An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm
Su et al. J Cheminform
An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm
Bo‑Han Su 3
MengY‑u Shen 3
Yeu‑Chern Harn 2
SanY‑uan Wang 3
Alioune Schurz 0 1
Chieh Lin 3
Olivia A. Lin 0 1
Yufeng J. Tseng 0 1 3
0 Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University , No. 1 Sec. 4, Roosevelt Road, Taipei 106 , Taiwan
1 Graduate Institute of Biomedical Elec‐ tronics and Bioinformatics, National Taiwan University , No. 1 Sec. 4, Roosevelt Road, Taipei 106 , Taiwan
2 Graduate Institute of Networking and Multimedia, National Taiwan University , No. 1 Sec. 4, Roosevelt Road, Taipei 106 , Taiwan
3 Department of Computer Science and Information Engineering, National Taiwan University , No. 1 Sec. 4, Roosevelt Road, Taipei 106 , Taiwan
The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time‑ consuming process and is limited by the available mass spectra of known natural products. Computer‑ aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP‑ complete. A dynamic programming (DP) algorithm can solve this NP‑ complete problem in pseudo‑ polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link (http://csccp.cmdm.tw/).
CASE; Natural products; Dynamic programming; Polynomial time
Background
Examining natural and therapeutic products is crucial for
drug development because many chemically synthesized
compounds have potentially serious toxicity and adverse
effects, while less toxic compounds extracted from
natural products could possibly be developed into new drug
candidates [
1
]. In addition, natural products often open
new chemical spaces not explored by synthetic
compounds produced by combinatorial chemistry and can
further expand the diversity and novelty of molecules
by extracting different natural sources, such as the deep
and cold seas [
2, 3
]. A review by Newman and Cragg [2]
indicated that 47% of new anti-cancer drugs from 1950
to 2006 were originally from or derived from natural
products. Recently, Butler et al. [
3
] reviewed 100 natural
products and natural products-derived compounds that
were either evaluated in clinical trials or in registration
at the end of 2013. They concluded that 50% of the
compounds were natural products or semi-synthetic natural
products, while the remaining compounds were classified
as natural products-derived compounds. The exploration
of new lead compounds from natural products and their
successful development into clinical trials will continue
to be a significant trend in drug discovery over the next
few years.
However, natural products-based drug discovery faces
many challenges [
4
], and the exploration of natural
products for new drug development was actually disfavored
by the pharmaceutical industry in the 2000s [
5
]. One of
the major hurdles is the extremely time-consuming
processes involved in the isolation and structural elucidation
of bioactive compounds from natural products composed
of complicated mixtures. Because the magnitude of the
natural products database is limited, high-throughput
screening methods cannot be used to effectively
identify potential natural products drugs. Many advances
in mass spectrometry (MS) and nuclear magnetic
resonance (NMR) automation techniques over the last two
decades have accelerated structural elucidation
processes for complex natural products mixtures. MS is a
common too (...truncated)