Available projects
Target Identification by Direct Observation of Protein-Ligand Complexes Using Native Mass Spectrometry
For small-molecule drugs, which comprise most of today’s medicines, a key challenge is the identification of the molecular target(s) underlying drug therapeutic effects. In this project, we will use native mass spectrometry as a base for an innovative chemical biology approach using a protein-ligand complex as a probe rather than a single compound used in routine chemical biology. We will apply our extensive tool set of tuberculosis specific protein targets and compounds known to have anti-TB cellular activity to establish our methodology. We will use the fast-growing, nonpathogenic bacterium M. smegmatis as a model mycobacterial system, which shares many features with the pathogenic M. tuberculosis. We will add protein-ligand complexes in the probe library to M. smegmatis cell lysate and fractionated components to determine the affinity (Kd) required to maintain the complexes. With this innovative technology, we propose to study both known and, unknown targets for compounds with anti-TB cellular activity.
Reference:
Escaping Bio-Assay Guided Isolation: Nature's Tools for Chemical Biology
This proposal aims to apply new methodologies for the detection of novel natural products within a fraction library or a Traditional Chinese Medicine based on proton nuclear magnetic resonance (NMR) metabolomic profiling techniques. For decades, natural product chemistry has centred on bio-assay guided isolation as the method of choice to identify novel biologically active compounds. As the number of isolated natural products has increased, it has become more difficult to isolate novel compounds. Having used bioassay-guided isolation for over four decades and reflecting on our recent results, we have developed NMR Fingerprints to transform the traditional process. NMR never lies as it detects every compound that has a proton. As NMR reveals all compounds containing hydrogen and is quantitative, it can be used to guarantee that all compounds within a fraction are isolated.
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PhenoTarget drug discovery from natural products using native mass spectrometry
TPrevious phenotypic HTS of the NatureBank Fraction Library against M. tuberculosis H37Rv, and other cell lines has resulted in the identification of phenotypic active fractions. Modern drug discovery and development is heavily dependent on rapid and insightful analytical methods. Native screening using high resolution electrospray ionization magnetic resonance mass spectrometry (MRMS) is a label-free, fast, accurate method that permits the direct observation of non-covalent protein-ligand complexes. The technique relies on non-denaturing electrospray-ionization (ESI) to firstly recognize multi-charged proteins in their near-native states. This project enables the identification of a single natural product from a complex mixture by its specific interaction with validated protein targets. The molecular weight mass information of the ligand allows rapid identification of the active ligand. This PhenoTarget approach identifies active natural products as well as their molecular targets in one experiment. References:
- Ali R. Elnaas, Darren Grice, Jianying Han, Yunjiang Feng, Angela Di Capua, Tin Mak, Joseph A. Laureanti, Garry W. Buchko, Peter J. Myler, Gregory Cook, Ronald J. Quinn and Miaomiao Liu*. Discovery of a Natural Product That Binds to the Mycobacterium tuberculosis Protein Rv1466 Using Native Mass Spectrometry. Molecules. 2020, 25 (10): 2384-2395
- Yan Xie, Yunjiang Feng, Tin Mak, Garry W. Buchko, Peter J. Myler, Miaomiao Liu and Ronald J. Quinn*. A PhenoTarget Approach for Identifying an Alkaloid Interacting with the Tuberculosis Protein Rv1466. Mar. Drugs. 2020, 18 (3): 149-162
Fragment based drug discovery from Traditional Chinese Medicines
The project will use fragment-based drug discovery to develop a database of known TCM pure compounds. There is no current database of fragment-sized TCM compounds in the world. This project will also compare the diversity of the TCM derived fragments against ring systems generated by Network analysis of the TCM database and against the full database using self orgainising maps (SOM). The TCM fragment library will be investigated to find chemical probes against tuberculosis proteins by the use of native mass spectrometry and an unbiased phenotypic assay using Zebra fish model. This will provide a comprehensive multidisciplinary approach to explain the function and understand the complexity of TCM metabolites towards drug discovery. References:
- Hoan Vu, Liliana Pedro, Tin Mak, Brendan McCormick, Jessica Rowley, Miaomiao Liu, Angela Di Capua, Billy Williams-Noonan, Ngoc B. Pham, Rebecca Pouwer, Bao Nguyen, Katherine T. Andrews, Tina Skinner-Adams, Jessica Kim, Wim G. J. Hol, Raymond Hui, Gregory J. Crowther, Wesley C. Van Voorhis and Ronald J. Quinn*. Fragment-based screening of a natural product library against 62 potential malaria drug targets employing native mass spectrometry. ACS Infect. Dis. 2018, 4 (4): 431–444
- Miaomiao Liu and Ronald J Quinn*. Fragment-based screening with natural products for novel antiparasitic disease drug discovery. Expert Opin. Drug Discov. 2019, 14(12):1283-1295
Building a database of bioactive natural products for Artificial Intelligence prediction of target
Currently, there is no established technique that allows the function of a compound produced by nature to be predicted by looking at its 2-dimensional chemical structure. We explore the opportunity for Artificial Intelligence to provide rationale interrogation of metabolites to predict their function. In this project, a database of bioactive natural products for Artificial Intelligence prediction of target will be established. An in silico scaffold analysis approach will be used to the known bioactive natural products. The approach decomposes a complex scaffold in every possible way, which then results in a network for each single molecule. This allows the exploration of the full scaffold diversity, such networks can be overlaid and hubs indicate similar properties. The database will be used to generate a Matrix on bioactive natural product scaffolds and their targets in order to train deep learning neural networks to predict function that is predominately based on metabolite interaction with protein targets.
Reference:
Decoding the Language of Nature: Is it time for Artificial Intelligence to predict the function of natural products based on structure
The project will address one of chemistry’s grand challenges: to find a function for every metabolite produced by Nature. Develop a method to predict the function of a compound produced by nature by looking at its chemical structure. Use artificial intelligence to build a metabolite-function knowledge base. The profound and specific biological activity of natural products coupled with their immediately recognizable structures suggests a code within these structures that we are not as yet aware of. The long-standing challenge is to be able to decode the functional information entangled in the structures of these metabolites, selected over millions of years by continuous evolution. Technological advances in artificial intelligence (AI), especially in the field of deep learning, hold the potential to make smart predictions based on explainable knowledge and patterns. Multi-faceted Big Data on the function of metabolites offers exciting new opportunities to apply state-of-the-art deep learning advances to pursuing the grand challenge of predicting biological function from the chemical structure of a metabolite. AI may tease out meaningful patterns and useful knowledge leading to integrated relationships and logic links between the metabolite structures and functions. However, significant groundwork in establishing the databases is still critically needed before real breakthroughs can happen.
Reference:
Miaomiao Liu, Peter Karuso, Yunjiang Feng, Esther Kellenberger, Fei Liu, Can Wang and Ronald J. Quinn*. Is it time for artificial intelligence to predict the function of natural products based on 2Dstructure?Med. Chem. Comm. 2019, 10, 1667-1677