Biological evaluation, molecular docking and DNA interaction studies of coordination compounds gleaned from a pyrazolone incorporated ligand
Authors: Lakshmanan Ramgeetha
Alagarraj Arunadevi
Natarajan Raman
Jeyaraman Porkodi
Journal: Nucleosides Nucleotides & Nucleic Acids (2019)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Anti-inflammatory activity; DNA interaction; MTT assay; Molecular docking.
Affiliations: Research Department of Chemistry, VHNSN College, Virudhunagar, India;, Department of Chemistry, The SFR College for Women, Sivakasi, India

Abstract:  In this work, we have synthesized a few novel mononuclear complexes of Cu(II), Co(II), Ni(II) and Zn(II) using a pyrazolone-derived Schiff base ligand. They were characterized by spectroscopic and analytical methods. The elemental analyses, UV-Vis, magnetic moment values and molar conductance of the complexes reveal that the complexes adopt an octahedral arrangement around the central metal ions. The interaction of complexes with CT-DNA was studied by absorption spectral titration and viscosity measurements. The observed data show that the complexes bind with CT-DNA via an intercalation mode. Efficient pUC18 DNA cleavage ability of the synthesized compounds was explored by gel electrophoresis. The antimicrobial activity of these compounds against a set of bacterial and fungal strains reveals that the complexes exhibit better activity than the free ligand. Moreover, all the complexes were evaluated against two cancer (HeLa and HepG2) and one normal (NHDF) cell lines. The data were compared with cisplatin. Anti-inflammatory activity has been experimentally validated which proves that theoretical predictions concur with the experimental results. In addition, molecular docking studies have been performed to consider the nature of binding mode and binding affinity of these compounds with DNA (1BNA) and protein (3hb5). These studies reveal that the mode of binding is intercalation and the complexes have higher binding energy scores than the free ligand. 

Retracted: No

PubPeer comments:
 The ideas presented in this paper are essentially same with those in the paper Y. Xu Q. Da, H. Wang (2011) A note on group decision-making procedure based on incomplete reciprocal relations. Soft Comput (2011) 15:1289-1300. doi: 10.1007 s00500-010-0662-3 See also the comment on https: pubpeer.com publications B0D925A3888436291ED0218B2F4251 However, this is not the point of this comment. This comment is to point out once again that **the normalization condition** $$w_1 + w_2 + cdots w_n =1, quad w_i ge 0,  forall i$$ **is incompatible with the notion of additive consistency of fuzzy relation preference relations.** The notion of additive consistency of a fuzzy preference relation was presented by Tanino is his 1984 paper T. Tanino: Fuzzy preference orderings in group decision making, Fuzzy Sets and Systems, Vol. 12, No. 2, pp. 117-131, 1984. https: doi.org 10.1016 0165-0114(84)90032-0 For a fuzzy preference relation $$R=(r_{ij})_{ntimes n}$$ taking values over $[0, 1]$ with the restriction $r_{ij} + r_{ji}=1$, it is additive consistent **if and only if** $$r_{ij} = r_{ik} - r_{jk} + 0.5, quad forall i, j, k$$ This requirement is actually $$r_{ij} -0.5= (r_{ik} -0.5) + ( r_{jk} - 0.5), quad forall i, j, k$$ Such a requirement is consistent with the notion of additive consistency in utility theory. That is, the relative differences characterize the preference information. As a result, a fuzzy preference relation is additive consistent **if and only if** it admits a representation of $$r_{ij} = 0.5(w_i - w_j) + 0.5 quad forall i, j$$ with an implicit condition that $$max_i w_i - min_i w_i le 1$$ to insure that the values of $r_{ij}$u2019s are in $[0, 1]$. **Such a representation is unique up to adding a same constant to each weight, i.e., translation invariant.** This result, as a full characterization of the additive consistency, was straightforward and clearly presented in Taninou2019s 1984 paper. However, **for some unknown reasons, this result was misinterpreted or even claimed not true.** The normalization condition $$w_1 + w_2 + cdots w_n =1, quad w_i ge 0,  forall i$$ was imposed to define the additive consistency of a fuzzy preference condition. It is obvious that the non-negativity on the utility values (or weights) of $w_i$u2019s and the equality constraint $w_1 + w_2 + cdots w_n =1$ cannot be guaranteed simultaneously for the additive consistency and hence are incompatible. **This mistake has been repeatedly pointed out in the literature**, for instance, M. Fedrizzi and M. Brunelli (2009). On the normalisation of a priority vector associated with a reciprocal relation. International Journal of General Systems Vol. 38, No. 5, 579u2013586 DOI: 10.1080 03081070902753606 However, this mistake still occurred frequently in some newly published papers dealing with the additive consistency of fuzzy preference relations and their variants and was even called a u201c**contribution**u201d by some ones. See the comment on https: pubpeer.com publications 7867CDD08E699F6D01C0E11F1693E0 This paper, as a note on a previous paper authored by Prof. Z.S. Xu and Prof. J. Chen, worked on another direction. The authors kept the normalization condition and changed the representation by $$r_{ij} = frac{n-1}{2}(w_i - w_j) + 0.5 quad forall i, j$$ Such an approach is technically correct. It works because all the positive utility values (or weights) may be scaled down to be in the interval $[0,1]$. It is just a rescaled version of Taninou2019s 1984 characterization in the case of additive consistency. To be clear, when a fuzzy preference relation is additive consistent, its corresponding weights can be shifted such that $$min_i w_i =0$$ As a consequence, $$w_1+ w_2+ cdots+ w_n le n-1$$ Denote the slack $$Delta = 1-frac{1}{n-1} sum_{i} w_i$$ and set $$w _i = frac{1}{n-1}w_i + frac{1}{n}Delta$$ Then, $$w _i ge 0, quad forall i$$ $$w _1 + w _2 + cdots + w _n =1$$ and also $$r_{ij} = frac{n-1}{2} (w _i - w _j) + 0.5, quad forall i,j$$ However, because the notion of additive consistency in its very essence, has nothing to do with the normalization condition, **the rescaled version of characterization works only for the case of additive consistent fuzzy preference relations.** The authors of this paper extended this rescaled version of characterization to the case of inconsistent fuzzy preference relations. The authors presented some linear programming based models (as corrections of those in the paper by Xu and Chen) to obtain a consistent one with least deviations, e.g., (M-5) on Page 773. It is known that a linear programming problem may admit multiple (infinitely many) optimal solutions and hence distort the preference information in this particular context. Moreover, it should be pointed out that **such an approach is technically deficient to deal with inconsistent fuzzy preference relations** because the normalization condition $$w_1 + w_2 + cdots w_n =1, quad w_i ge 0,  forall i$$ as the constraint in a linear programming model cannot guarantee that the values of $$r_{ij} = frac{n-1}{2}(w_i - w_j) + 0.5 quad forall i, j$$ are all in the interval $[0,1]$. This means that the obtained optimal solution, say by (M-5), may not necessarily define a fuzzy preference relation, let alone an additive consistent one. To illustrate this issue, consider the following example on the pairwise comparison on ${x_1, x_2, x_3}$ $$R=left[begin{array}{ccc} 0.5 1 1  0 0.5 1  0 0 0.5end{array} right]$$ It is clear that $R$ is not additive consistent but order consistent since it indicates a ranking as $$x_1 succ x_2 succ x_3$$ Set $n=3$ and the weights $$w_1 = x, quad w_2=yquad w_3 = 1-x-y$$ If a matrix of the form $$bigg(0.5+ frac{3-1}{2}(w_i-w_j)bigg)_{3times 3}$$ is desired with the minimum value of the total deviation to $R$, the objective function to be minimized is $$|x-y-0.5| + |2x+y-1.5| + |x+2y -1.5|$$ It can be verified that $$w_1 = 0.5,quad w_2=0.5,quad w_3 =0$$ is an optimal solution and provides the ranking as $$x_1 sim x_2 succ x_3$$ This implies that the preference information between $x_1$ and $x_2$ is distorted. On the other hand, it can also be verified that, with $0le epsilon 0.1$, $$w_1 = 0.6 -epsilon,quad w_2=0.4+epsilon,quad w_3 =0$$ is also an optimal solution with the ranking $$x_1 succ x_2 succ x_3$$ However, the fuzzy preference relation constructed by this group of weights is not a legitimate preference relation. For example, the preference relation defined by the weights $$w_1 = 0.6,quad w_2=0.4,quad w_3 =0$$ is $$left[begin{array}{ccc} 0.5 0.7 1.1  0.3 0.5 0.9  -0.1 0.1 0.5end{array} right]$$ Besides, it can also be verified that with $0le epsilon le 0.1$, $$w_1 = 0.7 -epsilon,quad w_2=0.3,quad w_3 =epsilon$$ is also an optimal solution but defines an illegitimate preference relation. ![file] ,