1
|
Zhang Y, Ge G, Xu X, Wu J. Ensemble-Based Virtual Screening Led to the Discovery of Novel Lead Molecules as Potential NMBAs. Molecules 2024; 29:1955. [PMID: 38731447 PMCID: PMC11085220 DOI: 10.3390/molecules29091955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/16/2024] [Accepted: 04/22/2024] [Indexed: 05/13/2024] Open
Abstract
Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs' active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations.
Collapse
Affiliation(s)
- Yi Zhang
- School of Medicine, Nanjing University, Nanjing 210093, China
- Jiangsu Key Laboratory of Central Nervous System Drug Research and Development, Jiangsu Nhwa Pharmaceutical Co., Ltd., Xuzhou 221116, China
| | - Gonghui Ge
- School of Pharmacy, China Medical University, Shenyang 110122, China
| | - Xiangyang Xu
- Jiangsu Key Laboratory of Central Nervous System Drug Research and Development, Jiangsu Nhwa Pharmaceutical Co., Ltd., Xuzhou 221116, China
| | - Jinhui Wu
- School of Medicine, Nanjing University, Nanjing 210093, China
| |
Collapse
|
2
|
Aghahasani R, Shiri F, Kamaladiny H, Haddadi F, Pirhadi S. Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery. BMC Chem 2024; 18:73. [PMID: 38615023 PMCID: PMC11016228 DOI: 10.1186/s13065-024-01175-6] [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: 10/13/2023] [Accepted: 03/28/2024] [Indexed: 04/15/2024] Open
Abstract
Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.
Collapse
Affiliation(s)
| | | | | | | | - Somayeh Pirhadi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| |
Collapse
|
3
|
Boldini D, Ballabio D, Consonni V, Todeschini R, Grisoni F, Sieber SA. Effectiveness of molecular fingerprints for exploring the chemical space of natural products. J Cheminform 2024; 16:35. [PMID: 38528548 DOI: 10.1186/s13321-024-00830-3] [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: 12/20/2023] [Accepted: 03/17/2024] [Indexed: 03/27/2024] Open
Abstract
Natural products are a diverse class of compounds with promising biological properties, such as high potency and excellent selectivity. However, they have different structural motifs than typical drug-like compounds, e.g., a wider range of molecular weight, multiple stereocenters and higher fraction of sp3-hybridized carbons. This makes the encoding of natural products via molecular fingerprints difficult, thus restricting their use in cheminformatics studies. To tackle this issue, we explored over 30 years of research to systematically evaluate which molecular fingerprint provides the best performance on the natural product chemical space. We considered 20 molecular fingerprints from four different sources, which we then benchmarked on over 100,000 unique natural products from the COCONUT (COlleCtion of Open Natural prodUcTs) and CMNPD (Comprehensive Marine Natural Products Database) databases. Our analysis focused on the correlation between different fingerprints and their classification performance on 12 bioactivity prediction datasets. Our results show that different encodings can provide fundamentally different views of the natural product chemical space, leading to substantial differences in pairwise similarity and performance. While Extended Connectivity Fingerprints are the de-facto option to encoding drug-like compounds, other fingerprints resulted to match or outperform them for bioactivity prediction of natural products. These results highlight the need to evaluate multiple fingerprinting algorithms for optimal performance and suggest new areas of research. Finally, we provide an open-source Python package for computing all molecular fingerprints considered in the study, as well as data and scripts necessary to reproduce the results, at https://github.com/dahvida/NP_Fingerprints .
Collapse
Affiliation(s)
- Davide Boldini
- TUM School of Natural Sciences, Department of Bioscience, Technical University of Munich, Center for Functional Protein Assemblies (CPA), 85748, Garching bei München, Germany.
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.zza Della Scienza, 1, 20126, Milan, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.zza Della Scienza, 1, 20126, Milan, Italy
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, P.zza Della Scienza, 1, 20126, Milan, Italy
| | - Francesca Grisoni
- Institute for Complex Molecular Systems and Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, Netherlands
| | - Stephan A Sieber
- TUM School of Natural Sciences, Department of Bioscience, Technical University of Munich, Center for Functional Protein Assemblies (CPA), 85748, Garching bei München, Germany
| |
Collapse
|
4
|
Vonka P, Rarova L, Bazgier V, Tichy V, Kolarova T, Holcakova J, Berka K, Kvasnica M, Oklestkova J, Kudova E, Strnad M, Hrstka R. Small change - big consequence: The impact of C15-C16 double bond in a D‑ring of estrone on estrogen receptor activity. J Steroid Biochem Mol Biol 2023; 233:106365. [PMID: 37468002 DOI: 10.1016/j.jsbmb.2023.106365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 07/21/2023]
Abstract
Estrogen receptor alpha (ER) is a key biomarker for breast cancer, and the presence or absence of ER in breast and other hormone-dependent cancers decides treatment regimens and patient prognosis. ER is activated after ligand binding - typically by steroid. 2682 steroid compounds were used in a molecular docking study to identify novel ligands for ER and to predict compounds that may show anticancer activity. The effect of the most promising compounds was determined by a novel luciferase reporter assay. Two compounds, 7 and 12, showing ER inhibitory activity comparable to clinical inhibitors such as tamoxifen or fulvestrant were selected. We propose that the inhibitory effect of compounds 7 and 12 on ER is related to the presence of a double bond in their D-ring, which may protect against ER activation by reducing the electron density of the keto group, or may undergo metabolism leading to an active compound. Western blotting revealed that compound 12 decreased the level of ER in the breast cancer cell line MCF7, which was associated with reduced expression of both isoforms of the progesterone receptor, a well-known downstream target of ER. However, compound 12 has a different mechanism of action from fulvestrant. Furthermore, we found that compound 12 interferes with mitochondrial functions, probably by disrupting the electron transport chain, leading to induction of the intrinsic apoptotic pathway even in ER-negative breast cancer cells. In conclusion, the combination of computational and experimental methods shown here represents a rapid approach to determine the activity of compounds towards ER. Our data will not only contribute to research focused on the regulation of ER activity but may also be useful for the further development of novel steroid receptor-targeted drugs applicable in clinical practice.
Collapse
Affiliation(s)
- Petr Vonka
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic; Laboratory of Growth Regulators, Faculty of Science of Palacký University & Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
| | - Lucie Rarova
- Department of Experimental Biology, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 78371 Olomouc, Czech Republic
| | - Vaclav Bazgier
- Department of Physical Chemistry, Faculty of Science, Palacký University Olomouc, třída 17. listopadu 12, 771 46 Olomouc, Czech Republic
| | - Vlastimil Tichy
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic
| | - Tamara Kolarova
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic
| | - Jitka Holcakova
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic
| | - Karel Berka
- Department of Physical Chemistry, Faculty of Science, Palacký University Olomouc, třída 17. listopadu 12, 771 46 Olomouc, Czech Republic
| | - Miroslav Kvasnica
- Laboratory of Growth Regulators, Faculty of Science of Palacký University & Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
| | - Jana Oklestkova
- Laboratory of Growth Regulators, Faculty of Science of Palacký University & Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
| | - Eva Kudova
- Institute of Organic Chemistry and Biochemistry AS CR, Flemingovo náměstí 2, 166 10, Praha 6, Czech Republic
| | - Miroslav Strnad
- Laboratory of Growth Regulators, Faculty of Science of Palacký University & Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
| | - Roman Hrstka
- Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Žlutý kopec 7, 656 53 Brno, Czech Republic; Laboratory of Growth Regulators, Faculty of Science of Palacký University & Institute of Experimental Botany of the Czech Academy of Sciences, Šlechtitelů 27, 783 71 Olomouc, Czech Republic.
| |
Collapse
|
5
|
Shen L, Feng H, Qiu Y, Wei GW. SVSBI: sequence-based virtual screening of biomolecular interactions. Commun Biol 2023; 6:536. [PMID: 37202415 PMCID: PMC10195826 DOI: 10.1038/s42003-023-04866-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023] Open
Abstract
Virtual screening (VS) is a critical technique in understanding biomolecular interactions, particularly in drug design and discovery. However, the accuracy of current VS models heavily relies on three-dimensional (3D) structures obtained through molecular docking, which is often unreliable due to the low accuracy. To address this issue, we introduce a sequence-based virtual screening (SVS) as another generation of VS models that utilize advanced natural language processing (NLP) algorithms and optimized deep K-embedding strategies to encode biomolecular interactions without relying on 3D structure-based docking. We demonstrate that SVS outperforms state-of-the-art performance for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for protein-protein interactions in five biological species. SVS has the potential to transform current practices in drug discovery and protein engineering.
Collapse
Affiliation(s)
- Li Shen
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Hongsong Feng
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA.
| |
Collapse
|
6
|
Zhao S, Zhang X, da Silva-Júnior EF, Zhan P, Liu X. Computer-aided drug design in seeking viral capsid modulators. Drug Discov Today 2023; 28:103581. [PMID: 37030533 DOI: 10.1016/j.drudis.2023.103581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/16/2023] [Accepted: 03/30/2023] [Indexed: 04/09/2023]
Abstract
Approved or licensed antiviral drugs have limited applications because of their drug resistance and severe adverse effects. By contrast, by stabilizing or destroying the viral capsid, compounds known as capsid modulators prevent viral replication by acting on new targets and, therefore, overcoming the problem of clinical drug resistance. For example. computer-aided drug design (CADD) methods, using strategies based on structures of biological targets (structure-based drug design; SBDD), such as docking, molecular dynamics (MD) simulations, and virtual screening (VS), have provided opportunities for fast and effective development of viral capsid modulators. In this review, we summarize the application of CADD in the discovery, optimization, and mechanism prediction of capsid-targeting small molecules, providing new insights into antiviral drug discovery modalities. Teaser: Computer-aided drug design will accelerate the development of viral capsid regulators, which brings new hope for the treatment of refractory viral diseases.
Collapse
Affiliation(s)
- Shujie Zhao
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology, Ministry of Education, School of Pharmaceutical Sciences, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, PR China
| | - Xujie Zhang
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology, Ministry of Education, School of Pharmaceutical Sciences, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, PR China
| | - Edeildo Ferreira da Silva-Júnior
- Institute of Chemistry and Biotechnology, Federal University of Alagoas, Lourival Melo Mota Avenue, 57072-970 Maceió, Alagoas, Brazil.
| | - Peng Zhan
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology, Ministry of Education, School of Pharmaceutical Sciences, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, PR China.
| | - Xinyong Liu
- Department of Medicinal Chemistry, Key Laboratory of Chemical Biology, Ministry of Education, School of Pharmaceutical Sciences, Shandong University, 44 West Culture Road, 250012 Jinan, Shandong, PR China.
| |
Collapse
|
7
|
Rehman AU, Khurshid B, Ali Y, Rasheed S, Wadood A, Ng HL, Chen HF, Wei Z, Luo R, Zhang J. Computational approaches for the design of modulators targeting protein-protein interactions. Expert Opin Drug Discov 2023; 18:315-333. [PMID: 36715303 PMCID: PMC10149343 DOI: 10.1080/17460441.2023.2171396] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 01/18/2023] [Indexed: 01/31/2023]
Abstract
BACKGROUND Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery. AREAS COVERED In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes. EXPERT OPINION Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.
Collapse
Affiliation(s)
- Ashfaq Ur Rehman
- Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California Irvine, Irvine, California, USA
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, Zhejiang, China
| | - Beenish Khurshid
- Department of Biochemistry, Abdul Wali Khan University Mardan, Pakistan
| | - Yasir Ali
- National Center for Bioinformatics, Quaid-e-Azam University, Islamabad, Pakistan
| | - Salman Rasheed
- National Center for Bioinformatics, Quaid-e-Azam University, Islamabad, Pakistan
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University Mardan, Pakistan
| | - Ho-Leung Ng
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, Kansas, USA
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, Zhejiang, China
| | - Zhiqiang Wei
- Medicinal Chemistry and Bioinformatics Center, Ocean University of China, Qingdao, Shandong, China
| | - Ray Luo
- Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California Irvine, Irvine, California, USA
| | - Jian Zhang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, Zhejiang, China
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
| |
Collapse
|
8
|
Szwabowski GL, Baker DL, Parrill AL. Application of computational methods for class A GPCR Ligand discovery. J Mol Graph Model 2023; 121:108434. [PMID: 36841204 DOI: 10.1016/j.jmgm.2023.108434] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/22/2023]
Abstract
G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.
Collapse
Affiliation(s)
| | - Daniel L Baker
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA
| | - Abby L Parrill
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
| |
Collapse
|
9
|
García JS, Puertas-Martín S, Redondo JL, Moreno JJ, Ortigosa PM. Improving drug discovery through parallelism. THE JOURNAL OF SUPERCOMPUTING 2023; 79:9538-9557. [PMID: 36687309 PMCID: PMC9842220 DOI: 10.1007/s11227-022-05014-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.
Collapse
Affiliation(s)
- Jerónimo S. García
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
| | - Savíns Puertas-Martín
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
- Information School, University of Sheffield, 221, Portobello Street, Sheffield, S1 4DP United Kingdom
| | - Juana L. Redondo
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
| | - Juan José Moreno
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
| | - Pilar M. Ortigosa
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
| |
Collapse
|
10
|
Henriquez-Figuereo A, Morán-Serradilla C, Angulo-Elizari E, Sanmartín C, Plano D. Small molecules containing chalcogen elements (S, Se, Te) as new warhead to fight neglected tropical diseases. Eur J Med Chem 2023; 246:115002. [PMID: 36493616 DOI: 10.1016/j.ejmech.2022.115002] [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: 10/28/2022] [Revised: 11/21/2022] [Accepted: 12/01/2022] [Indexed: 12/13/2022]
Abstract
Neglected tropical diseases (NTDs) encompass a group of infectious diseases with a protozoan etiology, high incidence, and prevalence in developing countries. As a result, economic factors constitute one of the main obstacles to their management. Endemic countries have high levels of poverty, deprivation and marginalization which affect patients and limit their access to proper medical care. As a matter of fact, statistics remain uncollected in some affected areas due to non-reporting cases. World Health Organization and other organizations proposed a plan for the eradication and control of the vector, although many of these plans were halted by the COVID-19 pandemic. Despite of the available drugs to treat these pathologies, it exists a lack of effectiveness against several parasite strains. Treatment protocols for diseases such as American trypanosomiasis (Chagas disease), leishmaniasis, and human African trypanosomiasis (HAT) have not achieved the desired results. Unfortunately, these drugs present limitations such as side effects, toxicity, teratogenicity, renal, and hepatic impairment, as well as high costs that have hindered the control and eradication of these diseases. This review focuses on the analysis of a collection of scientific shreds of evidence with the aim of identifying novel chalcogen-derived molecules with biological activity against Chagas disease, leishmaniasis and HAT. Compounds illustrated in each figure share the distinction of containing at least one chalcogen element. Sulfur (S), selenium (Se), and tellurium (Te) have been grouped and analyzed in accordance with their design strategy, chemical synthesis process and biological activity. After an exhaustive revision of the related literature on S, Se, and Te compounds, 183 compounds presenting excellent biological performance were gathered against the different causative agents of CD, leishmaniasis and HAT.
Collapse
Affiliation(s)
- Andreina Henriquez-Figuereo
- University of Navarra, School of Pharmacy and Nutrition, Department of Pharmaceutical Technology and Chemistry, Irunlarrea 1, 31008, Pamplona, Spain; Institute of Tropical Health, University of Navarra, Irunlarrea 1, 31008, Pamplona, Spain.
| | - Cristina Morán-Serradilla
- University of Navarra, School of Pharmacy and Nutrition, Department of Pharmaceutical Technology and Chemistry, Irunlarrea 1, 31008, Pamplona, Spain
| | - Eduardo Angulo-Elizari
- University of Navarra, School of Pharmacy and Nutrition, Department of Pharmaceutical Technology and Chemistry, Irunlarrea 1, 31008, Pamplona, Spain
| | - Carmen Sanmartín
- University of Navarra, School of Pharmacy and Nutrition, Department of Pharmaceutical Technology and Chemistry, Irunlarrea 1, 31008, Pamplona, Spain; Institute of Tropical Health, University of Navarra, Irunlarrea 1, 31008, Pamplona, Spain.
| | - Daniel Plano
- University of Navarra, School of Pharmacy and Nutrition, Department of Pharmaceutical Technology and Chemistry, Irunlarrea 1, 31008, Pamplona, Spain; Institute of Tropical Health, University of Navarra, Irunlarrea 1, 31008, Pamplona, Spain.
| |
Collapse
|
11
|
Tawaraishi T, Ochida A, Akao Y, Itono S, Kamaura M, Akther T, Shimada M, Canan S, Chowdhury S, Cao Y, Condroski K, Engkvist O, Francisco A, Ghosh S, Kaki R, Kelly JM, Kimura C, Kogej T, Nagaoka K, Naito A, Pairaudeau G, Radu C, Roberts I, Shum D, Watanabe NA, Xie H, Yonezawa S, Yoshida O, Yoshida R, Mowbray C, Perry B. Collaborative Virtual Screening Identifies a 2-Aryl-4-aminoquinazoline Series with Efficacy in an In Vivo Model of Trypanosoma cruzi Infection. J Med Chem 2023; 66:1221-1238. [PMID: 36607408 PMCID: PMC9884087 DOI: 10.1021/acs.jmedchem.2c00775] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Probing multiple proprietary pharmaceutical libraries in parallel via virtual screening allowed rapid expansion of the structure-activity relationship (SAR) around hit compounds with moderate efficacy against Trypanosoma cruzi, the causative agent of Chagas Disease. A potency-improving scaffold hop, followed by elaboration of the SAR via design guided by the output of the phenotypic virtual screening efforts, identified two promising hit compounds 54 and 85, which were profiled further in pharmacokinetic studies and in an in vivo model of T. cruzi infection. Compound 85 demonstrated clear reduction of parasitemia in the in vivo setting, confirming the interest in this series of 2-(pyridin-2-yl)quinazolines as potential anti-trypanosome treatments.
Collapse
Affiliation(s)
- Taisuke Tawaraishi
- Takeda
Pharmaceutical Company Limited, 26-1 Muraoka-Higashi 2-chrome, Fujisawa, Kanagawa 251-8555, Japan
| | - Atsuko Ochida
- Takeda
Pharmaceutical Company Limited, 26-1 Muraoka-Higashi 2-chrome, Fujisawa, Kanagawa 251-8555, Japan
| | - Yuichiro Akao
- Takeda
Pharmaceutical Company Limited, 26-1 Muraoka-Higashi 2-chrome, Fujisawa, Kanagawa 251-8555, Japan
| | - Sachiko Itono
- Takeda
Pharmaceutical Company Limited, 26-1 Muraoka-Higashi 2-chrome, Fujisawa, Kanagawa 251-8555, Japan
| | - Masahiro Kamaura
- Takeda
Pharmaceutical Company Limited, 26-1 Muraoka-Higashi 2-chrome, Fujisawa, Kanagawa 251-8555, Japan
| | - Thamina Akther
- Takeda
Pharmaceutical Company Limited, 26-1 Muraoka-Higashi 2-chrome, Fujisawa, Kanagawa 251-8555, Japan
| | - Mitsuyuki Shimada
- Takeda
Pharmaceutical Company Limited, 26-1 Muraoka-Higashi 2-chrome, Fujisawa, Kanagawa 251-8555, Japan
| | - Stacie Canan
- Celgene
Corporation, Celgene Global Health, 10300 Campus Point Drive, San Diego, California 92121, United States
| | - Sanjoy Chowdhury
- TCG
Lifesciences, Plot No-7,
Salt Lake Electronics Complex, BN Block, Sector V, Kolkata 700091, India
| | - Yafeng Cao
- WuXi
AppTec Company Ltd., 666 Gaoxin Road, East Lake High-Tech Development Zone, Wuhan 430075, People’s Republic of China
| | - Kevin Condroski
- Celgene
Corporation, Celgene Global Health, 10300 Campus Point Drive, San Diego, California 92121, United States
| | - Ola Engkvist
- AstraZeneca
Discovery Sciences, R&D, Pepparedsleden 1, 431 50 Mölndal, Sweden
| | - Amanda Francisco
- London School
of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, U.K.
| | - Sunil Ghosh
- TCG
Lifesciences, Plot No-7,
Salt Lake Electronics Complex, BN Block, Sector V, Kolkata 700091, India
| | - Rina Kaki
- Shionogi
& Co., Ltd, 3-1-1,
Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
| | - John M. Kelly
- London School
of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, U.K.
| | - Chiaki Kimura
- Shionogi
& Co., Ltd, 3-1-1,
Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
| | - Thierry Kogej
- AstraZeneca
Discovery Sciences, R&D, Pepparedsleden 1, 431 50 Mölndal, Sweden
| | - Kazuya Nagaoka
- Eisai
Co., Ltd, 1-3, Tokodai
5-chome, Tsukuba, Ibaraki 300-2635, Japan
| | - Akira Naito
- Shionogi
& Co., Ltd, 3-1-1,
Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
| | - Garry Pairaudeau
- AstraZeneca,
Discovery Sciences, R&D, The Darwin Building, 310 Milton Road, Milton, Cambridge CB4 0WG, U.K.
| | - Constantin Radu
- Institut
Pasteur Korea, 16, Daewangpangyo-ro
712 beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13488, Republic of Korea
| | - Ieuan Roberts
- AstraZeneca,
Discovery Sciences, R&D, The Darwin Building, 310 Milton Road, Milton, Cambridge CB4 0WG, U.K.
| | - David Shum
- Institut
Pasteur Korea, 16, Daewangpangyo-ro
712 beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do 13488, Republic of Korea
| | - Nao-aki Watanabe
- Eisai
Co., Ltd, 1-3, Tokodai
5-chome, Tsukuba, Ibaraki 300-2635, Japan
| | - Huanxu Xie
- WuXi
AppTec Company Ltd., 666 Gaoxin Road, East Lake High-Tech Development Zone, Wuhan 430075, People’s Republic of China
| | - Shuji Yonezawa
- Shionogi
& Co., Ltd, 3-1-1,
Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
| | - Osamu Yoshida
- Shionogi
& Co., Ltd, 3-1-1,
Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
| | - Ryu Yoshida
- Shionogi
& Co., Ltd, 3-1-1,
Futaba-cho, Toyonaka-shi, Osaka 561-0825, Japan
| | - Charles Mowbray
- Drugs for Neglected
Diseases Initiative, 15 Chemin Camille Vidart, Geneva 1202, Switzerland
| | - Benjamin Perry
- Drugs for Neglected
Diseases Initiative, 15 Chemin Camille Vidart, Geneva 1202, Switzerland,
| |
Collapse
|
12
|
Bort W, Mazitov D, Horvath D, Bonachera F, Lin A, Marcou G, Baskin I, Madzhidov T, Varnek A. Inverse QSAR: Reversing Descriptor-Driven Prediction Pipeline Using Attention-Based Conditional Variational Autoencoder. J Chem Inf Model 2022; 62:5471-5484. [PMID: 36332178 DOI: 10.1021/acs.jcim.2c01086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In order to better foramize it, the notorious inverse-QSAR problem (finding structures of given QSAR-predicted properties) is considered in this paper as a two-step process including (i) finding "seed" descriptor vectors corresponding to user-constrained QSAR model output values and (ii) identifying the chemical structures best matching the "seed" vectors. The main development effort here was focused on the latter stage, proposing a new attention-based conditional variational autoencoder neural-network architecture based on recent developments in attention-based methods. The obtained results show that this workflow was capable of generating compounds predicted to display desired activity while being completely novel compared to the training database (ChEMBL). Moreover, the generated compounds show acceptable druglikeness and synthetic accessibility. Both pharmacophore and docking studies were carried out as "orthogonal" in silico validation methods, proving that some of de novo structures are, beyond being predicted active by 2D-QSAR models, clearly able to match binding 3D pharmacophores and bind the protein pocket.
Collapse
Affiliation(s)
- William Bort
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Daniyar Mazitov
- Laboratory of Chemoinformatics and Molecular Modeling, A. M. Butlerov Institute of Chemistry, Kazan Federal University, 18, Kremlyovskaya str., 420008 Kazan, Russia
| | - Dragos Horvath
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Fanny Bonachera
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Arkadii Lin
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Gilles Marcou
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - Igor Baskin
- Department of Material Science and Engineering, Technion─Israel Institute of Technology, 3200003 Haifa, Israel
| | - Timur Madzhidov
- Laboratory of Chemoinformatics and Molecular Modeling, A. M. Butlerov Institute of Chemistry, Kazan Federal University, 18, Kremlyovskaya str., 420008 Kazan, Russia
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, UMR 7140 University of Strasbourg/CNRS, 4 rue Blaise Pascal, 67000 Strasbourg, France
| |
Collapse
|
13
|
Liu Z, Du J, Lin Z, Li Z, Liu B, Cui Z, Fang J, Xie L. DenovoProfiling: A webserver for de novo generated molecule library profiling. Comput Struct Biotechnol J 2022; 20:4082-4097. [PMID: 36016718 PMCID: PMC9379519 DOI: 10.1016/j.csbj.2022.07.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 01/10/2023] Open
Abstract
Various deep learning-based architectures for molecular generation have been proposed for de novo drug design. The flourish of the de novo molecular generation methods and applications has created a great demand for the visualization and functional profiling for the de novo generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the de novo molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.
Collapse
Key Words
- DDR1, Discovered potent discoidin domain receptor 1
- De novo drug design
- De novo molecule library
- Deep learning
- FBDD, Fragment-based drug design
- FDR, False discovery rate
- GAN, Generative adversarial networks
- HTS, High throughput screening
- LSTM, Long short-term memory
- Library profiling
- PCA, Principal components analysis
- RNN, Recurrent neural networks
- SCA, Scaffold-based classification approach
- VAE, Variational autoencoders
Collapse
Affiliation(s)
- Zhihong Liu
- School of Public Health, Xinxiang Medical University, Xinxiang, China
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Jiewen Du
- Beijing Jingpai Technology Co., Ltd., 1500-1, Hailong Building Z-Park, Beijing 100090, China
| | - Ziying Lin
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Ze Li
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Bingdong Liu
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Zongbin Cui
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
- Corresponding authors at: School of Public Health, Xinxiang Medical University, Xinxiang, China (L. Xie). Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China (J. Fang).
| | - Liwei Xie
- School of Public Health, Xinxiang Medical University, Xinxiang, China
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
- Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Corresponding authors at: School of Public Health, Xinxiang Medical University, Xinxiang, China (L. Xie). Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China (J. Fang).
| |
Collapse
|
14
|
Structure-Based Virtual Screening, Docking, ADMET, Molecular Dynamics, and MM-PBSA Calculations for the Discovery of Potential Natural SARS-CoV-2 Helicase Inhibitors from the Traditional Chinese Medicine. J CHEM-NY 2022. [DOI: 10.1155/2022/7270094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Continuing our antecedent work against COVID-19, a set of 5956 compounds of traditional Chinese medicine have been virtually screened for their potential against SARS-CoV-2 helicase (PDB ID: 5RMM). Initially, a fingerprint study with VXG, the ligand of the target enzyme, disclosed the similarity of 187 compounds. Then, a molecular similarity study declared the most similar 40 compounds. Subsequently, molecular docking studies were carried out to examine the binding modes and energies. Then, the most appropriate 26 compounds were subjected to in silico ADMET and toxicity studies to select the most convenient inhibitors to be: (1R,2S)-ephedrine (57), (1R,2S)-norephedrine (59), 2-(4-(pyrrolidin-1-yl)phenyl)acetic acid (84), 1-phenylpropane-1,2-dione (195), 2-methoxycinnamic acid (246), 2-methoxybenzoic acid (364), (R)-2-((R)-5-oxopyrrolidin-3-yl)-2-phenylacetic acid (405), (Z)-6-(3-hydroxy-4-methoxystyryl)-4-methoxy-2H-pyran-2-one (533), 8-chloro-2-(2-phenylethyl)-5,6,7-trihydroxy-5,6,7,8-tetrahydrochromone (637), 3-((1R,2S)-2-(dimethylamino)-1-hydroxypropyl)phenol (818), (R)-2-ethyl-4-(1-hydroxy-2-(methylamino)ethyl)phenol (5159), and (R)-2-((1S,2S,5S)-2-benzyl-5-hydroxy-4-methylcyclohex-3-en-1-yl)propane-1,2-diol (5168). Among the selected 12 compounds, the metabolites, compound 533 showed the best docking scores. Interestingly, the MD simulation studies for compound 533, the one with the highest docking score, over 100 ns showed its correct binding to SARS-CoV-2 helicase with low energy and optimum dynamics. Finally, MM-PBSA studies showed that 533 bonded favorably to SARS-CoV-2 helicase with a free energy value of −83 kJ/mol. Further, the free energy decomposition study determined the essential amino acid residues that contributed favorably to the binding process. The obtained results give a huge hope to find a cure for COVID-19 through further in vitro and in vivo studies for the selected compounds.
Collapse
|
15
|
Ligand-Enhanced Negative Images Optimized for Docking Rescoring. Int J Mol Sci 2022; 23:ijms23147871. [PMID: 35887220 PMCID: PMC9323918 DOI: 10.3390/ijms23147871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 12/04/2022] Open
Abstract
Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.
Collapse
|
16
|
Wu Y, Zhang B, Dong X, Ma S, Hu S. Discovery of Novel Small Molecule HDAC1, 2, 3 Inhibitors -- Combined
Receptor-Based and Ligand-Based Virtual Screening Strategy. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180819666211220124300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
This study aims to investigate and validate the potential drug target to HDAC1.
Background:
Human histone deacetylase 1 (HDAC1) can catalyze the deacetylation of histones belonging
to the family of human histone deacetylases (HDACs). Amide hydrolase HDAC1 plays a key role in
the development of many serious cancers such as prostate cancer, gastric cancer, lung cancer, esophageal
cancer, colon cancer, and breast cancer. Therefore, HDAC1 inhibitors, promoting the transcription of a
series of key genes such as the p53 gene and inhibiting the development of cancer through various downstream
mechanisms, have great potential for the treatment of cancer.
Objective:
The objective of this study is to discover new skeleton HDAC1 inhibitors efficiently and conveniently
with therapeutic potential for cancer.
Method:
Based on the crystal structure of HDAC1, through the combination of receptor-based and ligand-
based virtual screening from the commercial compound library, the top-ranked compounds are selected
for purchase through binding modes analysis, and their activities were verified through in vitro
HDAC1 inhibitory biological experiments.
Results:
Based on LeDock, 5ICN showed good distinguishing ability and was used as the receptor. According
to the results of the LeDock docking scoring from receptor-based virtual screening, 69 compounds
with binding energy less than -7.5 kcal/mol were obtained and used for ligand-based virtual
screening. A total of 21 novel compounds with high potential HDAC1 inhibitory activity were collected
by combining the similarity searching (NN) and the multinomial Naive Bayes machine learning model
(NB) methods. Through binding modes analysis, 10 compounds with different structures with potential
HDAC1 inhibitory activity were selected and screened HDAC1 inhibitory in vitro. May267 showed moderate
HDAC1 inhibitory activity, and the inhibition rate was 48% at a concentration of 20 μM.
Conclusion:
This study discovers novel small molecule HDAC1 inhibitors by combined receptor-based
and ligand-based virtual screening strategy, which provides an efficient method for the discovery of other
small molecule drugs. May267 shows moderate HDAC1 inhibitory activity, which can be further optimized
as a lead compound. However, it still has the problem of poor kinase selectivity to be solved.
Collapse
Affiliation(s)
- Yi Wu
- Department of General Surgery, Nanjing Medical University, Hangzhou First People’s Hospital, Hangzhou, Zhejiang
310006, P.R. China
| | - Bo Zhang
- Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology
and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People\'s Hospital, Cancer Center,
Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, P.R. China
| | - Xiaowu Dong
- Hangzhou Institute of Innovative
Medicine, Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang
University, Hangzhou, Zhejiang 310058, P.R. China
| | - Shenglin Ma
- Department of Oncology, Nanjing Medical University, Hangzhou First People’s Hospital, Hangzhou,
Zhejiang 310006, P.R. China
| | - Shengquan Hu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou,
Zhejiang 310058, P.R. China
| |
Collapse
|
17
|
Hutter MC. Differential Multimolecule Fingerprint for Similarity Search─Making Use of Active and Inactive Compound Sets in Virtual Screening. J Chem Inf Model 2022; 62:2726-2736. [PMID: 35613341 DOI: 10.1021/acs.jcim.2c00242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
In conventional fingerprint methods, the similarity between two molecules is calculated using the Tanimoto index as a numerical criterion. Thus, the query molecules in virtual screening should be most representative of the wanted compound class at hand. In the concept introduced here, all available active molecules form a multimolecule fingerprint in which the appearing features are weighted according to their respective frequency. The features of inactive molecules are treated likewise and the resulting values are subtracted from those of the active ones. The obtained differential multimolecule fingerprint (DMMFP) is thus specific for the respective class of compounds. To account for the noninteger representation within this fingerprint, a modified Sørensen-Dice coefficient is used to compute the similarity. Potentially active molecules yield positive scores, whereas presumably inactive ones are denoted by negative values. The concept was applied to Angiotensin-converting enzyme (ACE) inhibitors, β2-adrenoceptor ligands, leukotriene A4 hydrolase inhibitors, dopamine D3 antagonists, and cytochrome CYP2C9 substrates, for which experimental binding affinities are known and was tested against decoys from DUD-E and a further background database consisting of molecules from the dark chemical matter, which comprises compounds that appear as frequent hitters across multiple assays. Using the 166 publicly available keys of the MACCS fingerprint and the larger PubChem fingerprint, actives were recovered with very high sensitivity. Furthermore, three marketed ACE inhibitors as well as the carbonic anhydrase II inhibitor dorzolamide were detected in the dark chemical matter data set. For comparison, the DMMFP was also used with a Bayesian classifier, for which the specificity (correctly classified inactives) and likewise the accuracy was superior. Conversely, the similarity score produced by the Sørensen-Dice coefficient showed its potential for the early recognition of (potentially) active molecules.
Collapse
Affiliation(s)
- Michael C Hutter
- Center for Bioinformatics, Saarland University, Campus E2.1, 66123 Saarbruecken, Germany
| |
Collapse
|
18
|
Sepehri B, Ghavami R, Mahmoudi F, Irani M, Ahmadi R, Moradi D. Identifying SARS-CoV-2 main protease inhibitors by applying the computer screening of a large database of molecules. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:341-356. [PMID: 35502579 DOI: 10.1080/1062936x.2022.2050424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/02/2022] [Indexed: 06/14/2023]
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) at the end of 2019 affected global health. Its infection agent was called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wearing a mask, maintaining social distance, and vaccination are effective ways to prevent infection of SARS-CoV-2, but none of them help infected people. Targeting the enzymes of SARS-CoV-2 is an effective way to stop the replication of the virus in infected people and treat COVID-19 patients. SARS-CoV-2 main protease is a therapeutic target which the inhibition of its enzymatic activity prevents from the replication of SARS-CoV-2. A large database of molecules has been searched to identify new inhibitors for SARS-CoV-2 main protease enzyme. At the first step, ligand screening based on similarity search was used to select similar compounds to known SARS-CoV-2 main protease inhibitors. Then molecules with better predicted pharmacokinetic properties were selected. Structure-based virtual screening based on the application of molecular docking and molecular dynamics simulation methods was used to select more effective inhibitors among selected molecules in previous step. Finally two compounds were considered as SARS-CoV-2 main protease inhibitors.
Collapse
Affiliation(s)
- B Sepehri
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - R Ghavami
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - F Mahmoudi
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - M Irani
- Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - R Ahmadi
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| | - D Moradi
- Chemometrics Laboratory, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Iran
| |
Collapse
|
19
|
Nag S, Baidya ATK, Mandal A, Mathew AT, Das B, Devi B, Kumar R. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022; 12:110. [PMID: 35433167 PMCID: PMC8994527 DOI: 10.1007/s13205-022-03165-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 03/18/2022] [Indexed: 12/26/2022] Open
Abstract
A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.
Collapse
Affiliation(s)
- Sagorika Nag
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Anurag T. K. Baidya
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Abhimanyu Mandal
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Alen T. Mathew
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Bhanuranjan Das
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Bharti Devi
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Rajnish Kumar
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| |
Collapse
|
20
|
Vulpetti A, Lingel A, Dalvit C, Schiering N, Oberer L, Henry C, Lu Y. Efficient Screening of Target-Specific Selected Compounds in Mixtures by 19F NMR Binding Assay with Predicted 19F NMR Chemical Shifts. ChemMedChem 2022; 17:e202200163. [PMID: 35475323 DOI: 10.1002/cmdc.202200163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/26/2022] [Indexed: 11/06/2022]
Abstract
Ligand-based 19 F NMR screening is a highly effective and well-established hit-finding approach. The high sensitivity to protein binding makes it particularly suitable for fragment screening. Different criteria can be considered for generating fluorinated fragment libraries. One common strategy is to assemble a large, diverse, well-designed and characterized fragment library which is screened in mixtures, generated based on experimental 19 F NMR chemical shifts. Here, we introduce a complementary knowledge-based 19 F NMR screening approach, named 19 Focused screening, enabling the efficient screening of putative active molecules selected by computational hit finding methodologies, in mixtures assembled and on-the-fly deconvoluted based on predicted 19 F NMR chemical shifts. In this study, we developed a novel approach, named LEFshift , for 19 F NMR chemical shift prediction using rooted topological fluorine torsion fingerprints in combination with a random forest machine learning method. A demonstration of this approach to a real test case is reported.
Collapse
Affiliation(s)
- Anna Vulpetti
- Novartis Pharma AG, Global Discovery Chemistry, Novartis Campus, 4002, Basel, SWITZERLAND
| | - Andreas Lingel
- Novartis Institutes for BioMedical Research Basel, Global Discovery Chemistry, SWITZERLAND
| | - Claudio Dalvit
- Novartis Institutes for BioMedical Research Basel, Protease Platform, SWITZERLAND
| | - Nikolaus Schiering
- Novartis Institutes for BioMedical Research Basel, Protease Platform, SWITZERLAND
| | - Lukas Oberer
- Novartis Institutes for BioMedical Research Basel, Global Discovery Chemistry, SWITZERLAND
| | - Chrystelle Henry
- Novartis Institutes for BioMedical Research Basel, Protein Science, SWITZERLAND
| | - Yipin Lu
- Novartis Institutes for BioMedical Research Basel, Global Discovery Chemistry, SWITZERLAND
| |
Collapse
|
21
|
Venkatraman V, Colligan TH, Lesica GT, Olson DR, Gaiser J, Copeland CJ, Wheeler TJ, Roy A. Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets. Front Pharmacol 2022; 13:874746. [PMID: 35559261 PMCID: PMC9086895 DOI: 10.3389/fphar.2022.874746] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.
Collapse
Affiliation(s)
- Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas H. Colligan
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - George T. Lesica
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Daniel R. Olson
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Jeremiah Gaiser
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Conner J. Copeland
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Travis J. Wheeler
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Amitava Roy
- Department of Computer Science, University of Montana, Missoula, MT, United States
- Rocky Mountain Laboratories, Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, United States
| |
Collapse
|
22
|
Cheshmazar N, Hemmati S, Hamzeh-Mivehroud M, Sokouti B, Zessin M, Schutkowski M, Sippl W, Nozad Charoudeh H, Dastmalchi S. Development of New Inhibitors of HDAC1-3 Enzymes Aided by In Silico Design Strategies. J Chem Inf Model 2022; 62:2387-2397. [PMID: 35467871 DOI: 10.1021/acs.jcim.1c01557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Histone deacetylases (HDACs) are overexpressed in cancer, and their inhibition shows promising results in cancer therapy. In particular, selective class I HDAC inhibitors such as entinostat are proposed to be more beneficial in breast cancer treatment. Computational drug design is an inevitable part of today's drug discovery projects because of its unequivocal role in saving time and cost. Using three HDAC inhibitors trichostatin, vorinostat, and entinostat as template structures and a diverse fragment library, all synthetically accessible compounds thereof (∼3200) were generated virtually and filtered based on similarity against the templates and PAINS removal. The 298 selected structures were docked into the active site of HDAC I and ranked using a calculated binding affinity. Top-ranking structures were inspected manually, and, considering the ease of synthesis and drug-likeness, two new structures (3a and 3b) were proposed for synthesis and biological evaluation. The synthesized compounds were purified to a degree of more than 95% and structurally verified using various methods. The designed compounds 3a and 3b showed 65-80 and 5% inhibition on HDAC 1, 2, and 3 isoforms at a concentration of 10 μM, respectively. The novel compound 3a may be used as a lead structure for designing new HDAC inhibitors.
Collapse
Affiliation(s)
- Narges Cheshmazar
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz 5165665931, Iran.,Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665813, Iran.,Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 5166414766, Iran
| | - Salar Hemmati
- Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz 51656-65811, Iran
| | - Maryam Hamzeh-Mivehroud
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665813, Iran.,Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 5166414766, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665813, Iran
| | - Matthes Zessin
- Department of Enzymology, Institute of Biochemistry, Martin-Luther-University Halle-Wittenberg, 06120 Halle/Saale, Germany
| | - Mike Schutkowski
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, 06120 Halle/Saale, Germany
| | - Wolfgang Sippl
- Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, 06120 Halle/Saale, Germany
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz 5165665813, Iran.,Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz 5166414766, Iran.,Faculty of Pharmacy, Near East University, P.O. Box 99138, Nicosia, North Cyprus, Mersin 10, Turkey
| |
Collapse
|
23
|
ElHefnawi M, Jo E, Tolba MM, Fares M, Yang J, Shahbaaz M, Windisch MP. Drug repurposing through virtual screening and in vitro validation identifies tigecycline as a novel putative HCV polymerase inhibitor. Virology 2022; 570:9-17. [DOI: 10.1016/j.virol.2022.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/25/2022] [Accepted: 02/26/2022] [Indexed: 10/18/2022]
|
24
|
Rai BK, Sresht V, Yang Q, Unwalla R, Tu M, Mathiowetz AM, Bakken GA. TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics. J Chem Inf Model 2022; 62:785-800. [PMID: 35119861 DOI: 10.1021/acs.jcim.1c01346] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM) methods are limited by insufficient coverage of drug-like chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small-molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate compound library and leveraged massively parallel cloud computing resources for density functional theory (DFT) torsion scans of these fragments, generating a training data set of 1.2 million DFT energies. After training TorsionNet on this data set, we obtain a model that can rapidly predict the torsion energy profile of typical drug-like fragments with DFT-level accuracy. Importantly, our method also provides an uncertainty estimate for the predicted profiles without any additional calculations. In this report, we show that TorsionNet can accurately identify the preferred dihedral geometries observed in crystal structures. Our TorsionNet-based analysis of a diverse set of protein-ligand complexes with measured binding affinity shows a strong association between high ligand strain and low potency. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. TorsionNet500, a benchmark data set comprising 500 chemically diverse fragments with DFT torsion profiles (12k MM- and DFT-optimized geometries and energies), has been created and is made publicly available.
Collapse
Affiliation(s)
- Brajesh K Rai
- Simulation and Modeling Sciences, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Vishnu Sresht
- Simulation and Modeling Sciences, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Qingyi Yang
- Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Ray Unwalla
- Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Meihua Tu
- Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Alan M Mathiowetz
- Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States
| | - Gregory A Bakken
- Digital, Pfizer, Eastern Point Road, Groton, Connecticut 06340, United States
| |
Collapse
|
25
|
Suay-García B, Bueso-Bordils JI, Falcó A, Antón-Fos GM, Alemán-López PA. Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design. Int J Mol Sci 2022; 23:ijms23031620. [PMID: 35163543 PMCID: PMC8836228 DOI: 10.3390/ijms23031620] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/24/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.
Collapse
Affiliation(s)
- Beatriz Suay-García
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
- Correspondence:
| | - Jose I. Bueso-Bordils
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Antonio Falcó
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
| | - Gerardo M. Antón-Fos
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Pedro A. Alemán-López
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| |
Collapse
|
26
|
Ligand Based Virtual Screening Using Self-organizing Maps. Protein J 2022; 41:44-54. [PMID: 35022993 DOI: 10.1007/s10930-021-10030-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2021] [Indexed: 10/19/2022]
Abstract
Conventional drug discovery methods rely primarily on in-vitro experiments with a target molecule and an extensive set of small molecules to choose the suitable ligand. The exploration space for the selected ligand being huge; this approach is highly time-consuming and requires high capital for facilitation. Virtual screening, a computational technique used to reduce this search space and identify lead molecules, can speed up the drug discovery process. This paper proposes a ligand-based virtual screening method using an artificial neural network called self-organizing map (SOM). The proposed work uses two SOMs to predict the active and inactive molecules separately. This SOM based technique can uniquely label a small molecule as active, inactive, and undefined as well. This can reduce the number of false positives in the screening process and improve recall; compared to support vector machine and random forest based models. Additionally, by exploiting the parallelism present in the learning and classification phases of a SOM, a graphics processing unit (GPU) based model yields much better execution time. The proposed GPU-based SOM tool can successfully evaluate a large number of molecules in training and screening phases. The source code of the implementation and related files are available at https://github.com/jayarajpbalakrishnan/2_SOM_SCREEN.
Collapse
|
27
|
Al-Najjar BO, Saqallah FG, Abbas MA, Al-Hijazeen SZ, Sibai OA. P2Y 12 antagonists: Approved drugs, potential naturally isolated and synthesised compounds, and related in-silico studies. Eur J Med Chem 2022; 227:113924. [PMID: 34731765 DOI: 10.1016/j.ejmech.2021.113924] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 09/27/2021] [Accepted: 10/13/2021] [Indexed: 12/12/2022]
Abstract
P2Y12 is a platelet surface protein which is responsible for the amplification of P2Y1 response. It plays a crucial role in platelet aggregation and thrombus formation through an ADP-induced platelet activation mechanism. Despite that P2Y12 platelets' receptor is an excellent target for developing antiplatelet agents, only five approved medications are currently in clinical use which are classified into thienopyridines and nucleoside-nucleotide derivatives. In the past years, many attempts for developing new candidates as P2Y12 inhibitors have been made. This review highlights the importance and the role of P2Y12 receptor as part of the coagulation cascade, its reported congenital defects, and the type of assays which are used to verify and measure its activity. Furthermore, an overview is given of the clinically approved medications, the potential naturally isolated inhibitors, and the synthesised candidates which were tested either in-vitro, in-vivo and/or clinically. Finally, we outline the in-silico attempts which were carried out using virtual screening, molecular docking and dynamics simulations in efforts of designing novel P2Y12 antagonists. Various phytochemical classes might be considered as a corner stone for the discovery of novel P2Y12 inhibitors, whereas a wide range of ring systems can be deliberated as leading scaffolds in that area synthetically and theoretically.
Collapse
Affiliation(s)
- Belal O Al-Najjar
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, Al-Ahliyya Amman University, 19328, Amman, Jordan; Pharmacological and Diagnostic Research Lab, Al-Ahliyya Amman University, 19328, Amman, Jordan.
| | - Fadi G Saqallah
- Pharmaceutical Design and Simulation (PhDS) Laboratory, School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia
| | - Manal A Abbas
- Pharmacological and Diagnostic Research Lab, Al-Ahliyya Amman University, 19328, Amman, Jordan; Department of Medical Laboratory Sciences, Faculty of Allied Medical Sciences, Al-Ahliyya Amman University, 19328, Amman, Jordan
| | | | - Obada A Sibai
- Faculty of Pharmacy, Al-Ahliyya Amman University, 19328, Amman, Jordan
| |
Collapse
|
28
|
Virtual screening against Mycobacterium tuberculosis DNA gyrase: Applications and success stories. ANNUAL REPORTS IN MEDICINAL CHEMISTRY 2022. [DOI: 10.1016/bs.armc.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
29
|
Amendola G, Cosconati S. PyRMD: A New Fully Automated AI-Powered Ligand-Based Virtual Screening Tool. J Chem Inf Model 2021; 61:3835-3845. [PMID: 34270903 DOI: 10.1021/acs.jcim.1c00653] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Artificial intelligence (AI) algorithms are dramatically redefining the current drug discovery landscape by boosting the efficiency of its various steps. Still, their implementation often requires a certain level of expertise in AI paradigms and coding. This often prevents the use of these powerful methodologies by non-expert users involved in the design of new biologically active compounds. Here, the random matrix discriminant (RMD) algorithm, a high-performance AI method specifically tailored for the identification of new ligands, was implemented in a new fully automated tool, PyRMD. This ligand-based virtual screening tool can be trained using target bioactivity data directly downloaded from the ChEMBL repository without manual intervention. The software automatically splits the available training compounds into active and inactive sets and learns the distinctive chemical features responsible for the compounds' activity/inactivity. PyRMD was designed to easily screen millions of compounds in hours through an automated workflow and intuitive input files, allowing fine tuning of each parameter of the calculation. Additionally, PyRMD features a wealth of benchmark metrics, to accurately probe the model performance, which were used here to gauge its predictive potential and limitations. PyRMD is freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.
Collapse
Affiliation(s)
- Giorgio Amendola
- DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy
| | - Sandro Cosconati
- DiSTABiF, University of Campania Luigi Vanvitelli, Via Vivaldi 43, 81100 Caserta, Italy
| |
Collapse
|
30
|
Song YQ, Wu C, Wu KJ, Han QB, Miao XM, Ma DL, Leung CH. Ubiquitination Regulators Discovered by Virtual Screening for the Treatment of Cancer. Front Cell Dev Biol 2021; 9:665646. [PMID: 34055799 PMCID: PMC8149734 DOI: 10.3389/fcell.2021.665646] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/15/2021] [Indexed: 12/03/2022] Open
Abstract
The ubiquitin-proteasome system oversees cellular protein degradation in order to regulate various critical processes, such as cell cycle control and DNA repair. Ubiquitination can serve as a marker for mutation, chemical damage, transcriptional or translational errors, and heat-induced denaturation. However, aberrant ubiquitination and degradation of tumor suppressor proteins may result in the growth and metastasis of cancer. Hence, targeting the ubiquitination cascade reaction has become a potential strategy for treating malignant diseases. Meanwhile, computer-aided methods have become widely accepted as fast and efficient techniques for early stage drug discovery. This review summarizes ubiquitination regulators that have been discovered via virtual screening and their applications for cancer treatment.
Collapse
Affiliation(s)
- Ying-Qi Song
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau
| | - Chun Wu
- Department of Chemistry, Hong Kong Baptist University, Kowloon, Hong Kong
| | - Ke-Jia Wu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau
| | - Quan-Bin Han
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong
| | - Xiang-Min Miao
- Department of Chemistry, Hong Kong Baptist University, Kowloon, Hong Kong
| | - Dik-Lung Ma
- Department of Chemistry, Hong Kong Baptist University, Kowloon, Hong Kong
| | - Chung-Hang Leung
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau
| |
Collapse
|
31
|
Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches. Commun Chem 2021; 4:61. [PMID: 36697634 PMCID: PMC9814727 DOI: 10.1038/s42004-021-00498-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 03/24/2021] [Indexed: 01/28/2023] Open
Abstract
The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein-ligand binding affinity.
Collapse
|
32
|
Discovery of New Small Molecule Hits as Hepatitis B Virus Capsid Assembly Modulators: Structure and Pharmacophore-Based Approaches. Viruses 2021; 13:v13050770. [PMID: 33925540 PMCID: PMC8146408 DOI: 10.3390/v13050770] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 04/05/2021] [Accepted: 04/23/2021] [Indexed: 12/17/2022] Open
Abstract
Hepatitis B virus (HBV) capsid assembly modulators (CpAMs) have shown promise as potent anti-HBV agents in both preclinical and clinical studies. Herein, we report our efforts in identifying novel CpAM hits via a structure-based virtual screening against a small molecule protein-protein interaction (PPI) library, and pharmacophore-guided compound design and synthesis. Curated compounds were first assessed in a thermal shift assay (TSA), and the TSA hits were further evaluated in an antiviral assay. These efforts led to the discovery of two structurally distinct scaffolds, ZW-1841 and ZW-1847, as novel HBV CpAM hits, both inhibiting HBV in single-digit µM concentrations without cytotoxicity at 100 µM. In ADME assays, both hits displayed extraordinary plasma and microsomal stability. Molecular modeling suggests that these hits bind to the Cp dimer interfaces in a mode well aligned with known CpAMs.
Collapse
|
33
|
Berenger F, Kumar A, Zhang KYJ, Yamanishi Y. Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking. J Chem Inf Model 2021; 61:2341-2352. [PMID: 33861591 DOI: 10.1021/acs.jcim.0c01452] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method "lean-docking". To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.
Collapse
Affiliation(s)
- Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Japan
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Japan
| |
Collapse
|
34
|
Bitter taste in silico: A review on virtual ligand screening and characterization methods for TAS2R-bitterant interactions. Int J Pharm 2021; 600:120486. [PMID: 33744445 DOI: 10.1016/j.ijpharm.2021.120486] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/21/2021] [Accepted: 03/09/2021] [Indexed: 11/21/2022]
Abstract
The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications.
Collapse
|
35
|
Zhu K, Shen C, Tang C, Zhou Y, He C, Zuo Z. Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning. CHEMOSPHERE 2021; 265:129099. [PMID: 33272675 DOI: 10.1016/j.chemosphere.2020.129099] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 11/17/2020] [Accepted: 11/22/2020] [Indexed: 06/12/2023]
Abstract
The aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor, plays a crucial role in the regulation of xenobiotic metabolism. There are a large number of artificial or natural molecules in the environment that can activate AhR. In this study, we developed a virtual screening procedure to identify potential ligands of AhR. One structure-based method and two ligand-based methods were used for the virtual screening procedure. The results showed that the precision rate (0.96) and recall rate (0.64) of our procedure were significantly higher than those of a procedure used in a previous study, which suggests that supervised machine learning techniques can greatly improve the performance of virtual screening. Moreover, a pesticide dataset including 777 frequently used pesticides was screened. Seventy-seven pesticides were identified as potential AhR ligands by all three screening methods, among which 12 have never been previously reported as AhR agonists. Two non-agonist AhR ligands and 14 of the 77 pesticides were randomly selected for testing by in vitro and in vivo assays. All 14 pesticides showed different degrees of AhR agonistic activity, and none of the two non-agonist AhR ligand pesticides showed AhR agonistic activity, which suggests that our procedure had good robustness. Four of the pesticides were reported as AhR agonists for the first time, suggesting that these pesticides may need further toxicity assessment. In general, our procedure is a rapid, powerful and computationally inexpensive tool for predicting chemicals with AhR agonistic activity, which could be useful for environmental risk prediction and management.
Collapse
Affiliation(s)
- Kongyang Zhu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Chao Shen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Chen Tang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Yixi Zhou
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China
| | - Chengyong He
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China.
| | - Zhenghong Zuo
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian, 361005, China; State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, Fujian, 361005, China.
| |
Collapse
|
36
|
Shukla R, Henkel ND, Alganem K, Hamoud AR, Reigle J, Alnafisah RS, Eby HM, Imami AS, Creeden JF, Miruzzi SA, Meller J, Mccullumsmith RE. Signature-based approaches for informed drug repurposing: targeting CNS disorders. Neuropsychopharmacology 2021; 46:116-130. [PMID: 32604402 PMCID: PMC7688959 DOI: 10.1038/s41386-020-0752-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/30/2020] [Accepted: 06/22/2020] [Indexed: 12/15/2022]
Abstract
CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.
Collapse
Affiliation(s)
- Rammohan Shukla
- Department of Neurosciences, University of Toledo, Toledo, OH, USA.
| | | | - Khaled Alganem
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | | | - James Reigle
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Hunter M Eby
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Ali S Imami
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Justin F Creeden
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Scott A Miruzzi
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
| | - Jaroslaw Meller
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
- Department of Environmental Health, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Electrical Engineering and Computing Systems, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
| | - Robert E Mccullumsmith
- Department of Neurosciences, University of Toledo, Toledo, OH, USA
- Neurosciences Institute, ProMedica, Toledo, OH, USA
| |
Collapse
|
37
|
Wang SY, Liu X, Liu Y, Zhang HY, Zhang YB, Liu C, Song J, Niu JB, Zhang SY. Review of NEDDylation inhibition activity detection methods. Bioorg Med Chem 2021; 29:115875. [DOI: 10.1016/j.bmc.2020.115875] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/05/2020] [Accepted: 11/09/2020] [Indexed: 12/31/2022]
|
38
|
Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation. Molecules 2020; 25:molecules25204829. [PMID: 33092204 PMCID: PMC7594044 DOI: 10.3390/molecules25204829] [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: 09/30/2020] [Revised: 10/16/2020] [Accepted: 10/16/2020] [Indexed: 11/16/2022] Open
Abstract
CXCL12 are small pro-inflammatory chemo-attractant cytokines that bind to a specific receptor CXCR4 with a role in angiogenesis, tumor progression, metastasis, and cell survival. Globally, cancer metastasis is a major cause of morbidity and mortality. In this study, we targeted CXCL12 rather than the chemokine receptor (CXCR4) because most of the drugs failed in clinical trials due to unmanageable toxicities. Until now, no FDA approved medication has been available against CXCL12. Therefore, we aimed to find new inhibitors for CXCL12 through virtual screening followed by molecular dynamics simulation. For virtual screening, active compounds against CXCL12 were taken as potent inhibitors and utilized in the generation of a pharmacophore model, followed by validation against different datasets. Ligand based virtual screening was performed on the ChEMBL and in-house databases, which resulted in successive elimination through the steps of pharmacophore-based and score-based screenings, and finally, sixteen compounds of various interactions with significant crucial amino acid residues were selected as virtual hits. Furthermore, the binding mode of these compounds were refined through molecular dynamic simulations. Moreover, the stability of protein complexes, Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and radius of gyration were analyzed, which led to the identification of three potent inhibitors of CXCL12 that may be pursued in the drug discovery process against cancer metastasis.
Collapse
|
39
|
Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery. Sci Rep 2020; 10:16771. [PMID: 33033310 PMCID: PMC7545175 DOI: 10.1038/s41598-020-73681-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 09/18/2020] [Indexed: 12/30/2022] Open
Abstract
Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we performed comparative studies between deep neural networks (DNN) and other ligand-based virtual screening (LBVS) methods to demonstrate that DNN and random forest (RF) were superior in hit prediction efficiency. By using DNN, several triple-negative breast cancer (TNBC) inhibitors were identified as potent hits from a screening of an in-house database of 165,000 compounds. In broadening the application of this method, we harnessed the predictive properties of trained model in the discovery of G protein-coupled receptor (GPCR) agonist, by which computational structure-based design of molecules could be greatly hindered by lack of structural information. Notably, a potent (~ 500 nM) mu-opioid receptor (MOR) agonist was identified as a hit from a small-size training set of 63 compounds. Our results show that DNN could be an efficient module in hit prediction and provide experimental evidence that machine learning could identify potent hits in silico from a limited training set.
Collapse
|
40
|
Computational methods-guided design of modulators targeting protein-protein interactions (PPIs). Eur J Med Chem 2020; 207:112764. [PMID: 32871340 DOI: 10.1016/j.ejmech.2020.112764] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/09/2020] [Accepted: 08/16/2020] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.
Collapse
|
41
|
Stojanović L, Popović M, Tijanić N, Rakočević G, Kalinić M. Improved Scaffold Hopping in Ligand-Based Virtual Screening Using Neural Representation Learning. J Chem Inf Model 2020; 60:4629-4639. [DOI: 10.1021/acs.jcim.0c00622] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Miloš Popović
- Totient, Inc., Sinđelićeva 9, 11000 Belgrade, Serbia
| | | | | | - Marko Kalinić
- Totient, Inc., Sinđelićeva 9, 11000 Belgrade, Serbia
| |
Collapse
|
42
|
Gong J, Chen Y, Pu F, Sun P, He F, Zhang L, Li Y, Ma Z, Wang H. Understanding Membrane Protein Drug Targets in Computational Perspective. Curr Drug Targets 2020; 20:551-564. [PMID: 30516106 DOI: 10.2174/1389450120666181204164721] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/03/2018] [Accepted: 09/04/2018] [Indexed: 01/16/2023]
Abstract
Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.
Collapse
Affiliation(s)
- Jianting Gong
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Yongbing Chen
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Feng Pu
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Li Zhang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Yanwen Li
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Han Wang
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| |
Collapse
|
43
|
Coley CW, Eyke NS, Jensen KF. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angew Chem Int Ed Engl 2020; 59:22858-22893. [DOI: 10.1002/anie.201909987] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| |
Collapse
|
44
|
Coley CW, Eyke NS, Jensen KF. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt. Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201909987] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Connor W. Coley
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Natalie S. Eyke
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
| |
Collapse
|
45
|
Structure-based screening for discovery of sweet compounds. Food Chem 2020; 315:126286. [DOI: 10.1016/j.foodchem.2020.126286] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/10/2020] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
|
46
|
Park K, Ko YJ, Durai P, Pan CH. Machine learning-based chemical binding similarity using evolutionary relationships of target genes. Nucleic Acids Res 2020; 47:e128. [PMID: 31504818 PMCID: PMC6846180 DOI: 10.1093/nar/gkz743] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 08/13/2019] [Accepted: 08/20/2019] [Indexed: 12/21/2022] Open
Abstract
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information.
Collapse
Affiliation(s)
- Keunwan Park
- Natural Product Informatics Research Center, KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
| | - Young-Joon Ko
- Natural Product Informatics Research Center, KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea.,Department of Bioinformatics and Life Science, Soongsil University, Seoul 06978, Republic of Korea
| | - Prasannavenkatesh Durai
- Natural Product Informatics Research Center, KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
| | - Cheol-Ho Pan
- Natural Product Informatics Research Center, KIST Gangneung Institute of Natural Products, Gangneung 25451, Republic of Korea
| |
Collapse
|
47
|
Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem 2020; 8:343. [PMID: 32411671 PMCID: PMC7200080 DOI: 10.3389/fchem.2020.00343] [Citation(s) in RCA: 199] [Impact Index Per Article: 49.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 04/01/2020] [Indexed: 12/15/2022] Open
Abstract
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.
Collapse
Affiliation(s)
- Eduardo Habib Bechelane Maia
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil.,Federal Center for Technological Education of Minas Gerais-CEFET-MG, Belo Horizonte, Brazil
| | - Letícia Cristina Assis
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| | | | | | - Alex Gutterres Taranto
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| |
Collapse
|
48
|
Maia EHB, Medaglia LR, da Silva AM, Taranto AG. Molecular Architect: A User-Friendly Workflow for Virtual Screening. ACS OMEGA 2020; 5:6628-6640. [PMID: 32258898 PMCID: PMC7114615 DOI: 10.1021/acsomega.9b04403] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 03/06/2020] [Indexed: 05/02/2023]
Abstract
Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.
Collapse
Affiliation(s)
- Eduardo H. B. Maia
- Laboratório
de Quêmica Farmaĉutica Medicinal, Universidade Federal de São João Del-Rei, Divinópolis 35501-296, Minas Gerais, Brazil
- Centro
Federal de Educação Tecnológica de Minas Gerais,
CEFET-MG, Campus Divinópolis, Divinópolis 35503-822, MG, Brazil
| | | | - Alisson Marques da Silva
- Centro
Federal de Educação Tecnológica de Minas Gerais,
CEFET-MG, Campus Divinópolis, Divinópolis 35503-822, MG, Brazil
| | - Alex G. Taranto
- Laboratório
de Quêmica Farmaĉutica Medicinal, Universidade Federal de São João Del-Rei, Divinópolis 35501-296, Minas Gerais, Brazil
| |
Collapse
|
49
|
Abstract
INTRODUCTION Deep discriminative and generative neural-network models are becoming an integral part of the modern approach to ligand-based novel drug discovery. The variety of different architectures of neural networks, the methods of their training, and the procedures of generating new molecules require expert knowledge to choose the most suitable approach. AREAS COVERED Three different approaches to deep learning use in ligand-based drug discovery are considered: virtual screening, neural generative models, and mutation-based structure generation. Several architectures of neural networks for building either discriminative or generative models are considered in this paper, including deep multilayer neural networks, different kinds of convolutional neural networks, recurrent neural networks, and several types of autoencoders. Several kinds of learning frameworks are also considered, including adversarial learning and reinforcement learning. Different types of representations for generating molecules, including SMILES, graphs, and several alternative string representations are also considered. EXPERT OPINION Two kinds of problem should be solved in order to make the models built using deep neural networks, especially generative models, a valuable option in ligand-based drug discovery: the issue of interpretability and explainability of deep-learning models and the issue of synthetic accessibility of novel compounds designed by deep-learning algorithms.
Collapse
Affiliation(s)
- Igor I Baskin
- Faculty of Physics, M.V. Lomonosov Moscow State University , Moscow, Russia.,Butlerov Institute of Chemistry, Kazan Federal University , Kazan, Russia
| |
Collapse
|
50
|
Martinez-Mayorga K, Madariaga-Mazon A, Medina-Franco JL, Maggiora G. The impact of chemoinformatics on drug discovery in the pharmaceutical industry. Expert Opin Drug Discov 2020; 15:293-306. [PMID: 31965870 DOI: 10.1080/17460441.2020.1696307] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.
Collapse
Affiliation(s)
| | | | - José L Medina-Franco
- Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | | |
Collapse
|