Cluster analysis as selection and dereplication tool for the identification of new natural compounds from large sample sets.
Cluster analysis of gas-chromatographic (GC) data of ca. 500 bacterial isolates was used as an aid in detection and identification of new natural compounds. This approach reduces the number of GC/MS analysis (dereplication) and concomitantly improves the selection of samples with high probability to contain unknown natural products. Lipophilic bacterial extracts were derivatized and analyzed by GC under standardized conditions. A program was developed to convert chromatographic data into a two-dimensional matrix. Based on the results of hierarchical cluster analysis samples were selected for further investigation by GC/MS and NMR. This approach avoided unnecessary analysis of similar samples. By this method, the unusual oligoprenylsesquiterpenes 1 and 2 as well as new aromatic amides 7 and 8 were identified.
Böröczky
Katalin
K
Institute of Organic Chemistry, Technical University of Braunschweig, Hagenring 30, D-38106 Braunschweig.
Laatsch
Hartmut
H
Wagner-Döbler
Irene
I
Stritzke
Katja
K
Schulz
Stefan
S
D001419:Bacteria / Q000737:chemistry*; D001688:Biological Products / Q000737:chemistry* / Q000302:isolation & purification*; D016000:Cluster Analysis; D019295:Computational Biology; D006859:Hydrogen / Q000737:chemistry; D009682:Magnetic Resonance Spectroscopy; D013058:Mass Spectrometry; D015394:Molecular Structure
D001688:Biological Products; D006859:Hydrogen