An Integrated Strategy for the Detection, Dereplication, and Identification of DNA-Binding Biomolecules from Complex Natural Product Mixtures.
Hongyan MaHuiyun LiangShengxin CaiBarry R Oâ KeefeSusan L MooberryRobert H CichewiczPublished in: Journal of natural products (2020)
A fundamental factor in natural product drug discovery programs is the necessity to identify the active component(s) from complex chemical mixtures. Whereas this has traditionally been accomplished using bioassay-guided fractionation, we questioned whether alternative techniques could supplement and, in some cases, even supplant this approach. We speculated that a combination of ligand-fishing methods and modern analytical tools (e.g., LC-MS and online natural product databases) offered a route to enhance natural product drug discovery. Herein, a candidate solution referred to as the lickety-split ligand-affinity-based molecular angling system (LLAMAS) is described. This approach utilizes an ultrafiltration-based LC-PDA-MS/MS-guided DNA-binding assay in combination with the (i) Global Natural Products Social Molecular Networking, (ii) Dictionary of Natural Products, and (iii) SciFinder platforms to identify DNA binders in complex chemical mixtures. LLAMAS was initially vetted in tests using known small-molecule DNA binders and then optimized to a 96-well plate-based format. A set of 332 plant samples used in traditional Chinese medicine was screened for DNA-binding activity with LLAMAS, resulting in the identification of seven DNA-binding molecules, including berberine (12), palmatine (13), coptisine (14), fangchinoline (15), tetrandrine (16), daurisoline (17), and dauricine (18). These results demonstrate that LLAMAS is an effective natural product discovery platform for the efficient identification and dereplication of DNA-binding molecules from complex mixtures.
Keyphrases
- dna binding
- drug discovery
- transcription factor
- small molecule
- ionic liquid
- ms ms
- single molecule
- high throughput
- circulating tumor
- healthcare
- public health
- cell free
- machine learning
- protein protein
- artificial intelligence
- liquid chromatography tandem mass spectrometry
- liquid chromatography
- health information
- quantum dots
- ultra high performance liquid chromatography
- high resolution mass spectrometry