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Molecular descriptors to differentiate four gibberellins and seven anti-gibberellins were studied. DRAGON software and Cambridge Soft ChemOffice were used to calculate 212 descriptors. Of them, 48 showed statistically significant differences between gibberellins and anti-gibberellins which can be summarized as follows. Gibberellins contain as average 14.3 times more 7-membered rings, 14.3 times more 9-membered rings, 7.4 times more ring tertiary carbon (sp3), 6.1 times more terminal tertiary carbon (sp3) and 6.0 times more terminal quaternary carbon (sp3) than anti-gibberellins. Also gibberellins usually have ring quaternary carbon (sp3) and aliphatic secondary carbon (sp2) while anti-gibberellins usually do not. On the other hand, anti-gibberellins generally have aromatic ratio, aromatic bonds, nitrogen atoms, chlorine atoms, halogen atoms, benzene-like rings, aromatic carbon (sp2), unsubstituted benzene carbon (sp2) and substituted benzene carbon (sp2) while gibberellins usually do not. A dendrogram was obtained after conducting a hierarchical cluster analysis with data of chemical molecular descriptors with statistical significant differences (48). The dendrogram correctly classified gibberellins and anti-gibberellins in two independent branches.
Keywords: Chemo-informatics, Molecular descriptors, Plant growth regulators
Gibberellins are among the most important substances for regulating growth and morphogenesis in plant cell, tissue and organ culture [1,2]. They have been used, for instance, to control in vitro morphogenesis of sugarcane [3] pineapple [4], potato [5] bromeliads [6,7]. Modification of gibberellic acid levels are able to alter the biomass production, its allocation and may affect chemical resistance, but not tolerance [8]. The applications with gibberellic acid and abscisic acid to grapevines cv. Malbec may improve the transport of photo-assimilates from leaves to fruits by up-regulation of sugar transporters at different phenological stages [9].
MATERIALS AND METHODS
Our work was conducted with DRAGON software (version 5.5, 2007) and Cambridge Soft ChemOffice (version 12, 2010) with ChemDraw and Chem3D. We calculated 212 molecular descriptors. SPSS (Version 8.0 for Windows, SPSS Inc., New York, NY) was used to perform t-tests (p=0.05). The overall coefficients of variation (OCV) were calculated as described by Lorenzo et al. [14] (standard deviation/average) * 100). We considered the average values of the two growth regulators compared (gibberellins and anti-gibberellins) to calculate the standard deviation and average. The higher the difference between gibberellins and anti-gibberellins, the higher is the OCV. A dendrogram was obtained after conducting a hierarchical cluster analysis with data of chemical molecular descriptors. All variables were standardized (0-1) [15]
RESULTS AND DISCUSSION
In spite of 48 out of 212 molecular descriptors showed statistically significant differences between gibberellins and anti-gibberellins (Table 1), only 16 showed “High” OCVs (101.72 to 141.42%). Gibberellins have 14.3 times (2.00/0.14) more 7-membered rings, 14.3 times (2.00/0.14) more 9-membered rings, 7.4 times (5.25/0.71) more ring tertiary carbon (sp3), 6.1 times (5.25/0.86) more terminal tertiary carbon (sp3) and 6.0 times (1.75/0.29) more terminal quaternary carbon (sp3) than anti-gibberellins. Also gibberellins usually have ring quaternary carbon (sp3) and aliphatic secondary carbon (sp2) while anti-gibberellins usually do not. On the other hand, anti-gibberellins generally have aromatic ratio, aromatic bonds, nitrogen atoms, chlorine atoms, halogen atoms, benzene-like rings, aromatic carbon (sp2), unsubstituted benzene carbon (sp2) and substituted benzene carbon (sp2) while gibberellins usually do not.
With “Medium” OCVs (64.82 to 98.08%), some molecular descriptors also showed statistically significant differences between gibberellins and anti-gibberellins. Gibberellins averaged 5.54 times (15.00/2.71) more circuits, 4.26 times (5.50/1.29) more ring secondary carbon (sp3), 4.23 times (3.00/0.71) more 5-membered rings, 3.45 times (1.00/0.29) more 8-membered rings, 3.45 times (1.00/0.29) more carboxylic acid (aliphatic), 3.45 times (1.00/0.29) more secondary alcohols, 3.52 times (2.50/0.71) more hydroxyl groups, 3.22 times (5.50/1.71) more oxygen atoms, 2.91 times (3.75/1.29) more double bonds, 2.91 times (2.50/0.86) more donor atoms for H-bonds (N and O), 2.80 times (1.29/0.46) more hydrophilic factor, 2.69 times (5.00/1.86) more rings, 5.00 times (0.10/0.02) less rotatable bonds fraction and 3.14 times (3.14/1.00) less rotatable bonds than anti-gibberellins (Table 1).
Data of Table 1, used in the hierarchical cluster analysis, generated the dendrogram shown in Figure 2. The two groups of regulators were appropriately congregated in two independent branches. Molecular descriptors have been applied to describe biological activities in many studies [16,17] showing their applicability as an attractive tool for efficient (e.g.) drug design process. It has been studied the potential of innovative computational tools in processing of structurally complex natural products to predict their macromolecular targets and attempt to forecast their role in drug discovery. Rodrigues et al. [18] and Faulon et al. [19] proposed a unified method for predicting protein-chemical interactions based on the representation of a protein using its atomistic structure. There are models that are useful in identifying compounds with potential risk of inhibiting the CYP3A4 enzyme. Arimoto et al. [20] and Kombo et al. [21] showed that shape-based descriptors ROG and SXL correlate with off-target activity and solubility, which in turn influence clinical success. They searched a potential correlation between these shape-based descriptors and clinical success. With the rapid growth of public biological databases and biology-related web resources, abundant bioactivity data of small molecules and their targets are now available to the entire research community [22].
From the biochemical point of view, it is important to note that it is not possible to justify dissimilarities between different compounds that act completely different. Gibberellins are hormones and these molecules are only active in plants if they link to its receptor so its role is very specific and this specificity is due to the structure of this molecule [16]. On the other hand, the anti-gibberellins studied here are compounds that act directly in the action of some enzymes involved in gibberellins biosynthesis. So, in this case, it does not matter their structure as there is no connection with gibberellin receptors and whatever the structure of these compounds, they act blocking enzymes from gibberellin biosynthesis [10,13].
However, the procedure described here is effective to differentiate (chemically) gibberellins and anti-gibberellins. New potential growth regulators can be identified, although this preliminary result should be later tested experimentally. A similar chemo-informatic procedure was previously used by our group to compare auxins, cytokinins and gibberellins; although different molecular descriptors were analyzed [23-25].
AUTHOR CONTRIBUTION
I.A., D.G., L.P. and J.C.L. designed the research, analyzed the data and wrote the paper. J.C.L. had primary responsibility for the final content. All authors have read and approved the final manuscript.
ACKNOWLEDGEMENT
This research was supported by the Bioplant Centre (University of Ciego de Ávila, Cuba).
COMPLIANCE WITH ETHICAL STANDARDS
Conflict of interest
Authors do not have any conflict of interests.
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