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Parthasarathy A, Borrego EJ, Savka MA, Dobson RCJ, Hudson AO. Amino acid-derived defense metabolites from plants: A potential source to facilitate novel antimicrobial development. J Biol Chem 2021; 296:100438. [PMID: 33610552 PMCID: PMC8024917 DOI: 10.1016/j.jbc.2021.100438] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/23/2022] Open
Abstract
For millennia, humanity has relied on plants for its medicines, and modern pharmacology continues to reexamine and mine plant metabolites for novel compounds and to guide improvements in biological activity, bioavailability, and chemical stability. The critical problem of antibiotic resistance and increasing exposure to viral and parasitic diseases has spurred renewed interest into drug treatments for infectious diseases. In this context, an urgent revival of natural product discovery is globally underway with special attention directed toward the numerous and chemically diverse plant defensive compounds such as phytoalexins and phytoanticipins that combat herbivores, microbial pathogens, or competing plants. Moreover, advancements in “omics,” chemistry, and heterologous expression systems have facilitated the purification and characterization of plant metabolites and the identification of possible therapeutic targets. In this review, we describe several important amino acid–derived classes of plant defensive compounds, including antimicrobial peptides (e.g., defensins, thionins, and knottins), alkaloids, nonproteogenic amino acids, and phenylpropanoids as potential drug leads, examining their mechanisms of action, therapeutic targets, and structure–function relationships. Given their potent antibacterial, antifungal, antiparasitic, and antiviral properties, which can be superior to existing drugs, phytoalexins and phytoanticipins are an excellent resource to facilitate the rational design and development of antimicrobial drugs.
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Affiliation(s)
- Anutthaman Parthasarathy
- Rochester Institute of Technology, Thomas H. Gosnell School of Life Sciences, Rochester, New York, USA
| | - Eli J Borrego
- Rochester Institute of Technology, Thomas H. Gosnell School of Life Sciences, Rochester, New York, USA
| | - Michael A Savka
- Rochester Institute of Technology, Thomas H. Gosnell School of Life Sciences, Rochester, New York, USA
| | - Renwick C J Dobson
- Biomolecular Interaction Centre and School of Biological Sciences, University of Canterbury, Christchurch, New Zealand; Bio21 Molecular Science and Biotechnology Institute, Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville, Victoria, Australia
| | - André O Hudson
- Rochester Institute of Technology, Thomas H. Gosnell School of Life Sciences, Rochester, New York, USA.
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Eguchi R, Ono N, Hirai Morita A, Katsuragi T, Nakamura S, Huang M, Altaf-Ul-Amin M, Kanaya S. Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks. BMC Bioinformatics 2019; 20:380. [PMID: 31288752 PMCID: PMC6617615 DOI: 10.1186/s12859-019-2963-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 06/21/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousands of compounds in this class, few of their biosynthesis pathways are fully identified. In this study, we constructed a model to predict their precursors based on a novel kind of neural network called the molecular graph convolutional neural network. Molecular similarity is a crucial metric in the analysis of qualitative structure-activity relationships. However, it is sometimes difficult for current fingerprint representations to emphasize specific features for the target problems efficiently. It is advantageous to allow the model to select the appropriate features according to data-driven decisions for extracting more useful information, which influences a classification or regression problem substantially. RESULTS In this study, we applied a neural network architecture for undirected graph representation of molecules. By encoding a molecule as an abstract graph and applying "convolution" on the graph and training the weight of the neural network framework, the neural network can optimize feature selection for the training problem. By incorporating the effects from adjacent atoms recursively, graph convolutional neural networks can extract the features of latent atoms that represent chemical features of a molecule efficiently. In order to investigate alkaloid biosynthesis, we trained the network to distinguish the precursors of 566 alkaloids, which are almost all of the alkaloids whose biosynthesis pathways are known, and showed that the model could predict starting substances with an averaged accuracy of 97.5%. CONCLUSION We have showed that our model can predict more accurately compared to the random forest and general neural network when the variables and fingerprints are not selected, while the performance is comparable when we carefully select 507 variables from 18000 dimensions of descriptors. The prediction of pathways contributes to understanding of alkaloid synthesis mechanisms and the application of graph based neural network models to similar problems in bioinformatics would therefore be beneficial. We applied our model to evaluate the precursors of biosynthesis of 12000 alkaloids found in various organisms and found power-low-like distribution.
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Affiliation(s)
- Ryohei Eguchi
- Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.,Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan
| | - Naoaki Ono
- Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan. .,Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.
| | - Aki Hirai Morita
- Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan
| | - Tetsuo Katsuragi
- Department of Computer Science and Engineering, Toyohashi University of Technology, Hibarigaoka, Tempaku-cho, Toyohashi, Aichi, 441-8580, Japan
| | - Satoshi Nakamura
- Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.,Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan
| | - Ming Huang
- Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan
| | - Md Altaf-Ul-Amin
- Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan
| | - Shigehiko Kanaya
- Division of Science and Technology, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.,Data Science Center, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan
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