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Manochkumar J, Cherukuri AK, Kumar RS, Almansour AI, Ramamoorthy S, Efferth T. A critical review of machine-learning for "multi-omics" marine metabolite datasets. Comput Biol Med 2023; 165:107425. [PMID: 37696182 DOI: 10.1016/j.compbiomed.2023.107425] [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: 05/30/2023] [Revised: 07/12/2023] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.
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Affiliation(s)
- Janani Manochkumar
- School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
| | - Raju Suresh Kumar
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Abdulrahman I Almansour
- Department of Chemistry, College of Science, King Saud University, P. O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, India.
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany.
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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: 11] [Impact Index Per Article: 11.0] [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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Bocharova OA, Ionov NS, Kazeev IV, Shevchenko VE, Bocharov EV, Karpova RV, Sheychenko OP, Aksyonov AA, Chulkova SV, Kucheryanu VG, Revishchin AV, Pavlova GV, Kosorukov VS, Filimonov DA, Lagunin AA, Matveev VB, Pyatigorskaya NV, Stilidi IS, Poroikov VV. Computer-aided Evaluation of Polyvalent Medications' Pharmacological Potential. Multiphytoadaptogen as a Case Study. Mol Inform 2023; 42:e2200176. [PMID: 36075866 DOI: 10.1002/minf.202200176] [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: 07/23/2022] [Accepted: 09/08/2022] [Indexed: 01/12/2023]
Abstract
Many human diseases including cancer, degenerative and autoimmune disorders, diabetes and others are multifactorial. Pharmaceutical agents acting on a single target do not provide their efficient curation. Multitargeted drugs exhibiting pleiotropic pharmacological effects have certain advantages due to the normalization of the complex pathological processes of different etiology. Extracts of medicinal plants (EMP) containing multiple phytocomponents are widely used in traditional medicines for multifactorial disorders' treatment. Experimental studies of pharmacological potential for multicomponent compositions are quite expensive and time-consuming. In silico evaluation of EMP the pharmacological potential may provide the basis for selecting the most promising directions of testing and for identifying potential additive/synergistic effects. Multiphytoadaptogen (MPhA) containing 70 major phytocomponents of different chemical classes from 40 medicinal plant extracts has been studied in vitro, in vivo and in clinical researches. Antiproliferative and anti-tumor activities have been shown against some tumors as well as evidence-based therapeutic effects against age-related pathologies. In addition, the neuroprotective, antioxidant, antimutagenic, radioprotective, and immunomodulatory effects of MPhA were confirmed. Analysis of the PASS profiles of the biological activity of MPhA phytocomponents showed that most of the predicted anti-tumor and anti-metastatic effects were consistent with the results of laboratory and clinical studies. Antimutagenic, immunomodulatory, radioprotective, neuroprotective and anti-Parkinsonian effects were also predicted for most of the phytocomponents. Effects associated with positive effects on the male and female reproductive systems have been identified too. Thus, PASS and PharmaExpert can be used to evaluate the pharmacological potential of complex pharmaceutical compositions containing natural products.
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Affiliation(s)
- O A Bocharova
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - N S Ionov
- Institute of Biomedical Chemistry, 10, Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - I V Kazeev
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - V E Shevchenko
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - E V Bocharov
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - R V Karpova
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - O P Sheychenko
- All-Russian Scientific Research Institute of Medicinal and Aromatic Plants, 7 Grin Str., Moscow, 117216, Russia
| | - A A Aksyonov
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - S V Chulkova
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - V G Kucheryanu
- Research Institute of General Pathology and Pathophysiology, 8, Baltiyskaya Str., Moscow, 125315, Russia
| | - A V Revishchin
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, 5A Butlerova Str., Moscow, 117485, Russia
| | - G V Pavlova
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, 5A Butlerova Str., Moscow, 117485, Russia.,Sechenov First Moscow State Medical University (Sechenov University), 8, Trubetskaya Str., Moscow, 119991, Russia
| | - V S Kosorukov
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - D A Filimonov
- Institute of Biomedical Chemistry, 10, Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - A A Lagunin
- Institute of Biomedical Chemistry, 10, Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
| | - V B Matveev
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - N V Pyatigorskaya
- Sechenov First Moscow State Medical University (Sechenov University), 8, Trubetskaya Str., Moscow, 119991, Russia
| | - I S Stilidi
- Blokhin National Medical Research Center of Oncology, Kashirskoe shosse 24, Moscow, 115478, Russia
| | - V V Poroikov
- Institute of Biomedical Chemistry, 10, Bldg. 8, Pogodinskaya Str., Moscow, 119121, Russia
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A Brief Review of Machine Learning-Based Bioactive Compound Research. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12062906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Bioactive compounds are often used as initial substances for many therapeutic agents. In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired bioactive compounds in chemical spaces, such as those in natural products (NPs). This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds. It also provides an overview of recent research trends in machine learning-based prediction and the evaluation of bioactive compounds by listing real-world examples along with various input data. In addition, several ML-based approaches to identify specific bioactive compounds for cardiovascular and metabolic diseases are described. Overall, these approaches are important for the discovery of novel bioactive compounds and provide new insights into the machine learning basis for various traditional applications of bioactive compound-related research.
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Capecchi A, Reymond JL. Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning. J Cheminform 2021; 13:82. [PMID: 34663470 PMCID: PMC8524952 DOI: 10.1186/s13321-021-00559-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/02/2021] [Indexed: 01/13/2023] Open
Abstract
Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin ( https://tm.gdb.tools/map4/coconut_tmap/ ), and a support vector machine (SVM) trained with MAP4 correctly assigned the origin for 94% of plant, 89% of fungal, and 89% of bacterial NPs in this subset. An online tool based on an SVM trained with the entire subset correctly assigned the origin of further NPs with similar performance ( https://np-svm-map4.gdb.tools/ ). Origin information might be useful when searching for biosynthetic genes of NPs isolated from plants but produced by endophytic microorganisms.
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Affiliation(s)
- Alice Capecchi
- 1 Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland
| | - Jean-Louis Reymond
- 1 Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland.
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