Self-Organizing Maps of Molecular Descriptors for Sesquiterpene Lactones and Their Application to the Chemotaxonomy of the Asteraceae Family

Abstract

The Asteraceae, one of the largest families among angiosperms, is chemically characterised by the production of sesquiterpene lactones (SLs). A total of 1,111 SLs, which were extracted from 658 species, 161 genera, 63 subtribes and 15 tribes of Asteraceae, were represented and registered in two dimensions in the SISTEMATX, an in-house software system, and were associated with their botanical sources. The respective 11 block of descriptors: Constitutional, Functional groups, BCUT, Atom-centred, 2D autocorrelations, Topological, Geometrical, RDF, 3D-MoRSE, GETAWAY and WHIM were used as input data to separate the botanical occurrences through self-organising maps. Maps that were generated with each descriptor divided the Asteraceae tribes, with total index values between 66.7% and 83.6%. The analysis of the results shows evident similarities among the Heliantheae, Helenieae and Eupatorieae tribes as well as between the Anthemideae and Inuleae tribes. Those observations are in agreement with systematic classifications that were proposed by Bremer, which use mainly morphological and molecular data, therefore chemical markers partially corroborate with these classifications. The results demonstrate that the atom-centred and RDF descriptors can be used as a tool for taxonomic classification in low hierarchical levels, such as tribes. Descriptors obtained through fragments or by the two-dimensional representation of the SL structures were sufficient to obtain significant results, and better results were not achieved by using descriptors derived from three-dimensional representations of SLs. Such models based on physico-chemical properties can project new design SLs, similar structures from literature or even unreported structures in two-dimensional chemical space. Therefore, the generated SOMs can predict the most probable tribe where a biologically active molecule can be found according Bremer classification.

Analysis and fragmentation mechanisms of hirsutinolide‐type sesquiterpene lactones by ultra‐high‐performance liquid chromatography/electrospray ionization linear ion trap Orbitrap mass spectrometry

“…Sesquiterpene lactones (SLs) are an important class of secondary plant metabolites, mainly found in the Asteraceae family and including more than 5000 known structures inventoried in 2006 . SLs are divided into different chemical skeletons making them relevant as chemotaxonomic markers in Asteraceaes tribes . Moreover, many plants containing SLs are commonly used in folk medicine, and these metabolites have been investigated over many years because of their broad spectrum of biological activities .…”

“…[1] SLs are divided into different chemical skeletons making them relevant as chemotaxonomic markers in Asteraceaes tribes. [2][3][4] Moreover, many plants containing SLs are commonly used in folk medicine, [5][6][7] and these metabolites have been investigated over many years because of their broad spectrum of biological activities. [8,9] The increasing number of studies conducted since 2000 indicate that SLs are still promising in the search for new therapeutic molecules, especially in the field of inflammation and cancer.…”

Prediction of substituent types and positions on skeleton of eudesmane-type sesquiterpenes using generalized regression neural network (GRNN)

“…Artificial Neural Networks (ANNs) are defined as computational models with structures that are derived from a simplified concept of the brain, in which a number of nodes, called neurons, are interconnected in a network-like structure (Scotti et al, 2012). Due to its parallel nature, ANNs, could speed up the process of structural elucidation as the time-consuming sequential search (especially for large spectra library) and matching procedures (sequential comparison of an unknown target spectrum with the set of library spectra) employed by the conventional databases is avoided (Rufino et al, 2005).…”

Chemotaxonomy of three genera of the annonaceae family using self-organizing maps and 13C NMR data of diterpenes

“…36 Another very useful application is in the prediction and classification of spectra such as infrared, 39 mass, 40 and nuclear magnetic resonance [41][42][43] , including some QSAR studies. 44 In natural products chemistry, there are a few studies available showing applications of ANNs, such as the classification of Asteraceae tribes, 31,32,35 and the prediction of skeletal types. 45,46 13 C NMR (Nuclear Magnetic Resonance) data yield rich information about the molecular structure and are sufficiently sensitive to detect small differences in the molecule.…”

“…[22][23][24][25][26][27][28] Chemotaxonomic studies have been applied at several levels using different classes of secondary metabolites: superorders of angiosperms; 20,24,25 families such as Asteraceae, 29 Meliaceae, 27 Apocynaceae, 28 Lamiaceae; 30 tribes of Asteraceae. 23,[31][32][33][34][35] ANNs (Artificial Neural Networks) are a method or, more precisely, a set of methods, used extensively since the 1990s. Since ANNs are not restricted to linear correlations and can also take into account non-linear data correlations, they can be efficiently applied for modeling, prediction and classification.…”