1
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Ancajas CMF, Oyedele AS, Butt CM, Walker AS. Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products. Nat Prod Rep 2024; 41:1543-1578. [PMID: 38912779 PMCID: PMC11484176 DOI: 10.1039/d4np00009a] [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: 02/27/2024] [Indexed: 06/25/2024]
Abstract
Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.
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Affiliation(s)
| | | | - Caitlin M Butt
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
| | - Allison S Walker
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA.
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
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2
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Ouchene R, Zaatout N, Suzuki MT. An Overview on Nocardiopsis Species Originating From North African Biotopes as a Promising Source of Bioactive Compounds and In Silico Genome Mining Analysis of Three Sequenced Genomes. J Basic Microbiol 2024; 64:e2400046. [PMID: 38934516 DOI: 10.1002/jobm.202400046] [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: 01/30/2024] [Revised: 05/21/2024] [Accepted: 06/01/2024] [Indexed: 06/28/2024]
Abstract
Actinobacteria are renowned for their prolific production of diverse bioactive secondary metabolites. In recent years, there has been an increasing focus on exploring "rare" genera within this phylum for biodiscovery purposes, notably the Nocardiopsis genus, which will be the subject of the present study. Recognizing the absence of articles describing the research process of finding bioactive molecules from the genus Nocardiopsis in North African environments. We, therefore, present a historical overview of the discoveries of bioactive molecules of the genus Nocardiopsis originating from the region, highlighting their biological activities and associated reported molecules, providing a snapshot of the current state of the field, and offering insights into future opportunities and challenges for drug discovery. Additionally, we present a genome mining analysis of three genomes deposited in public databases that have been reported to be bioactive. A total of 36 biosynthetic gene clusters (BGCs) were identified, including those known to encode bioactive molecules. Notably, a substantial portion of the BGCs showed little to no similarity to those previously described, suggesting the possibility that the analyzed strains could be potential producers of new compounds. Further research on these genomes is essential to fully uncovering their biotechnological potential. Moving forward, we discuss the experimental designs adopted in the reported studies, as well as new avenues to guide the exploration of the Nocardiopsis genus in North Africa.
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Affiliation(s)
- Rima Ouchene
- Laboratoire de Microbiologie Appliquée (LMA), Faculté des Sciences de la Nature et de la Vie, Université de Bejaia, Bejaia, Algeria
- CNRS, Laboratoire de Biodiversité et Biotechnologies Microbiennes, LBBM, Sorbonne Université, Paris, France
| | - Nawel Zaatout
- Faculty of Natural and Life Sciences, University of Batna, Batna, Algeria
| | - Marcelino T Suzuki
- CNRS, Laboratoire de Biodiversité et Biotechnologies Microbiennes, LBBM, Sorbonne Université, Paris, France
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3
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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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4
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Sabotič J, Bayram E, Ezra D, Gaudêncio SP, Haznedaroğlu BZ, Janež N, Ktari L, Luganini A, Mandalakis M, Safarik I, Simes D, Strode E, Toruńska-Sitarz A, Varamogianni-Mamatsi D, Varese GC, Vasquez MI. A guide to the use of bioassays in exploration of natural resources. Biotechnol Adv 2024; 71:108307. [PMID: 38185432 DOI: 10.1016/j.biotechadv.2024.108307] [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: 07/24/2023] [Revised: 12/05/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Bioassays are the main tool to decipher bioactivities from natural resources thus their selection and quality are critical for optimal bioprospecting. They are used both in the early stages of compounds isolation/purification/identification, and in later stages to evaluate their safety and efficacy. In this review, we provide a comprehensive overview of the most common bioassays used in the discovery and development of new bioactive compounds with a focus on marine bioresources. We present a comprehensive list of practical considerations for selecting appropriate bioassays and discuss in detail the bioassays typically used to explore antimicrobial, antibiofilm, cytotoxic, antiviral, antioxidant, and anti-ageing potential. The concept of quality control and bioassay validation are introduced, followed by safety considerations, which are critical to advancing bioactive compounds to a higher stage of development. We conclude by providing an application-oriented view focused on the development of pharmaceuticals, food supplements, and cosmetics, the industrial pipelines where currently known marine natural products hold most potential. We highlight the importance of gaining reliable bioassay results, as these serve as a starting point for application-based development and further testing, as well as for consideration by regulatory authorities.
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Affiliation(s)
- Jerica Sabotič
- Department of Biotechnology, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.
| | - Engin Bayram
- Institute of Environmental Sciences, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - David Ezra
- Department of Plant Pathology and Weed Research, ARO, The Volcani Institute, P.O.Box 15159, Rishon LeZion 7528809, Israel
| | - Susana P Gaudêncio
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal; UCIBIO - Applied Biomolecular Sciences Unit, Department of Chemistry, Blue Biotechnology & Biomedicine Lab, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Berat Z Haznedaroğlu
- Institute of Environmental Sciences, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Nika Janež
- Department of Biotechnology, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
| | - Leila Ktari
- B3Aqua Laboratory, National Institute of Marine Sciences and Technologies, Carthage University, Tunis, Tunisia
| | - Anna Luganini
- Department of Life Sciences and Systems Biology, University of Turin, 10123 Turin, Italy
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Heraklion, Greece
| | - Ivo Safarik
- Department of Nanobiotechnology, Biology Centre, ISBB, CAS, Na Sadkach 7, 370 05 Ceske Budejovice, Czech Republic; Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute, Palacky University, Slechtitelu 27, 783 71 Olomouc, Czech Republic
| | - Dina Simes
- Centre of Marine Sciences (CCMAR), Universidade do Algarve, 8005-139 Faro, Portugal; 2GenoGla Diagnostics, Centre of Marine Sciences (CCMAR), Universidade do Algarve, Faro, Portugal
| | - Evita Strode
- Latvian Institute of Aquatic Ecology, Agency of Daugavpils University, Riga LV-1007, Latvia
| | - Anna Toruńska-Sitarz
- Department of Marine Biology and Biotechnology, Faculty of Oceanography and Geography, University of Gdańsk, 81-378 Gdynia, Poland
| | - Despoina Varamogianni-Mamatsi
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Heraklion, Greece
| | | | - Marlen I Vasquez
- Department of Chemical Engineering, Cyprus University of Technology, 3036 Limassol, Cyprus
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5
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Oselusi SO, Dube P, Odugbemi AI, Akinyede KA, Ilori TL, Egieyeh E, Sibuyi NR, Meyer M, Madiehe AM, Wyckoff GJ, Egieyeh SA. The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials. Comput Biol Med 2024; 169:107927. [PMID: 38184864 DOI: 10.1016/j.compbiomed.2024.107927] [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: 09/06/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.
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Affiliation(s)
- Samson O Oselusi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Phumuzile Dube
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Adeshina I Odugbemi
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, 7535, South Africa
| | - Kolajo A Akinyede
- Department of Science Technology, Biochemistry Unit, The Federal Polytechnic P.M.B.5351, Ado Ekiti, 360231, Nigeria
| | - Tosin L Ilori
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Elizabeth Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Nicole Rs Sibuyi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Mervin Meyer
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Abram M Madiehe
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Gerald J Wyckoff
- School of Pharmacy, Division of Pharmacology and Pharmaceutical Sciences, University of Missouri, Kansas City, MO, 64110-2446, United States
| | - Samuel A Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa.
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6
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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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7
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Gaudêncio SP, Bayram E, Lukić Bilela L, Cueto M, Díaz-Marrero AR, Haznedaroglu BZ, Jimenez C, Mandalakis M, Pereira F, Reyes F, Tasdemir D. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar Drugs 2023; 21:md21050308. [PMID: 37233502 DOI: 10.3390/md21050308] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.
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Affiliation(s)
- Susana P Gaudêncio
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- UCIBIO-Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Engin Bayram
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Lada Lukić Bilela
- Department of Biology, Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mercedes Cueto
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
| | - Ana R Díaz-Marrero
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
- Instituto Universitario de Bio-Orgánica (IUBO), Universidad de La Laguna, 38206 La Laguna, Spain
| | - Berat Z Haznedaroglu
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Carlos Jimenez
- CICA- Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spain
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, HCMR Thalassocosmos, 71500 Gournes, Crete, Greece
| | - Florbela Pereira
- LAQV, REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Fernando Reyes
- Fundación MEDINA, Avda. del Conocimiento 34, 18016 Armilla, Spain
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology (GEOMAR-Biotech), Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Am Kiel-Kanal 44, 24106 Kiel, Germany
- Faculty of Mathematics and Natural Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
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8
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Abstract
As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models-namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high-quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.
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Affiliation(s)
- Telmah Lluka
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
| | - Jonathan M Stokes
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada
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9
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Kaya Y, Demirci B, Uğurlu Aydın Z, Oybak Dönmez E, Baser KHC, Dönmez AA. Structural similarities of phytochemicals significantly contribute to species delimitation of Nigella and Garidella (Ranunculaceae). JOURNAL OF ESSENTIAL OIL RESEARCH 2022. [DOI: 10.1080/10412905.2022.2147591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Yasin Kaya
- Department of Biology, Faculty of Science, Hacettepe University, Ankara, Turkey
| | - Betül Demirci
- Department of Pharmacognosy, Faculty of Pharmacy, Anadolu University, Eskişehir, Turkey
| | | | - Emel Oybak Dönmez
- Department of Biology, Faculty of Science, Hacettepe University, Ankara, Turkey
| | - Kemal Hüsnü Can Baser
- Department of Pharmacognosy, Faculty of Pharmacy, Near East University, Nicosia, Northern, Cyprus
| | - Ali A. Dönmez
- Department of Biology, Faculty of Science, Hacettepe University, Ankara, Turkey
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10
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Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE. PubChem 2023 update. Nucleic Acids Res 2022; 51:D1373-D1380. [PMID: 36305812 PMCID: PMC9825602 DOI: 10.1093/nar/gkac956] [Citation(s) in RCA: 748] [Impact Index Per Article: 374.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 01/30/2023] Open
Abstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the 'standardize' option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.
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Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jie Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Asta Gindulyte
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jia He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Siqian He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Benjamin A Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Bo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Leonid Zaslavsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, 20894, USA
| | - Evan E Bolton
- To whom correspondence should be addressed. Tel: +1 301 451 1811; Fax: +1 301 480 4559;
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11
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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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12
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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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13
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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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14
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Gaudêncio SP, Pereira F. Predicting Antifouling Activity and Acetylcholinesterase Inhibition of Marine-Derived Compounds Using a Computer-Aided Drug Design Approach. Mar Drugs 2022; 20:md20020129. [PMID: 35200658 PMCID: PMC8879326 DOI: 10.3390/md20020129] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/28/2022] [Accepted: 02/06/2022] [Indexed: 11/19/2022] Open
Abstract
Biofouling is the undesirable growth of micro- and macro-organisms on artificial water-immersed surfaces, which results in high costs for the prevention and maintenance of this process (billion €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructure. To date, there are still no sustainable, economical and environmentally safe solutions to overcome this challenging phenomenon. A computer-aided drug design (CADD) approach comprising ligand- and structure-based methods was explored for predicting the antifouling activities of marine natural products (MNPs). In the CADD ligand-based method, 141 organic molecules extracted from the ChEMBL database and literature with antifouling screening data were used to build the quantitative structure–activity relationship (QSAR) classification model. An overall predictive accuracy score of up to 71% was achieved with the best QSAR model for external and internal validation using test and training sets. A virtual screening campaign of 14,492 MNPs from Encinar’s website and 14 MNPs that are currently in the clinical pipeline was also carried out using the best QSAR model developed. In the CADD structure-based approach, the 125 MNPs that were selected by the QSAR approach were used in molecular docking experiments against the acetylcholinesterase enzyme. Overall, 16 MNPs were proposed as the most promising marine drug-like leads as antifouling agents, e.g., macrocyclic lactam, macrocyclic alkaloids, indole and pyridine derivatives.
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Affiliation(s)
- Susana P. Gaudêncio
- Associate Laboratory i4HB—Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal;
- UCIBIO—Applied Molecular Biosciences Unit, Department of Chemistry, Blue Biotechnology and Biomedicine Lab, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Florbela Pereira
- LAQV, Department of Chemistry, NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
- Correspondence:
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15
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Wu W, Liu M, Geng JJ, Wang M. Teicoplanin combined with conventional vancomycin therapy for the treatment of pulmonary methicillin-resistant Staphylococcus aureus and Staphylococcus epidermidis infections. World J Clin Cases 2021; 9:10549-10556. [PMID: 35004986 PMCID: PMC8686121 DOI: 10.12998/wjcc.v9.i34.10549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/24/2021] [Accepted: 10/14/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Vancomycin and teicoplanin are both antibiotics that have significant antimicrobial effects on Gram-positive cocci.
AIM To explore the value of teicoplanin combined with conventional (vancomycin only) anti-infective therapy for the treatment of methicillin-resistant Staphylococcus aureus and Staphylococcus epidermidis pulmonary infections.
METHODS A total of 86 patients with methicillin-resistant Staphylococcus aureus or methicillin-resistant Staphylococcus epidermidis pulmonary infections, treated in our hospital between January 2018 and February 2020, were assigned to the study and control groups using a random number table method, with 43 patients in each group. The control group received conventional treatment (vancomycin), and the study group received both teicoplanin and conventional treatment. The following indicators were assessed in both groups: the time required for symptom relief, treatment effectiveness, serum levels of inflammatory factors (procalcitonin, interleukin-1β, tumor necrosis factor-α, C-reactive protein), clinical pulmonary infection scores before and after treatment, and the incidence of adverse reactions.
RESULTS Patients in the study group were observed to have faster cough and expectoration resolution, white blood cell count normalization, body temperature normalization, and rales disappearance than patients in the control group (all P < 0.05); the total rate of effectiveness was 93.02% in the study group, higher than the 76.74% in the control group (P < 0.05). The pre-treatment serum levels of procalcitonin, interleukin-1β, tumor necrosis factor-α, and C-reactive protein as well as the clinical pulmonary infection scores were similar among the patients in both groups. However, the post-treatment serum levels of procalcitonin, interleukin-1β, tumor necrosis factor-α, and C-reactive protein as well as the clinical pulmonary infection scores were significantly lower in the study group than in the control group (P < 0.05). There was no significant difference in the incidence of adverse reactions between the groups.
CONCLUSION Compared with conventional (vancomycin only) therapy, teicoplanin and vancomycin combination therapy for patients with pulmonary methicillin-resistant Staphylococcus aureus and methicillin-resistant Staphylococcus epidermidis infections can improve patient clinical symptoms, modulate serum inflammatory factor levels, and improve treatment efficacy, without increasing the risk of adverse reactions.
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Affiliation(s)
- Wei Wu
- Laboratory Medicine, Bejing Tongren Hospital, Capital Medical University, Beijing 100176, China
| | - Min Liu
- Department of General Practice, The Community Health Services Center in Lumen, Beijing 100080, China
| | - Jia-Jing Geng
- Laboratory Medicine, Bejing Tongren Hospital, Capital Medical University, Beijing 100176, China
| | - Mei Wang
- Laboratory Medicine, Bejing Tongren Hospital, Capital Medical University, Beijing 100176, China
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16
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Zuo W, Kwok HF. Development of Marine-Derived Compounds for Cancer Therapy. Mar Drugs 2021; 19:md19060342. [PMID: 34203870 PMCID: PMC8232666 DOI: 10.3390/md19060342] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/31/2021] [Accepted: 06/11/2021] [Indexed: 12/16/2022] Open
Abstract
Cancer has always been a threat to human health with its high morbidity and mortality rates. Traditional therapy, including surgery, chemotherapy and radiotherapy, plays a key role in cancer treatment. However, it is not able to prevent tumor recurrence, drug resistance and treatment side effects, which makes it a very attractive challenge to search for new effective and specific anticancer drugs. Nature is a valuable source of multiple pharmaceuticals, and most of the anticancer drugs are natural products or derived from them. Marine-derived compounds, such as nucleotides, proteins, peptides and amides, have also shed light on cancer therapy, and they are receiving a fast-growing interest due to their bioactive properties. Their mechanisms contain anti-angiogenic, anti-proliferative and anti-metastasis activities; cell cycle arrest; and induction of apoptosis. This review provides an overview on the development of marine-derived compounds with anticancer properties, both their applications and mechanisms, and discovered technologies.
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Affiliation(s)
- Weimin Zuo
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macao;
| | - Hang Fai Kwok
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macao;
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macao
- Correspondence:
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17
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Jin Y, Wang Z, Dong AY, Huang YQ, Hao GF, Song BA. Web repositories of natural agents promote pests and pathogenic microbes management. Brief Bioinform 2021; 22:6294160. [PMID: 34098581 DOI: 10.1093/bib/bbab205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 12/30/2022] Open
Abstract
The grand challenge to meet the increasing demands for food by a rapidly growing global population requires protecting crops from pests. Natural active substances play a significant role in the sustainable pests and pathogenic microbes management. In recent years, natural products- (NPs), antimicrobial peptides- (AMPs), medicinal plant- and plant essential oils (EOs)-related online resources have greatly facilitated the development of pests and pathogenic microbes control agents in an efficient and economical manner. However, a comprehensive comparison, analysis and summary of these existing web resources are still lacking. Here, we surveyed these databases of NPs, AMPs, medicinal plants and plant EOs with insecticidal, antibacterial, antiviral and antifungal activity, and we compared their functionality, data volume, data sources and applicability. We comprehensively discussed the limitation of these web resources. This study provides a toolbox for bench scientists working in the pesticide, botany, biomedical and pharmaceutical engineering fields. The aim of the review is to hope that these web resources will facilitate the discovery and development of potential active ingredients of pests and pathogenic microbes control agents.
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Affiliation(s)
- Yin Jin
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Zheng Wang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - An-Yu Dong
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Yuan-Qin Huang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Ge-Fei Hao
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
| | - Bao-An Song
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China
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18
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Atanasov AG, Zotchev SB, Dirsch VM, Supuran CT. Natural products in drug discovery: advances and opportunities. Nat Rev Drug Discov 2021; 20:200-216. [PMID: 33510482 PMCID: PMC7841765 DOI: 10.1038/s41573-020-00114-z] [Citation(s) in RCA: 1907] [Impact Index Per Article: 635.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2020] [Indexed: 02/07/2023]
Abstract
Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments - including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances - are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.
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Affiliation(s)
- Atanas G Atanasov
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland.
- Department of Pharmacognosy, University of Vienna, Vienna, Austria.
- Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia, Bulgaria.
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
| | - Sergey B Zotchev
- Department of Pharmacognosy, University of Vienna, Vienna, Austria
| | - Verena M Dirsch
- Department of Pharmacognosy, University of Vienna, Vienna, Austria
| | - Claudiu T Supuran
- Università degli Studi di Firenze, NEUROFARBA Dept, Sezione di Scienze Farmaceutiche, Florence, Italy.
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19
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Moumbock AFA, Gao M, Qaseem A, Li J, Kirchner P, Ndingkokhar B, Bekono BD, Simoben CV, Babiaka S, Malange YI, Sauter F, Zierep P, Ntie-Kang F, Günther S. StreptomeDB 3.0: an updated compendium of streptomycetes natural products. Nucleic Acids Res 2021; 49:D600-D604. [PMID: 33051671 PMCID: PMC7779017 DOI: 10.1093/nar/gkaa868] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/21/2022] Open
Abstract
Antimicrobial resistance is an emerging global health threat necessitating the rapid development of novel antimicrobials. Remarkably, the vast majority of currently available antibiotics are natural products (NPs) isolated from streptomycetes, soil-dwelling bacteria of the genus Streptomyces. However, there is still a huge reservoir of streptomycetes NPs which remains pharmaceutically untapped and a compendium thereof could serve as a source of inspiration for the rational design of novel antibiotics. Initially released in 2012, StreptomeDB (http://www.pharmbioinf.uni-freiburg.de/streptomedb) is the first and only public online database that enables the interactive phylogenetic exploration of streptomycetes and their isolated or mutasynthesized NPs. In this third release, there are substantial improvements over its forerunners, especially in terms of data content. For instance, about 2500 unique NPs were newly annotated through manual curation of about 1300 PubMed-indexed articles, published in the last five years since the second release. To increase interoperability, StreptomeDB entries were hyperlinked to several spectral, (bio)chemical and chemical vendor databases, and also to a genome-based NP prediction server. Moreover, predicted pharmacokinetic and toxicity profiles were added. Lastly, some recent real-world use cases of StreptomeDB are highlighted, to illustrate its applicability in life sciences.
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Affiliation(s)
- Aurélien F A Moumbock
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Mingjie Gao
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Ammar Qaseem
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Jianyu Li
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Pascal A Kirchner
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Bakoh Ndingkokhar
- Department of Organic Chemistry, University of Yaoundé I, P. O. Box 812, Yaoundé, Cameroon
| | - Boris D Bekono
- Department of Physics, Higher Teacher Training College, University of Yaoundé I, P. O. Box 47, Yaoundé, Cameroon
| | - Conrad V Simoben
- Department of Pharmaceutical Chemistry, Martin-Luther-Universität Halle-Wittenberg, Wolfgang-Langenbeck Straße 4, D-06120 Halle (Saale), Germany
| | - Smith B Babiaka
- Department of Chemistry, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Yvette I Malange
- Department of Chemistry, University of Buea, P. O. Box 63, Buea, Cameroon
| | - Florian Sauter
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Paul Zierep
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Fidele Ntie-Kang
- Department of Pharmaceutical Chemistry, Martin-Luther-Universität Halle-Wittenberg, Wolfgang-Langenbeck Straße 4, D-06120 Halle (Saale), Germany
- Department of Chemistry, University of Buea, P. O. Box 63, Buea, Cameroon
- Institute of Botany, Technische Universität Dresden, Zellescher Weg 20b, D-01217 Dresden, Germany
| | - Stefan Günther
- Institute of Pharmaceutical Sciences, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
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20
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Zhang R, Li X, Zhang X, Qin H, Xiao W. Machine learning approaches for elucidating the biological effects of natural products. Nat Prod Rep 2021; 38:346-361. [PMID: 32869826 DOI: 10.1039/d0np00043d] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.
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Affiliation(s)
- Ruihan Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xiaoli Li
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xingjie Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Huayan Qin
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Weilie Xiao
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
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21
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Carroll AR, Copp BR, Davis RA, Keyzers RA, Prinsep MR. Marine natural products. Nat Prod Rep 2021; 38:362-413. [PMID: 33570537 DOI: 10.1039/d0np00089b] [Citation(s) in RCA: 198] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
This review covers the literature published in 2019 for marine natural products (MNPs), with 719 citations (701 for the period January to December 2019) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms. The emphasis is on new compounds (1490 in 440 papers for 2019), together with the relevant biological activities, source organisms and country of origin. Pertinent reviews, biosynthetic studies, first syntheses, and syntheses that led to the revision of structures or stereochemistries, have been included. Methods used to study marine fungi and their chemical diversity have also been discussed.
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Affiliation(s)
- Anthony R Carroll
- School of Environment and Science, Griffith University, Gold Coast, Australia. and Griffith Institute for Drug Discovery, Griffith University, Brisbane, Australia
| | - Brent R Copp
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Rohan A Davis
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Australia and School of Enivironment and Science, Griffith University, Brisbane, Australia
| | - Robert A Keyzers
- Centre for Biodiscovery, School of Chemical and Physical Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Michèle R Prinsep
- Chemistry, School of Science, University of Waikato, Hamilton, New Zealand
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22
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Gaudêncio SP, Pereira F. A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition. Mar Drugs 2020; 18:E633. [PMID: 33322052 PMCID: PMC7764804 DOI: 10.3390/md18120633] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/04/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure-activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.
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Affiliation(s)
- Susana P. Gaudêncio
- Blue Biotechnology & Biomedicine Lab, UCIBIO—Applied Biomolecular Sciences Unit, Department of Chemistry, Faculty of Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal;
| | - Florbela Pereira
- LAQV, Department of Chemistry, Faculty for Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
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23
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Modelling the Anti-Methicillin-Resistant Staphylococcus Aureus (MRSA) Activity of Cannabinoids: A QSAR and Docking Study. CRYSTALS 2020. [DOI: 10.3390/cryst10080692] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Twenty-four cannabinoids active against MRSA SA1199B and XU212 were optimized at WB97XD/6-31G(d,p), and several molecular descriptors were obtained. Using a multiple linear regression method, several mathematical models with statistical significance were obtained. The robustness of the models was validated, employing the leave-one-out cross-validation and Y-scrambling methods. The entire data set was docked against penicillin-binding protein, iso-tyrosyl tRNA synthetase, and DNA gyrase. The most active cannabinoids had high affinity to penicillin-binding protein (PBP), whereas the least active compounds had low affinities for all of the targets. Among the cannabinoid compounds, Cannabinoid 2 was highlighted due to its suitable combination of both antimicrobial activity and higher scoring values against the selected target; therefore, its docking performance was compared to that of oxacillin, a commercial PBP inhibitor. The 2D figures reveal that both compounds hit the protein in the active site with a similar type of molecular interaction, where the hydroxyl groups in the aromatic ring of cannabinoids play a pivotal role in the biological activity. These results provide some evidence that the anti-Staphylococcus aureus activity of these cannabinoids may be related to the inhibition of the PBP protein; besides, the robustness of the models along with the docking and Quantitative Structure–Activity Relationship (QSAR) results allow the proposal of three new compounds; the predicted activity combined with the scoring values against PBP should encourage future synthesis and experimental testing.
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24
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The Biotechnological Potential of Secondary Metabolites from Marine Bacteria. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2019. [DOI: 10.3390/jmse7060176] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Marine habitats are a rich source of molecules of biological interest. In particular, marine bacteria attract attention with their ability to synthesize structurally diverse classes of bioactive secondary metabolites with high biotechnological potential. The last decades were marked by numerous discoveries of biomolecules of bacterial symbionts, which have long been considered metabolites of marine animals. Many compounds isolated from marine bacteria are unique in their structure and biological activity. Their study has made a significant contribution to the discovery and production of new natural antimicrobial agents. Identifying the mechanisms and potential of this type of metabolite production in marine bacteria has become one of the noteworthy trends in modern biotechnology. This path has become not only one of the most promising approaches to the development of new antibiotics, but also a potential target for controlling the viability of pathogenic bacteria.
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