm was used for thermostat
27
and barostat during the equilibration step (Berendsen,Postma,Van Gunsteren, Dinola, & Haak, 1984).All bonds
28
1
were constrained via the LINCS algorithm (Hess, Bekker, Berendsen, & Fraaije, 1997). In the final step or
29
production phase, a 50 ns MD simulation was performed under an NPT ensemble. In order to retain temperature
30
and pressure stable in production step, Nosé-Hoover thermostat and Parrinello-Rahman barostat were used
31
respectively by removing position restraints (Berendsen, Postma, Van Gunsteren, Dinola, & Haak, 1984).In
32
addition, the LINCS algorithm was used to constrain the lengths of hydrogen-containing bonds in this step
33
(Berendsen, Postma, Van Gunsteren, Dinola, & Haak,1984).The accessible surface area (ASA) and backbone
34
RMSF of protein during MD simulations of phase I were computed by using g_sas and g_rmsf modules of the
35
Gromacs package.
36
37
38
39
40
100000
0
Energy (kJ/mol)
0
10
20
30
40
50
-100000
Potential
-200000
Kinetic En.
-300000
-400000
-500000
Time (ns)
41
Fig. S1. The kinetic and potential energies of native SmtA during 50 ns MD simulations in phase I.
42
43
44
45
2
46
backbone rmsf
Standard deviation of ASA
0.25
0.2
0.15
0.1
0.05
0
0
3
6
9
12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57
47
Fig. S2. RMS fluctuation of protein backbone and standard deviation of accessible surface area (ASA) of SmtA
48
verses residue number in phase I during 50 ns MD simulation.
49
50
51
52
53
54
55
3
56
57
Fig. S3. The number of contacts below 0.6 nmbetweenCd 2+ ions and SmtA(A) and M-SmtA (B) during 50 ns
58
MD simulation in phase II and III.
59
60
PCA Analysis
61
Essential dynamics
62
Essential dynamics is a method which utilizes principal component analysis (PCA) on the actual coordinates of
63
the system and thus gives the essential motion of the protein or some atoms in phase space. Essential dynamics
64
are an efficient tool for monitoring protein dynamics in phase space because the observed motion is
65
unconstrained and represents the atomic fluctuations of the protein. The essential dynamics method divides the
66
conformational space of the protein into two subspecies, an essential subspace and a nonessential subspace. The
67
covariance matrix of carbon alpha atoms is diagnosed to obtain the eigenvectors and eigenvalues that provide
68
information about correlated motions throughout the protein.
69
4
The eigenvectors of the covariance matrix represent the principal components (PCs).
70
In fact, the eigenvectors represent the direction of motion, and the eigenvalues represent the amount of motion
71
along the eigenvectors. In addition, the eigenvector with the highest eigenvalue is considered as the first
72
principal component; the eigenvector with the second highest eigenvalue as the second principal component and
73
so on. It has been shown that the majority of protein motion scans be accounted for in the first some principal
74
components. Thus, the dynamics of a protein or some atoms can be analyzed by projecting its atomic motion
75
during a MD simulation onto its first two or three principal components (Sangeeta& Debjani, 2008).The
76
calculations of the eigenvectors and eigenvalues were carried out using the essential dynamics analysis
77
(Amadei, Linssen& Berendsen, 1993) with the aid of the Gromacs software package. We considered the carbon
78
alpha atoms in phase I, and cadmium ions in phases II and III for generating the covariance matrix.
79
The first few eigenvectors typically describe collective motions of the system, and contain the largest mean-
80
square fluctuations. In this work, eigenvectors of Cα in phase I and those of cadmium ions in phases II and III
81
were calculated for the global movement study of Cα and cadmium ions. The first four eigenvalues in phases I-
82
III have been shown in Table 3. The other eigenvalues were trivial. Projections onto eigenvectors are overall
83
coordinates that give information on how the system moves in the directions described by the eigenvectors
84
(Maisuradz, Liwo, & Scheraga, 2009).Projection of Cα motions ofSmtAin phase I and also cadmium ions in
85
phases II and III during the first three principal components (PCs) for 50 ns are shown as supplemental data in
86
Figs.S4, S5and S6, respectively.
87
The cosine content was introduced as a measure of the closeness of the PC to a cosine shape, which appeared to
88
be a good indicator for predicting, whether a trajectory has sampled a free-energy landscape sufficiently for
89
convergence. The value of the cosine content varies between 0 (no cosine shape) and 1 (perfect cosine shape).
90
When the cosine content of the first few PCs is close to 1 (an indication of bad sampling), the largest-scale
91
motions have a random diffusion in the protein dynamics. A cosine content of 0.2 for small peptides which
92
increases up to 0.5 for proteins shows good sampling (in production) and converging (in simulation) (Van Alten
93
et al., 1997). The cosine content of carbon alpha in phase I for eigenvector 1-3 is up to 0.075, suggesting
94
sampling had been good and simulation converged in phase I. Cosine contents of cadmium ions in phases II and
95
III and for the first three eigenvectors are respectively up to 0.027 and 0.006. These results indicate that the
96
samplings were good and cadmium ions did not have random diffusions in phases II and III. Thus, SmtA and
97
M-SmtAbiased Brownian motions of cadmium ions.
98
5
99
projection on eigenvector1
1.5
1
0.5
0
-0.5
0
10
20
30
40
50
-1
-1.5
Time (ns)
-2
100
projection on eigenvector2
1.5
1
0.5
0
0
10
20
30
40
50
-0.5
-1
Time (ns)
projection on eigenvector3
-1.5
1
101
0.5
0
-0.5
0
10
20
30
40
50
-1
-1.5
Time (ns)
102
103
Fig. S4. Projection of Cα motions of SmtA during 50 ns MD simulation onto its first 3 principal components in
104
phase I.
105
6
7
projection on
eigenvector1
5
3
1
-1 0
-3
10
20
30
40
50
-5
-7
Time (ns)
106
projection on eigenvector2
7
5
3
1
-1 0
10
20
30
40
50
-3
-5
-7
Time (ns)
107
projection on eigenvector3
7
5
3
1
-1 0
10
20
30
40
50
-3
-5
-7
Time (ns)
108
109
Fig. S5. Projection of cadmium ions during 50 ns MD simulation onto its first 3 principal components in phase
110
II.
111
7
projection on eigenvector1
7
5
3
1
-1 0
10
20
30
40
50
-3
-5
Time (ns)
-7
112
projection on eigenvector2
7
5
3
1
-1 0
10
20
30
40
50
-3
-5
Time (ns)
-7
113
projection on eigenvector3
7
5
3
1
-1 0
10
20
30
40
50
-3
-5
-7
Time (ns)
114
Fig. S6. Projection of cadmium ions during 50 ns MD simulation onto its first three principal components in
115
phase IV.
116
117
118
119
8
References
120
Amadei, A., Linssen, A.B., &Berendsen, H.J.C. (1993) Essential dynamics of proteins. Proteins, 17, 412–425.
121
Arnold, S., James, P.W., &Minou, B. (1979) Acidic polypeptides can assemble both histones and chromatin in
122
vitro at physiological ionic strength. PNAS USA, 76, 5000-5004.
123
Berendsen, H.J.C., Postma, J.P.M., Van Gunsteren, W.F.,Dinola, A.,& Haak, J.R. (1984) Molecular dynamics
124
with coupling to an external bath. J Chem Phys, 81, 3684–3690.
125
Brenner, B., Schoenberg, M., Schalovich, J.M., Greene, L.E.,& Eisenberg, E. (1982) Evidence for cross-bridge
126
attachment in relaxed muscle at low ionic strength. Proc Natl Acad Sci USA, 79, 7288-7291.
127
Darden, T., York, D.,& Pedersen, L. (1993) Particle mesh Ewald: An N log (N) method for Ewald sums in large
128
systems. J Chem Phys, 98, 10089-10092.
129
Hess, B., Bekker, H., Berendsen, H.J.C.,& Fraaije, J.G.E (1997) MLINCS: a linear constraint solver for
130
molecular simulations. J Comp Chem, 18, 1463-1472.
131
Maisuradz,G.G., Liwo, A.,& Scheraga, H.A (2009) Principal Component Analysis for Protein Folding
132
Dynamics. J Mol Biol, 385, 312–329.
133
Sangeeta, K.,& Debjani, R. (2008) Temperature-induced unfolding pathway of a type III antifreeze protein:
134
Insight from molecular dynamics simulation. J Mole Graph Model, 27, 88–94.
135
Van Aalten, D.M.F., De Groot, B.L., Findlay, J.B.C., Berendsen, H.J.C.,& Amadei AA (1997) comparison of
136
techniques for calculating protein essential dynamics. J Comput Chem, 18, 169-181.
137
Wu, G., Wolf, J.B., Ibrahim, A.F., Vadasz, S&Gunasinghe.M. (2006) Simplified gene synthesis: a one-step
138
approach to PCR-based gene construction. J Biotechnology, 124, 496–503.
139
140
9