[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Classification of Alkaloid Compounds Based on Subring Skeleton (SRS) Profiling: On Finding Relationship of Compounds with Metabolic Pathways

Abstract

Systematic representation of alkaloid biosynthetic pathways based on ring skeletons has been proposed because the skeleton nucleus of an alkaloid is the main criterion for determination in biosynthetic pathways. So the idea of ring skeletons was extended to apply classification of alkaloid compounds based on ring skeletons and to systematize alkaloid compounds and to examine the performance of this approach to predict biosynthetic pathways based on module elements. We constructed a 2-dimensional binary matrix corresponding to 2546 SRS and 478 pathway-known alkaloid compounds. Here, if ith substring skeleton is present in a target compound, the ith element was set to 1; otherwise, the ith element was set to 0. Relationship of alkaloid compounds with biosynthetic pathways are examined based on the dendrogram produced by Ward clustering method to the matrix. Of 12,243 alkaloid compounds accumulated in KNApSAcK Core DB (http://kanaya.naist.jp/knapsack_jsp/top.html), 3,124 compounds (25.5 %) correspond to the pathway-known ring skeletons (187 ring skeletons), but the remaining 9,119 (74.5%) compounds do not. By examining the sub-ring skeleton similarity of the remaining compounds, it might be possible to obtain clues of pathway information and systemization of all alkaloid compounds. Therefore, the present work focuses on comprehensive systematization of the alkaloid compounds and construction principles of ring skeletons in alkaloids based on subring skeleton profiling.