C4DT02257B

Dalton
Transactions
PAPER
Cite this: Dalton Trans., 2014, 43,
16680
Received 24th July 2014,
Accepted 9th September 2014
DOI: 10.1039/c4dt02257b
www.rsc.org/dalton
The unusual binding mechanism of Cu(II) ions to
the poly-histidyl domain of a peptide found in the
venom of an African viper†
Fabio Pontecchiani,a
Eyal Simonovsky,b,c
Robert Wieczorek,d
Nuno Barbosa,d
Magdalena Rowinska-Zyrek,d
Slawomir Potocki,d
Maurizio Remelli,*a
Yifat Miller*b,c
and Henryk Kozlowski*d
Copper complexes of a poly-His/poly-Gly peptide (EDDHHHHHHHHHGVGGGGGGGGGG-NH2), a
natural component of a snake venom, were studied by means of both experimental (thermodynamic,
spectroscopic and MS) techniques and molecular dynamics (MD) simulations and density functional
theory (DFT) calculations. This peptide proved to be an exceptionally effective copper chelator, forming
complexes which are thermodynamically more stable than those formed by both the albumin-like
ATCUN motif and several other poly-histidine protein fragments. We show that, in a poly-histidine stretch,
copper seems to prefer binding to residues separated by one amino acid and that a correlation between
an α-helical structure of the predicted complexes and their thermodynamic stability is observed.
Introduction
The snake has been a symbol of medicine and health care since
ancient times, due to its association with Asclepius, the Greek
god of medicine, represented with a serpent-entwined rod.
Nowadays, the interest in snake venoms as a possible source of
useful chemicals is very strong: several drugs derived from
snake venoms have been developed for the treatment of cardio-
vascular diseases or pain and many products containing snake
venom extracts are commercially available for skin care.1
The snake venoms are complex mixtures of metallo-
enzymes, proteins and polypeptides, containing more than 100
toxins.2
Snake venom metalloproteinases (SVMPs), constitut-
ing 30% of the total protein content of most viperine venoms,3
play a crucial role in the venom activity. The SVMPs are mainly
responsible at all levels for hemorrhagic activity of the venom;
they are phylogenetically closely related to the adamalysin
(or ADAM) family proteins and may contain one or more
calcium ions and one zinc ion.2
The zinc-binding domain
(–HEXXHXXGXXH–) is highly conserved in all of the metzincin
clan of metalloendopeptidases (comprising the ADAM family)
and contains three histidines separated by three and five resi-
dues.4
The various crystal structures of SVMPs show that the
three His residues and an oxygen atom of a water molecule are
tetrahedrally coordinated to the zinc ion.2
One of the most interesting questions that one can ask
with regard to the strong proteolytic activity of SVMPs is: why
does the venom not damage the venom glands of the snake?
Recently, three synergistic inhibitory mechanisms have been
demonstrated to abolish the metallopeptidase activity in situ:
(i) calcium chelation by citrate; (ii) acidic pH; and (iii) enzymatic
competitive inhibition by some peptides.5
High concentrations
of both citrate and peptides with strong metal-binding abilities
have been found in venoms, and the pH in the snake gland is
around 5. However, the dilution of the venom which follows the
inoculation into the victim and the pH buffering to the physio-
logical pH value diminish the inhibitory mechanism.
The relationship between the hemorrhagic activity of snake
venoms and metal ion homeostasis is an intriguing issue.
Snake venoms cause significant alterations of metal ion levels
in patients with snake bite: usually, a significant increase of
serum Cu(II) levels is observed6
and it has even been suggested
that serum copper/zinc ratios can be used as markers of patho-
physiological effects of snake envenomation.7
Several groups
have also shown that copper can inhibit various snake venom
serine proteases and metalloproteinases.8,9
The mechanism of
this activity remains unclear.
A recent mass spectrometric investigation on the venom of
an African viper (Atheris squamigera) revealed the presence of a
set of poly-His/poly-Gly peptides (pHpG), with the consensus
†Electronic supplementary information (ESI) available. See DOI: 10.1039/
c4dt02257b
a
Department of Chemical and Pharmaceutical Sciences, University of Ferrara, via
Fossato di Mortara 17, I-44121 Ferrara, Italy. E-mail: rmm@unife.it
b
Department of Chemistry, Ben Gurion University of the Negev, Beer-Sheva 84105,
Israel
c
Ilse Katz Institute for Nanoscale Science and Technology, Ben Gurion University of
the Negev, Beer-Sheva 84105, Israel. E-mail: ymiller@bgu.ac.il
d
Department of Chemistry, University of Wroclaw, F. Joliot-Curie 14, 50-383
Wroclaw, Poland. E-mail: henryk.kozlowski@chem.uni.wroc.pl
16680 | Dalton Trans., 2014, 43, 16680–16689 This journal is © The Royal Society of Chemistry 2014
Publishedon10September2014.DownloadedbyUniversityofSheffieldon27/11/201615:53:53.
View Article Online
View Journal | View Issue

sequence: EDDH9GVG10.10
One of these peptides is pHpG-1
(EDDH9GVG10); it had been proven to reduce (and completely
neutralize if the dose is sufficient) the hemorrhagic activity of
the Echisocellatus venom on the skin of mice.11
As one can see,
pHpG-1 consists of two long repeats of His and Gly. It is
remarkably well-known that poly-His sequences exist in many
proteins as recently reviewed by Rowinska-Zyrek et al.12
In
nature, poly-histidine sequences are found in chaperones of
urease and hydrogenase utilizing species, in metal trans-
porters, prion proteins, in histidine-rich glycoproteins, in anti-
microbial peptides or in numerous copper-binding proteins.
Furthermore, both natural and synthetic His-tags (sequences
of six His residues) are commercially used in protein purifi-
cation by means of immobilized metal affinity chromatography
(IMAC). The typical (His)6 tag is linked to the C- or N-terminus
of the protein which is meant to be purified, where it serves as
a molecular ‘anchor’ that binds to a metal ion (copper or
nickel), immobilized by chelation with nitrilotriacetic acid
(NTA) bound to a solid support.13
The peculiar, repetitive sequence of EDDHHHHHHHHH-
GVGGGGGGGGGG-NH2, a natural component of the venom of
Atheris squamigera, is a perfect example of a peptide which is
surprisingly rich in both histidines and glycines. Why did
Nature, among the 20 possible amino acids, choose two of
them (His and Gly) and arranged them in an awkward, repeti-
tive manner? This biology-inspired question is quite a puzzle
from the point of view of bioinorganic chemistry. Does this
sequence form even stronger complexes than the famous His-
tags? How does the metal binding affect the overall structure
of the biomolecule? Might metal complexes have any relevance
for biology? The most reasonable hypothesis would be to cor-
relate the His-rich sequence to a high metal-binding affinity
and the Gly-rich region to a given structure formation.
Concerning copper complexes, they are expected to be quite
stable, since all of the nine histidines present in the peptide
are theoretically able to coordinate copper ions and their sequen-
tial proximity seems to favor the complex formation. However,
describing the thermodynamics, the copper binding modes and
the predicted structure of complexes was not a trivial task; to
fully understand these phenomena, in the present work various
methods were used, both experimental (mass spectrometry, MS;
potentiometric titrations; UV-Vis absorption; circular dichroism,
CD; electron paramagnetic resonance spectroscopy, EPR) as well
as density functional theory calculations (DFT) and molecular
dynamics (MD) simulations had been applied with the aim to
gain information at the atomic level about the binding sites of
the copper along the pHpG-1 peptide.
Results and discussion
Protonation of the pHpG-1 peptide: insight into the residues
that can bind Cu(II)
The peptide pHpG-1 contains 13 sites involved in acid–base
equilibria. The most basic of them is the terminal amino
group, characterized by the highest protonation constant value
of this ligand (log K = 8.61, see Table 1): therefore, at pH
higher than approximately 10 the ligand is completely deproto-
nated and triply negatively charged (L3−
, see the distribution
diagram in Fig. 1). His residues are characterized by log K
values ranging from 7.24 to 3.05. Such low values are rather
unusual for histidine, but they are justified by the high charge
that the peptide assumes at high protonation degree. The pro-
tonation of the glutamic acid residue can be observed at the
boundary of the potentiometrically accessible pH range, since
its log K is also very low (2.0). Protonation equilibria of aspartic
acid residues could not be observed, since they are beyond the
pH detection limits of the glass electrode.
Cu(II)–pHpG-1 complexes: potentiometric and spectroscopic
results
The comparison of experimental curves obtained titrating
solutions containing the same amount of ligand in the pres-
ence or absence of the Cu(II) ion reveals that complex for-
mation starts at pH close to 3 (Fig. S2, ESI†). Under the
experimental conditions used, eight Cu(II) complexes are
formed in a detectable amount, in the explored pH range. All
of them are 1 : 1 and mononuclear, differently protonated and
charged, as shown in Table 2, where the corresponding log β
Fig. 1 Representative distribution diagram for the protonation of
pHpG-1 at 25 °C and I = 0.1 mol dm−3
(KCl). C°L = 4 × 10−4
mol dm−3
.
Table 1 Protonation constants of pHpG-1 at 25 °C and I = 0.1 mol dm−3
(KCl). The standard deviations are reported in parentheses as uncertain-
ties on the last significant figure
Log β Log K Residue
LH2−
8.61 (3) 8.61 Amine
LH2
−
15.85 (4) 7.24 His
LH3 22.96 (4) 7.11 His
LH4
+
29.31 (6) 6.35 His
LH5
2+
35.57 (5) 6.26 His
LH6
3+
41.34 (5) 5.77 His
LH7
4+
46.72 (4) 5.38 His
LH8
5+
51.83 (3) 5.11 His
LH9
6+
55.99 (3) 4.16 His
LH10
7+
59.04 (3) 3.05 His
LH11
8+
61.06 (4) 2.02 Glu
Dalton Transactions Paper
This journal is © The Royal Society of Chemistry 2014 Dalton Trans., 2014, 43, 16680–16689 | 16681
View Article Online

and log K values are reported, along with the suggested donor
atoms (see below). A representative distribution diagram is
shown in Fig. 2. The mass spectra measured under equimolar
metal-to-ligand conditions confirmed the formation of 1 : 1
complexes, considering both the position of the signals and
the isotopic patterns (Fig. S3, ESI†).
The visible absorption spectra (Fig. 3) show that the d–d
band of copper shifts from 800 nm (at pH 3) to 520 nm (at
pH 11), indicating the successive coordination of more nitro-
gen donor atoms, from both imidazole residues and the
peptide backbone. Two major changes can also be observed in
the CD spectra (Fig. S4, ESI†). First, at pH 6 the intensity of the
d–d band notably increases. This observation can be attributed
to the effect of binding of the first amide nitrogen, close to the
chiral centers of the ligand. The formation of a Cu(II)–Namide
coordination bond is also supported by the contemporary
onset of the characteristic UV band around 310 nm. Then, at
pH 9, the d–d band changes its sign, indicating an important
change in the coordination geometry. Finally, also EPR data
agree with the further involvement of nitrogen donors in the
complex-formation, starting from pH 3 (Fig. S5, ESI†).
Structural hypotheses for Cu(II) binding sites in pHpG-1
The first interaction between Cu(II) and the pHpG-1 peptide at
pH 3 can be attributed to either an imidazole ring of His resi-
dues or the terminal amino group. Spectroscopic data indicate
a 1 N complex and it seems likely that a variety of species
exists, with the same stoichiometry but differing in the nitro-
gen atom involved in complexation. Oxygen donors bound in
the equatorial position can be either from water molecules or
a carboxylic group of Asp or Glu.
At higher pH, additional imidazole nitrogens progressively
bind copper, as suggested by the spectrophotometric and EPR
data. The onset of the intense CD signal around pH 6.5 reveals
the substitution of one imidazole with an amide nitrogen,
forming the neutral [CuLH] complex, most likely characterized
by a 3N configuration in the equatorial plane of the complex. In
the alkaline pH range, amide nitrogens can effectively compete
with and substitute imidazoles in binding the Cu(II) ion.
Examination of the Cu(II) binding sites in pHpG-1 via
computational studies
To examine the structural Cu(II)–pHpG-1 complexes at the
atomic level, we applied DFT calculations in the gas phase and
MD simulations in solution. In both computational studies we
examined several binding sites along the poly-His sequence in
the pHpG-1 peptide and in the N-terminal domain that
include Glu and Asp acids that also have the ability to bind
Cu(II). It should be noted that there may be thousands of poss-
ible structures of Cu(II)–pHpG-1. The six models (Fig. 4) that
have been investigated by DFT and the ten models (Fig. 5) that
have been studied by MD simulations were carefully selected
to cover the most likely structures. In models A1–A3, M2, M3
and M5–M10 the Cu(II) binds to the poly-His domain in the
pHpG-1 peptide, while in models A4, A5, and M4 it binds
to both the poly-His domain and the N-terminal domain of
the peptide. Finally, in model M1 the Cu(II) binds only to
the N-terminal domain. Tables 3 and 4 report in detail the
binding sites of Cu(II) for each model.
Fig. 2 Representative distribution diagram for the complex-formation
of pHpG-1 with the Cu(II) ion, at 25 °C and I = 0.1 mol dm−3
(KCl). C°M =
4 × 10−4
mol dm−3
; M/L molar ratio = 1 : 1.2.
Fig. 3 Visible absorption spectra, at different pH values, for the system
Cu(II)/pHpG-1. C°M = 4 × 10−4
mol dm−3
; metal/ligand molar ratio =
1 : 1.2.
Table 2 Cu(II) complex-formation constants of pHpG-1 at 25 °C and I =
0.1 mol dm−3
(KCl). The standard deviations are reported in parentheses
as uncertainties on the last significant figure
Log β Log K Donors
[CuLH6]5+
48.67 (1) — 1Nim
[CuLH4]3+
40.09 (2) 4.58 2Nim
[CuLH3]2+
35.51 (2) 5.33 2Nim
[CuLH2]+
30.18 (3) 6.40 3Nim
[CuLH] 23.78 (3) 6.89 3Nim, N−
[CuL]−
16.89 (3) — 2Nim, 2N−
[CuLH−2]3−
1.12 (3) 9.29 2Nim, 2N−
[CuLH−3]4−
−8.17 (4) — 1Nim, 3N−
Paper Dalton Transactions
View Article Online

Defined secondary structures of pHpG-1 strongly stabilize its
Cu(II) complex
The intramolecular hydrogen bond network had been investi-
gated in models A1–A5. Model A1 has 13 hydrogen bonds,
Fig. 4 The optimized structures of Cu(II)–pHpG-1 complexes. Green tubes follow the peptide backbone. Binding sites with Cu(II) are detailed in
Table 3.
Table 3 The structural Cu(II)–pHpG-1 complexes computed using the
DFT level of theory
Model
Residues along the pHpG-1 peptide
interacting with Cu(II)
A1 H5, H7 and H9
A2 H4 and H6
A3 H11 and H12
A4 H10, H12 and G14 (via CvO)
A5 E1, H5 and H12
Fig. 5 The constructed models of Cu(II)–pHpG-1 complexes after simulations of 30 ns. Binding sites with Cu(II) are detailed in Table 4.
Table 4 Initial coordination of Cu(II) with residues in pHpG-1 peptide
Model
Residues along the pHpG-1 peptide
interacting with Cu(II)
M1 E1 and D3
M2 H7 and H9
M3 H4, H6 and H8
M4 E1, D3 and H12
M5 H4, H6 and H12
M6 H5, H7 and H11
M7 H6, H8 and H10
M8 H7, H9 and H11
M9 H8, H9 and H10
M10 H5, H11 and H12
View Article Online

models A2 and A3 have 12 hydrogen bonds, model A4 has 16
(the largest number of hydrogen bonds) and model A5 has 10
(the smallest number of hydrogen bonds). Hydrogen bonds
stabilize the folding of proteins and peptides to form some-
times β-sheet and α-helical structures. Herein, we found that
in all models A1–A5 the hydrogen bonds form stable helical
structures. Interestingly, in each model one can see two 3–10
type helix and one α-helix.
To examine whether one can see α-helical structures in sol-
vated Cu(II)–pHpG-1 complexes, we applied molecular
dynamics (MD) simulations for models M1–M10. To this aim,
we constructed models in which Cu(II) interacts with two or
three imidazole groups of His, carboxylic groups of Glu or Asp
and water. Fig. 5 shows the final ten simulated models and the
final coordination of the peptide with Cu(II). Interestingly, in
two of the simulated models, in which initially only imidazole
groups of His interacted with Cu(II), spontaneously during the
MD simulations the Cu(II) interacted also with carboxylic
groups of Glu (model M2) and Asp (model M10). One can see
that in the result of MD simulations among the ten models,
models M2, M3, M6 and M8 showed a well-defined secondary
structure of an α-helix in the Gly-rich domain of the
peptide and only the model M4 contained the antiparallel
β-sheet structure in the N-terminal domain (EDD(H)9) (Fig. S1,
ESI†). Interestingly, models M2, M3, M4, M6 and M8 are
energetically more stable (Table S1, ESI†) and preferred (Fig. 6)
compared to the other models that did not show a well-
defined secondary structure (excluding model M1, in which
Cu(II) interacts with Glu1 and Asp3 and not with His residues,
as in all the other models). It should be noted that only
preferred models with populations above 10% were con-
sidered. These five models consist mostly of the populations of
Cu(II)–pHpG-1 complex models (∼56% of the populations).
Yet, one cannot neglect that also disordered complexes may
be formed (∼44% of the populations), but still the majority
of the populations consists of complexes with a secondary
structure.
Coordination of Cu(II) with solvating water molecules
Since we are interested in the structure and thermodynamics
of the complex in solution and all experimental procedures are
performed in water solution, it is thus interesting to examine
the number of water molecules that can interact with Cu(II), in
addition to donor groups of the ligand. It is known that Cu(II)
has a coordination number of 4, 5 or 6 in biological systems.16
In the models presented in Fig. 5, Cu(II) binds to His and/or
Asp and/or Glu, but we suggest that it is also coordinated to
water molecules to form coordination numbers of 4, 5 or 6. To
this aim, we followed the number of water molecules that co-
ordinate with the Cu(II) along the MD simulations, considering
a distance cut-off of 2.17 Å between Cu(II) and O atoms of the
water molecule for each model.
Fig. 7 shows the distribution of the number of water mole-
cules that coordinate with Cu(II) at this cut-off distance. In the
majority of the ten models, two water molecules participate in
the coordination with Cu(II). The maximum coordination with
Cu(II), including the water molecule(s) for most of the models,
is 5.
Fig. 6 Calculated populations of the ten constructed models of the
Cu(II)–pHpG-1 complex using the GBMV14,15
and Monte-Carlo simu-
lations. Binding sites with Cu(II) are detailed in Table 4.
Fig. 7 The distribution of the number of water molecules of the Cu(II)–
pHpG-1 complex that coordinate with the Cu(II) ions in models M1–M10
according to the cut-off distance of Cu(II)–oxygen atom of H2O water
molecules of 2.17 Å. Binding sites with Cu(II) are detailed in Table 4. We
based the cut-off distance on the experimental distances range of
Cu2+
–O atoms of water in metalloproteins for various coordination
numbers.17
View Article Online

Comparing the thermodynamic stability of the Cu(II)–pHpG-1
complex with other Cu(II)–peptide complexes
The best way to compare the complex ability of different
ligands towards a metal ion or vice versa is to build up compe-
tition diagrams, calculated using the protonation and binary
complex-formation constants, excluding the formation of
ternary species. Of course, this is an approximation, but it
allows comparing the stability of complexes with different
stoichiometries, charges and protonation degrees.
In Fig. 8 the Cu(II) binding strength of the pHpG-1 peptide
is compared with that of the peptide DAHK-Am, corresponding
to the N-terminal metal binding site of albumin (also named
the ATCUN motif). The plot shows that more than 80% of
copper is bound to pHpG-1 at all pH values. A similar result
(Fig. S6, ESI†) is obtained comparing the binding strength of
pHpG-1 with that of other short peptides with a similar
sequence at their N-terminus, but lacking the His residues. In
this case, the short peptides can compete with pHpG-1 only at
low pH, where both the terminal amine and imidazole nitro-
gens can anchor the metal. The conclusion is that the role
played by the terminal amine in complex formation is negli-
gible in the presence of many His residues.
A very good ligand for Cu(II) is the octarepeat domain of the
human prion protein, containing four His residues spaced
with seven other residues, repeated four times. Depending on
both the pH and the ligand/metal ratio, the octarepeat frag-
ment can form either tetranuclear, binuclear or mononuclear
species.18,19
The pHpG-1 peptide binds Cu(II) much more
strongly than these species (Fig. 9).
Poly-His sequences are not rare in proteins. A good example
is given by Hpn protein, secreted by Helicobacter pylori and
involved in nickel trafficking, containing 47% of His in its
sequence with two stretches of 6 and 7 consecutive histi-
dines.21
Hpn forms strong complexes with Cu(II) ions, whose
structures are very similar, at physiological pH, to that hypo-
thesized for pHpG-1.22
However, when the two ligands are in
competition, most of the copper binds pHpG-1 (Fig. 10). The
same behavior was found when building up the competition
diagram for the system Cu(II)/pHpG-1/Ac-HHHHHH-Am, where
the latter peptide mimics the His-tag (containing six consecu-
tive His) used for protein purification in IMAC (see Fig. S7,
ESI†). The reasons for this behavior can be the impact of
different non-binding residues surrounding the binding site
(enthalpic reason) or the possibility of pHpG-1 to form many
different structures containing the same set of donor atoms,
due to its higher number of consecutive histidines (entropic
reason). In addition, the computational results reported above
show the capability of pHpG-1 to bind copper with three imid-
azole rings, in different ways. Finally, the structuring pro-
perties of the metal ions towards the peptide, inducing the
formation of α-helices and several H-bonds, can lead to a
binding site protected from the solvent and more resistant
towards metal hydrolysis.
Conclusions
The pHpG-1 peptide derived from the venom of Atheris squami-
gera is an unusual and fascinating molecule, able to bind
Cu(II) in a variety of coordination modes. Protonation and
copper complex-formation constants have been determined
Fig. 8 Calculated competition diagram for a ternary solution contain-
ing Cu(II), pHpG-1 and the protected peptide DAHK-Am, corresponding
to the ATCUN motif. The total concentration of each component is 1 ×
10−3
mol dm−3
. The equilibrium constants for the system Cu(II)/
DAHK-Am are taken from ref. 20.
Fig. 9 Calculated competition diagram for a solution containing Cu(II),
pHpG-1 and the octarepeat domain of the human prion protein. The
total concentration of each component is 1 × 10−3
mol dm−3
.
Fig. 10 Calculated competition diagram for a solution containing Cu(II),
pHpG-1 and the protected metal-binding fragment THHHHYHGG of
Hpn protein secreted by Helicobacter pylori. The total concentration of
each component is 1 × 10−3
mol dm−3
.
View Article Online

potentiometrically; the stoichiometry of the formed species
have been checked by ESI-MS; structural hypotheses on the
main species have been suggested after combining potentio-
metric, UV-Vis absorption, CD and EPR data. DFT calculations
and molecular modeling showed the preferred copper binding
sites and established a correlation between a given structure
and metal binding affinity.
Each one of the nine consecutive histidines is able to co-
ordinate the Cu(II) ion and their proximity seems to favor the
complex formation. Cu(II) has a multiplicity of imidazole nitro-
gen atoms to bind to; as a consequence, given a complex stoi-
chiometry, a variety of coordination modes and structures are
possible.
A detailed analysis of numerous experimental and theore-
tical results leads to three main, significant conclusions. First,
from the thermodynamic point of view, all the complexes are
extremely stable; the high number of different combinations
of possible binding sites can be the entropic reason for this
remarkable stability. The pHpG-1 peptide has much more
affinity towards Cu(II) ions than any protein fragment studied
so far, including the albumin-like ATCUN motif, the poly-histi-
dine region of Hpn and the so-called octarepeat domain of the
human prion protein. The Cu(II)–pHpG-1 complex is more
stable than each one of them throughout the entire pH range.
Second, in a sequence of consecutive histidines, copper
seems to prefer binding to histidine residues separated by
exactly one amino acid, in particular to His4, 6 and 8 or to
His7, 9 and 11. This is in agreement with our previous find-
ings for copper complexation of the hexa-histidine tag;17
this
is also a valuable piece of information that can help to predict
or estimate copper binding sites in poly-histidine regions.
Third, there is a pronounced correlation between the well-
defined secondary structure of the predicted complexes and
their thermodynamic stability; complexes in which the Gly-
rich domain is α-helically shaped are energetically more stable
than models without a well-defined secondary structure.
As further developments of the present investigation
focused on pHpG-1 solutions containing equimolar concen-
trations of Cu(II) and the peptide, we are on the way to studying
other divalent metal ions and to extending the ligand/metal
ratio to higher values, in order to explore the possibility of
intermolecular crosslinking by the metal ions.
Experimental section
Materials
The C-protected peptide pHpG-1 (EDDH9GVG10-Am) was pur-
chased from Selleck Chemicals (Huston, TX) (certified purity:
99.35%) and was used as received. Its purity was potentio-
metrically checked.
CuCl2 was an extra pure product (Sigma-Aldrich); the con-
centration of its stock solution was determined by ICP-MS.
The carbonate-free stock solution of 0.1 mol dm−3
KOH was
purchased from Sigma-Aldrich and then potentiometrically
standardized with potassium hydrogen phthalate as the
primary standard. The HCl stock solution was prepared by
diluting concentrated HCl (Sigma-Aldrich) and then standar-
dized with KOH. All sample solutions were prepared with fresh
doubly distilled water. The ionic strength was adjusted to
0.1 mol dm−3
by adding KCl (Sigma-Aldrich). Grade A glass-
ware was employed throughout.
Potentiometric measurements
Stability constants for proton and Cu(II) complexes were calcu-
lated from potentiometric titration curves registered over the
pH range 2.5–10.5 at 25 °C and ionic strength 0.1 mol dm−3
(KCl) using a total volume of 1.5–2.0 cm3
. The pH-metric titra-
tions were performed with a MOLSPIN pH-meter system
equipped with a Russel CMAW 711 semi-combined electrode,
calibrated in proton concentrations using HCl.23
Alkali was
added with a 0.500 cm3
micrometer syringe, which was cali-
brated by both weight titration and the titration of standard
materials. The purities and the exact concentrations of the
ligand solutions were determined by the Gran method.24
The
ligand concentration was about 4 × 10−4
mol dm−3
, and the
metal-to-ligand ratio was 1 : 1.2.
The HYPERQUAD and SUPERQUAD programs were used
for the overall (β) and step (K) stability constant calcu-
lations.25,26
Standard deviations were given by the program
itself and refer to random errors only. The speciation and com-
petition diagrams were computed with the HYSS program.27
Spectroscopic measurements
The absorption spectra were recorded on a Varian Cary 300 Bio
spectrophotometer in the range 200–800 nm. Circular di-
chroism (CD) spectra were recorded on a Jasco J 715 spectropo-
larimeter in the 230–800 nm range. Electron paramagnetic
(EPR) spectra were recorded in liquid nitrogen on a Bruker
ELEXSYS E500 CW-EPR spectrometer at X-band frequency
(9.5 GHz) and equipped with an ER 036TM NMR Teslameter
and an E41 FC frequency counter. 30% ethylene glycol was
used as a cryoprotectant for EPR measurements.
The concentrations of solutions used for spectroscopic
studies were similar to those employed in the potentiometric
experiment. The UV-Vis, CD and EPR spectroscopic parameters
were calculated from the spectra obtained at the pH values
corresponding to the maximum concentration of each parti-
cular species on the basis of distribution diagrams.
Mass spectrometric measurements
High-resolution mass spectra were obtained on a Bruker
Q-FTMS spectrometer (Bruker Daltonik, Bremen, Germany),
equipped with an Apollo II electrospray ionization source with
an ion funnel. The mass spectrometer was operated in the
negative ion mode. The instrumental parameters were as
follows: scan range m/z 400–1600, dry gas nitrogen, tempera-
ture 170 °C, and ion energy 5 eV. Capillary voltage was opti-
mized to the highest S/N ratio and it was 4500 V. The small
changes of voltage (±500 V) did not significantly affect the opti-
mized spectra. The samples (metal/ligand in a 1 : 1 or 1 : 2 stoi-
chiometry, [ligand]tot = 5 × 10−4
mol dm−3
) were prepared in a
View Article Online

1 : 1 MeOH–H2O mixture at different pH values ranging from
2.9 to 8.8. The sample was infused at a flow rate of 3 μL min−1
.
The instrument was calibrated externally with the Tunemix™
mixture (Bruker Daltonik, Germany) in the quadratic
regression mode. Data were processed using the Bruker
Compass DataAnalysis 4.0 program. The mass accuracy for the
calibration was better than 5 ppm, enabling together with the
true isotopic pattern (using SigmaFit) an unambiguous confir-
mation of the elemental composition of the obtained complex.
Density functional theory (DFT) calculations
Quantum chemistry methods are useful tools to predict the
structure and stability of the complexes.28–30
We computed five
structural Cu(II)–pHpG-1 complexes using the DFT level of
theory (Table 3). Molecular orbital studies on Cu(II)–pHpG-1
peptide complexes with a ratio of 1 : 1 have been done on the
DFT level of theory. The starting structure for DFT calculations
was generated on the basis of the amino acid sequence of the
pHpG-1 peptide after 85 ps simulation at 300 K, without
cutoffs using BIO+ implementation of the CHARMM force
field. All DFT calculations were performed with the Gaussian
09 suite of programs31
using the M06-2X32
hybrid functional
and the 6-31G basis set. We have found five Cu(II)–pHpG-1
peptide complexes applying the DFT calculations.
Constructions of Cu(II)–pHpG-1 complex models for molecular
dynamics (MD) simulations
Since the peptide pHpG-1 shows a lack of crystal structure, we
applied the Phyre 2 program33
to predict the folding state of
this peptide (Fig. S1, ESI†). We then coordinated Cu(II) with
various residues along the sequence of the peptide using our
previous protocol of constraints of metal binding with pep-
tides and proteins.34–38
We constructed models in which Cu(II)
interacts with two or three imidazole groups of His and car-
boxylic groups of Glu or Asp and water, taking into account all
the possibilities for the Cu(II)–pHpG-1 system. We selected ten
models of the Cu(II)–pHpG-1 complex among the variety of the
possible modes that Cu(II) can use to bind the peptide pHpG-1
(Table 4).
Molecular dynamics (MD) simulations protocol
The ten models were first minimized as we have performed
previously for amyloids and other peptides.34–38
MD simu-
lations of the solvated models were performed in the NPT
ensemble using NAMD39
with the CHARMM27 force-field.40,41
The models were energy minimized and explicitly solvated in a
TIP3P water box42,43
with a minimum distance of 15 Å from
each edge of the box. Each water molecule within 2.5 Å of the
models was removed. Counter ions were added at random
locations to neutralize the models charge. The Langevin piston
method44,45
with a decay period of 100 fs and a damping time
of 50 fs was used to maintain a constant pressure of 1 atm. A
temperature of 310 K was controlled by a Langevin thermostat
with a damping coefficient of 10 ps.39
The short-range van der
Waals interactions were calculated using the switching func-
tion, with a twin range cut-off of 10.0 and 12.0 Å. Long-range
electrostatic interactions were calculated using the particle
mesh Ewald method with a cutoff of 12.0 Å.46,47
The equations
of motion were integrated using the leapfrog integrator with a
step of 1 fs. The solvated systems were energy minimized for
2000 conjugated gradient steps, where the hydrogen bonding
distance between the β-sheet in each oligomer was fixed in the
range 2.2–2.5 Å. The counter ions and water molecules were
allowed to move. The hydrogen atoms were constrained to the
equilibrium bond using the SHAKE algorithm.48
The minimized solvated systems were energy minimized for
5000 additional conjugate gradient steps and 20 000 heating
steps at 250 K, with all atoms being allowed to move. Then,
the system was heated from 250 K to 310 K for 300 ps and
equilibrated at 310 K for 300 ps. All simulations were run for
30 ns at 310 K. Parameterizations for the Cu(II)–peptide com-
plexes had been performed for all MD simulations. The force
constant values for the Cu(II)–N atom and the Cu(II)–O atom
are in the range of 10–50 kcal mol−2
.
Generalized Born method with molecular volume (GBMV)
The relative conformational energies can be compared only for
models that have the same sequence and number of peptides,
thus the relative conformational energies had been computed
for all ten models. To obtain the relative conformational ener-
gies of the ten models, the model trajectories of the last 5 ns
were first extracted from the explicit MD simulations excluding
the water molecules – a total of 500 conformations for each
oligomer. The solvation energies of all conformations were
calculated using the GMBV.14,15
In the GBMV calculations, the
dielectric constant of water was set to 80. The hydrophobic
solvent-accessible surface area (SASA) term factor was set to
0.00592 kcal (mol Å)−1
. Each conformation was minimized
using 1000 cycles, and the conformational energy was evalu-
ated by grid-based GBMV.
A total of 5000 conformations (500 for each model) were
used to construct the energy landscape of the ten models and
to evaluate the conformer probabilities using Monte Carlo
(MC) simulations. In the first step, one conformation of con-
former i and one conformation of conformer j were randomly
selected. Then, the Boltzmann factor was computed as e−(Ej−Ei)/kT
,
where Ei and Ej are the conformational energies evaluated
using the GBMV calculations for conformations i and j,
respectively, k is the Boltzmann constant and T is the absolute
temperature (298 K used here). If the value of the Boltzmann
factor was larger than the random number, then the move
from conformation i to conformation j was allowed. After
1 million steps, the conformations ‘visited’ for each conformer
were counted. Finally, the relative probability of model n was
evaluated as Pn = Nn/Ntotal, where Pn is the population of model
n, Nn is the total number of conformations visited for model n,
and Ntotal is the total steps. The advantages of using MC simu-
lations to estimate conformer probability lie in their good
numerical stability and the control that they allow of transition
probabilities among several conformers.
Using all ten models and 5000 conformations (500 for each
model) generated from the MD simulations, we estimated the
View Article Online

overall stability and populations for each conformer based on
the MD simulations, with the energy landscape being com-
puted with GBMV for these ten models. The group of these ten
models is likely to represent only a very small percentage of
the ensemble. Nevertheless, the carefully selected models
cover the most likely structures.
Acknowledgements
This research was partially supported by grant no. 2011128
from the United States–Israel Binational Science Foundation
(BSF). All simulations were performed using the high-perform-
ance computational facilities of the Miller lab in the BGU HPC
computational center. The support of the BGU HPC compu-
tational center staﬀ is greatly appreciated. The financial
support from University of Ferrara (FAR 2012) is gratefully
acknowledged.
References
1 F. J. Vonk, K. Jackson, R. Doley, F. Madaras, P. J. Mirtschin
and N. Vidal, Bioessays, 2011, 33, 269–279.
2 T. S. Kang, D. Georgieva, N. Genov, M. T. Murakami,
M. Sinha, R. P. Kumar, P. Kaur, S. Kumar, S. Dey,
S. Sharma, A. Vrielink, C. Betzel, S. Takeda, R. K. Arni,
T. P. Singh and R. M. Kini, FEBS J., 2011, 278, 4544–4576.
3 J. W. Fox and S. M. T. Serrano, FEBS J., 2008, 275, 3016–
3030.
4 A. Onoda, T. Suzuki, H. Ishizuka, R. Sugiyama, S. Ariyasu
and T. Yamamura, J. Pept. Sci., 2009, 15, 832–841.
5 R. Marques-Porto, I. Lebrun and D. C. Pimenta, Comp.
Biochem. Physiol., 2008, 147, 424–433.
6 C. Ustun, I. Tegin and M. F. Geyik, J. Microbiol. Infect. Dis.,
2013, 3, 71–74.
7 A. Al-Asmari, N. A. Osman, A. Aziz, A. M. Aman and
M. W. Khan, J. Biol. Sci., 2013, 1, 8–14.
8 D. L. Menaldo, C. P. Bernardes, N. A. Santos-Filho,
L. d. A. Moura, A. L. Fuly, E. C. Arantes and S. V. Sampaio,
Biochimie, 2012, 94, 2545–2558.
9 A. F. Wahby, A. M. Abdel-Aty and E. M. El-Kady, Toxicon,
2012, 59, 329–337.
10 P. Favreau, O. Cheneval, L. Menin, S. Michalet,
H. Gaertner, F. Principaud, R. Thai, A. Menez, P. Bulet and
R. Stocklin, Rapid Commun. Mass Spectrom., 2007, 21, 406–
412.
11 S. C. Wagstaﬀ, P. Favreau, O. Cheneval, G. D. Laing,
M. C. Wilkinson, R. L. Miller, R. Stoecklin and
R. A. Harrison, Biochem. Biophys. Res. Commun., 2008, 365,
650–656.
12 M. Rowinska-Zyrek, D. Witkowska, S. Potocki, M. Remelli
and H. Kozlowski, New J. Chem., 2013, 37, 58–70.
13 H. Block, B. Maertens, A. Spriestersbach, N. Brinker,
J. Kubicek, R. Fabis, J. Labahn and F. Schafer, in Guide to
Protein Purification, ed. R. R. Burgess and M. P. Deutscher,
Elsevier Academic Press Inc, San Diego, 2nd edn, 2009,
vol. 463, pp. 439–473.
14 M. S. Lee, F. R. Salsbury and C. L. Brooks, J. Chem. Phys.,
2002, 116, 10606–10614.
15 M. S. Lee, M. Feig, F. R. Salsbury and C. L. Brooks,
J. Comput. Chem., 2003, 24, 1348–1356.
16 http://mespeus.bch.ed.ac.uk/tanna/qg3.htm.
17 J. Watly, E. Simonovsky, R. Wieczorek, N. Barbosa, Y. Miller
and H. Kozlowski, Inorg. Chem., 2014, 53, 6675–6683.
18 D. Valensin, M. Luczkowski, F. M. Mancini, A. Legowska,
E. Gaggelli, G. Valensin, K. Rolka and H. Kozlowski, Dalton
Trans., 2004, 1284–1293.
19 H. Kozlowski, M. Luczkowski, M. Remelli and D. Valensin,
Coord. Chem. Rev., 2012, 256, 2129–2141.
20 M. Sokolowska, A. Krezel, M. Dyba, Z. Szewczuk and
W. Bal, Eur. J. Biochem., 2002, 269, 1323–1331.
21 J. V. Gilbert, J. Ramakrishna, F. W. Sunderman, A. Wright
and A. G. Plaut, Infect. Immun., 1995, 63, 2682–2688.
22 D. Witkowska, R. Politano, M. Rowinska-Zyrek, R. Guerrini,
M. Remelli and H. Kozlowski, Chem. – Eur. J., 2012, 18,
11088–11099.
23 H. M. Irving, M. G. Miles and L. D. Pettit, Anal. Chim. Acta,
1967, 38, 475–488.
24 G. Gran, Acta Chem. Scand., 1950, 4, 559–577.
25 P. Gans, A. Sabatini and A. Vacca, J. Chem. Soc., Dalton
Trans., 1985, 1195–1200.
26 P. Gans, A. Sabatini and A. Vacca, Talanta, 1996, 43, 1739–
1753.
27 L. Alderighi, P. Gans, A. Ienco, D. Peters, A. Sabatini and
A. Vacca, Coord. Chem. Rev., 1999, 184, 311–318.
28 E. Gumienna-Kontecka, G. Berthon, I. O. Fritsky,
R. Wieczorek, Z. Latajka and H. Kozlowski, J. Chem. Soc.,
Dalton Trans., 2000, 4201–4208.
29 Z. Latajka, Z. Mielke, A. Olbert-Majkut, R. Wieczorek and
K. G. Tokhadze, Phys. Chem. Chem. Phys., 1999, 1, 2441–
2448.
30 M. Wierzejewska and R. Wieczorek, Chem. Phys., 2003, 287,
169–181.
31 M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria,
M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone,
B. Mennucci, G. A. Petersson, H. Nakatsuji, M. Caricato,
X. Li, H. P. Hratchian, A. F. Izmaylov, J. Bloino, G. Zheng,
J. L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda,
J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao,
H. Nakai, T. Vreven, J. J. A. Montgomery, J. E. Peralta,
F. Ogliaro, M. Bearpark, J. J. Heyd, E. Brothers,
K. N. Kudin, V. N. Staroverov, R. Kobayashi, J. Normand,
K. Raghavachari, A. Rendell, J. C. Burant, S. S. Iyengar,
J. Tomasi, M. Cossi, N. Rega, J. M. Millam, M. Klene,
J. E. Knox, J. B. Cross, V. Bakken, C. Adamo, J. Jaramillo,
R. Gomperts, R. E. Stratmann, O. Yazyev, A. J. Austin,
R. Cammi, C. Pomelli, J. W. Ochterski, R. L. Martin,
K. Morokuma, V. G. Zakrzewski, G. A. Voth, P. Salvador,
J. J. Dannenberg, S. Dapprich, A. D. Daniels, Ö. Farkas,
J. B. Foresman, J. V. Ortiz, J. Cioslowski and D. J. Fox,
Gaussian, Inc., Wallingford, CT, 2009.
View Article Online

32 Y. Zhao and D. G. Truhlar, Theor. Chem. Acc., 2008, 120,
215–241.
33 http://www.sbg.bio.ic.ac.uk/servers/phyre2/html/page.cgi?
id=index.
34 V. Wineman-Fisher, R. Simkovitch, S. Shomer,
R. Gepshtein, D. Huppert, M. Saif, K. Kallio,
S. J. Remington and Y. Miller, Phys. Chem. Chem. Phys.,
2014, 16, 11196–11208.
35 N. Zeytuni, R. Uebe, M. Maes, G. Davidov, M. Baram,
O. Raschdorf, M. Nadav-Tsubery, S. Kolusheva, R. Bitton,
G. Goobes, A. Friedler, Y. Miller, D. Schueler and
R. Zarivach, PLoS One, 2014, 9, e92141.
36 Y. Raz, B. Rubinov, M. Matmor, H. Rapaport, G. Ashkenasy
and Y. Miller, Chem. Commun., 2013, 49, 6561–6563.
37 Y. Raz and Y. Miller, PLoS One, 2013, 8, e73303.
38 Y. Raz, J. Adler, A. Vogel, H. A. Scheidt, T. Haeupl, B. Abel,
D. Huster and Y. Miller, Phys. Chem. Chem. Phys., 2014, 16,
7710–7717.
39 L. Kale, R. Skeel, M. Bhandarkar, R. Brunner, A. Gursoy,
N. Krawetz, J. Phillips, A. Shinozaki, K. Varadarajan and
K. Schulten, J. Comput. Phys., 1999, 151, 283–312.
40 A. D. MacKerell, D. Bashford, M. Bellott, R. L. Dunbrack,
J. D. Evanseck, M. J. Field, S. Fischer, J. Gao, H. Guo, S. Ha,
D. Joseph-McCarthy, L. Kuchnir, K. Kuczera, F. T. K. Lau,
C. Mattos, S. Michnick, T. Ngo, D. T. Nguyen, B. Prodhom,
W. E. Reiher, B. Roux, M. Schlenkrich, J. C. Smith, R. Stote,
J. Straub, M. Watanabe, J. Wiorkiewicz-Kuczera, D. Yin and
M. Karplus, J. Phys. Chem. B, 1998, 102, 3586–3616.
41 B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States,
S. Swaminathan and M. Karplus, J. Comput. Chem., 1983, 4,
187–217.
42 M. W. Mahoney and W. L. Jorgensen, J. Chem. Phys., 2000,
112, 8910–8922.
43 W. L. Jorgensen, J. Chandrasekhar, J. D. Madura,
R. W. Impey and M. L. Klein, J. Chem. Phys., 1983, 79, 926–
935.
44 K. Tu, D. J. Tobias and M. L. Klein, Biophys. J., 1995, 69,
2558–2562.
45 S. E. Feller, Y. H. Zhang, R. W. Pastor and B. R. Brooks,
J. Chem. Phys., 1995, 103, 4613–4621.
46 U. Essmann, L. Perera, M. L. Berkowitz, T. Darden, H. Lee
and L. G. Pedersen, J. Chem. Phys., 1995, 103, 8577–8593.
47 T. Darden, D. York and L. Pedersen, J. Chem. Phys., 1993,
98, 10089–10092.
48 J. P. Ryckaert, G. Ciccotti and H. J. C. Berendsen, J. Comput.
Phys., 1977, 23, 327–341.
View Article Online

C4DT02257B

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Viewers also liked

Viewers also liked (13)

Similar to C4DT02257B

Similar to C4DT02257B (20)

C4DT02257B