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Zhang S, Liu Y, Jin S, Xia T, Song H, Cao C, Liao Y, Pan R, Yan M, Chang Q. Exploration of novel human neutrophil elastase inhibitors from natural compounds: Virtual screening, in vitro, molecular dynamics simulation and in vivo study. Eur J Pharmacol 2024; 982:176825. [PMID: 39159715 DOI: 10.1016/j.ejphar.2024.176825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 07/02/2024] [Accepted: 07/18/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND Human neutrophil elastase (HNE) is an important contributor to lung diseases such as acute lung injury (ALI) or acute respiratory distress syndrome. Therefore, this study aimed to identify natural HNE inhibitors with anti-inflammatory activity through machine learning algorithms, in vitro assays, molecular dynamic simulation, and an in vivo ALI assay. METHODS Based on the optimized Discovery Studio two-dimensional molecular descriptors, combined with different molecular fingerprints, six machine learning models were established using the Naïve Bayesian (NB) method to identify HNE inhibitors. Subsequently, the optimal model was utilized to screen 6925 drug-like compounds obtained from the Traditional Chinese Medicine Systems Pharmacy Database and Analysis Platform (TCMSP), followed by ADMET analysis. Finally, 10 compounds with reported anti-inflammatory activity were selected to determine their inhibitory activities against HNE in vitro, and the compounds with the best activity were selected for a 100 ns molecular dynamics simulation and its anti-inflammatory effect was evaluated using Poly (I:C)-induced ALI model. RESULTS The evaluation of the in vitro HNE inhibition efficiency of the 10 selected compounds showed that the flavonoid tricetin had the strongest inhibitory effect on HNE. The molecular dynamics simulation indicated that the binding of tricetin to HNE was relatively stable throughout the simulation. Importantly, in vivo experiments indicated that tricetin treatment substantially improved the Poly (I:C)-induced ALI. CONCLUSION The proposed NB model was proved valuable for exploring novel HNE inhibitors, and natural tricetin was screened out as a novel HNE inhibitor, which was confirmed by in vitro and in vivo assays for its inhibitory activities.
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Affiliation(s)
- Shanshan Zhang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Yongguang Liu
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Suwei Jin
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Tianji Xia
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Hongbin Song
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Chenxi Cao
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Yonghong Liao
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Ruile Pan
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Mingzhu Yan
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
| | - Qi Chang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
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2
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Schaduangrat N, Khemawoot P, Jiso A, Charoenkwan P, Shoombuatong W. MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information. Sci Rep 2024; 14:24764. [PMID: 39433940 PMCID: PMC11494111 DOI: 10.1038/s41598-024-75487-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2024] [Accepted: 10/07/2024] [Indexed: 10/23/2024] Open
Abstract
Migraine is considered one of the debilitating primary headache conditions with an estimated worldwide occurrence of approximately 14-15%, contributing highly to factors responsible for global disability. Calcitonin gene-related peptide (CGRP) is a neuropeptide that plays a crucial role in the pathophysiology of migraines and thus, its inhibition can help relieve migraine symptoms. However, conventional process of CGRP drug development has been laborious and time-consuming with incurred costs exceeding one billion dollars. On the other hand, machine learning (ML)-based approaches that are capable of accurately identifying CGRP inhibitors could greatly facilitate in expediting the discovery of novel CGRP drugs. Therefore, this study proposes a novel and high-accuracy meta-model, namely MetaCGRP, that can precisely identify CGRP inhibitors. To the best of our knowledge, MetaCGRP is the first SMILES-based approach that has been developed to identify CGRP inhibitors without the use of 3D structural information. In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. Finally, we employed the feature selection method to optimize the multi-view features and determine the best feature subset to enable the construction of the meta-model. Both cross-validation and independent tests indicated that MetaCGRP clearly outperforms several conventional ML classifiers, with accuracies of 0.898 and 0.799 on the training and independent test datasets, respectively. In addition, MetaCGRP in conjunction with molecular docking was utilized to identify five potential natural product candidates from Thai herbal pharmacopoeia and analyze their binding affinity and interactions to CGRP. To facilitate community-wide efforts in expediting the discovery of novel CGRP inhibitors, a user-friendly web server for MetaCGRP is freely available at https://pmlabqsar.pythonanywhere.com/MetaCGRP .
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Affiliation(s)
- Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Phisit Khemawoot
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, 10540, Thailand
| | - Apisada Jiso
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, 10540, Thailand
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand.
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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3
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Wang Y, Sui Y, Yao J, Jiang H, Tian Q, Tang Y, Ou Y, Tang J, Tan N. Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study. Brief Bioinform 2024; 25:bbae362. [PMID: 39073832 DOI: 10.1093/bib/bbae362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/21/2024] [Accepted: 07/13/2024] [Indexed: 07/30/2024] Open
Abstract
Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.
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Affiliation(s)
- Yinyin Wang
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Yihang Sui
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Jiaqi Yao
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Hong Jiang
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Qimeng Tian
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130 Meilong Road, Shanghai 200237, China
| | - Yongyu Ou
- Beijing Haiyan Pharmaceutical Co., Ltd., Yangtze River Pharmaceutical Group, No. 16 Shengmingyuan Road, Beijing 102206, PR China
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki FI-00290, Finland
| | - Ninghua Tan
- Department of TCMs Pharmaceuticals, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 639 Longmian Avenue, Nanjing 211198, PR China
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4
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Bas TG, Duarte V. Biosimilars in the Era of Artificial Intelligence-International Regulations and the Use in Oncological Treatments. Pharmaceuticals (Basel) 2024; 17:925. [PMID: 39065775 PMCID: PMC11279612 DOI: 10.3390/ph17070925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
This research is based on three fundamental aspects of successful biosimilar development in the challenging biopharmaceutical market. First, biosimilar regulations in eight selected countries: Japan, South Korea, the United States, Canada, Brazil, Argentina, Australia, and South Africa, represent the four continents. The regulatory aspects of the countries studied are analyzed, highlighting the challenges facing biosimilars, including their complex approval processes and the need for standardized regulatory guidelines. There is an inconsistency depending on whether the biosimilar is used in a developed or developing country. In the countries observed, biosimilars are considered excellent alternatives to patent-protected biological products for the treatment of chronic diseases. In the second aspect addressed, various analytical AI modeling methods (such as machine learning tools, reinforcement learning, supervised, unsupervised, and deep learning tools) were analyzed to observe patterns that lead to the prevalence of biosimilars used in cancer to model the behaviors of the most prominent active compounds with spectroscopy. Finally, an analysis of the use of active compounds of biosimilars used in cancer and approved by the FDA and EMA was proposed.
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Affiliation(s)
- Tomas Gabriel Bas
- Escuela de Ciencias Empresariales, Universidad Católica del Norte, Coquimbo 1781421, Chile;
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5
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Shahab M, Danial M, Duan X, Khan T, Liang C, Gao H, Chen M, Wang D, Zheng G. Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis. J Biomol Struct Dyn 2024; 42:3874-3886. [PMID: 37232453 DOI: 10.1080/07391102.2023.2216278] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 05/15/2023] [Indexed: 05/27/2023]
Abstract
The rise of antibiotic-resistant Mycobacterium tuberculosis (Mtb) has reduced the availability of medications for tuberculosis therapy, resulting in increased morbidity and mortality globally. Tuberculosis spreads from the lungs to other parts of the body, including the brain and spine. Developing a single drug can take several decades, making drug discovery costly and time-consuming. Machine learning algorithms like support vector machines (SVM), k-nearest neighbor (k-NN), random forest (RF) and Gaussian naive base (GNB) are fast and effective and are commonly used in drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. For the training of the models, a dataset of 307 was downloaded from BindingDB. Among 307 compounds, 85 compounds were labeled as active, having an IC50 below 58 mM, while 222 compounds were labeled inactive against thymidylate kinase, with 87.2% accuracy. The developed models were subjected to an external ZINC dataset of 136,564 compounds. Furthermore, we performed the 100-ns dynamic simulation and post trajectories analysis of compounds having good interaction and score in molecular docking. As compared to the standard reference compound, the top three hits revealed greater stability and compactness. In conclusion, our predicted hits can inhibit thymidylate kinase overexpression to combat Mycobacterium tuberculosis.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Muhammad Shahab
- State Key Laboratories of Chemical Resources Engineering Beijing University of Chemical Technology, Beijing, China
| | - Muhammad Danial
- University of Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, China
| | - Xiuyuan Duan
- State Key Laboratories of Chemical Resources Engineering Beijing University of Chemical Technology, Beijing, China
| | - Taimur Khan
- State Key Laboratories of Chemical Resources Engineering Beijing University of Chemical Technology, Beijing, China
| | - Chaoqun Liang
- State Key Laboratories of Chemical Resources Engineering Beijing University of Chemical Technology, Beijing, China
| | - Hanzi Gao
- State Key Laboratories of Chemical Resources Engineering Beijing University of Chemical Technology, Beijing, China
| | - Meiyu Chen
- State Key Laboratories of Chemical Resources Engineering Beijing University of Chemical Technology, Beijing, China
| | - Daixi Wang
- State Key Laboratories of Chemical Resources Engineering Beijing University of Chemical Technology, Beijing, China
| | - Guojun Zheng
- State Key Laboratories of Chemical Resources Engineering Beijing University of Chemical Technology, Beijing, China
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6
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Arora S, Chettri S, Percha V, Kumar D, Latwal M. Artifical intelligence: a virtual chemist for natural product drug discovery. J Biomol Struct Dyn 2024; 42:3826-3835. [PMID: 37232451 DOI: 10.1080/07391102.2023.2216295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shefali Arora
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Sukanya Chettri
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Versha Percha
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Mamta Latwal
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
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7
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Gahl M, Kim HW, Glukhov E, Gerwick WH, Cottrell GW. PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning. JOURNAL OF NATURAL PRODUCTS 2024; 87:567-575. [PMID: 38349959 PMCID: PMC10960629 DOI: 10.1021/acs.jnatprod.3c00879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/22/2024] [Accepted: 01/22/2024] [Indexed: 02/15/2024]
Abstract
Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a "within-one" measure that reaches 93.0% accuracy.
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Affiliation(s)
- Martha Gahl
- Department
of Computer Science and Engineering, University
of California San Diego, La Jolla, California 92093, United States
| | - Hyun Woo Kim
- Center
for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
- College
of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University-Seoul, Gyeonggi-do 04620, Republic of Korea
| | - Evgenia Glukhov
- Center
for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
| | - William H. Gerwick
- Center
for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, United States
- Skaggs
School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
| | - Garrison W. Cottrell
- Department
of Computer Science and Engineering, University
of California San Diego, La Jolla, California 92093, United States
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8
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Ji X, Li Q, Liu Z, Wu W, Zhang C, Sui H, Chen M. Identification of Active Components for Sports Supplements: Machine Learning-Driven Classification and Cell-Based Validation. ACS OMEGA 2024; 9:11347-11355. [PMID: 38496927 PMCID: PMC10938306 DOI: 10.1021/acsomega.3c07395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/25/2024] [Accepted: 02/05/2024] [Indexed: 03/19/2024]
Abstract
The identification of active components is critical for the development of sports supplements. However, high-throughput screening of active components remains a challenge. This study sought to construct prediction models to screen active components from herbal medicines via machine learning and validate the screening by using cell-based assays. The six constructed models had an accuracy of >0.88. Twelve randomly selected active components from the screening were tested for their active potency on C2C12 cells, and 11 components induced a significant increase in myotube diameters and protein synthesis. The effect and mechanism of luteolin among the 11 active components as potential sports supplements were then investigated by using immunofluorescence staining and high-content imaging analysis. It showed that luteolin increased the skeletal muscle performance via the activation of PGC-1α and MAPK signaling pathways. Thus, high-throughput prediction models can be effectively used to screen active components as sports supplements.
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Affiliation(s)
- Xiaoning Ji
- State
Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di
Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
- NHC
key laboratory of food safety risk assessment, China National Center for Food Safety Risk Assessment, Beijing 100022, China
| | - Qiuyun Li
- NMPA
Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial
Key Laboratory of Tropical Disease Research, Food Safety and Health
Research Center, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Zhaoping Liu
- NHC
key laboratory of food safety risk assessment, China National Center for Food Safety Risk Assessment, Beijing 100022, China
| | - Weiliang Wu
- NMPA
Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial
Key Laboratory of Tropical Disease Research, Food Safety and Health
Research Center, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Chaozheng Zhang
- NHC
key laboratory of food safety risk assessment, China National Center for Food Safety Risk Assessment, Beijing 100022, China
| | - Haixia Sui
- NHC
key laboratory of food safety risk assessment, China National Center for Food Safety Risk Assessment, Beijing 100022, China
| | - Min Chen
- State
Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di
Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
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9
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Riedling O, Walker AS, Rokas A. Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning. Microbiol Spectr 2024; 12:e0340023. [PMID: 38193680 PMCID: PMC10846162 DOI: 10.1128/spectrum.03400-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many fungal SMs have one or more medically and industrially important activities (e.g., antifungal, antibacterial, and antitumor). The genes necessary for fungal SM biosynthesis are typically located right next to each other in the genome and are known as biosynthetic gene clusters (BGCs). However, whether fungal SM bioactivity can be predicted from specific attributes of genes in BGCs remains an open question. We adapted machine learning models that predicted SM bioactivity from bacterial BGC data with accuracies as high as 80% to fungal BGC data. We trained our models to predict the antibacterial, antifungal, and cytotoxic/antitumor bioactivity of fungal SMs on two data sets: (i) fungal BGCs (data set comprised of 314 BGCs) and (ii) fungal (314 BGCs) and bacterial BGCs (1,003 BGCs). We found that models trained on fungal BGCs had balanced accuracies between 51% and 68%, whereas training on bacterial and fungal BGCs had balanced accuracies between 56% and 68%. The low prediction accuracy of fungal SM bioactivities likely stems from the small size of the data set; this lack of data, coupled with our finding that including bacterial BGC data in the training data did not substantially change accuracies currently limits the application of machine learning approaches to fungal SM studies. With >15,000 characterized fungal SMs, millions of putative BGCs in fungal genomes, and increased demand for novel drugs, efforts that systematically link fungal SM bioactivity to BGCs are urgently needed.IMPORTANCEFungi are key sources of natural products and iconic drugs, including penicillin and statins. DNA sequencing has revealed that there are likely millions of biosynthetic pathways in fungal genomes, but the chemical structures and bioactivities of >99% of natural products produced by these pathways remain unknown. We used artificial intelligence to predict the bioactivities of diverse fungal biosynthetic pathways. We found that the accuracies of our predictions were generally low, between 51% and 68%, likely because the natural products and bioactivities of only very few fungal pathways are known. With >15,000 characterized fungal natural products, millions of putative biosynthetic pathways present in fungal genomes, and increased demand for novel drugs, our study suggests that there is an urgent need for efforts that systematically identify fungal biosynthetic pathways, their natural products, and their bioactivities.
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Affiliation(s)
- Olivia Riedling
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, USA
| | - Allison S. Walker
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, USA
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, USA
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Yang F, Jia L, Zhou HC, Huang JN, Hou MY, Liu FT, Prabhu N, Li ZJ, Yang CB, Zou C, Nordlund P, Wang JG, Dai LY. Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma. Acta Pharmacol Sin 2024; 45:391-404. [PMID: 37803139 PMCID: PMC10789809 DOI: 10.1038/s41401-023-01167-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/05/2023] [Indexed: 10/08/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.
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Affiliation(s)
- Fan Yang
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China
- Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Guangzhou, 510632, China
| | - Lin Jia
- College of Pharmacy, Shenzhen Technology University, Shenzhen, 518118, China
| | - Hong-Chao Zhou
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China
| | - Jing-Nan Huang
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China
| | - Meng-Yun Hou
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China
| | - Feng-Ting Liu
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China
| | - Nayana Prabhu
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore
| | - Zhi-Jie Li
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China
| | - Chuan-Bin Yang
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China
| | - Chang Zou
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China
- Department of Clinical Medical Research Center, The First Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, 518020, China
| | - Pär Nordlund
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore
- Department of Oncology and Pathology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Ji-Gang Wang
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China.
- Artemisinin Research Center, and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| | - Ling-Yun Dai
- Department of Geriatrics, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital (the Second Clinical Medical College of Jinan University; the First Affiliated Hospital of Southern University of Science and Technology), Shenzhen, 518020, China.
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore.
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11
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Mikutis S, Lawrinowitz S, Kretzer C, Dunsmore L, Sketeris L, Rodrigues T, Werz O, Bernardes GJL. Machine Learning Uncovers Natural Product Modulators of the 5-Lipoxygenase Pathway and Facilitates the Elucidation of Their Biological Mechanisms. ACS Chem Biol 2024; 19:217-229. [PMID: 38149598 PMCID: PMC10804367 DOI: 10.1021/acschembio.3c00725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/10/2023] [Accepted: 12/12/2023] [Indexed: 12/28/2023]
Abstract
Machine learning (ML) models have made inroads into chemical sciences, with optimization of chemical reactions and prediction of biologically active molecules being prime examples thereof. These models excel where physical experiments are expensive or time-consuming, for example, due to large scales or the need for materials that are difficult to obtain. Studies of natural products suffer from these issues─this class of small molecules is known for its wealth of structural diversity and wide-ranging biological activities, but their investigation is hindered by poor synthetic accessibility and lack of scalability. To facilitate the evaluation of these molecules, we designed ML models that predict which natural products can interact with a particular target or a relevant pathway. Here, we focused on discovering natural products that are capable of modulating the 5-lipoxygenase (5-LO) pathway that plays key roles in lipid signaling and inflammation. These computational approaches led to the identification of nine natural products that either directly inhibit the activity of the 5-LO enzyme or affect the cellular 5-LO pathway. Further investigation of one of these molecules, deltonin, led us to discover a new cell-type-selective mechanism of action. Our ML approach helped deorphanize natural products as well as shed light on their mechanisms and can be broadly applied to other use cases in chemical biology.
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Affiliation(s)
- Sigitas Mikutis
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Stefanie Lawrinowitz
- Department
of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich Schiller University Jena, Philosophenweg 14, 07743 Jena, Germany
| | - Christian Kretzer
- Department
of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich Schiller University Jena, Philosophenweg 14, 07743 Jena, Germany
| | - Lavinia Dunsmore
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Laurynas Sketeris
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Tiago Rodrigues
- Instituto
de Investigação do Medicamento (iMed), Faculdade de
Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisbon, Portugal
| | - Oliver Werz
- Department
of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich Schiller University Jena, Philosophenweg 14, 07743 Jena, Germany
| | - Gonçalo J. L. Bernardes
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
- Instituto
de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal
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12
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Wu J, Deng S, Yu X, Wu Y, Hua X, Zhang Z, Huang Y. Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 123:155201. [PMID: 37976693 DOI: 10.1016/j.phymed.2023.155201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/23/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Astragali Radix (AR) is a widely used herbal medicine. The quality of AR is influenced by several key factors, including the production area, growth mode, species, and grade. However, the markers currently used to distinguish these factors primarily focus on secondary metabolites, and their validation on large-scale samples is lacking. PURPOSE This study aims to discover reliable markers and develop classification models for identifying the production area, growth mode, species, and grade of AR. METHODS A total of 366 batches of AR crude slices were collected from six provinces in China and divided into learning (n = 191) and validation (n = 175) sets. Three ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods were developed and validated for determining 22 primary and 10 secondary metabolites in AR methanol extract. Based on the quantification data, seven machine learning algorithms, such as Nearest Neighbors and Gradient Boosted Trees, were applied to screen the potential markers and build the classification models for identifying the four factors associated with AR quality. RESULTS Our analysis revealed that secondary metabolites (e.g., astragaloside IV, calycosin-7-O-β-D-glucoside, and ononin) played a crucial role in evaluating AR quality, particularly in identifying the production area and species. Additionally, fatty acids (e.g., behenic acid and lignoceric acid) were vital in determining the growth mode of AR, while amino acids (e.g., alanine and phenylalanine) were helpful in distinguishing different grades. With both primary and secondary metabolites, the Nearest Neighbors algorithm-based model was constructed for identifying each factor of AR, achieving good classification accuracy (>70%) on the validation set. Furthermore, a panel of four metabolites including ononin, astragaloside II, pentadecanoic acid, and alanine, allowed for simultaneous identification of all four factors of AR, offering an accuracy of 86.9%. CONCLUSION Our findings highlight the potential of integrating large-scale targeted metabolomics and machine learning approaches to accurately identify the quality-associated factors of AR. This study opens up possibilities for enhancing the evaluation of other herbal medicines through similar methodologies, and further exploration in this area is warranted.
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Affiliation(s)
- Jing Wu
- Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China; Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Shaoqian Deng
- Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China
| | - Xinyue Yu
- Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China
| | - Yanlin Wu
- National Institutes for Food and Drug Control, Beijing, 102629, China
| | - Xiaoyi Hua
- Department of Traditional Chinese Medicine Testing, Wuxi Center for Drug Safety Control, Wuxi, 214028, China
| | - Zunjian Zhang
- Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China.
| | - Yin Huang
- Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Ministry of Education, Nanjing, 210009, China; Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
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13
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He Z, Yuan J, Zhang Y, Li R, Mo M, Wang Y, Ti H. Recent advances towards natural plants as potential inhibitors of SARS-Cov-2 targets. PHARMACEUTICAL BIOLOGY 2023; 61:1186-1210. [PMID: 37605622 PMCID: PMC10446791 DOI: 10.1080/13880209.2023.2241518] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/29/2023] [Accepted: 07/23/2023] [Indexed: 08/23/2023]
Abstract
CONTEXT Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still ongoing and currently the most striking epidemic disease. With the rapid global spread of SARS-CoV-2 variants, new antivirals are urgently needed to avert a more serious crisis. Inhibitors from traditional medicines or natural plants have shown promising results to fight COVID-19 with different mechanisms of action. OBJECTIVES To provide comprehensive and promising approaches to the medical community in the fight against this epidemic by reviewing potential plant-derived anti-SARS-CoV-2 inhibitors. METHODS Structural databases such as TCMSP (http://lsp.nwu.edu.cn/tcmsp.php), TCM Database @ Taiwan (http://tcm.cmu.edu.tw/), BATMAN-TCM (http://bionet.ncpsb.org/batman-tcm/) and TCMID (http://www.megabionet.org/tcmid/), as well as PubMed, Sci Finder, Research Gate, Science Direct, CNKI, Web of Science and Google Scholar were searched for relevant articles on TCMs and natural products against SARS-CoV-2. RESULTS Seven traditional Chinese medicines formulas have unique advantages in regulating the immune system for treating COVID-19. The plant-derived natural compounds as anti-SARS-CoV-2 inhibitors were identified based on 5 SARS-CoV-2 key proteins, namely, angiotensin-converting enzyme 2 (ACE2), 3 C-like protease (3CLpro), papain-like protease (PLpro), spike (S) protein, and nucleocapsid (N) protein. CONCLUSIONS A variety of natural products, such as flavonoids, terpenoids, phenols, and alkaloids, were identified, which could be used as potential SASR-Cov-2 inhibitors. These shed new light on the efficient discovery of SASR-Cov-2 inhibitors from natural products.
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Affiliation(s)
- Zhouman He
- School of Chinese Medicinal Resource, Guangdong Pharmaceutical University, Guangzhou, P. R. China
| | - Jia Yuan
- School of Chinese Medicinal Resource, Guangdong Pharmaceutical University, Guangzhou, P. R. China
| | - Yuanwen Zhang
- School of Chinese Medicinal Resource, Guangdong Pharmaceutical University, Guangzhou, P. R. China
| | - Runfeng Li
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, P. R. China
| | - Meilan Mo
- School of Chinese Medicinal Resource, Guangdong Pharmaceutical University, Guangzhou, P. R. China
| | - Yutao Wang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, P. R. China
| | - Huihui Ti
- School of Chinese Medicinal Resource, Guangdong Pharmaceutical University, Guangzhou, P. R. China
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14
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Hu G, Qiu M. Machine learning-assisted structure annotation of natural products based on MS and NMR data. Nat Prod Rep 2023; 40:1735-1753. [PMID: 37519196 DOI: 10.1039/d3np00025g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.
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Affiliation(s)
- Guilin Hu
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China.
- University of the Chinese Academy of Sciences, Beijing 100049, People's Republic of China
| | - Minghua Qiu
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China.
- University of the Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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15
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Yuan Y, Shi C, Zhao H. Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products. ACS Synth Biol 2023; 12:2650-2662. [PMID: 37607352 PMCID: PMC10615616 DOI: 10.1021/acssynbio.3c00234] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.
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Affiliation(s)
- Yujie Yuan
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Chengyou Shi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Departments of Chemistry, Biochemistry, and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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Liu Y, Li H, Luo Z, Yu Y, Yang J, Zhang M, Law BYK, Huang Z, Li W. Artesunate, a new antimalarial clinical drug, exhibits potent anti-AML activity by targeting the ROS/Bim and TFRC/Fe 2+ pathways. Br J Pharmacol 2023; 180:701-720. [PMID: 36368726 DOI: 10.1111/bph.15986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 10/20/2022] [Accepted: 10/29/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND AND PURPOSE Artesunate, approved by the Food and Drug Administration in 2020 as a new treatment for severe malaria, also shows anti-tumour activity against acute myeloid leukaemia (AML). However, the underlying molecular mechanism(s) of artesunate-induced apoptosis and differentiation of AML is not clearly elucidated. EXPERIMENTAL APPROACH The biological effects of artesunate on AML were explored in vitro, using cells from AML patients and leukaemia cell lines, and in vivo, using female C57BL/6 or nude nu/nu BALB/c mice. Underlying mechanisms in vitro were examined with the Trypan blue dye exclusion assay, western blotting and flow cytometry. Effects of artesunate in C57BL/6 mice intravenously injected with murine AML cells (C1498-GFP) were assessed by numbers of AML cells and by survival. KEY RESULTS In vitro, artesunate promoted apoptosis and differentiation in both leukaemia cell lines and patient-derived primary leukaemia cells. Mechanistically, artesunate promoted cell apoptosis by triggering reactive oxygen species (ROS) production and increasing expression of the pro-apoptotic protein Bim. Interestingly, transferrin receptor 1 (TFRC)-mediated regulation of intracellular iron homeostasis also played an essential role in AML cell differentiation induced by artesunate. In vivo, artesunate slowed AML progression and prolonged survival in a mouse leukaemia model. Notably, artesunate displayed no apparent toxicity towards healthy haematopoietic stem cells, bone marrow mononuclear cells or experimental animals. CONCLUSION AND IMPLICATIONS Artesunate is a safe agent with significant anti-leukaemia effects in mice and may serve as a promising chemotherapeutic strategy for patients with AML, based on two different mechanisms, targeting the ROS/Bim and the TFRC/Fe2+ pathways.
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Affiliation(s)
- Yi Liu
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei, PR China
| | - Han Li
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei, PR China
| | - Zhihong Luo
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei, PR China
| | - You Yu
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei, PR China
| | - Jingzhao Yang
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei, PR China
| | - Min Zhang
- Department of Hematology, Union Hospital of Huazhong University of Science and Technology, Wuhan, Hubei, PR China
| | - Betty Yuen Kwan Law
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau SAR, PR China
| | - Zan Huang
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei, PR China
| | - Wenhua Li
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, Hubei, PR China
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17
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Shin SH, Hur G, Kim NR, Park JHY, Lee KW, Yang H. A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor. Food Funct 2023; 14:1869-1883. [PMID: 36723137 DOI: 10.1039/d2fo03466b] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
As a type of stress hormone, glucocorticoids (GCs) affect numerous physiological pathways by binding to the glucocorticoid receptor (GR) and regulating the transcription of various genes. However, when GCs are dysregulated, the resulting hypercortisolism may contribute to various metabolic disorders, including obesity. Thus, attempts have been made to discover potent GR antagonists that can reverse excess-GC-related metabolic diseases. Phytochemicals are a collection of valuable bioactive compounds that are known for their wide variety of chemotypes. Recently, various computational methods have been developed to obtain active phytochemicals that can modulate desired target proteins. In this study, we developed a workflow comprising two consecutive quantitative structure-activity relationship-based machine learning models to discover novel GR-antagonizing phytochemicals. These two models collectively identified 65 phytochemicals that bind to and antagonize GR. Of these, nine commercially available phytochemicals were validated for GR-antagonist and anti-obesity activities. In particular, we confirmed that demethylzeylasteral, a phytochemical of the Tripterygium wilfordii Radix, exhibits potent anti-obesity activity in vitro through GR antagonism.
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Affiliation(s)
- Seo Hyun Shin
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Gihyun Hur
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Na Ra Kim
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Jung Han Yoon Park
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ki Won Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea. .,Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.,Advanced Institutes of Convergence Technology, Seoul National University, Suwon, 16229, Republic of Korea
| | - Hee Yang
- Department of Food and Nutrition, Kookmin University, Seoul 02707, Republic of Korea.
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18
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Park SY, Kim YW, Song YR, Bak SB, Jang YP, Kim IK, Kim JH, Kim CE. Compound-level identification of sasang constitution type-specific personalized herbal medicine using data science approach. Heliyon 2023; 9:e13692. [PMID: 36852049 PMCID: PMC9957892 DOI: 10.1016/j.heliyon.2023.e13692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
Introduction Sasang Constitutional Medicine (SCM) is a type of traditional Korean medicine where patients are classified as one of four Sasang constitution types (Sasang type) and medications consisting of medicinal herbs are prescribed according to the Sasang type. Despite the importance of personalized medicine, the operation mechanism is largely unknown. To gain a better understanding, we investigated the compound information that composes Sasang type-specific personalized herbal medicines on both multivariate and univariate levels. Methods Five machine learning classifiers including extremely randomized trees (ERT) were trained to investigate whether the Sasang type can be explained by compound information at the multivariate level. Hierarchical clustering was conducted to determine whether compounds are processed distributedly or specifically. Taxonomic and biosynthetic analyses were conducted on these compounds. A univariate level statistical test was conducted to provide more robust Sasang type-specific compound information. Results Using the trained ERT classifier, sixty important compounds were extracted. The sixty compounds were clustered into three groups, corresponding to each Sasang type-prominent compounds, suggesting that most compounds have specific preference for the Sasang type. Structural and biosynthetic characteristics of these Sasang type-prominent compounds were determined based on taxonomy and pathway analyses. Fourteen compounds showed statistically significant relevance with the Sasang type. Additionally, we predicted the Sasang type of unknown herbs, which were confirmed by their biological effects in functional assays. Conclusion This study investigated the personalized herbal medicines of the SCM using compound information. This study provided information on the chemical characteristics of the compounds that are essential for classifying the Sasang type of medicinal herbs, as well as predictions regarding the Sasang type of the commonly used but unidentified medicinal herbs.
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Affiliation(s)
- Sa-Yoon Park
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, 13120, Republic of Korea
| | - Young Woo Kim
- Department of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea.,School of Korean Medicine, Dongguk University, Gyeongju, 38066, Republic of Korea
| | - Yu Rim Song
- School of Korean Medicine, Dongguk University, Gyeongju, 38066, Republic of Korea
| | - Seon Been Bak
- School of Korean Medicine, Dongguk University, Gyeongju, 38066, Republic of Korea
| | - Young Pyo Jang
- College of Pharmacy, Kyung Hee University, Seoul, 02447, South Korea
| | - Il-Kon Kim
- Department of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea
| | - Ji-Hwan Kim
- Department of Sasang Constitutional Medicine, Gil Hospital of Korean Medicine, Gachon University, Incheon, 21565, Republic of Korea
| | - Chang-Eop Kim
- Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, 13120, Republic of Korea
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19
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Zhao JX, Yue JM. Frontier studies on natural products: moving toward paradigm shifts. Sci China Chem 2023. [DOI: 10.1007/s11426-022-1512-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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20
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Gonzalez-Pastor R, Carrera-Pacheco SE, Zúñiga-Miranda J, Rodríguez-Pólit C, Mayorga-Ramos A, Guamán LP, Barba-Ostria C. Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts. Molecules 2023; 28:1068. [PMID: 36770734 PMCID: PMC9920787 DOI: 10.3390/molecules28031068] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/24/2023] Open
Abstract
Natural extracts have been and continue to be used to treat a wide range of medical conditions, from infectious diseases to cancer, based on their convenience and therapeutic potential. Natural products derived from microbes, plants, and animals offer a broad variety of molecules and chemical compounds. Natural products are not only one of the most important sources for innovative drug development for animal and human health, but they are also an inspiration for synthetic biology and chemistry scientists towards the discovery of new bioactive compounds and pharmaceuticals. This is particularly relevant in the current context, where antimicrobial resistance has risen as a global health problem. Thus, efforts are being directed toward studying natural compounds' chemical composition and bioactive potential to generate drugs with better efficacy and lower toxicity than existing molecules. Currently, a wide range of methodologies are used to analyze the in vitro activity of natural extracts to determine their suitability as antimicrobial agents. Despite traditional technologies being the most employed, technological advances have contributed to the implementation of methods able to circumvent issues related to analysis capacity, time, sensitivity, and reproducibility. This review produces an updated analysis of the conventional and current methods to evaluate the antimicrobial activity of natural compounds.
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Affiliation(s)
- Rebeca Gonzalez-Pastor
- Biomedical Research Center (CENBIO), Eugenio Espejo School of Health Sciences, Universidad UTE, Quito 170527, Ecuador
| | - Saskya E. Carrera-Pacheco
- Biomedical Research Center (CENBIO), Eugenio Espejo School of Health Sciences, Universidad UTE, Quito 170527, Ecuador
| | - Johana Zúñiga-Miranda
- Biomedical Research Center (CENBIO), Eugenio Espejo School of Health Sciences, Universidad UTE, Quito 170527, Ecuador
| | - Cristina Rodríguez-Pólit
- Biomedical Research Center (CENBIO), Eugenio Espejo School of Health Sciences, Universidad UTE, Quito 170527, Ecuador
| | - Arianna Mayorga-Ramos
- Biomedical Research Center (CENBIO), Eugenio Espejo School of Health Sciences, Universidad UTE, Quito 170527, Ecuador
| | - Linda P. Guamán
- Biomedical Research Center (CENBIO), Eugenio Espejo School of Health Sciences, Universidad UTE, Quito 170527, Ecuador
| | - Carlos Barba-Ostria
- School of Medicine, College of Health Sciences, Universidad San Francisco de Quito (USFQ), Quito 170901, Ecuador
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Proteomic Analysis of Human Breast Cancer MCF-7 Cells to Identify Cellular Targets of the Anticancer Pigment OR3 from Streptomyces coelicolor JUACT03. Appl Biochem Biotechnol 2023; 195:236-252. [PMID: 36070163 DOI: 10.1007/s12010-022-04128-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2022] [Indexed: 01/13/2023]
Abstract
Search for ideal compounds with known pathways of anticancer mechanism is still a priority research focus for cancer, as it continues to be a major health challenge across the globe. Hence, in the present study, anticancer potential of a yellow pigment fraction, OR3, isolated from Streptomyces coelicolor JUACT03 was assessed on the breast cancer cell line MCF-7. TLC-fractionated OR3 pigment was subjected to HPLC and GC-MS analysis for characterization and identification of the bioactive component. MCF-7 cells were treated with IC50 concentration of OR3 and the molecular alterations were analyzed using mass spectrometry-based quantitative proteomic analysis. Bioinformatics tools such as STRING analysis and Ingenuity Pathway Analysis were performed to analyze proteomics data and to identify dysregulated signaling pathways. As per our obtained data, OR3 treatment decreased cell proliferation and induced apoptotic cell death due to significant dysregulation of protein expressions in MCF-7 cells. Altered expression included the ribosomal, mRNA processing and vesicle-mediated transport proteins as a result of OR3 treatment. Downregulation of MAPK proteins, NFkB, and estradiol signaling was identified in OR3-treated MCF-7 cells. Mainly eIF2, mTOR, and eIF4 signaling pathways were altered in OR3-treated cells. GC-MS data indicated the presence of novel compounds in OR3 fraction. It can be concluded that OR3 exhibits potent anticancer activity on the breast cancer cells mainly through altering the expression and affecting the signaling proteins which are involved in different cell proliferation/apoptotic pathways thereby causing inhibition of cancer cell proliferation, survival and metastasis.
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Ogawa K, Sakamoto D, Hosoki R. Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications. Chem Pharm Bull (Tokyo) 2023; 71:486-494. [PMID: 37394596 DOI: 10.1248/cpb.c23-00039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.
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Affiliation(s)
- Keiko Ogawa
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University
| | - Daiki Sakamoto
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University
| | - Rumiko Hosoki
- Laboratory of Regulatory Science, College of Pharmaceutical Sciences, Ritsumeikan University
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23
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Karim MB, Kanaya S, Altaf‐Ul‐Amin M. Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network. Mol Inform 2022; 41:e2100247. [PMID: 35014190 PMCID: PMC9400908 DOI: 10.1002/minf.202100247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/09/2022] [Indexed: 11/20/2022]
Abstract
The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and similarities among metabolites and for the prediction of chemical reactions etc. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property of plant metabolites. We developed the first DNN model using the fingerprint set of metabolites as features. In the second DNN model, we searched the similarities among fingerprints using correlation and used one representative feature from each group of highly correlated fingerprints. In the third model, the fingerprints of metabolites were used to find structurally similar chemical compound clusters. Form each cluster a representative metabolite is selected and made part of the training dataset. The second model reduced the number of features where the third model achieved better classification results for test data. In both cases, we applied the simple graph clustering method to cluster the corresponding network. The correlation-based DNN model reduced some features while retaining an almost similar performance compared to the first DNN model. The third model improves classification results for test data by capturing wider variance within training data using graph clustering method. This third model is somewhat novel approach and can be applied to build DNN models for other purposes.
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24
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Xia Z, Kong F, Wang K, Zhang X. Role of N6-Methyladenosine Methylation Regulators in the Drug Therapy of Digestive System Tumours. Front Pharmacol 2022; 13:908079. [PMID: 35754499 PMCID: PMC9218687 DOI: 10.3389/fphar.2022.908079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/17/2022] [Indexed: 12/12/2022] Open
Abstract
Digestive system tumours, including stomach, colon, esophagus, liver and pancreatic tumours, are serious diseases affecting human health. Although surgical treatment and postoperative chemoradiotherapy effectively improve patient survival, current diagnostic and therapeutic strategies for digestive system tumours lack sensitivity and specificity. Moreover, the tumour's tolerance to drug therapy is enhanced owing to tumour cell heterogeneity. Thus, primary or acquired treatment resistance is currently the main hindrance to chemotherapy efficiency. N6-methyladenosine (m6A) has various biological functions in RNA modification. m6A modification, a key regulator of transcription expression, regulates RNA metabolism and biological processes through the interaction of m6A methyltransferase ("writers") and demethylase ("erasers") with the binding protein decoding m6A methylation ("readers"). Additionally, m6A modification regulates the occurrence and development of tumours and is a potential driving factor of tumour drug resistance. This review systematically summarises the regulatory mechanisms of m6A modification in the drug therapy of digestive system malignancies. Furthermore, it clarifies the related mechanisms and therapeutic prospects of m6A modification in the resistence of digestive system malignancies to drug therapy.
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Affiliation(s)
- Zhelin Xia
- Department of Pharmacy, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
| | - Fanhua Kong
- Zhongnan Hospital of Wuhan University, Institute of Hepatobiliary Diseases of Wuhan University, Transplant Center of Wuhan University, National Quality Control Center for Donated Organ Procurement, Hubei Key Laboratory of Medical Technology on Transplantation, Hubei Clinical Research Center for Natural Polymer Biological Liver, Hubei Engineering Center of Natural Polymer-based Medical Materials, Wuhan, China
| | - Kunpeng Wang
- Department of General Surgery Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, China
| | - Xin Zhang
- Department of Pharmacy, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China
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25
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InflamNat: web-based database and predictor of anti-inflammatory natural products. J Cheminform 2022; 14:30. [PMID: 35659771 PMCID: PMC9167499 DOI: 10.1186/s13321-022-00608-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 05/09/2022] [Indexed: 11/30/2022] Open
Abstract
Natural products (NPs) are a valuable source for anti-inflammatory drug discovery. However, they are limited by the unpredictability of the structures and functions. Therefore, computational and data-driven pre-evaluation could enable more efficient NP-inspired drug development. Since NPs possess structural features that differ from synthetic compounds, models trained with synthetic compounds may not perform well with NPs. There is also an urgent demand for well-curated databases and user-friendly predictive tools. We presented a comprehensive online web platform (InflamNat, http://www.inflamnat.com/ or http://39.104.56.4/) for anti-inflammatory natural product research. InflamNat is a database containing the physicochemical properties, cellular anti-inflammatory bioactivities, and molecular targets of 1351 NPs that tested on their anti-inflammatory activities. InflamNat provides two machine learning-based predictive tools specifically designed for NPs that (a) predict the anti-inflammatory activity of NPs, and (b) predict the compound-target relationship for compounds and targets collected in the database but lacking existing relationship data. A novel multi-tokenization transformer model (MTT) was proposed as the sequential encoder for both predictive tools to obtain a high-quality representation of sequential data. The experimental results showed that the proposed predictive tools achieved an AUC value of 0.842 and 0.872 in the prediction of anti-inflammatory activity and compound-target interactions, respectively.
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26
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Hassam M, Shamsi JA, Khan A, Al-Harrasi A, Uddin R. Prediction of inhibitory activities of small molecules against Pantothenate synthetase from Mycobacterium tuberculosis using Machine Learning models. Comput Biol Med 2022; 145:105453. [DOI: 10.1016/j.compbiomed.2022.105453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/18/2022] [Accepted: 03/23/2022] [Indexed: 11/03/2022]
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27
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Jukič M, Bren U. Machine Learning in Antibacterial Drug Design. Front Pharmacol 2022; 13:864412. [PMID: 35592425 PMCID: PMC9110924 DOI: 10.3389/fphar.2022.864412] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/28/2022] [Indexed: 12/17/2022] Open
Abstract
Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.
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Affiliation(s)
- Marko Jukič
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
| | - Urban Bren
- Laboratory of Physical Chemistry and Chemical Thermodynamics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
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28
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Moshawih S, Goh HP, Kifli N, Idris AC, Yassin H, Kotra V, Goh KW, Liew KB, Ming LC. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem Biol Drug Des 2022; 100:185-217. [PMID: 35490393 DOI: 10.1111/cbdd.14062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 04/15/2022] [Accepted: 04/23/2022] [Indexed: 11/28/2022]
Abstract
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.
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Affiliation(s)
- Said Moshawih
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hui Poh Goh
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Azam Che Idris
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hayati Yassin
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Vijay Kotra
- Faculty of Pharmacy, Quest International University, Perak, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Kai Bin Liew
- Faculty of Pharmacy, University of Cyberjaya, Cyberjaya, Malaysia
| | - Long Chiau Ming
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
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29
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Tao Xue H, Stanley-Baker M, Wai Kin Kong A, Leung Li H, Wen Bin Goh W. Data considerations for predictive modeling applied to the discovery of bioactive natural products. Drug Discov Today 2022; 27:2235-2243. [DOI: 10.1016/j.drudis.2022.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
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30
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Simonetti SO, Kaufman TS, Larghi EL. Conjugation of Carbohydrates with Quinolines: A Powerful Synthetic Tool. European J Org Chem 2022. [DOI: 10.1002/ejoc.202200107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Sebastián O. Simonetti
- Instituto de Química Rosario: Instituto de Quimica Rosario Química Orgánica Suipacha 531 S2002LRK Rosario ARGENTINA
| | - Teodoro S. Kaufman
- Instituto de Química Rosario: Instituto de Quimica Rosario Química Orgánica Suipacha 531 S2002LRK Rosario ARGENTINA
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31
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Xu T, Xu M, Zhu W, Chen CZ, Zhang Q, Zheng W, Huang R. Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling. J Med Chem 2022; 65:4590-4599. [PMID: 35275639 PMCID: PMC8936051 DOI: 10.1021/acs.jmedchem.1c01372] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Indexed: 12/12/2022]
Abstract
Identification of anti-SARS-CoV-2 compounds through traditional high-throughput screening (HTS) assays is limited by high costs and low hit rates. To address these challenges, we developed machine learning models to identify compounds acting via inhibition of the entry of SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like (3CL) protease. The optimal classification models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values of >0.78. Experimental validation showed that the best performing models increased the assay hit rate by 2.1-fold for viral entry inhibitors and 10.4-fold for 3CL protease inhibitors compared to those of the original drug repurposing screens. Twenty-two compounds showed potent (<5 μM) antiviral activities in a SARS-CoV-2 live virus assay. In conclusion, machine learning models can be developed and used as a complementary approach to HTS to expand compound screening capacities and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery.
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Affiliation(s)
- Tuan Xu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Miao Xu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Wei Zhu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Catherine Z Chen
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Qi Zhang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Wei Zheng
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
| | - Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States
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32
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Vivek-Ananth RP, Sahoo AK, Srivastava A, Samal A. Virtual screening of phytochemicals from Indian medicinal plants against the endonuclease domain of SFTS virus L polymerase. RSC Adv 2022; 12:6234-6247. [PMID: 35424542 PMCID: PMC8982020 DOI: 10.1039/d1ra06702h] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 02/16/2022] [Indexed: 12/25/2022] Open
Abstract
Severe fever with thrombocytopenia syndrome virus (SFTSV) causes a highly infectious disease with reported mortality in the range 2.8% to 47%. The replication and transcription of the SFTSV genome is performed by L polymerase, which has both an RNA dependent RNA polymerase domain and an N-terminal endonuclease (endoN) domain. Due to its crucial role in the cap-snatching mechanism required for initiation of viral RNA transcription, the endoN domain is an ideal antiviral drug target. In this virtual screening study for the identification of potential inhibitors of the endoN domain of SFTSV L polymerase, we have used molecular docking and molecular dynamics (MD) simulation to explore the natural product space of 14 011 phytochemicals from Indian medicinal plants. After generating a heterogeneous ensemble of endoN domain structures reflecting conformational diversity of the corresponding active site using MD simulations, ensemble docking of the phytochemicals was performed against the endoN domain structures. Apart from the ligand binding energy from docking, our virtual screening workflow imposes additional filters such as drug-likeness, non-covalent interactions with key active site residues, toxicity and chemical similarity with other hits, to identify top 5 potential phytochemical inhibitors of endoN domain of SFTSV L polymerase. Further, the stability of the protein–ligand docked complexes for the top 5 potential inhibitors was analyzed using MD simulations. The potential phytochemical inhibitors, predicted in this study using contemporary computational methods, are expected to serve as lead molecules in future experimental studies towards development of antiviral drugs against SFTSV. Virtual screening of a large phytochemical library from Indian medicinal plants to identify potential endonuclease inhibitors against emerging virus SFTSV.![]()
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Affiliation(s)
- R P Vivek-Ananth
- The Institute of Mathematical Sciences (IMSc) Chennai 600113 India .,Homi Bhabha National Institute (HBNI) Mumbai 400094 India
| | - Ajaya Kumar Sahoo
- The Institute of Mathematical Sciences (IMSc) Chennai 600113 India .,Homi Bhabha National Institute (HBNI) Mumbai 400094 India
| | - Ashutosh Srivastava
- Discipline of Biological Engineering, Indian Institute of Technology Gandhinagar Gandhinagar 382355 India
| | - Areejit Samal
- The Institute of Mathematical Sciences (IMSc) Chennai 600113 India .,Homi Bhabha National Institute (HBNI) Mumbai 400094 India
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33
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Deng LJ, Deng WQ, Fan SR, Chen MF, Qi M, Lyu WY, Qi Q, Tiwari AK, Chen JX, Zhang DM, Chen ZS. m6A modification: recent advances, anticancer targeted drug discovery and beyond. Mol Cancer 2022; 21:52. [PMID: 35164788 PMCID: PMC8842557 DOI: 10.1186/s12943-022-01510-2] [Citation(s) in RCA: 178] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 01/15/2022] [Indexed: 12/12/2022] Open
Abstract
Abnormal N6-methyladenosine (m6A) modification is closely associated with the occurrence, development, progression and prognosis of cancer, and aberrant m6A regulators have been identified as novel anticancer drug targets. Both traditional medicine-related approaches and modern drug discovery platforms have been used in an attempt to develop m6A-targeted drugs. Here, we provide an update of the latest findings on m6A modification and the critical roles of m6A modification in cancer progression, and we summarize rational sources for the discovery of m6A-targeted anticancer agents from traditional medicines and computer-based chemosynthetic compounds. This review highlights the potential agents targeting m6A modification for cancer treatment and proposes the advantage of artificial intelligence (AI) in the discovery of m6A-targeting anticancer drugs. Three stages of m6A-targeting anticancer drug discovery: traditional medicine-based natural products, modern chemical modification or synthesis, and artificial intelligence (AI)-assisted approaches for the future.
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Affiliation(s)
- Li-Juan Deng
- Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Wei-Qing Deng
- Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, China
| | - Shu-Ran Fan
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, China
| | - Min-Feng Chen
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, China
| | - Ming Qi
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, China
| | - Wen-Yu Lyu
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, China
| | - Qi Qi
- Clinical Translational Center for Targeted Drug, Department of Pharmacology, School of Medicine, Jinan University, Guangzhou, 510632, People's Republic of China
| | - Amit K Tiwari
- Department of Pharmacology and Experimental Therapeutics, The University of Toledo, Toledo, OH, USA
| | - Jia-Xu Chen
- Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
| | - Dong-Mei Zhang
- Guangdong Province Key Laboratory of Pharmacodynamic Constituents of Traditional Chinese Medicine and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou, 510632, China.
| | - Zhe-Sheng Chen
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY, 11439, USA.
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34
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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35
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Wang Z, Zhang W, Liu B. Computational Analysis of Synthetic Planning: Past and Future. CHINESE J CHEM 2021. [DOI: 10.1002/cjoc.202100273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Zhuang Wang
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
| | - Wenhan Zhang
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
| | - Bo Liu
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
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36
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Nishimura S. Marine natural products targeting the eukaryotic cell membrane. J Antibiot (Tokyo) 2021; 74:769-785. [PMID: 34493848 DOI: 10.1038/s41429-021-00468-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/16/2021] [Accepted: 07/01/2021] [Indexed: 02/07/2023]
Abstract
The cell membrane, with high fluidity and alternative curvatures, maintains the robust integrity to distinguish inner and outer space of cells or organelles. Lipids are the main components of the cell membrane, but their functions are largely unknown. Even the visualization of lipids is not straightforward since modification of lipids often hampers its correct physical properties. Many natural products target cell membranes, some of which are used as pharmaceuticals and/or research tools. They show specific recognition on lipids, and thus exhibit desired pharmacological effects and unique biological phenotypes. This review is a catalog of marine natural products that target eukaryotic cell membranes. Chemical structures, biological activities, and molecular mechanisms are summarized. I hope that this review will be helpful for readers to notice the potential of marine natural products in the exploration of the function of lipids and the druggability of eukaryotic cell membranes.
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Affiliation(s)
- Shinichi Nishimura
- Department of Biotechnology, Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo, Japan.
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37
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Bach E, Passaglia LMP, Jiao J, Gross H. Burkholderia in the genomic era: from taxonomy to the discovery of new antimicrobial secondary metabolites. Crit Rev Microbiol 2021; 48:121-160. [PMID: 34346791 DOI: 10.1080/1040841x.2021.1946009] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Species of Burkholderia are highly versatile being found not only abundantly in soil, but also as plants and animals' commensals or pathogens. Their complex multireplicon genomes harbour an impressive number of polyketide synthase (PKS) and nonribosomal peptide-synthetase (NRPS) genes coding for the production of antimicrobial secondary metabolites (SMs), which have been successfully deciphered by genome-guided tools. Moreover, genome metrics supported the split of this genus into Burkholderia sensu stricto (s.s.) and five new other genera. Here, we show that the successful antimicrobial SMs producers belong to Burkholderia s.s. Additionally, we reviewed the occurrence, bioactivities, modes of action, structural, and biosynthetic information of thirty-eight Burkholderia antimicrobial SMs shedding light on their diversity, complexity, and uniqueness as well as the importance of genome-guided strategies to facilitate their discovery. Several Burkholderia NRPS and PKS display unusual features, which are reflected in their structural diversity, important bioactivities, and varied modes of action. Up to now, it is possible to observe a general tendency of Burkholderia SMs being more active against fungi. Although the modes of action and biosynthetic gene clusters of many SMs remain unknown, we highlight the potential of Burkholderia SMs as alternatives to fight against new diseases and antibiotic resistance.
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Affiliation(s)
- Evelise Bach
- Departamento de Genética and Programa de Pós-graduação em Genética e Biologia Molecular, Instituto de Biociências, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Luciane Maria Pereira Passaglia
- Departamento de Genética and Programa de Pós-graduação em Genética e Biologia Molecular, Instituto de Biociências, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil
| | - Junjing Jiao
- Department for Pharmaceutical Biology, Pharmaceutical Institute, University of Tübingen, Tübingen, Germany
| | - Harald Gross
- Department for Pharmaceutical Biology, Pharmaceutical Institute, University of Tübingen, Tübingen, Germany
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Jeon J, Kang S, Kim HU. Predicting biochemical and physiological effects of natural products from molecular structures using machine learning. Nat Prod Rep 2021; 38:1954-1966. [PMID: 34047331 DOI: 10.1039/d1np00016k] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.
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Affiliation(s)
- Junhyeok Jeon
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Seongmo Kang
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea and BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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40
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Daley SK, Cordell GA. Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix. Nat Prod Commun 2021. [DOI: 10.1177/1934578x211003029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.
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Affiliation(s)
| | - Geoffrey A. Cordell
- Natural Products Inc., Evanston, IL, USA
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, USA
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41
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Huang M, Lu JJ, Ding J. Natural Products in Cancer Therapy: Past, Present and Future. NATURAL PRODUCTS AND BIOPROSPECTING 2021; 11:5-13. [PMID: 33389713 PMCID: PMC7933288 DOI: 10.1007/s13659-020-00293-7] [Citation(s) in RCA: 231] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 12/15/2020] [Indexed: 05/02/2023]
Abstract
Natural products, with remarkable chemical diversity, have been extensively investigated for their anticancer potential for more than a half-century. The collective efforts of the community have achieved the tremendous advancements, bringing natural products to clinical use and discovering new therapeutic opportunities, yet the challenges remain ahead. With remarkable changes in the landscape of cancer therapy and growing role of cutting-edge technologies, we may have come to a crossroads to revisit the strategies to understand nature products and to explore their therapeutic utility. This review summarizes the key advancements in nature product-centered cancer research and calls for the implementation of systematic approaches, new pharmacological models, and exploration of emerging directions to revitalize natural products search in cancer therapy.
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Affiliation(s)
- Min Huang
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jin-Jian Lu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Jian Ding
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
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Prihoda D, Maritz JM, Klempir O, Dzamba D, Woelk CH, Hazuda DJ, Bitton DA, Hannigan GD. The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability. Nat Prod Rep 2021; 38:1100-1108. [PMID: 33245088 DOI: 10.1039/d0np00055h] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas, machine learning has excelled in natural language processing. As such methods have excelled at understanding written languages (e.g. English), they are also being applied to biological problems to better understand the "genomic language". In this review we focus on recent advances in applying machine learning to natural products and genomics, and how those advances are improving our understanding of natural product biology, chemistry, and drug discovery. We discuss machine learning applications in genome mining (identifying biosynthetic signatures in genomic data), predictions of what structures will be created from those genomic signatures, and the types of activity we might expect from those molecules. We further explore the application of these approaches to data derived from complex microbiomes, with a focus on the human microbiome. We also review challenges in leveraging machine learning approaches in the field, and how the availability of other "omics" data layers provides value. Finally, we provide insights into the challenges associated with interpreting machine learning models and the underlying biology and promises of applying machine learning to natural product drug discovery. We believe that the application of machine learning methods to natural product research is poised to accelerate the identification of new molecular entities that may be used to treat a variety of disease indications.
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Affiliation(s)
- David Prihoda
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague, Czech Republic and Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Julia M Maritz
- Exploratory Science Center, Merck & Co., Inc., Cambridge, MA, USA.
| | - Ondrej Klempir
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague, Czech Republic
| | - David Dzamba
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague, Czech Republic
| | | | - Daria J Hazuda
- Exploratory Science Center, Merck & Co., Inc., Cambridge, MA, USA.
| | - Danny A Bitton
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague, Czech Republic
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Maglangit F, Yu Y, Deng H. Bacterial pathogens: threat or treat (a review on bioactive natural products from bacterial pathogens). Nat Prod Rep 2021; 38:782-821. [PMID: 33119013 DOI: 10.1039/d0np00061b] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Covering: up to the second quarter of 2020 Threat or treat? While pathogenic bacteria pose significant threats, they also represent a huge reservoir of potential pharmaceuticals to treat various diseases. The alarming antimicrobial resistance crisis and the dwindling clinical pipeline urgently call for the discovery and development of new antibiotics. Pathogenic bacteria have an enormous potential for natural products drug discovery, yet they remained untapped and understudied. Herein, we review the specialised metabolites isolated from entomopathogenic, phytopathogenic, and human pathogenic bacteria with antibacterial and antifungal activities, highlighting those currently in pre-clinical trials or with potential for drug development. Selected unusual biosynthetic pathways, the key roles they play (where known) in various ecological niches are described. We also provide an overview of the mode of action (molecular target), activity, and minimum inhibitory concentration (MIC) towards bacteria and fungi. The exploitation of pathogenic bacteria as a rich source of antimicrobials, combined with the recent advances in genomics and natural products research methodology, could pave the way for a new golden age of antibiotic discovery. This review should serve as a compendium to communities of medicinal chemists, organic chemists, natural product chemists, biochemists, clinical researchers, and many others interested in the subject.
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Affiliation(s)
- Fleurdeliz Maglangit
- Department of Biology and Environmental Science, College of Science, University of the Philippines Cebu, Lahug, Cebu City, 6000, Philippines. and Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, UK.
| | - Yi Yu
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (MOE), Hubei Province Engineering and Technology Research Centre for Fluorinated Pharmaceuticals, School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China.
| | - Hai Deng
- Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, UK.
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Yoo S, Yang HC, Lee S, Shin J, Min S, Lee E, Song M, Lee D. A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds. Front Pharmacol 2020; 11:584875. [PMID: 33519445 PMCID: PMC7845697 DOI: 10.3389/fphar.2020.584875] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 11/06/2020] [Indexed: 12/25/2022] Open
Abstract
Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several in vitro methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost. To overcome these limitations, several in silico methods have been proposed for conducting large-scale analysis, but they are still limited in terms of dealing with incomplete and heterogeneous natural compound data. Here, we propose a deep learning-based approach to identify the medicinal uses of natural compounds by exploiting massive and heterogeneous drug and natural compound data. The rationale behind this approach is that deep learning can effectively utilize heterogeneous features to alleviate incomplete information. Based on latent knowledge, molecular interactions, and chemical property features, we generated 686 dimensional features for 4,507 natural compounds and 2,882 approved and investigational drugs. The deep learning model was trained using the generated features and verified drug indication information. When the features of natural compounds were applied as input to the trained model, potential efficacies were successfully predicted with high accuracy, sensitivity, and specificity.
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Affiliation(s)
- Sunyong Yoo
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - Hyung Chae Yang
- Department of Otorhinolaryngology-Head and Neck Surgery, Chonnam National University Medical School and Chonnam National University Hospital, Gwangju, South Korea
| | - Seongyeong Lee
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - Jaewook Shin
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - Seyoung Min
- School of Electronics and Computer Engineering, Chonnam National University, Gwangju, South Korea
| | - Eunjoo Lee
- Big Data Steering Department, National Health Insurance Service, Wonju, South Korea
| | - Minkeun Song
- Department of Physical and Rehabilitation Medicine, Research Institute of Medical Science, Cardiovascular Research Institute, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Doheon Lee
- Bio-Synergy Research Center, Daejeon, South Korea
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
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45
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Medina-Franco JL, Saldívar-González FI. Cheminformatics to Characterize Pharmacologically Active Natural Products. Biomolecules 2020; 10:E1566. [PMID: 33213003 PMCID: PMC7698493 DOI: 10.3390/biom10111566] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/11/2020] [Accepted: 11/14/2020] [Indexed: 12/19/2022] Open
Abstract
Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.
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Affiliation(s)
- José L. Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico;
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Capecchi A, Reymond JL. Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning. Biomolecules 2020; 10:E1385. [PMID: 32998475 PMCID: PMC7600738 DOI: 10.3390/biom10101385] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 09/22/2020] [Accepted: 09/25/2020] [Indexed: 12/20/2022] Open
Abstract
Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.
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Affiliation(s)
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland;
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