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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [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: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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2
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Dey S, Samadder A, Nandi S. Current Role of Nanotechnology Used in Food Processing Industry to Control Food Additives and Exploring Their Biochemical Mechanisms. Curr Drug Targets 2021; 23:513-539. [PMID: 34915833 DOI: 10.2174/1389450123666211216150355] [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: 03/16/2021] [Revised: 07/25/2021] [Accepted: 09/02/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND With the advent of food additives centuries ago, the human race has found ways to improve and maintain the safety of utility, augment the taste, color, texture, nutritional value, and appearance of the food. Since the 19th century, when the science behind food spoilage was discerned, the use of food additives in food preservation has been increasing worldwide and at a fast pace to get along with modern lifestyles. Although food additives are thought to be used to benefit the food market, some of them are found to be associated with several health issues at an alarming rate. Studies are still going on regarding the mechanisms by which food additives affect public health. Therefore, an attempt has been made to find out the remedies by exploiting technologies that may convey new properties of food additives that can only enhance the quality of food without having any systemic side effects. Thus, this review focuses on the applications of nanotechnology in the production of nano-food additives and evaluates its success regarding reduction in the health-related hazards collaterally maintaining the food nutrient value. METHODOLOGY A thorough literature study was performed using scientific databases like PubMed, Science Direct, Scopus, Web of Science for determining the design of the study, and each article was checked for citation and referred to formulate the present review article. CONCLUSION Nanotechnology can be applied in the food processing industry to control the unregulated use of food additives and to intervene in the biochemical mechanisms at a cellular and physiological level for the ensuring safety of food products. The prospective of nano-additive of chemical origin could be useful to reduce risks of hazards related to human health that are caused majorly due to the invasion of food contaminants (either intentional or non-intentional) into food, though this area still needs scientific validation. Therefore, this review provides comprehensive knowledge on different facets of food contaminants and also serves as a platform of ideas for encountering health risk problems about the design of improved versions of nano-additives.
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Affiliation(s)
- Sudatta Dey
- Cytogenetics and Molecular Biology Laboratory, Department of Zoology, University of Kalyani, Kalyani, Nadia-741235. India
| | - Asmita Samadder
- Cytogenetics and Molecular Biology Laboratory, Department of Zoology, University of Kalyani, Kalyani, Nadia-741235. India
| | - Sisir Nandi
- Department of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research (GIPER) (Affiliated to Uttarakhand Technical University). Kashipur-244713. India
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3
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Moumbock AFA, Li J, Tran HTT, Hinkelmann R, Lamy E, Jessen HJ, Günther S. ePharmaLib: A Versatile Library of e-Pharmacophores to Address Small-Molecule (Poly-)Pharmacology. J Chem Inf Model 2021; 61:3659-3666. [PMID: 34236848 DOI: 10.1021/acs.jcim.1c00135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Bioactive compounds oftentimes bind to several target proteins, thereby exhibiting polypharmacology. Experimentally determining these interactions is however laborious, and structure-based virtual screening (SBVS) of bioactive compounds could expedite drug discovery by prioritizing hits for experimental validation. Here, we present ePharmaLib, a library of 15,148 e-pharmacophores modeled from solved structures of pharmaceutically relevant protein-ligand complexes of the screening Protein Data Bank (sc-PDB). ePharmaLib can be used for target fishing of phenotypic hits, side effect predictions, drug repurposing, and scaffold hopping. In retrospective SBVS, a good balance was obtained between computational efficiency and predictive accuracy. As a proof of concept, we carried out prospective SBVS in conjunction with a photometric assay, which inferred that the mechanism of action of neopterin (an endogenous immunomodulator) putatively stems from its inhibition (IC50 = 18 μM) of the human purine nucleoside phosphorylase. This ready-to-use library is freely available at http://www.pharmbioinf.uni-freiburg.de/epharmalib.
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Affiliation(s)
- Aurélien F A Moumbock
- Institute of Pharmaceutical Sciences, Faculty of Chemistry and Pharmacy, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany.,Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, Albert-Ludwigs-Universität Freiburg, Albertstraße 21, D-79104 Freiburg, Germany
| | - Jianyu Li
- Institute of Pharmaceutical Sciences, Faculty of Chemistry and Pharmacy, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
| | - Hoai T T Tran
- Institute of Pharmaceutical Sciences, Faculty of Chemistry and Pharmacy, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany.,Molecular Preventive Medicine, University Medical Center and Faculty of Medicine, Albert-Ludwigs-Universität Freiburg, Engesserstaße 4, D-79108 Freiburg, Germany
| | - Rahel Hinkelmann
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, Albert-Ludwigs-Universität Freiburg, Albertstraße 21, D-79104 Freiburg, Germany
| | - Evelyn Lamy
- Molecular Preventive Medicine, University Medical Center and Faculty of Medicine, Albert-Ludwigs-Universität Freiburg, Engesserstaße 4, D-79108 Freiburg, Germany
| | - Henning J Jessen
- Institute of Organic Chemistry, Faculty of Chemistry and Pharmacy, Albert-Ludwigs-Universität Freiburg, Albertstraße 21, D-79104 Freiburg, Germany
| | - Stefan Günther
- Institute of Pharmaceutical Sciences, Faculty of Chemistry and Pharmacy, Albert-Ludwigs-Universität Freiburg, Hermann-Herder-Straße 9, D-79104 Freiburg, Germany
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4
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Tortosa V, Pietropaolo V, Brandi V, Macari G, Pasquadibisceglie A, Polticelli F. Computational Methods for the Identification of Molecular Targets of Toxic Food Additives. Butylated Hydroxytoluene as a Case Study. Molecules 2020; 25:E2229. [PMID: 32397407 PMCID: PMC7248939 DOI: 10.3390/molecules25092229] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 11/24/2022] Open
Abstract
Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use of this synthetic food additive. In addition, the estimated daily intake of BHT has been demonstrated to exceed the recommended acceptable threshold. In the present work, using BHT as a case study, the usefulness of computational techniques, such as reverse screening and molecular docking, in identifying protein-ligand interactions of food additives at the bases of their toxicological effects has been probed. The computational methods here employed have been useful for the identification of several potential unknown targets of BHT, suggesting a possible explanation for its toxic effects. In silico analyses can be employed to identify new macromolecular targets of synthetic food additives and to explore their functional mechanisms or side effects. Noteworthy, this could be important for the cases in which there is an evident lack of experimental studies, as is the case for BHT.
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Affiliation(s)
- Valentina Tortosa
- Department of Sciences, Roma Tre University, 00146 Rome, Italy; (V.T.); (V.P.); (V.B.); (G.M.); (A.P.)
| | - Valentina Pietropaolo
- Department of Sciences, Roma Tre University, 00146 Rome, Italy; (V.T.); (V.P.); (V.B.); (G.M.); (A.P.)
| | - Valentina Brandi
- Department of Sciences, Roma Tre University, 00146 Rome, Italy; (V.T.); (V.P.); (V.B.); (G.M.); (A.P.)
| | - Gabriele Macari
- Department of Sciences, Roma Tre University, 00146 Rome, Italy; (V.T.); (V.P.); (V.B.); (G.M.); (A.P.)
| | - Andrea Pasquadibisceglie
- Department of Sciences, Roma Tre University, 00146 Rome, Italy; (V.T.); (V.P.); (V.B.); (G.M.); (A.P.)
| | - Fabio Polticelli
- Department of Sciences, Roma Tre University, 00146 Rome, Italy; (V.T.); (V.P.); (V.B.); (G.M.); (A.P.)
- National Institute of Nuclear Physics, Roma Tre University, 00146 Rome, Italy
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5
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Djikic T, Vucicevic J, Laurila J, Radi M, Veljkovic N, Xhaard H, Nikolic K. Deciphering Imidazoline Off‐targets by Fishing in the Class A of GPCR field. Mol Inform 2020; 39:e1900165. [DOI: 10.1002/minf.201900165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 02/20/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Teodora Djikic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| | - Jelica Vucicevic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| | - Jonne Laurila
- Research Center for Integrative Physiology and Pharmacology, Institute of BiomedicineUniversity of Turku FI-20014 Turun yliopisto, Turku Finland
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del FarmacoUniversità degli Studi di Parma Viale delle Scienze, 27/A 43124 Parma Italy
| | - Nevena Veljkovic
- Laboratory for bioinformatics and computational chemistry, Institute of Nuclear Sciences VincaUniversity of Belgrade Mihaila Petrovica Alasa 14 11001 Belgrade Serbia
| | - Henri Xhaard
- Division of Pharmaceutical Chemistry, Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of PharmacyUniversity of Helsinki P.O. Box 56 FI-00014 Helsinki Finland
| | - Katarina Nikolic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
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7
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Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev 2019; 151-152:169-190. [PMID: 31071378 DOI: 10.1016/j.addr.2019.05.001] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 04/14/2019] [Accepted: 05/02/2019] [Indexed: 02/07/2023]
Abstract
Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.
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Affiliation(s)
- Parichehr Hassanzadeh
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| | - Fatemeh Atyabi
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
| | - Rassoul Dinarvand
- Nanotechnology Research Center, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran 13169-43551, Iran.
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8
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Moumbock AF, Li J, Mishra P, Gao M, Günther S. Current computational methods for predicting protein interactions of natural products. Comput Struct Biotechnol J 2019; 17:1367-1376. [PMID: 31762960 PMCID: PMC6861622 DOI: 10.1016/j.csbj.2019.08.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 08/09/2019] [Accepted: 08/23/2019] [Indexed: 01/08/2023] Open
Abstract
Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.
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Affiliation(s)
| | | | | | | | - Stefan Günther
- Institute of Pharmaceutical Sciences, Research Group Pharmaceutical Bioinformatics, Albert-Ludwigs-Universität Freiburg, Germany
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9
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De Vita S, Lauro G, Ruggiero D, Terracciano S, Riccio R, Bifulco G. Protein Preparation Automatic Protocol for High-Throughput Inverse Virtual Screening: Accelerating the Target Identification by Computational Methods. J Chem Inf Model 2019; 59:4678-4690. [DOI: 10.1021/acs.jcim.9b00428] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Simona De Vita
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Gianluigi Lauro
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Dafne Ruggiero
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Stefania Terracciano
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Raffaele Riccio
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Giuseppe Bifulco
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
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10
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Lapillo M, Tuccinardi T, Martinelli A, Macchia M, Giordano A, Poli G. Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies. Int J Mol Sci 2019; 20:ijms20051023. [PMID: 30818741 PMCID: PMC6429110 DOI: 10.3390/ijms20051023] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 01/03/2023] Open
Abstract
The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.
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Affiliation(s)
| | | | | | - Marco Macchia
- Department of Pharmacy, University of Pisa, 56126 Pisa, Italy.
| | - Antonio Giordano
- Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA.
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy.
| | - Giulio Poli
- Department of Pharmacy, University of Pisa, 56126 Pisa, Italy.
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van der Krieken SE, Popeijus HE, Bendik I, Böhlendorf B, Konings MCJM, Tayyeb J, Mensink RP, Plat J. Large-Scale Screening of Natural Products Transactivating Peroxisome Proliferator-Activated Receptor α Identifies 9S-Hydroxy-10E,12Z,15Z-Octadecatrienoic Acid and Cymarin as Potential Compounds Capable of Increasing Apolipoprotein A-I Transcription in Hum. Lipids 2019; 53:1021-1030. [DOI: 10.1002/lipd.12116] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 10/18/2018] [Accepted: 11/27/2018] [Indexed: 01/16/2023]
Affiliation(s)
- Sophie E. van der Krieken
- NUTRIM School of Nutrition and Translational Research in Metabolism, Department of Nutrition and Movement Sciences; Maastricht University; PO Box 616, 6200 MD, Maastricht The Netherlands
| | - Herman E. Popeijus
- NUTRIM School of Nutrition and Translational Research in Metabolism, Department of Nutrition and Movement Sciences; Maastricht University; PO Box 616, 6200 MD, Maastricht The Netherlands
| | - Igor Bendik
- DSM Nutritional Products Ltd, Research and Development, Human Nutrition and Health; PO Box 2676, Basel Switzerland
| | - Bettina Böhlendorf
- DSM Nutritional Products Ltd, Research and Development, Human Nutrition and Health; PO Box 2676, Basel Switzerland
| | - Maurice C. J. M. Konings
- NUTRIM School of Nutrition and Translational Research in Metabolism, Department of Nutrition and Movement Sciences; Maastricht University; PO Box 616, 6200 MD, Maastricht The Netherlands
| | - Jehad Tayyeb
- NUTRIM School of Nutrition and Translational Research in Metabolism, Department of Nutrition and Movement Sciences; Maastricht University; PO Box 616, 6200 MD, Maastricht The Netherlands
| | - Ronald P. Mensink
- NUTRIM School of Nutrition and Translational Research in Metabolism, Department of Nutrition and Movement Sciences; Maastricht University; PO Box 616, 6200 MD, Maastricht The Netherlands
| | - Jogchum Plat
- NUTRIM School of Nutrition and Translational Research in Metabolism, Department of Nutrition and Movement Sciences; Maastricht University; PO Box 616, 6200 MD, Maastricht The Netherlands
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12
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Huang H, Zhang G, Zhou Y, Lin C, Chen S, Lin Y, Mai S, Huang Z. Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds. Front Chem 2018; 6:138. [PMID: 29868550 PMCID: PMC5954125 DOI: 10.3389/fchem.2018.00138] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 04/09/2018] [Indexed: 12/13/2022] Open
Abstract
This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.
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Affiliation(s)
- Hongbin Huang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Guigui Zhang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Yuquan Zhou
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Chenru Lin
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Suling Chen
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Yutong Lin
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Shangkang Mai
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Zunnan Huang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
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Patel H, Brinkjost T, Koch O. PyGOLD: a python based API for docking based virtual screening workflow generation. Bioinformatics 2018; 33:2589-2590. [PMID: 28398502 DOI: 10.1093/bioinformatics/btx197] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 04/05/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Molecular docking is one of the successful approaches in structure based discovery and development of bioactive molecules in chemical biology and medicinal chemistry. Due to the huge amount of computational time that is still required, docking is often the last step in a virtual screening approach. Such screenings are set as workflows spanned over many steps, each aiming at different filtering task. These workflows can be automatized in large parts using python based toolkits except for docking using the docking software GOLD. However, within an automated virtual screening workflow it is not feasible to use the GUI in between every step to change the GOLD configuration file. Thus, a python module called PyGOLD was developed, to parse, edit and write the GOLD configuration file and to automate docking based virtual screening workflows. Availability and Implementation The latest version of PyGOLD, its documentation and example scripts are available at: http://www.ccb.tu-dortmund.de/koch or http://www.agkoch.de. PyGOLD is implemented in Python and can be imported as a standard python module without any further dependencies. Contact oliver.koch@agkoch.de, oliver.koch@tu-dortmund.de. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology.,Department of Computer Science, TU Dortmund University, 44227 Dortmund, Germany
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14
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The potential role of in silico approaches to identify novel bioactive molecules from natural resources. Future Med Chem 2017; 9:1665-1686. [PMID: 28841048 DOI: 10.4155/fmc-2017-0124] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].
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Maccari G, Deodato D, Fiorucci D, Orofino F, Truglio GI, Pasero C, Martini R, De Luca F, Docquier JD, Botta M. Design and synthesis of a novel inhibitor of T. Viride chitinase through an in silico target fishing protocol. Bioorg Med Chem Lett 2017; 27:3332-3336. [PMID: 28610983 DOI: 10.1016/j.bmcl.2017.06.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 06/01/2017] [Accepted: 06/03/2017] [Indexed: 12/31/2022]
Abstract
In the last ten years, we identified and developed a new therapeutic class of antifungal agents, the macrocyclic amidinoureas. These compounds are active against several Candida species, including clinical isolates resistant to currently available antifungal drugs. The mode of action of these molecules is still unknown. In this work, we developed an in-silico target fishing procedure to identify a possible target for this class of compounds based on shape similarity, inverse docking procedure and consensus score rank-by-rank. Chitinase enzyme emerged as possible target. To confirm this hypothesis a novel macrocyclic derivative has been produced, specifically designed to increase the inhibition of the chitinase. Biological evaluation highlights a stronger enzymatic inhibition for the new derivative, while its antifungal activity drops probably because of pharmacokinetic issues. Collectively, our data suggest that chitinase represent at least one of the main target of macrocyclic amidinoureas.
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Affiliation(s)
- Giorgio Maccari
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy
| | - Davide Deodato
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy
| | - Diego Fiorucci
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy
| | - Francesco Orofino
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy
| | - Giuseppina I Truglio
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy
| | - Carolina Pasero
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy
| | - Riccardo Martini
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy
| | - Filomena De Luca
- Department of Medical Biotechnology, University of Siena, I-53100 Siena, Italy
| | - Jean-Denis Docquier
- Department of Medical Biotechnology, University of Siena, I-53100 Siena, Italy; Lead Discovery Siena s.r.l, Via Vittorio Alfieri 31, I-53019 Castelnuovo Berardenga, Italy
| | - Maurizio Botta
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy; Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, BioLife Science Building, Suite 333, 1900 N 12th Street, Philadelphia, PA 19122, USA; Lead Discovery Siena s.r.l, Via Vittorio Alfieri 31, I-53019 Castelnuovo Berardenga, Italy.
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16
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Shaikh N, Sharma M, Garg P. An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking. MOLECULAR BIOSYSTEMS 2016; 12:1006-14. [PMID: 26822863 DOI: 10.1039/c5mb00650c] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Proteochemometric (PCM) methods, which use descriptors of both the interacting species, i.e. drug and the target, are being successfully employed for the prediction of drug-target interactions (DTI). However, unavailability of non-interacting dataset and determining the applicability domain (AD) of model are a main concern in PCM modeling. In the present study, traditional PCM modeling was improved by devising novel methodologies for reliable negative dataset generation and fingerprint based AD analysis. In addition, various types of descriptors and classifiers were evaluated for their performance. The Random Forest and Support Vector Machine models outperformed the other classifiers (accuracies >98% and >89% for 10-fold cross validation and external validation, respectively). The type of protein descriptors had negligible effect on the developed models, encouraging the use of sequence-based descriptors over the structure-based descriptors. To establish the practical utility of built models, targets were predicted for approved anticancer drugs of natural origin. The molecular recognition interactions between the predicted drug-target pair were quantified with the help of a reverse molecular docking approach. The majority of predicted targets are known for anticancer therapy. These results thus correlate well with anticancer potential of the selected drugs. Interestingly, out of all predicted DTIs, thirty were found to be reported in the ChEMBL database, further validating the adopted methodology. The outcome of this study suggests that the proposed approach, involving use of the improved PCM methodology and molecular docking, can be successfully employed to elucidate the intricate mode of action for drug molecules as well as repositioning them for new therapeutic applications.
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Affiliation(s)
- Naeem Shaikh
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
| | - Mahesh Sharma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), S. A. S. Nagar, Punjab 160062, India.
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17
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Nikolic K, Mavridis L, Djikic T, Vucicevic J, Agbaba D, Yelekci K, Mitchell JBO. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies. Front Neurosci 2016; 10:265. [PMID: 27375423 PMCID: PMC4901078 DOI: 10.3389/fnins.2016.00265] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/25/2016] [Indexed: 11/13/2022] Open
Abstract
HIGHLIGHTSMany CNS targets are being explored for multi-target drug design New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds QSAR, virtual screening and docking methods increase the potential of rational drug design
The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D1-R/D2-R/5-HT2A-R/H3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.
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Affiliation(s)
- Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Lazaros Mavridis
- School of Biological and Chemical Sciences, Queen Mary University of London London, UK
| | - Teodora Djikic
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University Istanbul, Turkey
| | - Jelica Vucicevic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Danica Agbaba
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Kemal Yelekci
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University Istanbul, Turkey
| | - John B O Mitchell
- EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews St Andrews, UK
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18
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Xie H, Wen H, Qin M, Xia J, Zhang D, Liu L, Liu B, Liu Q, Jin Q, Chen X. In silico drug repositioning for the treatment of Alzheimer's disease using molecular docking and gene expression data. RSC Adv 2016. [DOI: 10.1039/c6ra21941a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
We provided a computational drug repositioning method for the treatment of Alzheimer's disease.
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Affiliation(s)
- Hongbo Xie
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Haixia Wen
- Department of Physiology
- Harbin Medical University
- Harbin
- P. R. China
| | - Mingze Qin
- School of Pharmaceutical Engineering
- Shenyang Pharmaceutical University
- Shenyang 110016
- P. R. China
| | - Jie Xia
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Denan Zhang
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Lei Liu
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Bo Liu
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Qiuqi Liu
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Qing Jin
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
| | - Xiujie Chen
- College of Bioinformatics Science and Technology
- Harbin Medical University
- Harbin 150081
- P. R. China
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