International Journal of Inorganic ChemistryVolume 2012 (2012), Article ID 265249, 9 pagesdoi:10.1155/2012/265249

Research Article

Speciation Studies of L-Histidine Complexes of Pb(II), Cd(II), and Hg(II) in DMSO-Water Mixtures

K. Bharath Kumar Naik, B. Ananda Kumar, S. Raju, and G. Nageswara Rao

Department of Inorganic & Analytical Chemistry, School of Chemistry, Andhra University, Visakhapatnam 530003, India

Received 5 November 2011; Revised 21 January 2012; Accepted 23 January 2012

Academic Editor: Wei-Yin Sun

Copyright © 2012 K. Bharath Kumar Naik et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Equilibrium study on complex formation of L-histidine with Pb(II), Cd(II), and Hg(II) has been investigated pH metrically in DMSO-water mixtures (0–60% v/v) at 303 K and 0.16 mol L⁻¹ ionic strength. The predominant species detected for Pb(II) and Cd(II) are ML₂H₄, ML₂H₃, ML₂H₂, ML₂H, and ML₂ and those for Hg(II) are ML₂H₄, ML₂H₃, ML₂, and ML. The appropriateness of experimental conditions is verified by introducing errors intentionally in the concentrations of ingredients. The models containing different numbers of species were refined by using the computer program MINIQUAD75. The best-fit chemical models were arrived at based on statistical parameters. The trend in variation of stability constants of the complexes with dielectric constant of the medium is attributed to the electrostatic and nonelectrostatic forces. The species distribution and the plausible equilibria for the formation of the species are also presented.

1. Introduction

The speciation study of toxic metal ion complexes is useful to understand the role played by the active site cavities in biological molecules and the bonding behavior of protein residues with the metal ion. The species refined and their relative concentrations under the experimental conditions represent the possible forms of aminoacids in biofluids.

Due to its numerous uses and high persistence, lead is a major environmental contaminant [1]. Lead is toxic even at low concentrations for living organisms, who can absorb it in various ways [2]. Lead intake by humans can be due to the consumption of crop plants grown on soils with high plant-available metal concentrations [3]. It can, however, migrate through the soil with dissolved organic matter [4] or mobilized by certain plants [5]. Moreover, carried from the air to the soils as fine particles, lead could be released more easily in soil solution [6].

Cadmium causes iron deficiency by binding to cysteine, glutamate, aspartate, and histidine ligands [7]. Cadmium inhibits enzymes that participate in bilirubine conjunction [8]. It increases urine Ca²⁺ excretion which can cause severe bone pathology [9]. The possible effects of long term low-level exposure to cadmium are of concern because it is readily distributed to tissues of liver and kidney, which are the main target organs in acute and chronic cadmium exposure [10, 11]. Other tissues involved in cadmium toxicity include the testis, heart, bone, eye, and brain [12].

Mercury is one of the most toxic elements and has negative health effects in human populations, highly dependent on fish consumption [13, 14]. Recent research concluded that neither vitamin B12 nor the acetyl-CoA pathways are required for bacterial methylation of mercury [15]. Cysteine enhances Hg methylation by facilitating uptake of Hg²⁺ and promoting enzymatic formation of monomethylmercury [16–18]. Humans are exposed to Hg primarily as monomethylmercury [19], which is the form of Hg that accumulates readily in organisms and biomagnifies in food webs [20].

L-Histidine (His) is one of the strongest metal coordinating ligands among the aminoacids and plays an important role in the binding of metal ions by proteins. Most of the active sites of the biomolecules have histidine as one of the amino acid residues. It has three potential metal-binding sites, namely, carboxylate oxygen (Ocaboxyl), imidazole imidonitrogen (Nim) and aminonitrogen (Nam) [21] (Figure 1). Due to the high reactivity of its imidazole group, histidine residue is often found at the active site of enzymes and involved directly in catalysis [22]. It controls the transmission of metals in biological bases [23] and has been reported to act as a neurotransmitter or neuromodulator in mammalian central nervous system. His is involved in a large number of biochemical processes such as biosynthesis of histamine, secretion of prolactin and antidiuretic hormone, and production of red and white blood cells. It possesses vasodilating and hypotensive actions and may boost the activity of soothing alpha waves in the brain. His is used in the treatment of anemia, allergies, rheumatoid arthritis, and other inflammatory reactions [24–28].

Figure 1: L-Histidine.

Dimethyl sulfoxide (DMSO) has the largest dielectric constant of the common dipolar aprotic solvents and it has been termed the super solvent. Another important phenomenon about the structure of the DMSO molecule is its trigonal pyramidal geometry. There is a highly directional lone pair of electrons at the apex of the pyramid, which helps in complexation.

His is an important component of active sites of biomolecules and there is a very likely hood for toxic metals to interact with His. Hence the speciation of the title systems has been studied in DMSO-water mixtures. DMSO has been chosen as a medium to decrease the dielectric constant of the medium, since the permittivity of active site cavities is very low [29]. Protonation equilibria of L-His in DMSO-water mixtures have already been studied in this laboratory [30].

2. Experimental

2.1. Materials

L-Histidine.HCl (E-Merck, Germany) solution (0.05 mol L⁻¹) was prepared in triple-distilled deionised water by maintaining 0.05 mol L⁻¹ nitric acid concentration to increase the solubility. DMSO (Qualigens, India) was used as received. 2 mol L⁻¹ sodium nitrate (Qualigens, India) was prepared to maintain the ionic strength in the titrand. Solutions of Pb(II), Cd(II), and Hg(II) nitrates (0.05 mol L⁻¹) were prepared by dissolving G.R. Grade (E-Merck, Germany) salts in triple-distilled water maintaining 0.05 mol L⁻¹ nitric acid to suppress the hydrolysis of metal salts. All the solutions were standardized by standard methods. To assess the errors that might have crept into the determination of the concentrations, the data were subjected to analysis of variance of one way classification [31]. The strengths of alkali and mineral acid were determined using the Gran plot method [32, 33].

2.2. Apparatus

The titrimetric data were obtained using ELICO (Model LI-120) pH meter (readability 0.01), which was calibrated with 0.05 mol L⁻¹ potassium hydrogen phthalate in acidic region and 0.01 mol L⁻¹ borax solution in basic region. The glass electrode was equilibrated in a well stirred DMSO-water mixture containing the inert electrolyte. All the titrations were carried out in the medium containing varying concentrations of DMSO-water mixtures (0–60% v/v) by maintaining an ionic strength of 0.16 mol L⁻¹ with sodium chloride at  K. The effect of variation in asymmetry potential, liquid junction potential, activity coefficient, sodium ion error, and dissolved carbon dioxide on the response of glass electrode was accounted for in the form of correction factor [34, 35].

2.3. Procedure

For the determination of stability constants of metal-ligand binary species, initially titrations of strong acid with alkali were carried out at regular intervals to check whether complete equilibration was achieved. Then the calomel electrode was refilled with DMSO-water mixture of equivalent composition as that of titrand. In each of the titrations, the titrand consisted of approximately 1 mmol mineral acid in a total volume of 50 mL. Titrations with different ratios (1 : 2.5, 1 : 3.75 and 1 : 5.0 in the case of Pb(II) and Cd(II) and 1 : 7.5, 1 : 8.5 and 1 : 10.0 in the case of Hg(II)) of metal-to-ligand were carried out with 0.4 mol L⁻¹ sodium hydroxide. Other experimental details are given elsewhere [36].

2.4. Modeling Strategy

The computer program SCPHD [37] was used to calculate the correction factor. By using the pH-metric titration data, the binary stability constants were calculated with the computer program MINIQUAD75 [38], which exploit the advantage of the constrained least-squares method in the initial refinement and reliable convergence of Marquardt algorithm. During the refinement of binary systems, the correction factor and the protonation constants of histidine were fixed. The variation of stability constants with the dielectric constant of the medium was analyzed on electrostatic grounds on the basis of solute-solute and solute-solvent interactions.

3. Result and Discussion

The results of exhaustive modeling for the Cd(II)-His system in 40% DMSO-water mixture are given in Table 1. The models gave better statistics as the number of species was increased, confirming better fit. There was no further improvement in the fit on inclusion of some more species in the model containing CdL₂H₄, CdL₂H3, CdL₂H₂, CdL₂H, and CdL₂. This indicates that the final model appropriately fits the experimental data. Such exhaustive modeling was performed for all the systems. The results of the final best-fit models that contain the stoichiometry of the complex species and their overall formation constants along with some of the important statistical parameters are given in Table 2. Very low-standard deviation in overall stability constants (log β) signifies the precision of these constants. The small values of (sum of squares of deviations in concentrations of ingredients at all experimental points) corrected for degrees of freedom, small values of mean, standard deviation, and mean deviation for the systems are validated by the residual analysis [39].

Table 1: Exhaustive modeling of Cd(II)-His system in 40% v/v DMSO-water mixture. pH range = 2.0–8.0, .

Model No.
log (SD)

Skewness
Kurtosis
χ ²
-factor

120
121
122
123
124

1
12.15 (9)
—
—
—
—
10.927
−1.43
4.96
176.10
0.016

2
—
18.87 (6)
—
—
—
4.649
0.00
6.26
114.71
0.010

3
—
—
25.44 (25)
—
—
34.123
−0.20
12.56
359.17
0.028

4
—
—
—
Rejected
—
—
—
—
—
—

5
—
—
—
—
Rejected
—
—
—
—
—

6
11.27 (12)
18.79 (5)
—
—
—
2.740
−1.32
5.35
100.94
0.008

7
12.11 (7)
—
25.20 (7)
—
—
2.427
−1.36
5.33
100.56
0.007

8
12.15 (9)
—
—
Rejected
—
10.978
−1.43
4.96
176.10
0.016

9
12.15 (9)
—
—
—
Rejected
10.928
−1.43
4.96
176.10
0.016

10
—
18.92 (7)
24.74 (12)
—
—
3.687
0.50
9.42
116.12
0.009

11
—
19.26 (11)
—
30.78 (17)
—
3.448
0.43
8.66
83.63
0.009

12
—
18.87 (6)
—
—
Rejected
4.649
0.00
6.26
114.71
0.010

13
—
—
25.43 (26)
Rejected
—
34.123
−0.20
12.56
359.17
0.028

14
—
—
25.64 (29)
—
35.53 (41)
33.541
−0.20
13.11
412.88
0.028

15
—
—
—
Rejected
Rejected
—
—
—
—
—

16
11.54 (7)
18.75 (4)
24.91 (5)
—
—
1.036
−1.93
6.96
137.95
0.004

17
11.63 (11)
19.17 (8)
—
30.72 (12)
—
1.642
−1.04
4.00
69.05
0.006

18
11.26 (12)
18.88 (5)
—
—
Rejected
2.740
−1.32
5.35
100.94
0.008

19
12.11 (7)
—
25.19 (6)
Rejected
—
2.427
−1.36
5.33
100.56
0.007

20
12.25 (7)
—
25.39 (7)
—
35.34 (11)
1.979
−1.22
6.41
53.76
0.006

21
12.91 (21)
—
—
31.06 (25)
Rejected
8.500
−1.43
5.41
121.02
0.014

22
—
19.16 (12)
24.71 (21)
30.59 (21)
—
3.316
0.55
10.31
151.88
0.008

23
—
19.04 (8)
24.96 (13)
—
35.17 (19)
3.484
0.73
10.39
107.52
0.009

24
—
19.42 (11)
—
31.00 (15)
35.25 (17)
3.168
0.73
9.82
62.68
0.008

25
—
—
25.64 (29)
Rejected
35.53 (40)
33.541
−0.20
13.11
412.88
0.028

26
11.62 (7)
18.89 (6)
24.94 (6)
30.16 (19)
—
0.957
−2.04
7.71
159.99
0.004

27
11.70 (6)
18.87 (4)
25.12 (5)
—
35.20 (8)
0.766
−1.06
5.55
36.56
0.004

28
12.25 (7)
—
25.39 (7)
Rejected
35.34 (11)
1.979
−1.22
6.41
53.76
0.006

29
—
19.32 (11)
24.92 (19)
30.81 (19)
35.28 (16)
2.989
0.93
12.16
126.63
0.008

30
11.80 (11)
19.34 (7)
—
30.96 (10)
35.20 (12)
1.382
−0.50
3.58
26.22
0.005

31
11.78 (6)
19.05 (6)
25.15 (5)
30.45 (13)
35.25 (7)
0.630
−0.75
5.64
56.26
0.003

; m = number of species; NP = number of experimental points; SD = standard deviation.

Table 2: Best-fit chemical models of His complexes with Pb(II), Cd(II), and Hg(II) in DMSO-water mixtures.

DMS % v/v
log (SD)
NP

Skewness
Kurtosis
χ ²
-factor
pH range

110
120
121
122
123
124

Pb(II)

0.0
—
9.37 (40)
18.36 (40)
25.07 (41)
31.10 (42)
36.90 (42)
38
0.137
0.67
7.15
32.11
0.002
3.5–9.0

10.0
—
9.65 (29)
19.29 (28)
26.02 (28)
31.78 (30)
37.54 (29)
29
0.385
0.06
2.45
2.66
0.004
3.5–9.0

20.0
—
9.10 (30)
18.59 (30)
25.29 (31)
31.07 (33)
36.67 (35)
19
0.094
0.64
4.22
5.74
0.001
3.7–9.0

30.0
—
9.80 (13)
19.21 (10)
25.65 (12)
30.82 (19)
36.43 (16)
44
0.328
0.59
4.19
10.67
0.004
3.5–9.0

40.0
—
10.81 (34)
19.64 (32)
25.90 (34)
31.35 (39)
36.70 (39)
47
1.323
0.35
3.14
18.38
0.008
3.5–9.0

50.0
—
10.03 (9)
19.29 (8)
25.31 (9)
30.22 (16)
35.46 (15)
45
0.270
1.39
9.68
18.30
0.003
3.5–9.0

60.0
—
9.77 (16)
19.07 (13)
25.02 (17)
29.90 (29)
35.02 (30)
49
0.915
0.86
5.16
9.90
0.006
3.5–9.0

Cd(II)

0.0
—
10.25 (14)
17.80 (15)
24.35 (16)
30.55 (17)
36.26 (18)
50
0.282
1.85
12.03
59.28
0.003
3.0–8.0

10.0
—
10.63 (26)
18.36 (26)
25.02 (29)
31.19 (29)
36.96 (30)
49
0.522
2.45
13.66
68.13
0.005
3.0–8.0

20.0
—
10.22 (12)
17.63 (12)
23.97 (20)
29.88 (28)
34.95 (69)
37
0.362
1.50
7.91
18.23
0.003
3.0–8.0

30.0
—
11.01 (8)
18.55 (7)
24.90 (10)
30.55 (14)
35.85 (18)
44
0.244
0.77
6.03
13.82
0.003
3.5–8.0

40.0
—
11.78 (6)
19.05 (6)
25.15 (5)
30.45 (13)
35.25 (7)
98
0.630
−0.75
5.64
56.26
0.003
2.0–8.0

50.0
—
12.11 (7)
19.28 (6)
25.22 (6)
30.24 (16)
35.01 (8)
96
0.751
−0.93
6.80
80.00
0.004
2.0–8.0

60.0
—
12.10 (7)
18.91 (6)
24.83 (5)
29.68 (17)
—
22
0.356
1.38
6.32
5.52
0.003
3.5–8.5

Hg(II)

0.0
10.09 (24)
13.18 (34)
—
—
33.83 (23)
35.21 (87)
90
0.824
−0.78
4.70
47.16
0.004
2.0–8.0

10.0
12.42 (49)
16.78 (85)
—
—
31.60 (65)
—
95
99.96
−0.71
6.23
307.70
.0494
2.0–8.0

20.0
11.57 (33)
14.78 (55)
—
—
32.38 (86)
35.81 (40)
65
4.295
0.19
5.38
107.97
.0103
2.0–9.0

30.0
11.57 (32)
14.59 (35)
—
—
34.56 (29)
37.27 (36)
65
0.533
−0.26
7.16
9.68
0.003
2.0–9.0

40.0
12.31 (20)
16.13 (23)
—
—
34.55 (14)
36.83 (22)
36
0.192
−0.20
4.15
15.70
0.001
2.0–9.0

50.0
12.31 (31)
15.58 (34)
—
—
35.00 (29)
37.36 (36)
106
0.650
−0.55
4.19
20.31
0.003
2.0–9.0

60.0
12.49 (22)
15.55 (28)
—
—
34.87 (19)
37.40 (24)
105
0.668
−0.09
3.40
23.67
0.003
2.0–9.0

3.1. Residual Analysis

In data analysis with least squares methods, the residuals (the differences between the experimental data and the data simulated based on model parameters) are assumed to follow Gaussian or normal distribution. When the data are fit into the models, the residuals should ideally be equal to zero. If statistical measures of the residuals and the errors assumed in the models are not significantly different from each other, the model is said to be adequate. Further, a model is considered adequate only if the residuals do not show any trend. Respecting the hypothesis that the errors are random, the residuals are tested for normal distribution. Such tests are χ ², Skewness, Kurtosis, and R-factor. These statistical parameters show that the best-fit models portray the metal-ligand species in DMSO-water mixtures, as discussed below.

In the present study, the χ ² values are less than the table values, and so the models are accepted. The kurtosis values in this study indicate that the residuals form leptokurtic pattern. The values of skewness recorded in Table 2 are between 0.06 and 1.39 for Pb(II), −0.93 and 2.45 for Cd(II) and −0.78, and 0.19 for Hg(II). These data evince that the residuals form part of a normal distribution. Hence, least square method can be applied to the present data. The sufficiency of the model is further evident from crystallographic -values. These statistical parameters thus show that the best-fit models portray the metal-ligand species in DMSO media.

3.2. Effect of Systematic Errors on Best Fit Model

In order to rely upon the best-fit chemical model for critical evaluation and application under varied experimental conditions with different accuracies of data acquisition, an investigation was undertaken by introducing pessimistic errors in the influential parameters like concentrations of alkali, mineral acid, ligand, metal, , and volume (Table 3). The order of the ingredients that influence the magnitudes of stability constants due to incorporation of errors is alkali > acid > metal > ligand > volume > log F. Some species were even rejected when errors were introduced in the concentrations. The rejection of some species and increased standard deviations in the stability constants on introduction of errors confirm the suitability of the experimental conditions (concentrations of ingredients) and choice of the best-fit models.

Table 3: Effect of errors in influential parameters on Pb(II)-His complex stability constants in 50% v/v DMSO-water mixture.

Ingredient
% Error
log (SD)

120
121
122
123
124

0
10.03 (9)
19.29 (8)
25.31 (9)
30.22 (16)
35.46 (15)

−5
14.52 (13)
21.26 (11)
26.28 (17)
31.92 (12)
Rejected

Acid
−2
11.44 (11)
20.00 (9)
25.38 (12)
30.93 (13)
34.98 (34)

+2
Rejected
18.62 (22)
25.32 (27)
Rejected
35.88 (35)

+5
Rejected
16.50 (75)
25.12(78)
Rejected
36.29 (86)

−5
Rejected
15.84 (35)
24.62 (35)
Rejected
35.53 (50)

−2
Rejected
18.55 (17)
25.25 (21)
Rejected
35.71 (30)

Alkali
+2
11.48 (11)
19.97 (9)
25.37 (12)
30.88 (13)
35.28 (23)

+5
13.99 (9)
20.67 (8)
25.71 (14)
31.37 (9)
Rejected

−5
10.00 (17)
19.20 (14)
25.37 (20)
Rejected
35.82 (26)

−2
10.05 (13)
19.29 (12)
25.36 (13)
30.18 (27)
35.66 (20)

Ligand
+2
10.01 (7)
19.31 (6)
25.28 (7)
30.27 (11)
35.22 (14)

+5
10.02 (8)
19.36 (6)
25.24 (7)
30.34 (11)
34.73 (26)

−5
9.94 (12)
19.26 (9)
25.34 (11)
30.32 (18)
35.61 (17)

−2
9.99 (11)
19.28 (8)
25.32 (10)
30.27 (17)
35.51 (16)

Log F
+2
10.06 (9)
19.31 (7)
25.30 (9)
30.19 (16)
35.39 (15)

+5
10.12 (8)
19.34 (6)
25.30 (8)
30.15 (15)
35.30 (15)

−5
10.17 (8)
19.31 (6)
25.22 (8)
30.13 (15)
35.27 (15)

−2
10.08 (8)
19.30 (7)
25.27 (9)
30.18 (16)
35.38 (15)

Metal
+2
10.06 (8)
19.31 (7)
25.30 (9)
30.19 (16)
35.39 (15)

+5
10.12 (8)
19.34 (6)
25.30 (8)
30.15 (15)
35.30 (15)

−5
9.98 (9)
19.24 (8)
25.27 (9)
30.17 (17)
35.40 (16)

−2
10.01 (9)
19.27 (8)
25.30 (9)
30.21 (16)
35.43 (15)

Volume
+2
10.04 (9)
19.31 (7)
25.33 (9)
30.24 (16)
35.47 (15)

+5
10.07 (9)
19.34 (7)
25.36 (9)
30.28 (16)
35.50 (15)

3.3. Effect of Solvent

The linear variation of stability constants (log β) of his complexes with Pb(II), Cd(II), and Hg(II) with variation of ( is the dielectric constant) of DMSO-water mixtures are given in Figure 2. DMSO is a polar aprotic and coordinating solvent. It is a structure former and it enhances the water structure in DMSO-water mixtures; hence, it removes water from coordination sphere of metal ions, making it more reactive towards the ligands. As a result, the stability of the complexes is expected to increase. At the same time, it is a coordinating solvent and competes with the ligands for coordinating the metals. This decreases the stability of the complexes. Hence, the stability of the complexes is expected to either increase or decrease. The linear variation indicates that electrostatic forces dominate the equilibrium process under the present experimental conditions.

(a)

(b)

(c)

Figure 2: Variation of stability constant values of His complexes with reciprocal of dielectric constant () of DMSO-water mixtures (a) Pb(II); (b) Cd(II); (c) Hg(II); (star) log (square) log ; (circle) log ; (triangle up) log ; (triangle down) log ; (diamond) log .

3.4. Distribution Diagrams

Histidine is a tridentate ligand that has one dissociable (carboxylate group) and two associable (amino, imidazole) protons. The different forms of His are    LH, and L⁻ in the pH range below 4.0, 2.0–6.0, 6.0–10.0, and above 9.0, respectively. Hence, the plausible binary metal-ligand complexes can be predicted from these data. The present investigation reveals the existence of ML₂H₄, ML₂H₃, ML₂H₂, ML₂H, and ML₂ for Pb(II), and Cd(II). Hg(II) forms ML₂H₄, ML₂H₃, ML₂, and ML species. The formation of various his complex species is shown in the following equilibria. The species distribution diagrams in DMSO-water mixtures are shown in Figure 3. They indicate that the his complexes of Pb(II), Cd(II), and Hg(II) are formed in the pH range 2.0–9.0. At lower pH, species are formed from free-metal ion that interacts with form of the ligand [Equilibrium (1)]. The species may be formed from M(II) and or by the deprotonation of (Equilibria (2) & (3)). In the same manner, the species ML₂H₂ may be formed either from free-metal ion and / (Equilibria (4) & (5)) or deprotonation of and (Equilibria (6) & (7)). At lower pH, the interaction of free-metal ion with or (Equilibria (4) & (5)) and at higher pH, deprotonation of , and (Equilibria (6) & (7)) results in the formation of ML₂H₂ species. Species ML₂H is formed from M(II) and or or at lower pH, deprotonation of , , and species at higher pH (Equilibria (8)–(12)). The ML₂ species are formed from interaction of free-metal ion, with or LH (Equilibria 15 & 16) or ML⁺ and LH (Equilibrium (17)) or deprotonation of , , , and ML₂H⁺ species (Equilibria (18)–(21)). In the case of Hg(II) (Figure 2(c)) the ML species are formed from interaction of free-metal ion and or (Equilibria (13) & (14)). It is formed at near neutral pH.

(a)

(b)

(c)

Figure 3: Distribution diagrams of binary complexes of His in 50% v/v DMSO-water mixture: (a) Pb(II), (b) Cd(II) and (c) Hg(II).

4. Conclusions

The following conclusions have been drawn from the modeling studies of the L-Histidine complexes of Pb(II), Cd(II) and Hg(II) in DMSO-water mixture. (1) L-Histidine forms both protonated and unprotonated complexes under pH range 2.0–9.0. The species detected for the toxic metals Pb(II) and Cd(II) are ML₂H₄, ML₂H₃, ML₂H₂, ML₂H, and ML₂. ML₂H₄, ML₂H₃, ML₂, and ML species are detected the case of Hg(II). (2) The linear variation of stability constants as a function of of the medium indicates the dominance of electrostatic forces over nonelectrostatic forces. The linear increasing trend with DMSO content supports the dominance of the structure forming nature of DMSO over its complexing ability. (3) The order of ingredients in influencing the magnitudes of stability constants due to incorporation of errors in their concentrations is alkali > acid > metal > ligand > volume > log F. (4) Some species are stabilized due to electrostatic interactions and some are destabilized due to the decreased dielectric constant.

Acknowledgment

The authors (K. B. K. Naik, B. A. Kumar, S. Raju) thank The University Grants Commission, New Delhi, India for financial support under Faculty Development Programme.

References

Y. Chen, C. Wang, and Z. Wang, “Residues and source identification of persistent organic pollutants in farmland soils irrigated by effluents from biological treatment plants,” Environment International, vol. 31, no. 6, pp. 778–783, 2005.

R. L. Canfield Jr., C. R. Henderson, D. A. Cory-Slechta, C. Cox, T. A. Jusko, and B. P. Lanphear, “Intellectual impairment in children with blood lead concentrations below 10 μg per deciliter,” New England Journal of Medicine, vol. 348, no. 16, pp. 1517–1526, 2003.

P. D. Alexander, B. J. Alloway, and A. M. Dourado, “Genotypic variations in the accumulation of Cd, Cu, Pb and Zn exhibited by six commonly grown vegetables,” Environmental Pollution, vol. 144, no. 3, pp. 736–745, 2006.

M. Cecchi, C. Dumat, A. Alric, B. Felix-Faure, P. Pradere, and M. Guiresse, “Multi-metal contamination of a calcic cambisol by fallout from a lead-recycling plant,” Geoderma, vol. 144, no. 1-2, pp. 287–298, 2008.

M. Arshad, J. Silvestre, E. Pinelli et al., “A field study of lead phytoextraction by various scented Pelargonium cultivars,” Chemosphere, vol. 71, no. 11, pp. 2187–2192, 2008.

G. J. K. Komarnicki, “Lead and cadmium in indoor air and the urban environment,” Environmental Pollution, vol. 136, no. 1, pp. 47–61, 2005.

J. M. Castagnetto, S. W. Hennessy, V. A. Roberts, E. D. Getzoff, J. A. Tainer, and M. E. Pique, “MDB: the Metalloprotein Database and Browser at The Scripps Research Institute,” Nucleic Acids Research, vol. 30, no. 1, pp. 379–382, 2002.

L. Zeneli, H. Paçarizi, N. M. Daci, M. Daci-Ajvazi, and A. Prenaj, “The effects of air pollution and smoking on cadmium concentration in human blood and correlation with biochemical parameters,” American Journal of Biochemistry and Biotechnology, vol. 5, no. 2, pp. 59–62, 2009.

S. Takebayashi, S. Jimi, M. Segawa, and Y. Kiyoshi, “Cadmium induces osteomalacia mediated by proximal tubular atrophy and disturbances of phosphate reabsorption. A study of 11 autopsies,” Pathology Research and Practice, vol. 196, no. 9, pp. 653–663, 2000.

S. Chater, T. Douki, A. Favier, M. Sakly, and H. Abdelmelek, “Changes in antioxidant status and biochemical parameters after orally cadmium administration in females rats,” Acta Biologica Hungarica, vol. 60, no. 1, pp. 79–88, 2009.

S. O. Asagba and F. O. Obi, “Effect of cadmium on kidney and liver cell membrane integrity and antioxidant enzyme status: implications for Warri River cadmium level,” Tropical Journal of Environmental Science and Health, vol. 3, no. 1, pp. 33–39, 2000.

S. O. Asagba, “Alterations in activities of tissue enzymes in oral cadmium Toxicity,” Nigerian Journal of Science and Environment, vol. 6, pp. 91–102, 2007.

D. Mergler, H. A. Anderson, L. H. M. Chan et al., “Methylmercury exposure and health effects in humans: a worldwide concern,” Ambio, vol. 36, no. 1, pp. 3–11, 2007.

U. S. Environmental Protection Agency, 2010

E. B. Ekstrom and F. M. M. Morel, “Cobalt limitation of growth and mercury methylation in sulfate-reducing bacteria,” Environmental Science and Technology, vol. 42, no. 1, pp. 93–99, 2008.

L. Landner, “Biochemical model for the biological methylation of mercury suggested from methylation studies in vivo with neurospora crassa,” Nature, vol. 230, no. 5294, pp. 452–454, 1971.

J. K. Schaefer and F. M. M. Morel, “High methylation rates of mercury bound to cysteine by Geobacter sulfurreducens,” Nature Geoscience, vol. 2, no. 2, pp. 123–126, 2009.

R. Sparling, “Biogeochemistry: mercury methylation made easy,” Nature Geoscience, vol. 2, no. 2, pp. 92–93, 2009.

W. F. Fitzgerald, C. H. Lamborg, and C. R. Hammerschmidt, “Marine biogeochemical cycling of mercury,” Chemical Reviews, vol. 107, no. 2, pp. 641–662, 2007.

J. G. Wiener, D. P. Krabbenhoft, G. H. Heinz, and A. M. Scheuhammer, “Ecotoxicology of mercury,” in Handbook of Ecotoxicology, Chapter 16, p. 407, CRC Press, 2nd edition, 461.

P. Deschamps, P. P. Kulkarni, M. Gautam-Basak, and B. Sarkar, “The saga of copper(II)-L-histidine,” Coordination Chemistry Reviews, vol. 249, no. 9-10, pp. 895–909, 2005.

T. E. Creighton, Encyclopedia of Molecular Biology, vol. 2, Wiley, New York, NY, USA, 1999.

C. Guo Nan, W. Xiao Ping, D. Jian Ping, and C. Hong Qing, “A study on electrochemistry of histidine and its metabolites based on the diazo coupling reaction,” Talanta, vol. 49, no. 2, pp. 319–330, 1999.

A. K. Vinnikova, R. C. Kukreja, and M. L. Hess, “Singlet oxygen-induced inhibition of cardiac sarcolemmal Na⁺K⁺-ATPase,” Journal of Molecular and Cellular Cardiology, vol. 24, no. 5, pp. 465–470, 1992.

N. G. Sitton, J. S. Dixon, C. Astbury, R. J. Francis, H. A. Bird, and V. Wright, “Kinetic investigations into the possible cause of low serum histidine in rheumatoid arthritis,” Annals of the Rheumatic Diseases, vol. 47, no. 1, pp. 48–52, 1988.

E. Hitomi-Ohmura, N. Amano, Y. Aoyama, and A. Yoshida, “The effect of a histidine-excess diet on cholesterol synthesis and degradation in rats,” Lipids, vol. 27, no. 10, pp. 755–760, 1992.

Y. Aoyama, T. Tsuda, E. Hitomi-Ohmura, and A. Yoshida, “Effect of dietary excess-histidine on fructose 1,6-bisphosphatase and 6-phosphofructokinase activities, and activation of fructose 1,6-bisphosphatase by basic amino acids in rat liver,” International Journal of Biochemistry, vol. 24, no. 6, pp. 981–985, 1992.

Q. Cai, G. Takemura, and M. Ashraf, “Antioxidative properties of histidine and its effect on myocardial injury during ischemia/reperfusion in isolated rat heart,” Journal of Cardiovascular Pharmacology, vol. 25, no. 1, pp. 147–155, 1995.

H. Sigel, R. B. Martin, and R. Tribolet, “An estimation of the equivalent solution dielectric constant in the active-site cavity of metalloenzymes. Dependence of carboxylate—metal-ion complex stabilities on the polarity of mixed aqueous/organic solvents,” European Journal of Biochemistry, vol. 152, no. 1, pp. 187–193, 1985.

S. Raju, B. Ananda Kumar, G. Pushpa Raju, K. Bharath Kumar Naik, and G. N. Rao, “Protonation equilibria of L-glutamic acid and L-histidine in Dmso-water mixtures,” Current Chemical Research. In press

R. S. Rao and G. N. Rao, Computer Applications in Chemistry, Himalaya Publishing House, Mumbai, India, 2005.

G. Gran, “Determination of the equivalence point in potentiometric titrations. Part II,” The Analyst, vol. 77, no. 920, pp. 661–670, 1952.

G. Gran, “Equivalence volumes in potentiometric titrations,” Analytica Chimica Acta, vol. 206, pp. 111–123, 1988.

M. P. Latha, V. M. Rao, T. S. Rao, and G. N. Rao, “Chemical speciation of Pb(II), Cd(II), Hg(II), Co(II), Ni(II), Cu(II) and Zn(II) binary complexes of L-methionine in 1,2-propanediol-water mixtures,” Bulletin of the Chemical Society of Ethiopia, vol. 21, no. 3, pp. 363–372, 2007.

B. B. V. Sailaja, T. Kebede, G. N. Rao, and M. S. P. Rao, “Effect of micelles on speciation of ternary complexes of Uranyl(VI) with oxalic and malonic acids,” Proceedings of National Academy of Sciences, India, vol. 74, no. 4, pp. 399–412, 2004.

N. Padmaja, M. S. Babu, G. N. Rao, R. S. Rao, and K. V. Ramana, “Effect of urea on the speciation of 1,10-phenanthroline complexes of toxic metal ions-computer augmented modelling studies,” Polyhedron, vol. 9, no. 20, pp. 2497–2506, 1990.

G. N. Rao, Complex equilibria of biological importance in aquo organic media-Computer augmented modeling studies, Ph.D. thesis, Andhra University, Visakhapatnam, India, 1989.

P. Gans, A. Sabatini, and A. Vacca, “An improved computer program for the computation of formation constants from potentiometric data,” Inorganica Chimica Acta, vol. 18, pp. 237–239, 1976.

G. N. Rao and A. Ramakrishna, “Speciation studies of nickel(II) complexes of L-glutamine and succinic acid in urea-water mixtures,” Proceedings of National Academy of Sciences, India, vol. 75, no. 4, pp. 245–248, 2005.