Database for Rapid Dereplication of Known Natural Products Using Data from MS and Fast NMR Experiments

The discovery of novel and/or new
bioactive natural products from
biota sources is often confounded by the reisolation of known natural
products. Dereplication strategies that involve the analysis of NMR
and MS spectroscopic data to infer structural features present in
purified natural products in combination with database searches of
these substructures provide an efficient method to rapidly identify
known natural products. Unfortunately this strategy has been hampered
by the lack of publically available and comprehensive natural product databases
and open source cheminformatics tools. A new platform, DEREP-NP, has
been developed to help solve this problem. DEREP-NP uses the open
source cheminformatics program DataWarrior to generate a database
containing counts of 65 structural fragments present in 229 358
natural product structures derived from plants, animals, and microorganisms,
published before 2013 and freely available in the nonproprietary Universal
Natural Products Database (UNPD). By counting the number of times
one or more of these structural features occurs in an unknown compound,
as deduced from the analysis of its NMR (1H, HSQC, and/or
HMBC) and/or MS data, matching structures carrying the same numeric
combination of searched structural features can be retrieved from
the database. Confirmation that the matching structure is the same
compound can then be verified through literature comparison of spectroscopic
data. This methodology can be applied to both purified natural products
and fractions containing a small number of individual compounds that
are often generated as screening libraries. The utility of DEREP-NP
has been verified through the analysis of spectra derived from compounds
(and fractions containing two or three compounds) isolated from plant,
marine invertebrate, and fungal sources. DEREP-NP is freely available
at https://github.com/clzani/DEREP-NP and will help to streamline the natural product discovery process.