1
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Luo Q, Wang S, Li HY, Zheng L, Mu Y, Guo J. Benchmarking reverse docking through AlphaFold2 human proteome. Protein Sci 2024; 33:e5167. [PMID: 39276010 PMCID: PMC11400627 DOI: 10.1002/pro.5167] [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: 05/24/2024] [Revised: 08/21/2024] [Accepted: 08/24/2024] [Indexed: 09/16/2024]
Abstract
Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.
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Affiliation(s)
- Qing Luo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
| | - Sheng Wang
- Shanghai Zelixir Biotech Company Ltd., China
| | - Hoi Yeung Li
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Liangzhen Zheng
- Shenzhen Zelixir Biotech Company Ltd., China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, Singapore
| | - Jingjing Guo
- Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
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2
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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024; 19:1043-1069. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [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: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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3
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Shim HS, Iaconelli J, Shang X, Li J, Lan ZD, Jiang S, Nutsch K, Beyer BA, Lairson LL, Boutin AT, Bollong MJ, Schultz PG, DePinho RA. TERT activation targets DNA methylation and multiple aging hallmarks. Cell 2024; 187:4030-4042.e13. [PMID: 38908367 DOI: 10.1016/j.cell.2024.05.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 04/03/2024] [Accepted: 05/23/2024] [Indexed: 06/24/2024]
Abstract
Insufficient telomerase activity, stemming from low telomerase reverse transcriptase (TERT) gene transcription, contributes to telomere dysfunction and aging pathologies. Besides its traditional function in telomere synthesis, TERT acts as a transcriptional co-regulator of genes pivotal in aging and age-associated diseases. Here, we report the identification of a TERT activator compound (TAC) that upregulates TERT transcription via the MEK/ERK/AP-1 cascade. In primary human cells and naturally aged mice, TAC-induced elevation of TERT levels promotes telomere synthesis, blunts tissue aging hallmarks with reduced cellular senescence and inflammatory cytokines, and silences p16INK4a expression via upregulation of DNMT3B-mediated promoter hypermethylation. In the brain, TAC alleviates neuroinflammation, increases neurotrophic factors, stimulates adult neurogenesis, and preserves cognitive function without evident toxicity, including cancer risk. Together, these findings underscore TERT's critical role in aging processes and provide preclinical proof of concept for physiological TERT activation as a strategy to mitigate multiple aging hallmarks and associated pathologies.
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Affiliation(s)
- Hong Seok Shim
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jonathan Iaconelli
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Xiaoying Shang
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jiexi Li
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zheng D Lan
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shan Jiang
- Institute for Applied Cancer Science, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kayla Nutsch
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Brittney A Beyer
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Luke L Lairson
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Adam T Boutin
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Michael J Bollong
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Peter G Schultz
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Ronald A DePinho
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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4
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Stefan SM, Rafehi M. Medicinal polypharmacology-a scientific glossary of terminology and concepts. Front Pharmacol 2024; 15:1419110. [PMID: 39092220 PMCID: PMC11292611 DOI: 10.3389/fphar.2024.1419110] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 04/30/2024] [Indexed: 08/04/2024] Open
Abstract
Medicinal polypharmacology is one answer to the complex reality of multifactorial human diseases that are often unresponsive to single-targeted treatment. It is an admittance that intrinsic feedback mechanisms, crosstalk, and disease networks necessitate drugs with broad modes-of-action and multitarget affinities. Medicinal polypharmacology grew to be an independent research field within the last two decades and stretches from basic drug development to clinical research. It has developed its own terminology embedded in general terms of pharmaceutical drug discovery and development at the intersection of medicinal chemistry, chemical biology, and clinical pharmacology. A clear and precise language of critical terms and a thorough understanding of underlying concepts is imperative; however, no comprehensive work exists to this date that could support researchers in this and adjacent research fields. In order to explore novel options, establish interdisciplinary collaborations, and generate high-quality research outputs, the present work provides a first-in-field glossary to clarify the numerous terms that have originated from various individual disciplines.
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Affiliation(s)
- Sven Marcel Stefan
- Medicinal Chemistry and Systems Polypharmacology, Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology (LIED), University of Lübeck and University Medical Center Schleswig-Holstein (UKSH), Lübeck, Germany
- Department of Biopharmacy, Medical University of Lublin, Lublin, Poland
| | - Muhammad Rafehi
- Institute of Clinical Pharmacology, University Medical Center Göttingen, Göttingen, Germany
- Department of Medical Education, Augsburg University Medicine, Augsburg, Germany
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5
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Brennan RJ, Jenkinson S, Brown A, Delaunois A, Dumotier B, Pannirselvam M, Rao M, Ribeiro LR, Schmidt F, Sibony A, Timsit Y, Sales VT, Armstrong D, Lagrutta A, Mittlestadt SW, Naven R, Peri R, Roberts S, Vergis JM, Valentin JP. The state of the art in secondary pharmacology and its impact on the safety of new medicines. Nat Rev Drug Discov 2024; 23:525-545. [PMID: 38773351 DOI: 10.1038/s41573-024-00942-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2024] [Indexed: 05/23/2024]
Abstract
Secondary pharmacology screening of investigational small-molecule drugs for potentially adverse off-target activities has become standard practice in pharmaceutical research and development, and regulatory agencies are increasingly requesting data on activity against targets with recognized adverse effect relationships. However, the screening strategies and target panels used by pharmaceutical companies may vary substantially. To help identify commonalities and differences, as well as to highlight opportunities for further optimization of secondary pharmacology assessment, we conducted a broad-ranging survey across 18 companies under the auspices of the DruSafe leadership group of the International Consortium for Innovation and Quality in Pharmaceutical Development. Based on our analysis of this survey and discussions and additional research within the group, we present here an overview of the current state of the art in secondary pharmacology screening. We discuss best practices, including additional safety-associated targets not covered by most current screening panels, and present approaches for interpreting and reporting off-target activities. We also provide an assessment of the safety impact of secondary pharmacology screening, and a perspective on opportunities and challenges in this rapidly developing field.
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Affiliation(s)
| | | | | | | | | | | | - Mohan Rao
- Janssen Research & Development, San Diego, CA, USA
- Neurocrine Biosciences, San Diego, CA, USA
| | - Lyn Rosenbrier Ribeiro
- UCB Biopharma, Braine-l'Alleud, Belgium
- AstraZeneca, Cambridge, UK
- Grunenthal, Berkshire, UK
| | | | | | - Yoav Timsit
- Novartis Biomedical Research, Cambridge, MA, USA
- Blueprint Medicines, Cambridge, MA, USA
| | | | - Duncan Armstrong
- Novartis Biomedical Research, Cambridge, MA, USA
- Armstrong Pharmacology, Macclesfield, UK
| | | | | | - Russell Naven
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Novartis Biomedical Research, Cambridge, MA, USA
| | - Ravikumar Peri
- Takeda Pharmaceuticals, Cambridge, MA, USA
- Alexion Pharmaceuticals, Wilmington, DE, USA
| | - Sonia Roberts
- Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - James M Vergis
- Faegre Drinker Biddle and Reath, LLP, Washington, DC, USA
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6
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Stefan K, Namasivayam V, Stefan SM. Computer-aided pattern scoring - A multitarget dataset-driven workflow to predict ligands of orphan targets. Sci Data 2024; 11:530. [PMID: 38783061 PMCID: PMC11116543 DOI: 10.1038/s41597-024-03343-8] [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: 02/28/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
The identification of lead molecules and the exploration of novel pharmacological drug targets are major challenges of medical life sciences today. Genome-wide association studies, multi-omics, and systems pharmacology steadily reveal new protein networks, extending the known and relevant disease-modifying proteome. Unfortunately, the vast majority of the disease-modifying proteome consists of 'orphan targets' of which intrinsic ligands/substrates, (patho)physiological roles, and/or modulators are unknown. Undruggability is a major challenge in drug development today, and medicinal chemistry efforts cannot keep up with hit identification and hit-to-lead optimization studies. New 'thinking-outside-the-box' approaches are necessary to identify structurally novel and functionally distinctive ligands for orphan targets. Here we present a unique dataset that includes critical information on the orphan target ABCA1, from which a novel cheminformatic workflow - computer-aided pattern scoring (C@PS) - for the identification of novel ligands was developed. Providing a hit rate of 95.5% and molecules with high potency and molecular-structural diversity, this dataset represents a suitable template for general deorphanization studies.
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Affiliation(s)
- Katja Stefan
- University of Oslo and Oslo University Hospital, Department of Pathology, Rikshospitalet, Sognsvannsveien 20, 0372, Oslo, Norway
- University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck Institute of Experimental Dermatology, Medical Systems Biology Division, Medicinal Chemistry and Systems Polypharmacology, Ratzeburger Allee 160, 23538, Lübeck, Germany
| | - Vigneshwaran Namasivayam
- University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck Institute of Experimental Dermatology, Medical Systems Biology Division, Medicinal Chemistry and Systems Polypharmacology, Ratzeburger Allee 160, 23538, Lübeck, Germany.
- University of Bonn, Pharmaceutical Institute, Department of Pharmaceutical and Cellbiological Chemistry, An der Immenburg 4, 53121, Bonn, Germany.
| | - Sven Marcel Stefan
- University of Oslo and Oslo University Hospital, Department of Pathology, Rikshospitalet, Sognsvannsveien 20, 0372, Oslo, Norway.
- University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck Institute of Experimental Dermatology, Medical Systems Biology Division, Medicinal Chemistry and Systems Polypharmacology, Ratzeburger Allee 160, 23538, Lübeck, Germany.
- Medical University of Lublin, Department of Biopharmacy, Chodzki 4a, 20-093, Lublin, Poland.
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7
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Rafehi M, Möller M, Ismail Al-Khalil W, Stefan SM. Medicinal Polypharmacology in the Clinic - Translating the Polypharmacolome into Therapeutic Benefit. Pharm Res 2024; 41:411-417. [PMID: 38366233 DOI: 10.1007/s11095-024-03656-8] [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: 10/21/2023] [Accepted: 01/07/2024] [Indexed: 02/18/2024]
Abstract
Drugs with multiple targets, often annotated as 'unselective', 'promiscuous', 'multitarget', or 'polypharmacological', are widely considered in both academic and industrial research as a high risk due to the likelihood of adverse effects. However, retrospective analyses have shown that particularly approved drugs bear rich polypharmacological profiles. This raises the question whether our perception of the specificity paradigm ('one drug-one target concept') is correct - and if specifically multitarget drugs should be developed instead of being rejected. These questions provoke a paradigm shift - regarding the development of polypharmacological drugs not as a 'waste of investment', but acknowledging the existence of a 'lack of investment'. This perspective provides an insight into modern drug development highlighting latest drug candidates that have not been assessed in a broader polypharmacology-based context elsewhere embedded in a historic framework of classical and modern approved multitarget drugs. The article shall be an inspiration to the scientific community to re-consider current standards, and more, to evolve to a better understanding of polypharmacology from a challenge to an opportunity.
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Affiliation(s)
- Muhammad Rafehi
- Department of Medical Education Augsburg, Augsburg University Medicine, Stenglinstr. 2, 86156, Augsburg, Germany.
- Institute of Clinical Pharmacology, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Germany.
| | - Marius Möller
- Medical Systems Biology Group, Lübeck Institute of Experimental Dermatology (LIED), University of Lübeck and University Medical Center Schleswig-Holstein, Ratzeburger Allee 160, 23538, Lübeck, Germany
| | - Wouroud Ismail Al-Khalil
- Institute of Clinical Pharmacology, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Germany
| | - Sven Marcel Stefan
- Medicinal Chemistry and Systems Polypharmacology, Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology (LIED), University of Lübeck and University Medical Center Schleswig-Holstein, Ratzeburger Allee 160, 23538, Lübeck, Germany.
- Department of Pathology, University of Oslo and Oslo University Hospital, Sognsvannsveien 20, 0372, Oslo, Norway.
- Department of Biopharmacy, Medical University of Lublin, Chodzki 4a, Lublin, 20-093, Poland.
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8
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Stefan SM, Rafehi M. Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development. Drug Dev Res 2024; 85:e22125. [PMID: 37920929 DOI: 10.1002/ddr.22125] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/23/2023] [Accepted: 10/12/2023] [Indexed: 11/04/2023]
Abstract
At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.
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Affiliation(s)
- Sven Marcel Stefan
- Drug Development and Chemical Biology, Lübeck Institute of Experimental Dermatology (LIED), University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany
- Translational Neurodegeneration Research and Neuropathology Lab, Department of Pathology, Section of Neuropathology and Oslo University Hospital, University of Oslo, Oslo, Norway
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
| | - Muhammad Rafehi
- Department of Medical Education, Augsburg University Medicine, Augsburg, Germany
- Institute of Clinical Pharmacology, University Medical Center Göttingen, Göttingen, Germany
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9
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Fan M, Jin C, Li D, Deng Y, Yao L, Chen Y, Ma YL, Wang T. Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review. Front Pharmacol 2023; 14:1289901. [PMID: 38035021 PMCID: PMC10682728 DOI: 10.3389/fphar.2023.1289901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023] Open
Abstract
The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.
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Affiliation(s)
- Mengyue Fan
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ching Jin
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, United States
| | - Daping Li
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yingshan Deng
- College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lin Yao
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yongjun Chen
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yu-Ling Ma
- Oxford Chinese Medicine Research Centre, University of Oxford, Oxford, United Kingdom
| | - Taiyi Wang
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
- Oxford Chinese Medicine Research Centre, University of Oxford, Oxford, United Kingdom
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10
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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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11
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Su Y, Bai Q, Tao H, Xu B. Prospects for the application of traditional Chinese medicine network pharmacology in food science research. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023. [PMID: 36882903 DOI: 10.1002/jsfa.12541] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
There has always been a particular difficulty with in-depth research on the mechanisms of food nutrition and bioactivity. The main function of food is to meet the nutritional needs of the human body, rather than to exert a therapeutic effect. Its relatively modest biological activity makes it difficult to study from the perspective of general pharmacological models. With the popularity of functional foods and the concept of dietary therapy, and the development of information and multi-omics technology in food research, research into these mechanisms is moving towards a more microscopic future. Network pharmacology has accumulated nearly 20 years of research experience in traditional Chinese medicine (TCM), and there has been no shortage of work from this perspective on the medicinal functions of food. Given the similarity between the concept of 'multi-component-multi-target' properties of food and TCM, we think that network pharmacology is applicable to the study of the complex mechanisms of food. Here we review the development of network pharmacology, summarize its application to 'medicine and food homology', and propose a methodology based on food characteristics for the first time, demonstrating its feasibility for food research. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yuanyuan Su
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Qiong Bai
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Hongxun Tao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Bin Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
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12
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Lightfoot HL, Smith GF. Targeting RNA with small molecules-A safety perspective. Br J Pharmacol 2023. [PMID: 36631428 DOI: 10.1111/bph.16027] [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: 02/24/2022] [Revised: 06/30/2022] [Accepted: 12/20/2022] [Indexed: 01/13/2023] Open
Abstract
RNA is a major player in cellular function, and consequently can drive a number of disease pathologies. Over the past several years, small molecule-RNA targeting (smRNA targeting) has developed into a promising drug discovery approach. Numerous techniques, tools, and assays have been developed to support this field, and significant investments have been made by pharmaceutical and biotechnology companies. To date, the focus has been on identifying disease validated primary targets for smRNA drug development, yet RNA as a secondary (off) target for all small molecule drug programs largely has been unexplored. In this perspective, we discuss structure, target, and mechanism-driven safety aspects of smRNAs and highlight how these parameters can be evaluated in drug discovery programs to produce potentially safer drugs.
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Affiliation(s)
- Helen L Lightfoot
- Safety and Mechanistic Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Graham F Smith
- Data Science and AI, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
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13
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Ona G, Berrada A, Bouso JC. Communalistic use of psychoactive plants as a bridge between traditional healing practices and Western medicine: A new path for the Global Mental Health movement. Transcult Psychiatry 2022; 59:638-651. [PMID: 34665080 DOI: 10.1177/13634615211038416] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The Global Mental Health (GMH) movement aims to provide urgently needed treatment to those with mental illness, especially in low- and middle-income countries. Due to the complexity of providing mental health services to people from various cultures, there is much debate among GMH advocates regarding the best way to proceed. While biomedical interventions offer some degree of help, complementary approaches should focus on the social/community aspects. Many cultures conduct traditional rituals involving the communal use of psychoactive plants. We propose that these practices should be respected, protected, and promoted as valuable tools with regard to mental health care at the community level. The traditional use of psychoactive plants promotes community engagement and participation, and they are relatively affordable. Furthermore, the worldviews and meaning-making systems of local population are respected. The medical systems surrounding the use of psychoactive plants can be explained in biomedical terms, and many recently published clinical trials have demonstrated their therapeutic potential. Psychoactive plants and associated rituals offer potential benefits as complementary aspects of mental health services. They should be considered as such by international practitioners and advocates of the GMH movement.
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Affiliation(s)
- Genís Ona
- ICEERS - International Center for Ethnobotanical Education, Research, and Service, Barcelona, Spain.,Department of Anthropology, Philosophy and Social Work, Medical Anthropology Research Center (MARC), 16777Universitat Rovira i Virgili, Tarragona, Spain
| | - Ali Berrada
- Unidad de Medicina Interna, 16548Hospital del Mar, Barcelona, Spain
| | - José Carlos Bouso
- ICEERS - International Center for Ethnobotanical Education, Research, and Service, Barcelona, Spain.,Department of Anthropology, Philosophy and Social Work, Medical Anthropology Research Center (MARC), 16777Universitat Rovira i Virgili, Tarragona, Spain
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14
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Ona G, Balant M, Bouso JC, Gras A, Vallès J, Vitales D, Garnatje T. The Use of Cannabis sativa L. for Pest Control: From the Ethnobotanical Knowledge to a Systematic Review of Experimental Studies. Cannabis Cannabinoid Res 2022; 7:365-387. [PMID: 34612729 PMCID: PMC9418361 DOI: 10.1089/can.2021.0095] [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] [Indexed: 11/12/2022] Open
Abstract
Background: Despite the benefits that synthetic pesticides have provided in terms of pest and disease control, they cause serious long-term consequences for both the environment and living organisms. Interest in eco-friendly products has subsequently increased in recent years. Methods: This article briefly analyzes the available ethnobotanical evidence regarding the use of Cannabis sativa as a pesticide and offers a systematic review of experimental studies. Results: Our findings indicate that both ethnobotanical and experimental procedures support the use of C. sativa as a pesticide, as remarkable toxicity has been observed against pest organisms. The results included in the systematic review of experimental studies (n=30) show a high degree of heterogeneity, but certain conclusions can be extracted to guide further research. For instance, promising pesticide properties were reported for most of the groups of species tested, especially Arachnida and Insecta; the efficacy of C. sativa as a pesticide can be derived from a wide variety of compounds that it contains and possible synergistic effects; it is crucial to standardize the phytochemical profile of C. sativa plants used as well as to obtain easily reproducible results; appropriate extraction methods should be explored; and upper inflorescences of the plant may be preferred for the production of the essential oil, but further studies should explore better other parts of the plant. Conclusion: In the coming years, as new findings are produced, the promising potential of C. sativa as a pesticide will be elucidated, and reviews such as the present one constitute useful basic tools to make these processes easier.
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Affiliation(s)
- Genís Ona
- International Center for Ethnobotanical Education, Research, and Service (ICEERS), Barcelona, Catalonia, Spain
- Medical Anthropology Research Center (MARC), Universitat Rovira i Virgili, Tarragona, Catalonia, Spain
- Department of Psychology and Research Center for Behavior Assessment (CRAMC), Universitat Rovira i Virgili, Tarragona, Catalonia, Spain
- Institut Botànic de Barcelona (IBB, CSIC-Ajuntament de Barcelona), Barcelona, Catalonia, Spain
| | - Manica Balant
- Institut Botànic de Barcelona (IBB, CSIC-Ajuntament de Barcelona), Barcelona, Catalonia, Spain
- Laboratori de Botànica (UB)—Unitat associada al CSIC, Facultat de Farmàcia i Ciències de l'Alimentació, Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - José Carlos Bouso
- International Center for Ethnobotanical Education, Research, and Service (ICEERS), Barcelona, Catalonia, Spain
- Medical Anthropology Research Center (MARC), Universitat Rovira i Virgili, Tarragona, Catalonia, Spain
- Department of Neurosciences and Behavior, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil
| | - Airy Gras
- Institut Botànic de Barcelona (IBB, CSIC-Ajuntament de Barcelona), Barcelona, Catalonia, Spain
| | - Joan Vallès
- Laboratori de Botànica (UB)—Unitat associada al CSIC, Facultat de Farmàcia i Ciències de l'Alimentació, Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Daniel Vitales
- Institut Botànic de Barcelona (IBB, CSIC-Ajuntament de Barcelona), Barcelona, Catalonia, Spain
- Laboratori de Botànica (UB)—Unitat associada al CSIC, Facultat de Farmàcia i Ciències de l'Alimentació, Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Teresa Garnatje
- Institut Botànic de Barcelona (IBB, CSIC-Ajuntament de Barcelona), Barcelona, Catalonia, Spain
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15
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Panda G, Mishra N, Sharma D, Kutum R, Bhoyar RC, Jain A, Imran M, Senthilvel V, Divakar MK, Mishra A, Garg P, Banerjee P, Sivasubbu S, Scaria V, Ray A. Comprehensive Assessment of Indian Variations in the Druggable Kinome Landscape Highlights Distinct Insights at the Sequence, Structure and Pharmacogenomic Stratum. Front Pharmacol 2022; 13:858345. [PMID: 35865963 PMCID: PMC9294532 DOI: 10.3389/fphar.2022.858345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
India confines more than 17% of the world’s population and has a diverse genetic makeup with several clinically relevant rare mutations belonging to many sub-group which are undervalued in global sequencing datasets like the 1000 Genome data (1KG) containing limited samples for Indian ethnicity. Such databases are critical for the pharmaceutical and drug development industry where diversity plays a crucial role in identifying genetic disposition towards adverse drug reactions. A qualitative and comparative sequence and structural study utilizing variant information present in the recently published, largest curated Indian genome database (IndiGen) and the 1000 Genome data was performed for variants belonging to the kinase coding genes, the second most targeted group of drug targets. The sequence-level analysis identified similarities and differences among different populations based on the nsSNVs and amino acid exchange frequencies whereas a comparative structural analysis of IndiGen variants was performed with pathogenic variants reported in UniProtKB Humsavar data. The influence of these variations on structural features of the protein, such as structural stability, solvent accessibility, hydrophobicity, and the hydrogen-bond network was investigated. In-silico screening of the known drugs to these Indian variation-containing proteins reveals critical differences imparted in the strength of binding due to the variations present in the Indian population. In conclusion, this study constitutes a comprehensive investigation into the understanding of common variations present in the second largest population in the world and investigating its implications in the sequence, structural and pharmacogenomic landscape. The preliminary investigation reported in this paper, supporting the screening and detection of ADRs specific to the Indian population could aid in the development of techniques for pre-clinical and post-market screening of drug-related adverse events in the Indian population.
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Affiliation(s)
- Gayatri Panda
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India
| | - Neha Mishra
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India
| | - Disha Sharma
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Rintu Kutum
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
- Ashoka University, Sonipat, India
| | - Rahul C. Bhoyar
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Abhinav Jain
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Mohamed Imran
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Vigneshwar Senthilvel
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Mohit Kumar Divakar
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Anushree Mishra
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Parth Garg
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India
| | - Priyanka Banerjee
- Institute for Physiology, Charité-University Medicine Berlin, Berlin, Germany
| | - Sridhar Sivasubbu
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Vinod Scaria
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Arjun Ray
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla, India
- *Correspondence: Arjun Ray,
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16
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Paguigan ND, Yan Y, Karthikeyan M, Chase K, Carter J, Leavitt LS, Lim AL, Lin Z, Memon T, Christensen S, Bentzen BH, Schmitt N, Reilly CA, Teichert RW, Raghuraman S, Olivera BM, Schmidt EW. The Tunicate Metabolite 2-(3,5-Diiodo-4-methoxyphenyl)ethan-1-amine Targets Ion Channels of Vertebrate Sensory Neurons. ACS Chem Biol 2021; 16:1654-1662. [PMID: 34423964 DOI: 10.1021/acschembio.1c00328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Marine tunicates produce defensive amino-acid-derived metabolites, including 2-(3,5-diiodo-4-methoxyphenyl)ethan-1-amine (DIMTA), but their mechanisms of action are rarely known. Using an assay-guided approach, we found that out of the many different sensory cells in the mouse dorsal root ganglion (DRG), DIMTA selectively affected low-threshold cold thermosensors. Whole-cell electrophysiology experiments using DRG cells, channels expressed in Xenopus oocytes, and human cell lines revealed that DIMTA blocks several potassium channels, reducing the magnitude of the afterhyperpolarization and increasing the baseline intracellular calcium concentration [Ca2+]i of low-threshold cold thermosensors. When injected into mice, DIMTA increased the threshold of cold sensation by >3 °C. DIMTA may thus serve as a lead in the further design of compounds that inhibit problems in the cold-sensory system, such as cold allodynia and other neuropathic pain conditions.
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Affiliation(s)
- Noemi D. Paguigan
- Department of Medicinal Chemistry, University of Utah, Salt Lake City, Utah 81112, United States
| | - Yannan Yan
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Manju Karthikeyan
- Department of Biology, University of Utah, Salt Lake City, Utah 81112, United States
| | - Kevin Chase
- Department of Biology, University of Utah, Salt Lake City, Utah 81112, United States
| | - Jackson Carter
- Department of Biology, University of Utah, Salt Lake City, Utah 81112, United States
| | - Lee S. Leavitt
- Department of Biology, University of Utah, Salt Lake City, Utah 81112, United States
| | - Albebson L. Lim
- Department of Medicinal Chemistry, University of Utah, Salt Lake City, Utah 81112, United States
| | - Zhenjian Lin
- Department of Medicinal Chemistry, University of Utah, Salt Lake City, Utah 81112, United States
| | - Tosifa Memon
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah 81112, United States
| | - Sean Christensen
- Department of Biology, University of Utah, Salt Lake City, Utah 81112, United States
| | - Bo H. Bentzen
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Nicole Schmitt
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Christopher A. Reilly
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah 81112, United States
| | - Russell W. Teichert
- Department of Biology, University of Utah, Salt Lake City, Utah 81112, United States
| | | | - Baldomero M. Olivera
- Department of Biology, University of Utah, Salt Lake City, Utah 81112, United States
| | - Eric W. Schmidt
- Department of Medicinal Chemistry, University of Utah, Salt Lake City, Utah 81112, United States
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17
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GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds. Sci Rep 2021; 11:9510. [PMID: 33947911 PMCID: PMC8097070 DOI: 10.1038/s41598-021-88939-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 04/12/2021] [Indexed: 02/02/2023] Open
Abstract
The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews' correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.
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18
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Boutin JA, Jockers R. Melatonin controversies, an update. J Pineal Res 2021; 70:e12702. [PMID: 33108677 DOI: 10.1111/jpi.12702] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/11/2020] [Accepted: 10/20/2020] [Indexed: 12/13/2022]
Abstract
Melatonin was discovered more than 60 years ago. Since then, several seminal discoveries have allowed us to define its function as a neuroendocrine hormone and its molecular targets in mammals and many other species. However, many fundamental issues have not yet been solved such as the subcellular localization of melatonin synthesis and the full spectrum of its molecular targets. In addition, a considerable number of controversies persist in the field, mainly concerning how many functions melatonin has. Altogether, this illustrates how "immature" the field still is. The intention of this opinion article is to note the controversies and limitations in the field, to initiate a discussion and to make proposals/guidelines to overcome them and move the field forward.
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Affiliation(s)
- Jean A Boutin
- Institut de Recherches Internationales SERVIER, Suresnes Cedex, France
| | - Ralf Jockers
- INSERM, CNRS, Institut Cochin, Université de Paris, Paris, France
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19
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Oña G, Bouso JC. Therapeutic Potential of Natural Psychoactive Drugs for Central Nervous System Disorders: A Perspective from Polypharmacology. Curr Med Chem 2021; 28:53-68. [PMID: 31830883 DOI: 10.2174/0929867326666191212103330] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Revised: 10/01/2019] [Accepted: 10/02/2019] [Indexed: 11/22/2022]
Abstract
In the drug development, the formation of highly selective ligands has been unsuccessful in the treatment of central nervous system disorders. Multi-target ligands, from the polypharmacology paradigm, are being proposed as treatments for these complex disorders, since they offer enhanced efficacy and a strong safety profile. Natural products are the best examples of multi-target compounds, so they are of high interest within this paradigm. Additionally, recent research on psychoactive drugs of natural origin, such as ayahuasca and cannabis, has demonstrated the promising therapeutic potential for the treatment of some psychiatric and neurological disorders. In this text, we describe how research on psychoactive drugs can be effectively combined with the polypharmacology paradigm, providing ayahuasca and cannabis research as examples. The advantages and disadvantages are also discussed.
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Affiliation(s)
- Genís Oña
- International Center for Ethnobotanical Education, Research and Service (ICEERS), Barcelona, Spain
| | - José Carlos Bouso
- International Center for Ethnobotanical Education, Research and Service (ICEERS), Barcelona, Spain
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20
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Mayr F, Möller G, Garscha U, Fischer J, Rodríguez Castaño P, Inderbinen SG, Temml V, Waltenberger B, Schwaiger S, Hartmann RW, Gege C, Martens S, Odermatt A, Pandey AV, Werz O, Adamski J, Stuppner H, Schuster D. Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction. Int J Mol Sci 2020; 21:E7102. [PMID: 32993084 PMCID: PMC7582679 DOI: 10.3390/ijms21197102] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 09/19/2020] [Accepted: 09/21/2020] [Indexed: 12/01/2022] Open
Abstract
Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature's treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)-a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.
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Affiliation(s)
- Fabian Mayr
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Gabriele Möller
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; (G.M.); (J.A.)
| | - Ulrike Garscha
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, University Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, Germany; (U.G.); (J.F.)
| | - Jana Fischer
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, University Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, Germany; (U.G.); (J.F.)
| | - Patricia Rodríguez Castaño
- Pediatric Endocrinology, Diabetology and Metabolism, University Children’s Hospital Bern, Freiburgstrasse 15, 3010 Bern, Switzerland; (P.R.C.); (A.V.P.)
- Department of Biomedical Research, University of Bern, Freiburgstrasse 15, 3010 Bern, Switzerland
| | - Silvia G. Inderbinen
- Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland; (S.G.I.); (A.O.)
| | - Veronika Temml
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Birgit Waltenberger
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Stefan Schwaiger
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Rolf W. Hartmann
- Helmholtz Institute of Pharmaceutical Research Saarland (HIPS), Department for Drug Design and Optimization, Campus E8.1, 66123 Saarbrücken, Germany;
- Saarland University, Pharmaceutical and Medicinal Chemistry, Campus E8.1, 66123 Saarbrücken, Germany
| | - Christian Gege
- University of Heidelberg, Institute of Pharmacy and Molecular Biotechnology (IPMB), Medicinal Chemistry, Im Neuenheimer Feld 364, 69120 Heidelberg, Germany;
| | - Stefan Martens
- Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, 38010 San Michele all’Adige, Italy;
| | - Alex Odermatt
- Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland; (S.G.I.); (A.O.)
| | - Amit V. Pandey
- Pediatric Endocrinology, Diabetology and Metabolism, University Children’s Hospital Bern, Freiburgstrasse 15, 3010 Bern, Switzerland; (P.R.C.); (A.V.P.)
- Department of Biomedical Research, University of Bern, Freiburgstrasse 15, 3010 Bern, Switzerland
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, 07743 Jena, Germany;
| | - Jerzy Adamski
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; (G.M.); (J.A.)
- Lehrstuhl für Experimentelle Genetik, Technische Universität München, Emil-Erlenmeyer-Forum 5, 85356 Freising-Weihenstephan, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
| | - Hermann Stuppner
- Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria; (F.M.); (V.T.); (B.W.); (S.S.); (H.S.)
| | - Daniela Schuster
- Institute of Pharmacy, Department of Pharmaceutical and Medicinal Chemistry, Paracelsus Medical University Salzburg, Strubergasse 21, 5020 Salzburg, Austria
- Institute of Pharmacy/Pharmaceutical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria
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21
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Gupta MN, Roy I. Drugs, host proteins and viral proteins: how their promiscuities shape antiviral design. Biol Rev Camb Philos Soc 2020; 96:205-222. [PMID: 32918378 DOI: 10.1111/brv.12652] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 08/24/2020] [Accepted: 08/27/2020] [Indexed: 12/12/2022]
Abstract
The reciprocal nature of drug specificity and target specificity implies that the same is true for their respective promiscuities. Protein promiscuity has two broadly different types of footprint in drug design. The first is relaxed specificity of binding sites for substrates, inhibitors, effectors or cofactors. The second involves protein-protein interactions of regulatory processes such as signal transduction and transcription, and here protein intrinsic disorder plays an important role. Both viruses and host cells exploit intrinsic disorder for their survival, as do the design and discovery programs for antivirals. Drug action, strictly speaking, always relies upon promiscuous activity, with drug promiscuity enlarging its scope. Drug repurposing searches for additional promiscuity on the part of both the drug and the target in the host. Understanding the subtle nuances of these promiscuities is critical in the design of novel and more effective antivirals.
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Affiliation(s)
- Munishwar Nath Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India
| | - Ipsita Roy
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, Punjab, 160062, India
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22
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Daurichromenic Acid from the Chinese Traditional Medicinal Plant Rhododendron dauricum Inhibits Sphingomyelin Synthase and Aβ Aggregation. Molecules 2020; 25:molecules25184077. [PMID: 32906602 PMCID: PMC7571127 DOI: 10.3390/molecules25184077] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 08/31/2020] [Accepted: 09/02/2020] [Indexed: 01/05/2023] Open
Abstract
Species of the genus Rhododendron have been used in traditional Chinese medicine, with the medicinal herb "Manshanfong" used as an expectorant and for the treatment of acute bronchitis. Daurichromenic acid (DCA), a constituent of Rhododendron dauricum, is a meroterpenoid with antibacterial, anti-HIV, and anti-inflammatory activities. However, the mechanisms underlying these pharmacologic activities are poorly understood. To develop new drugs based on DCA, more information is required regarding its interactions with biomolecules. The present study showed that DCA inhibits the activity of the enzyme sphingomyelin synthase, with an IC50 of 4 µM. The structure-activity relationships between DCA and sphingomyelin synthase were evaluated using derivatives and cyclized hongoquercin A. In addition, DCA was found to inhibit amyloid β aggregation. These results may help in the design of effective drugs based on DCA.
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23
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X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes. Int J Mol Sci 2020; 21:ijms21113782. [PMID: 32471121 PMCID: PMC7312685 DOI: 10.3390/ijms21113782] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/18/2020] [Accepted: 05/24/2020] [Indexed: 12/11/2022] Open
Abstract
(1) Background: Compounds with multitarget activity are of interest in basic research to explore molecular foundations of promiscuous binding and in drug discovery as agents eliciting polypharmacological effects. Our study has aimed to systematically identify compounds that form complexes with proteins from distinct classes and compare their bioactive conformations and molecular properties. (2) Methods: A large-scale computational investigation was carried out that combined the analysis of complex X-ray structures, ligand binding modes, compound activity data, and various molecular properties. (3) Results: A total of 515 ligands with multitarget activity were identified that included 70 organic compounds binding to proteins from different classes. These multiclass ligands (MCLs) were often flexible and surprisingly hydrophilic. Moreover, they displayed a wide spectrum of binding modes. In different target structure environments, binding shapes of MCLs were often similar, but also distinct. (4) Conclusions: Combined structural and activity data analysis identified compounds with activity against proteins with distinct structures and functions. MCLs were found to have greatly varying shape similarity when binding to different protein classes. Hence, there were no apparent canonical binding shapes indicating multitarget activity. Rather, conformational versatility characterized MCL binding.
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Lautié E, Russo O, Ducrot P, Boutin JA. Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes. Front Pharmacol 2020; 11:397. [PMID: 32317969 PMCID: PMC7154113 DOI: 10.3389/fphar.2020.00397] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 03/16/2020] [Indexed: 12/11/2022] Open
Abstract
The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.
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Affiliation(s)
- Emmanuelle Lautié
- Centro de Valorização de Compostos Bioativos da Amazônia (CVACBA)-Instituto de Ciências Biológicas, Universidade Federal do Pará (UFPA), Belém, Brazil
| | - Olivier Russo
- Institut de Recherches Internationales SERVIER, Suresnes, France
| | - Pierre Ducrot
- Molecular Modelling Department, 'PEX Biotechnologie, Chimie & Biologie, Institut de Recherches SERVIER, Croissy-sur-Seine, France
| | - Jean A Boutin
- Institut de Recherches Internationales SERVIER, Suresnes, France
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Yao CH, Wang L, Stancliffe E, Sindelar M, Cho K, Yin W, Wang Y, Patti GJ. Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software. Anal Chem 2020; 92:1856-1864. [PMID: 31804057 DOI: 10.1021/acs.analchem.9b03811] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .
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Affiliation(s)
| | | | | | | | | | - Weitong Yin
- Department of Mathematics and Statistics , University of North Carolina at Charlotte , Charlotte , North Carolina 28223 , United States
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Pinzi L, Rastelli G. Identification of Target Associations for Polypharmacology from Analysis of Crystallographic Ligands of the Protein Data Bank. J Chem Inf Model 2019; 60:372-390. [PMID: 31800237 DOI: 10.1021/acs.jcim.9b00821] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The design of a chemical entity that potently and selectively binds to a biological target of therapeutic relevance has dominated the scene of drug discovery so far. However, recent findings suggest that multitarget ligands may be endowed with superior efficacy and be less prone to drug resistance. The Protein Data Bank (PDB) provides experimentally validated structural information about targets and bound ligands. Therefore, it represents a valuable source of information to help identifying active sites, understanding pharmacophore requirements, designing novel ligands, and inferring structure-activity relationships. In this study, we performed a large-scale analysis of the PDB by integrating different ligand-based and structure-based approaches, with the aim of identifying promising target associations for polypharmacology based on reported crystal structure information. First, the 2D and 3D similarity profiles of the crystallographic ligands were evaluated using different ligand-based methods. Then, activity data of pairs of similar ligands binding to different targets were inspected by comparing structural information with bioactivity annotations reported in the ChEMBL, BindingDB, BindingMOAD, and PDBbind databases. Afterward, extensive docking screenings of ligands in the identified cross-targets were made in order to validate and refine the ligand-based results. Finally, the therapeutic relevance of the identified target combinations for polypharmacology was evaluated from comparison with information on therapeutic targets reported in the Therapeutic Target Database (TTD). The results led to the identification of several target associations with high therapeutic potential for polypharmacology.
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Affiliation(s)
- Luca Pinzi
- Department of Life Sciences , University of Modena and Reggio Emilia , Via Giuseppe Campi 103 , 41125 Modena , Italy
| | - Giulio Rastelli
- Department of Life Sciences , University of Modena and Reggio Emilia , Via Giuseppe Campi 103 , 41125 Modena , Italy
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27
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Brown DG, Smith GF, Wobst HJ. Promiscuity of in Vitro Secondary Pharmacology Assays and Implications for Lead Optimization Strategies. J Med Chem 2019; 63:6251-6275. [PMID: 31714773 DOI: 10.1021/acs.jmedchem.9b01625] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
We conducted an analysis on screening data generated from 1445 compounds against a panel of 130 enzymes, ion channels, and receptors to assess secondary pharmacological risks. Hit rates of these targets as well as physicochemical properties for those hits were evaluated. A majority of targets yielded hits with higher clogP, molecular weight, and more basic character than inactive compounds. Although most targets favored lipophilic hits, the average clogP of hits at a given target did not correlate with its hit rate. Furthermore, a matched pair analysis was completed to determine structural changes that impacted off-target activities. A correlation of binding assays used in this analysis illustrated that some pharmacologically related binding assays are highly correlative and may be substituted for a smaller set of surrogate assays.
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Affiliation(s)
- Dean G Brown
- Hit Discovery, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Waltham, Massachusetts 02451, United States
| | - Graham F Smith
- Data Science and Artificial Intelligence, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Heike J Wobst
- Neuroscience, BioPharmaceuticals R&D, AstraZeneca, Waltham, Massachusetts 02451, United States
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28
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Fifield AL, Hanavan PD, Faigel DO, Sergienko E, Bobkov A, Meurice N, Petit JL, Polito A, Caulfield TR, Castle EP, Copland JA, Mukhopadhyay D, Pal K, Dutta SK, Luo H, Ho TH, Lake DF. Molecular Inhibitor of QSOX1 Suppresses Tumor Growth In Vivo. Mol Cancer Ther 2019; 19:112-122. [PMID: 31575656 DOI: 10.1158/1535-7163.mct-19-0233] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 07/01/2019] [Accepted: 09/24/2019] [Indexed: 11/16/2022]
Abstract
Quiescin sulfhydryl oxidase 1 (QSOX1) is an enzyme overexpressed by many different tumor types. QSOX1 catalyzes the formation of disulfide bonds in proteins. Because short hairpin knockdowns (KD) of QSOX1 have been shown to suppress tumor growth and invasion in vitro and in vivo, we hypothesized that chemical compounds inhibiting QSOX1 enzymatic activity would also suppress tumor growth, invasion, and metastasis. High throughput screening using a QSOX1-based enzymatic assay revealed multiple potential QSOX1 inhibitors. One of the inhibitors, known as "SBI-183," suppresses tumor cell growth in a Matrigel-based spheroid assay and inhibits invasion in a modified Boyden chamber, but does not affect viability of nonmalignant cells. Oral administration of SBI-183 inhibits tumor growth in 2 independent human xenograft mouse models of renal cell carcinoma. We conclude that SBI-183 warrants further exploration as a useful tool for understanding QSOX1 biology and as a potential novel anticancer agent in tumors that overexpress QSOX1.
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Affiliation(s)
- Amber L Fifield
- School of Life Sciences, Arizona State University, Tempe, Arizona
| | | | - Douglas O Faigel
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Phoenix, Arizona
| | - Eduard Sergienko
- Assay Development, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California
| | - Andrey Bobkov
- Assay Development, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California
| | | | | | - Alysia Polito
- Hematology/Oncology, Mayo Clinic, Scottsdale, Arizona
| | - Thomas R Caulfield
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida.,Mayo Graduate School, Neurobiology of Disease, Mayo Clinic, Jacksonville, Florida.,Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida.,Health Sciences Research, Division of Biomedical Statistics & Informatics, Mayo Clinic, Jacksonville, Florida.,Center for Individualized Medicine, Mayo Clinic, Jacksonville, Florida
| | - Erik P Castle
- Department of Urology, Mayo Clinic, Phoenix, Arizona
| | - John A Copland
- Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida
| | | | - Krishnendu Pal
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Jacksonville, Florida
| | - Shamit K Dutta
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Jacksonville, Florida
| | - Huijun Luo
- Division of Hematology/Oncology, Mayo Clinic, Phoenix, Arizona
| | - Thai H Ho
- Division of Hematology/Oncology, Mayo Clinic, Phoenix, Arizona.
| | - Douglas F Lake
- School of Life Sciences, Arizona State University, Tempe, Arizona.
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29
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Bofill A, Jalencas X, Oprea TI, Mestres J. The human endogenous metabolome as a pharmacology baseline for drug discovery. Drug Discov Today 2019; 24:1806-1820. [PMID: 31226432 DOI: 10.1016/j.drudis.2019.06.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 05/17/2019] [Accepted: 06/12/2019] [Indexed: 01/01/2023]
Abstract
We have limited understanding of the variation in in vitro affinities of drugs for their targets. An analysis of a highly curated set of 815 interactions between 566 drugs and 129 primary targets reveals that 71% of drug-target affinities have values above that of the corresponding endogenous ligand, 96% of them fitting within a range of two orders of magnitude. Our findings suggest that the evolutionary optimised affinity of endogenous ligands for their native proteins can serve as a baseline for the primary pharmacology of drugs. We show that the degree of off-target selectivity and safety risks of drugs derived from their secondary pharmacology depend very much on that baseline. Thus, we propose a new approach for estimating safety margins.
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Affiliation(s)
- Andreu Bofill
- Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
| | - Xavier Jalencas
- Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
| | - Tudor I Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA; UNM Comprehensive Cancer Center, Albuquerque, NM, USA; Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jordi Mestres
- Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, 08003 Barcelona, Catalonia, Spain.
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30
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Hu Y, Bajorath J. SAR Matrix Method for Large-Scale Analysis of Compound Structure-Activity Relationships and Exploration of Multitarget Activity Spaces. Methods Mol Biol 2019; 1825:339-352. [PMID: 30334212 DOI: 10.1007/978-1-4939-8639-2_11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
As the number of compounds and the volume of bioactivity data rapidly grow, advanced computational methods are required to study structure-activity relationships (SARs) on a large scale. Herein, the SAR matrix (SARM) methodology is described that was designed to systematically extract structural relationships between bioactive compounds from large databases, explore structure-activity relationships, and navigate multitarget activity spaces, which is one of the core tasks in chemogenomics. In addition, the SARM approach was designed to visualize structural and structure-activity relationships, which is often of critical importance for making this information available in an intuitive form for practical applications.
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Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany.
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31
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Ikram N, Mirza MU, Vanmeert M, Froeyen M, Salo-Ahen OMH, Tahir M, Qazi A, Ahmad S. Inhibition of Oncogenic Kinases: An In Vitro Validated Computational Approach Identified Potential Multi-Target Anticancer Compounds. Biomolecules 2019; 9:E124. [PMID: 30925835 PMCID: PMC6523505 DOI: 10.3390/biom9040124] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 03/20/2019] [Accepted: 03/21/2019] [Indexed: 12/16/2022] Open
Abstract
Tumorigenesis in humans is a multistep progression that imitates genetic changes leading to cell transformation and malignancy. Oncogenic kinases play a central role in cancer progression, rendering them putative targets for the design of anti-cancer drugs. The presented work aims to identify the potential multi-target inhibitors of oncogenic receptor tyrosine kinases (RTKs) and serine/threonine kinases (STKs). For this, chemoinformatics and structure-based virtual screening approaches were combined with an in vitro validation of lead hits on both cancerous and non-cancerous cell lines. A total of 16 different kinase structures were screened against ~739,000 prefiltered compounds using diversity selection, after which the top hits were filtered for promising pharmacokinetic properties. This led to the identification of 12 and 9 compounds against RTKs and STKs, respectively. Molecular dynamics (MD) simulations were carried out to better comprehend the stability of the predicted hit kinase-compound complexes. Two top-ranked compounds against each kinase class were tested in vitro for cytotoxicity, with compound F34 showing the most promising inhibitory activity in HeLa, HepG2, and Vero cell lines with IC50 values of 145.46 μM, 175.48 μM, and 130.52 μM, respectively. Additional docking of F34 against various RTKs was carried out to support potential multi-target inhibition. Together with reliable MD simulations, these results suggest the promising potential of identified multi-target STK and RTK scaffolds for further kinase-specific anti-cancer drug development toward combinatorial therapies.
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Affiliation(s)
- Nazia Ikram
- Institute of Molecular Biology and Biotechnology, The University of Lahore, 54000 Lahore, Pakistan.
| | - Muhammad Usman Mirza
- Centre for Research in Molecular Medicine, The University of Lahore, 54000 Lahore, Pakistan.
- Department of Pharmaceutical and Pharmacological Sciences, Rega Institute for Medical Research, Medicinal Chemistry, University of Leuven, B-3000 Leuven, Belgium.
| | - Michiel Vanmeert
- Department of Pharmaceutical and Pharmacological Sciences, Rega Institute for Medical Research, Medicinal Chemistry, University of Leuven, B-3000 Leuven, Belgium.
| | - Matheus Froeyen
- Department of Pharmaceutical and Pharmacological Sciences, Rega Institute for Medical Research, Medicinal Chemistry, University of Leuven, B-3000 Leuven, Belgium.
| | - Outi M H Salo-Ahen
- Structural Bioinformatics Laboratory, Faculty of Science and Engineering, Biochemistry, Åbo Akademi University, FI-20520 Turku, Finland.
- Pharmaceutical Sciences Laboratory, Faculty of Science and Engineering, Pharmacy, Åbo Akademi University, FI-20520 Turku, Finland.
| | - Muhammad Tahir
- Centre for Research in Molecular Medicine, The University of Lahore, 54000 Lahore, Pakistan.
| | - Aamer Qazi
- Centre for Research in Molecular Medicine, The University of Lahore, 54000 Lahore, Pakistan.
| | - Sarfraz Ahmad
- Institute of Pharmaceutical Sciences, Riphah University, 54000 Lahore, Pakistan.
- Department of Chemistry, Faculty of Sciences, University Malaya, 59100, Kuala Lumpur, Malaysia.
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Abstract
Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.
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Affiliation(s)
- Ryan Byrne
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.
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Tolmacheva IA, Nazarov AV, Eroshenko DV, Grishko VV. Synthesis, cytotoxic evaluation, and molecular docking studies of the semi-synthetic "triterpenoid-steroid" hybrids. Steroids 2018; 140:131-143. [PMID: 30315840 DOI: 10.1016/j.steroids.2018.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 10/01/2018] [Accepted: 10/07/2018] [Indexed: 10/28/2022]
Abstract
Synthetic transformations of steroids for drug discovery and improvement of drug effectiveness have been an important part of modern medicinal chemistry and pharmaceutical sciences. Pentacyclic triterpenoids, being represented in the nature by various structures and biogenetically related to steroids, can largely expand the spectrum of biologically active steroidal agents via synthesis of the so-called "triterpenoid-steroid" hybrids. In the presented work, the nitrile anion cyclizations of 3,4-secolupane and 3,4-seco-oleanane nitriles and follow-up synthetic transformations of the cyclized products with formation of the gemm-dimethyl-free A ring "triterpenoid-steroid" hybrids were studied. Furthermore, the resulting cyclic compounds were modified at C3, C4, and/or C5 positions of ring A, as well as at C20, C28, and C30 positions of the isopropylidene moiety in the case of lupane triterpenoids. The cytotoxic effect of the synthesized compounds against seven cancer cell lines HEp-2, HCT 116, MS, RD TE32, A549, MCF7, and PC3 was evaluated. The in silico identification of potential anticancer protein targets with regard to the compounds, which were active at micromolar concentrations against tested cell lines, was carried out. The molecular docking studies showed that compound 19, which demonstrated most pronounced cytotoxicity (IC50 0.64-3.17 μM) against all tested cell lines, fits well the active sites of CDK6 and HER2/neu.
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Affiliation(s)
- Irina A Tolmacheva
- Institute of Technical Chemistry, Perm Federal Scientific Centre, Ural Branch, Russian Academy of Sciences, 3 Akad. Korolev str., 614013 Perm, Russia
| | - Alexey V Nazarov
- Institute of Technical Chemistry, Perm Federal Scientific Centre, Ural Branch, Russian Academy of Sciences, 3 Akad. Korolev str., 614013 Perm, Russia
| | - Daria V Eroshenko
- Institute of Technical Chemistry, Perm Federal Scientific Centre, Ural Branch, Russian Academy of Sciences, 3 Akad. Korolev str., 614013 Perm, Russia
| | - Victoria V Grishko
- Institute of Technical Chemistry, Perm Federal Scientific Centre, Ural Branch, Russian Academy of Sciences, 3 Akad. Korolev str., 614013 Perm, Russia.
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Antolín AA, Mestres J. Dual Inhibitors of PARPs and ROCKs. ACS OMEGA 2018; 3:12707-12712. [PMID: 30411017 PMCID: PMC6210072 DOI: 10.1021/acsomega.8b02337] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 09/24/2018] [Indexed: 05/16/2023]
Abstract
Recent network and system biology analyses suggest that most complex diseases are regulated by robust and highly interconnected pathways that could be better modulated by small molecules binding to multiple biological targets. These pieces of evidence recently led to devote efforts on identifying single chemical entities that bind to two different disease-relevant targets. Here, we first predicted in silico and later confirmed in vitro that UPF 1069, a known bioactive poly(ADP-ribose) polymerase-1/2 (PARP1/2) molecule, and hydroxyfasudil, a known bioactive Rho-associated protein kinase-1/2 (ROCK1/2) molecule, have low-micromolar cross-affinity for ROCK1/2 and PARP1/2, respectively. These molecules can now be regarded as chemical seeds from which pharmacological tools could be generated to study the impact of dual inhibition of PARPs and ROCKs in preclinical models of a variety of complex diseases where both targets are involved.
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Siramshetty VB, Preissner R, Gohlke BO. Exploring Activity Profiles of PAINS and Their Structural Context in Target–Ligand Complexes. J Chem Inf Model 2018; 58:1847-1857. [DOI: 10.1021/acs.jcim.8b00385] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Vishal B. Siramshetty
- Structural Bioinformatics Group, Charité-Universitätsmedizin Berlin, 10115 Berlin, Germany
- BB3R - Berlin Brandenburg 3R Graduate School, Freie Universität Berlin, 14195 Berlin, Germany
| | - Robert Preissner
- Structural Bioinformatics Group, Charité-Universitätsmedizin Berlin, 10115 Berlin, Germany
- BB3R - Berlin Brandenburg 3R Graduate School, Freie Universität Berlin, 14195 Berlin, Germany
| | - Bjoern-Oliver Gohlke
- Structural Bioinformatics Group, Charité-Universitätsmedizin Berlin, 10115 Berlin, Germany
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36
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Siramshetty VB, Preissner R. Drugs as habitable planets in the space of dark chemical matter. Drug Discov Today 2017; 23:481-486. [PMID: 28709991 DOI: 10.1016/j.drudis.2017.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 06/13/2017] [Accepted: 07/07/2017] [Indexed: 10/19/2022]
Abstract
A recent study demonstrated antifungal activity of dark chemical matter (DCM) compounds that were otherwise inactive in more than 100 HTS assays. These compounds were proposed to possess unique activity and 'clean' safety profiles. Here, we present an outlook of the promiscuity and safety of these compounds by retrospectively comparing their chemical and biological spaces with those of drugs. Significant amounts of marketed drugs (16%), withdrawn drugs (16.5%) and natural compounds (3.5%) share structural identity with DCM. Compound promiscuity assessment indicates that dark matter compounds could potentially interact with multiple biological targets. Further, thousands of DCM compounds showed presence of frequent-hitting pan-assay interference compound (PAINS) substructures. In light of these observations, filtering these compounds from screening libraries can be an irrevocable loss.
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Affiliation(s)
- Vishal B Siramshetty
- Structural Bioinformatics Group, Institute of Physiology & Experimental and Clinical Research Center (ECRC), Charité - University Medicine Berlin, Berlin, Germany; BB3R - Berlin Brandenburg 3R Graduate School, Free University of Berlin, Berlin, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Robert Preissner
- Structural Bioinformatics Group, Institute of Physiology & Experimental and Clinical Research Center (ECRC), Charité - University Medicine Berlin, Berlin, Germany; BB3R - Berlin Brandenburg 3R Graduate School, Free University of Berlin, Berlin, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
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37
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Boezio B, Audouze K, Ducrot P, Taboureau O. Network-based Approaches in Pharmacology. Mol Inform 2017; 36. [PMID: 28692140 DOI: 10.1002/minf.201700048] [Citation(s) in RCA: 189] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 06/21/2017] [Indexed: 12/23/2022]
Abstract
In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example.
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Affiliation(s)
- Baptiste Boezio
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
| | - Karine Audouze
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Olivier Taboureau
- Université Paris Diderot - Inserm UMR-S973, MTi, 75205, Paris Cedex 13, 75013, Paris, France
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Entering the 'big data' era in medicinal chemistry: molecular promiscuity analysis revisited. Future Sci OA 2017; 3:FSO179. [PMID: 28670471 PMCID: PMC5481856 DOI: 10.4155/fsoa-2017-0001] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Accepted: 01/26/2017] [Indexed: 12/26/2022] Open
Abstract
The 'big data' concept plays an increasingly important role in many scientific fields. Big data involves more than unprecedentedly large volumes of data that become available. Different criteria characterizing big data must be carefully considered in computational data mining, as we discuss herein focusing on medicinal chemistry. This is a scientific discipline where big data is beginning to emerge and provide new opportunities. For example, the ability of many drugs to specifically interact with multiple targets, termed promiscuity, forms the molecular basis of polypharmacology, a hot topic in drug discovery. Compound promiscuity analysis is an area that is much influenced by big data phenomena. Different results are obtained depending on chosen data selection and confidence criteria, as we also demonstrate.
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39
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Luo Q, Zhao L, Hu J, Jin H, Liu Z, Zhang L. The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing. PLoS One 2017; 12:e0171433. [PMID: 28196116 PMCID: PMC5308821 DOI: 10.1371/journal.pone.0171433] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Accepted: 01/20/2017] [Indexed: 12/28/2022] Open
Abstract
Target fishing often relies on the use of reverse docking to identify potential target proteins of ligands from protein database. The limitation of reverse docking is the accuracy of current scoring funtions used to distinguish true target from non-target proteins. Many contemporary scoring functions are designed for the virtual screening of small molecules without special optimization for reverse docking, which would be easily influenced by the properties of protein pockets, resulting in scoring bias to the proteins with certain properties. This bias would cause lots of false positives in reverse docking, interferring the identification of true targets. In this paper, we have conducted a large-scale reverse docking (5000 molecules to 100 proteins) to study the scoring bias in reverse docking by DOCK, Glide, and AutoDock Vina. And we found that there were actually some frequency hits, namely interference proteins in all three docking procedures. After analyzing the differences of pocket properties between these interference proteins and the others, we speculated that the interference proteins have larger contact area (related to the size and shape of protein pockets) with ligands (for all three docking programs) or higher hydrophobicity (for Glide), which could be the causes of scoring bias. Then we applied the score normalization method to eliminate this scoring bias, which was effective to make docking score more balanced between different proteins in the reverse docking of benchmark dataset. Later, the Astex Diver Set was utilized to validate the effect of score normalization on actual cases of reverse docking, showing that the accuracy of target prediction significantly increased by 21.5% in the reverse docking by Glide after score normalization, though there was no obvious change in the reverse docking by DOCK and AutoDock Vina. Our results demonstrate the effectiveness of score normalization to eliminate the scoring bias and improve the accuracy of target prediction in reverse docking. Moreover, the properties of protein pockets causing scoring bias to certain proteins we found here can provide the theory basis to further optimize the scoring functions of docking programs for future research.
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Affiliation(s)
- Qiyao Luo
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Liang Zhao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Jianxing Hu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Hongwei Jin
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
- * E-mail: (ZL); (LZ)
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
- * E-mail: (ZL); (LZ)
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40
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Backman TWH, Evans DS, Girke T. Large-scale bioactivity analysis of the small-molecule assayed proteome. PLoS One 2017; 12:e0171413. [PMID: 28178331 PMCID: PMC5298297 DOI: 10.1371/journal.pone.0171413] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 01/20/2017] [Indexed: 12/12/2022] Open
Abstract
This study presents an analysis of the small molecule bioactivity profiles across large quantities of diverse protein families represented in PubChem BioAssay. We compared the bioactivity profiles of FDA approved drugs to non-FDA approved compounds, and report several distinct patterns characteristic of the approved drugs. We found that a large fraction of the previously reported higher target promiscuity among FDA approved compounds, compared to non-FDA approved bioactives, was frequently due to cross-reactivity within rather than across protein families. We identified 804 potentially novel protein target candidates for FDA approved drugs, as well as 901 potentially novel target candidates with active non-FDA approved compounds, but no FDA approved drugs with activity against these targets. We also identified 486348 potentially novel compounds active against the same targets as FDA approved drugs, as well as 153402 potentially novel compounds active against targets without active FDA approved drugs. By quantifying the agreement among replicated screens, we estimated that more than half of these novel outcomes are reproducible. Using biclustering, we identified many dense clusters of FDA approved drugs with enriched activity against a common set of protein targets. We also report the distribution of compound promiscuity using a Bayesian statistical model, and report the sensitivity and specificity of two common methods for identifying promiscuous compounds. Aggregator assays exhibited greater accuracy in identifying highly promiscuous compounds, while PAINS substructures were able to identify a much larger set of "middle range" promiscuous compounds. Additionally, we report a large number of promiscuous compounds not identified as aggregators or PAINS. In summary, the results of this study represent a rich reference for selecting novel drug and target protein candidates, as well as for eliminating candidate compounds with unselective activities.
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Affiliation(s)
- Tyler William H. Backman
- Department of Bioengineering, University of California Riverside, Riverside, California, United States of America
- Institute for Integrative Genome Biology, University of California Riverside, Riverside, California, United States of America
| | - Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California Riverside, Riverside, California, United States of America
- * E-mail:
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41
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Cheeseman M, Chessum NEA, Rye CS, Pasqua AE, Tucker M, Wilding B, Evans LE, Lepri S, Richards M, Sharp SY, Ali S, Rowlands M, O’Fee L, Miah A, Hayes A, Henley AT, Powers M, te Poele R, De Billy E, Pellegrino L, Raynaud F, Burke R, van Montfort RLM, Eccles SA, Workman P, Jones K. Discovery of a Chemical Probe Bisamide (CCT251236): An Orally Bioavailable Efficacious Pirin Ligand from a Heat Shock Transcription Factor 1 (HSF1) Phenotypic Screen. J Med Chem 2017; 60:180-201. [PMID: 28004573 PMCID: PMC6014687 DOI: 10.1021/acs.jmedchem.6b01055] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Indexed: 12/20/2022]
Abstract
Phenotypic screens, which focus on measuring and quantifying discrete cellular changes rather than affinity for individual recombinant proteins, have recently attracted renewed interest as an efficient strategy for drug discovery. In this article, we describe the discovery of a new chemical probe, bisamide (CCT251236), identified using an unbiased phenotypic screen to detect inhibitors of the HSF1 stress pathway. The chemical probe is orally bioavailable and displays efficacy in a human ovarian carcinoma xenograft model. By developing cell-based SAR and using chemical proteomics, we identified pirin as a high affinity molecular target, which was confirmed by SPR and crystallography.
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Affiliation(s)
- Matthew
D. Cheeseman
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Nicola E. A. Chessum
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Carl S. Rye
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - A. Elisa Pasqua
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Michael
J. Tucker
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Birgit Wilding
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Lindsay E. Evans
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Susan Lepri
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Meirion Richards
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Swee Y. Sharp
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Salyha Ali
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
- Division
of Structural Biology at The Institute of
Cancer Research, London SW7 3RP, United Kingdom
| | - Martin Rowlands
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Lisa O’Fee
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Asadh Miah
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Angela Hayes
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Alan T. Henley
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Marissa Powers
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Robert te Poele
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Emmanuel De Billy
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Loredana Pellegrino
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Florence Raynaud
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Rosemary Burke
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Rob L. M. van Montfort
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
- Division
of Structural Biology at The Institute of
Cancer Research, London SW7 3RP, United Kingdom
| | - Suzanne A. Eccles
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Paul Workman
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
| | - Keith Jones
- Cancer
Research UK Cancer Therapeutics Unit at The Institute of Cancer Research, London SW7 3RP, United Kingdom
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42
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Joshi T, Yan D, Hamed O, Tannheimer SL, Phillips GB, Wright CD, Kim M, Salmon M, Newton R, Giembycz MA. GS-5759, a Bifunctional β2-Adrenoceptor Agonist and Phosphodiesterase 4 Inhibitor for Chronic Obstructive Pulmonary Disease with a Unique Mode of Action: Effects on Gene Expression in Human Airway Epithelial Cells. J Pharmacol Exp Ther 2016; 360:324-340. [DOI: 10.1124/jpet.116.237743] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 12/05/2016] [Indexed: 12/31/2022] Open
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43
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Gilberg E, Jasial S, Stumpfe D, Dimova D, Bajorath J. Highly Promiscuous Small Molecules from Biological Screening Assays Include Many Pan-Assay Interference Compounds but Also Candidates for Polypharmacology. J Med Chem 2016; 59:10285-10290. [PMID: 27809519 DOI: 10.1021/acs.jmedchem.6b01314] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
In PubChem screening assays, 466 highly promiscuous compounds were identified that were examined for known pan-assay interference compounds (PAINS) and aggregators using publicly available filters. These filters detected 210 PAINS and 67 aggregators. Compounds passing the filters included additional PAINS that were not detected, mostly due to tautomerism, and a variety of other potentially reactive compounds currently not encoded as PAINS. For a subset of compounds passing the filters, there was no evidence of potential artifacts. These compounds are considered candidates for further exploring multitarget activities and the molecular basis of polypharmacology.
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Affiliation(s)
- Erik Gilberg
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Swarit Jasial
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Dilyana Dimova
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany
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44
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Comparing atom-based with residue-based descriptors in predicting binding site similarity: do backbone atoms matter? Future Med Chem 2016; 8:1871-1885. [PMID: 27629811 DOI: 10.4155/fmc-2016-0077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
AIM We question the level of detail required in protein 3D-representation to detect site similarity which is relevant for polypharmacology prediction. RESULTS We modified the in-house program SiteAlign to replace generic pharmacophoric descriptors of cavity-lining amino acids by descriptors accounting for solvent exposure. Benchmarking the novel, atom-based, method (SiteAlign2) revealed no global improvement of performance. However, in the rare cases of no sequence or global structure similarities between the compared proteins, SiteAlign2 was more successful if backbone atoms are key determinants of ligand binding. CONCLUSION SiteAlign suits the comparison of binding sites for close or distant homologs. SiteAlign2 provides a better insight into the physical model of site similarity between nonhomologs, but at the expense of an increased sensitivity to atomic coordinates.
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45
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Charting the chemical space around the (iso)indoline scaffold, a comprehensive approach towards multitarget directed ligands. Bioorg Med Chem Lett 2016; 26:4211-5. [DOI: 10.1016/j.bmcl.2016.07.055] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 07/20/2016] [Accepted: 07/22/2016] [Indexed: 01/09/2023]
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46
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Stumpfe D, Dimova D, Bajorath J. Computational Method for the Systematic Identification of Analog Series and Key Compounds Representing Series and Their Biological Activity Profiles. J Med Chem 2016; 59:7667-76. [PMID: 27501131 DOI: 10.1021/acs.jmedchem.6b00906] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
A computational methodology is introduced for detecting all unique series of analogs in large compound data sets, regardless of chemical relationships between analogs. No prior knowledge of core structures or R-groups is required, which are automatically determined. The approach is based upon the generation of retrosynthetic matched molecular pairs and analog networks from which distinct series are isolated. The methodology was applied to systematically extract more than 17 000 distinct series from the ChEMBL database. For comparison, analog series were also isolated from screening compounds and drugs. Known biological activities were mapped to series from ChEMBL, and in more than 13 000 of these series, key compounds were identified that represented substitution sites of all analogs within a series and its complete activity profile. The analog series, key compounds, and activity profiles are made freely available as a resource for medicinal chemistry applications.
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Affiliation(s)
- Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Dilyana Dimova
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstrasse 2, D-53113 Bonn, Germany
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47
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Maryanoff BE. Phenotypic Assessment and the Discovery of Topiramate. ACS Med Chem Lett 2016; 7:662-5. [PMID: 27437073 PMCID: PMC4948003 DOI: 10.1021/acsmedchemlett.6b00176] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 06/13/2016] [Indexed: 12/25/2022] Open
Abstract
![]()
The
role of phenotypic assessment in drug discovery is discussed,
along with the discovery and development of TOPAMAX (topiramate),
a billion-dollar molecule for the treatment of epilepsy and migraine.
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Affiliation(s)
- Bruce E. Maryanoff
- Baruch S. Blumberg Institute, 3805 Old Easton Road, Doylestown, Pennsylvania 18902, United States
- Department of Chemistry, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
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48
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Bajorath J. Analyzing Promiscuity at the Level of Active Compounds and Targets. Mol Inform 2016; 35:583-587. [DOI: 10.1002/minf.201600030] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 04/08/2016] [Indexed: 01/20/2023]
Affiliation(s)
- Jürgen Bajorath
- Department of Life Science Informatics, Bonn-Aachen International Center for Information Technology; Rheinische Friedrich-Wilhelms-Universität Bonn; Dahlmannstr. 2 D-53113 Bonn Germany)
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49
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Núñez-Vivanco G, Valdés-Jiménez A, Besoaín F, Reyes-Parada M. Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach. J Cheminform 2016; 8:19. [PMID: 27092185 PMCID: PMC4834829 DOI: 10.1186/s13321-016-0131-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Accepted: 04/04/2016] [Indexed: 11/15/2022] Open
Abstract
Background Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D patterns such as allosteric sites, enzyme-cofactor interaction motifs, etc. This makes necessary the development of new ligand-independent methods to search and compare 3D patterns in all available protein structures. Results Here we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility of Geomfinder, which was able to discriminate between similar and different 3D patterns related to binding sites of common substrates in a range of diverse proteins. Conclusions Geomfinder allows detecting similar 3D patterns between any two pair of protein structures, regardless of the divergency among their amino acids sequences. Although the software is not intended for simultaneous multiple comparisons in a large number of proteins, it can be particularly useful in cases such as the structure-based design of multitarget drugs, where a detailed analysis of 3D patterns similarities between a few selected protein targets is essential. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0131-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gabriel Núñez-Vivanco
- Escuela de Ingeniería Civil en Bioinformática, Universidad de Talca, Avenida Lircay s/n, Talca, Chile ; Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Talca, Chile
| | - Alejandro Valdés-Jiménez
- Escuela de Ingeniería Civil en Bioinformática, Universidad de Talca, Avenida Lircay s/n, Talca, Chile
| | - Felipe Besoaín
- Escuela de Ingeniería Civil en Bioinformática, Universidad de Talca, Avenida Lircay s/n, Talca, Chile ; Estudis d'Informática, Multimedia i Telecomunicacio, Universitat Oberta de Catalunya, Rambla del Poblenou 15, Barcelona, Spain ; Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Av. Carl Friedrich Gauss, 5, Castelldefels, Barcelona, Spain
| | - Miguel Reyes-Parada
- School of Medicine, Faculty of Medical Sciences, Universidad de Santiago de Chile, Avenida Libertador Bernardo O'Higgins 3363, Santiago, Chile ; Facultad de Ciencias de la Salud, Universidad Autonóma de Chile, 5 Poniente 1670, Talca, Chile
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50
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Determining the Degree of Promiscuity of Extensively Assayed Compounds. PLoS One 2016; 11:e0153873. [PMID: 27082988 PMCID: PMC4833426 DOI: 10.1371/journal.pone.0153873] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 04/05/2016] [Indexed: 11/21/2022] Open
Abstract
In the context of polypharmacology, an emerging concept in drug discovery, promiscuity is rationalized as the ability of compounds to specifically interact with multiple targets. Promiscuity of drugs and bioactive compounds has thus far been analyzed computationally on the basis of activity annotations, without taking assay frequencies or inactivity records into account. Most recent estimates have indicated that bioactive compounds interact on average with only one to two targets, whereas drugs interact with six or more. In this study, we have further extended promiscuity analysis by identifying the most extensively assayed public domain compounds and systematically determining their promiscuity. These compounds were tested in hundreds of assays against hundreds of targets. In our analysis, assay promiscuity was distinguished from target promiscuity and separately analyzed for primary and confirmatory assays. Differences between the degree of assay and target promiscuity were surprisingly small and average and median degrees of target promiscuity of 2.6 to 3.4 and 2.0 were determined, respectively. Thus, target promiscuity remained at a low level even for most extensively tested active compounds. These findings provide further evidence that bioactive compounds are less promiscuous than drugs and have implications for pharmaceutical research. In addition to a possible explanation that drugs are more extensively tested for additional targets, the results would also support a “promiscuity enrichment model” according to which promiscuous compounds might be preferentially selected for therapeutic efficacy during clinical evaluation to ultimately become drugs.
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