Multi-scale analysis of structural variability of Caryophyllaceae saponins by a simplex machine learning approach

Nabil Semmar,Muhammad Farman,Soumaya Cheikh Ali,Asma Hammami-Semmar

doi:10.3390/mol2net-03-04839

Abstract

A mass conservation law-based chemometric approach was developed to extract smoothed processes governing inter- and intra-molecular variability of structural diversity in metabolic pools. The approach consisted of a machine-learning method using simplex rule to calculate a complete set of smoothed barycentric molecules from iterated linear combinations between molecular classes (glycosylation classes). An application to four glycosylation levels (GLs) of Caryophyllaceae saponins highlighted aglycone-dependent variations of glycosylations, especially for gypsogenic acid (GA) which showed high 28-glucosylation levels. Quillaic acid (QA) and gypsogenin (Gyp) showed closer variation ranges of GLs, but differed by relationships between glycosylated carbons toward different sugars. Relative GLs of carbons C3 and C28 showed associative (positive), competitive (negative) or independent (unsensitive) trends conditioned by the aglycone type (GA, Gyp) and molecular (total) GLs (the four classes): 28-glucosylation and 28-xylosylation showed negative global trends in Gyp vs GLs-depending trends in QA. Also, relative levels of 3-galactosylation and 3-xylosylation varied by unsensitive ways in Gyp vs positive trends in QA. These preliminary results revealed higher metabolic tensions (competitions) between considered glycosylations in Gyp vs more associative processes in QA. In conclusion, glycosylations of GA and QA were relatively distant whereas Gyp occupied intermediate position.

Highlights

The Caryophyllaceae plant family was proved to be a wide source of saponins essentially based on three triterpenic skeleton including gypsogenin (Gyp), quillaic acid (QA)and gypsogenic acid (GA) [1].Apart from the sapogenin type, structural variability of Caryophyllaceae saponins showed multi-factorial and multi-scale aspects due to different glycosylation levels (GLs) and glycosylation types essentially occurring at the carbons C3 and C28.By considering a wide dataset of 205 Caryophyllaceae saponins based onGyp, QA andGAwith different GL(2 to 9), a machine learning approach was applied to extract key information on inter- and intra-molecular regulatory processes governing the observed structural diversityin relation to aglycones (a), glycosylation levelsand types (b, c) and substitution carbons(d) [2]
Illustrations are given for 28-Xyl vs 28-Glc (Figure1) and 3-Xyl vs 3-Gal (Figure2): GA was markedly distant from Gyp and QA indicating some specific glycosylation orders
Simplex-based machine learning applied to structural variability of Caryophyllaceae saponins highlighted strong differentiation in metabolic glycosylation governed by the aglycone type, molecular

Summary

Introduction

The Caryophyllaceae plant family was proved to be a wide source of saponins essentially based on three triterpenic skeleton (aglycones or sapogenins) including gypsogenin (Gyp), quillaic acid (QA)and gypsogenic acid (GA) [1].Apart from the sapogenin type, structural variability of Caryophyllaceae saponins showed multi-factorial and multi-scale aspects due to different glycosylation levels (GLs) and glycosylation types essentially occurring at the carbons C3 and C28.By considering a wide dataset of 205 Caryophyllaceae saponins based onGyp, QA andGAwith different GL(2 to 9), a machine learning approach was applied to extract key information on inter- and intra-molecular regulatory processes governing the observed structural diversityin relation to aglycones (a), glycosylation levelsand types (b, c) and substitution carbons(d) [2]. Gypsogenic acid (GA) [1].Apart from the sapogenin type, structural variability of Caryophyllaceae saponins showed multi-factorial and multi-scale aspects due to different glycosylation levels (GLs) and glycosylation types essentially occurring at the carbons C3 and C28. In silico combinations between saponin structures belonging to different molecular classes (GLs) provided a complete set of simulated theoretical molecules from which significant trends within and between glycosylated carbons were revealed to govern structural variability at inter-molecular scale.

Methods

Results

Conclusion