101
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Nedeljković N, Dobričić V, Bošković J, Vesović M, Bradić J, Anđić M, Kočović A, Jeremić N, Novaković J, Jakovljević V, Vujić Z, Nikolić M. Synthesis and Investigation of Anti-Inflammatory Activity of New Thiourea Derivatives of Naproxen. Pharmaceuticals (Basel) 2023; 16:ph16050666. [PMID: 37242450 DOI: 10.3390/ph16050666] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023] Open
Abstract
The aim of the study was a synthesis and investigation of the dose-dependent anti-inflammatory effect of new thiourea derivatives of naproxen with selected aromatic amines and esters of aromatic amino acids. The results of the in vivo study indicate that derivatives of m-anisidine (4) and N-methyl tryptophan methyl ester (7) showed the most potent anti-inflammatory activity four hours after injection of carrageenan, with the percentage of inhibition of 54.01% and 54.12%, respectively. In vitro assays of COX-2 inhibition demonstrated that none of the tested compounds achieved 50% inhibition at concentrations lower than 100 µM. On the other hand, the aromatic amine derivatives (1-5) accomplished significant inhibition of 5-LOX, and the lowest IC50 value was observed for compound 4 (0.30 μM). High anti-edematous activity of compound 4 in the rat paw edema model, together with potent inhibition of 5-LOX, highlight this compound as a promising anti-inflammatory agent.
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Affiliation(s)
- Nikola Nedeljković
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
| | - Vladimir Dobričić
- Department of Pharmaceutical Chemistry, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Jelena Bošković
- Department of Pharmaceutical Chemistry, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Marina Vesović
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
| | - Jovana Bradić
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, Svetozara Markovica 69, 34000 Kragujevac, Serbia
| | - Marijana Anđić
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, Svetozara Markovica 69, 34000 Kragujevac, Serbia
| | - Aleksandar Kočović
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, Svetozara Markovica 69, 34000 Kragujevac, Serbia
| | - Nevena Jeremić
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- 1st Moscow State Medical, University IM Sechenov, Trubetskaya 8/2, 119991 Moscow, Russia
| | - Jovana Novaković
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, Svetozara Markovica 69, 34000 Kragujevac, Serbia
| | - Vladimir Jakovljević
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- Department of Physiology, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
- Department of Human Pathology, 1st Moscow State Medical, University IM Sechenov, Trubetskaya 8/2, 119991 Moscow, Russia
| | - Zorica Vujić
- Department of Pharmaceutical Chemistry, University of Belgrade-Faculty of Pharmacy, Vojvode Stepe 450, 11221 Belgrade, Serbia
| | - Miloš Nikolić
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovica 69, 34000 Kragujevac, Serbia
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102
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Iqbal D, Rehman MT, Alajmi MF, Alsaweed M, Jamal QMS, Alasiry SM, Albaker AB, Hamed M, Kamal M, Albadrani HM. Multitargeted Virtual Screening and Molecular Simulation of Natural Product-like Compounds against GSK3β, NMDA-Receptor, and BACE-1 for the Management of Alzheimer's Disease. Pharmaceuticals (Basel) 2023; 16:ph16040622. [PMID: 37111379 PMCID: PMC10143309 DOI: 10.3390/ph16040622] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/18/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
The complexity of Alzheimer's disease (AD) and several side effects of currently available medication inclined us to search for a novel natural cure by targeting multiple key regulatory proteins. We initially virtually screened the natural product-like compounds against GSK3β, NMDA receptor, and BACE-1 and thereafter validated the best hit through molecular dynamics simulation (MDS). The results demonstrated that out of 2029 compounds, only 51 compounds exhibited better binding interactions than native ligands, with all three protein targets (NMDA, GSK3β, and BACE) considered multitarget inhibitors. Among them, F1094-0201 is the most potent inhibitor against multiple targets with binding energy -11.7, -10.6, and -12 kcal/mol, respectively. ADME-T analysis results showed that F1094-0201 was found to be suitable for CNS drug-likeness in addition to their other drug-likeness properties. The MDS results of RMSD, RMSF, Rg, SASA, SSE and residue interactions indicated the formation of a strong and stable association in the complex of ligands (F1094-0201) and proteins. These findings confirm the F1094-0201's ability to remain inside target proteins' binding pockets while forming a stable complex of protein-ligand. The free energies (MM/GBSA) of BACE-F1094-0201, GSK3β-F1094-0201, and NMDA-F1094-0201 complex formation were -73.78 ± 4.31 kcal mol-1, -72.77 ± 3.43 kcal mol-1, and -52.51 ± 2.85 kcal mol-1, respectively. Amongst the target proteins, F1094-0201 have a more stable association with BACE, followed by NMDA and GSK3β. These attributes of F1094-0201 indicate it as a possible option for the management of pathophysiological pathways associated with AD.
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Affiliation(s)
- Danish Iqbal
- Department of Health Information Management, College of Applied Medical Sciences, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia
| | - Md Tabish Rehman
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Mohamed F Alajmi
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Mohammed Alsaweed
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia
| | - Qazi Mohammad Sajid Jamal
- Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Al Bukayriyah 52741, Saudi Arabia
| | - Sharifa M Alasiry
- Critical Care Nursing, Department of Nursing, College of Applied Medical Sciences, Majmaah University, Al-Majmaah 15341, Saudi Arabia
| | - Awatif B Albaker
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Munerah Hamed
- Department of Pathology, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia
| | - Mehnaz Kamal
- Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Hind Muteb Albadrani
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia
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103
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Bernal FA, Schmidt TJ. A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †. Molecules 2023; 28:molecules28083399. [PMID: 37110632 PMCID: PMC10144340 DOI: 10.3390/molecules28083399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.
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Affiliation(s)
- Freddy A Bernal
- University of Münster, Institute of Pharmaceutical Biology and Phytochemistry (IPBP), PharmaCampus-Corrensstraße 48, 48149 Münster, Germany
| | - Thomas J Schmidt
- University of Münster, Institute of Pharmaceutical Biology and Phytochemistry (IPBP), PharmaCampus-Corrensstraße 48, 48149 Münster, Germany
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104
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Hansel-Harris AT, Santos-Martins D, Bruciaferri N, Tillack AF, Holcomb M, Forli S. Ringtail: A Python Tool for Efficient Management and Storage of Virtual Screening Results. J Chem Inf Model 2023; 63:1858-1864. [PMID: 36976961 PMCID: PMC10713006 DOI: 10.1021/acs.jcim.3c00166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries in early stage drug discovery. As the size of compound libraries which can feasibly be screened grows, so do the challenges in result management and storage. Here we introduce Ringtail, a new Python tool in the AutoDock Suite for efficient storage and analysis of virtual screening data based on portable SQLite databases. Ringtail is designed to work with AutoDock-GPU and AutoDock Vina out-of-the-box. Its modular design also allows for easy extension to support input file types from other docking software, different storage solutions, and incorporation into other applications. Ringtail's SQLite database output can dramatically reduce the required disk storage (36-46 fold) by selecting individual poses to store and by taking advantage of the relational database format. Filtering times are also dramatically reduced, requiring minutes to filter millions of ligands. Thus, Ringtail is a tool that can immediately integrate into existing virtual screening pipelines using AutoDock-GPU and Vina, and is scriptable and modifiable to fit specific user needs.
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Affiliation(s)
- Althea T Hansel-Harris
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
| | - Diogo Santos-Martins
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
| | - Niccolò Bruciaferri
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
| | - Andreas F Tillack
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
| | - Matthew Holcomb
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, California 92037, United States
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105
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Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023; 616:673-685. [PMID: 37100941 DOI: 10.1038/s41586-023-05905-z] [Citation(s) in RCA: 135] [Impact Index Per Article: 135.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 03/01/2023] [Indexed: 04/28/2023]
Abstract
Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.
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Affiliation(s)
- Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
- Center for New Technologies in Drug Discovery and Development, Bridge Institute, Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA.
- Department of Chemistry, University of Southern California, Los Angeles, CA, USA.
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106
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Sharma MK, Parashar S, Sharma D, Jakhar K, Lal K, Pandya NU, Om H. Synthesis, characterization, docking and antimicrobial studies of binol based amide linked symmetrical bistriazoles. J INDIAN CHEM SOC 2023. [DOI: 10.1016/j.jics.2023.100973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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107
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Abdelrady YA, Ashraf NM, Hamid A, Thabet HS, Sayed AM, Salem SH, Hassanein EHM, Sayed AM. In silico assessment of diterpenes as potential inhibitors of SARS-COV-2 main protease. Future Virol 2023; 18:295-308. [PMID: 38052000 PMCID: PMC10207350 DOI: 10.2217/fvl-2022-0163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/03/2023] [Indexed: 12/07/2023]
Abstract
Aim We aimed to investigate the potential inhibitory effects of diterpenes on SARS-CoV-2 main protease (Mpro). Materials & methods We performed a virtual screening of diterpenoids against Mpro using molecular docking, molecular dynamics simulation and absorption, distribution, metabolism and excretion) analysis. Results Some tested compounds followed Lipinski's rule and showed drug-like properties. Some diterpenoids possessed remarkable binding affinities with SARS-CoV-2 Mpro and drug-like pharmacokinetic properties. Three derivatives exhibited structural deviations lower than 1 Å. Conclusion The findings of the study suggest that some of the diterpenes could be candidates as potential inhibitors for Mpro of SARS-CoV-2.
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Affiliation(s)
| | - Naeem Mahmood Ashraf
- School of Biochemistry & Biotechnology, University of the Punjab, Lahore, Pakistan
| | - Arslan Hamid
- LIMES Institute (AG-Netea), University of Bonn, Bonn, Germany
| | - Hayam S Thabet
- Microbiology department, Faculty of Veterinary Medicine, Assiut University, 71526, Egypt
| | - Asmaa M Sayed
- Botany & Microbiology Department, Faculty of Science, Assiut University, Egypt
| | - Shimaa H Salem
- Botany & Microbiology Department, Faculty of Science, Assiut University, Egypt
| | - Emad HM Hassanein
- Department of Pharmacology & Toxicology, Faculty of Pharmacy, Al-Azhar University, Assiut Branch, Assiut, 71524, Egypt
| | - Ahmed M Sayed
- Biochemistry Laboratory, Chemistry Department, Faculty of Science, Assiut University, 71516, Egypt
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108
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Integrated computational and experimental approach for novel anti-leishmanial molecules by targeting Dephospho-coenzyme A kinase. Int J Biol Macromol 2023; 232:123441. [PMID: 36708902 DOI: 10.1016/j.ijbiomac.2023.123441] [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: 10/14/2022] [Revised: 01/07/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023]
Abstract
Coenzyme A acts as a necessary cofactor for many enzymes and is a part of many biochemical processes. One of the critical enzymes involved in Coenzyme A synthesis is Dephospho-coenzyme A-kinase (DPCK). In this study, we have used integrated computational and experimental approaches for promising inhibitors of DPCK using the natural products available in the ZINC database for anti-leishmanial drug development. The top hit compounds chosen after molecular docking were Veratramine, Azulene, Hupehenine, and Hederagenin. The free binding energy of Veratramine, Azulene, Hupehenine, and Hederagenin was estimated. Besides the favourable binding point, the ligands also showed good hydrogen bonding and other interactions with key residues of the enzyme's active site. The natural compounds were also experimentally investigated for their effect on the L. donovani promastigotes and murine macrophage (J774A.1). A good antileishmanial activity by the compounds on the promastigotes was observed as estimated by the MTT assay. The in-vitro experiments revealed that Hupehenine (IC50 = 7.34 ± 0.37 μM) and Veratramine (IC50 = 12.46 ± 2.28 μM) exhibited better inhibition than Hederagenin (IC50 = 23.36 ± 0.54 μM) and Azulene (IC50 = 24.42 ± 3.28 μM). This work has identified novel anti-leishmanial molecules possibly acting through the inhibition of DPCK.
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109
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Sobral PS, Luz VCC, Almeida JMGCF, Videira PA, Pereira F. Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors. Int J Mol Sci 2023; 24:ijms24065908. [PMID: 36982981 PMCID: PMC10054797 DOI: 10.3390/ijms24065908] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/16/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.
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Affiliation(s)
- Patrícia S Sobral
- LAQV and REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Vanessa C C Luz
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - João M G C F Almeida
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Paula A Videira
- UCIBIO, Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
| | - Florbela Pereira
- LAQV and REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal
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110
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Du L, Geng C, Zeng Q, Huang T, Tang J, Chu Y, Zhao K. Dockey: a modern integrated tool for large-scale molecular docking and virtual screening. Brief Bioinform 2023; 24:7034216. [PMID: 36764832 DOI: 10.1093/bib/bbad047] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/12/2023] Open
Abstract
Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.
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Affiliation(s)
- Lianming Du
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
- Institute for Advanced Study, Chengdu University, Chengdu 610106, China
| | - Chaoyue Geng
- College of Food and Biological Engineering, Chengdu University, Chengdu 610106, China
| | - Qianglin Zeng
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
| | - Ting Huang
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
| | - Jie Tang
- College of Food and Biological Engineering, Chengdu University, Chengdu 610106, China
| | - Yiwen Chu
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
| | - Kelei Zhao
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
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111
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De Vita S, Chini MG, Bifulco G, Lauro G. Target identification by structure-based computational approaches: Recent advances and perspectives. Bioorg Med Chem Lett 2023; 83:129171. [PMID: 36739998 DOI: 10.1016/j.bmcl.2023.129171] [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: 08/05/2022] [Revised: 12/15/2022] [Accepted: 02/01/2023] [Indexed: 02/05/2023]
Abstract
The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.
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Affiliation(s)
- Simona De Vita
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy
| | - Maria Giovanna Chini
- Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone, 86090 Pesche (IS), Italy
| | - Giuseppe Bifulco
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy.
| | - Gianluigi Lauro
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy.
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112
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Li R, Wu J, He F, Xu Q, Yin K, Li S, Li W, Wei A, Zhang L, Zhang XH, Zhang B. Rational design, synthesis, antifungal evaluation and docking studies of antifungal peptide CGA-N12 analogues based on the target CtKRE9. Bioorg Chem 2023; 132:106355. [PMID: 36669359 DOI: 10.1016/j.bioorg.2023.106355] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 11/02/2022] [Accepted: 01/08/2023] [Indexed: 01/12/2023]
Abstract
Candida tropicalis is a major non-albicans species that causes invasive candidiasis. CGA-N12, an anti-Candida peptide found by our group, disrupted cell wall architecture by inhibiting the activity of the protein killer-resistant 9 (KRE9), a β-1,6-glucan synthase specific to Candida spp. and plants. Herein, a set of CGA-N12 analogues were rationally designed based on the interaction networks between CGA-N12 and C. tropicalis KRE9 (CtKRE9). Seven CGA-N12 analogues with significantly improved antifungal activity against C. tropicalis were screened by reducing the docking energy of CGA-N12 and CtKRE9 and increasing the number of positive charges on CGA-N12 based on a stable three-dimensional model of CtKRE9. CGA-N12 and its analogues exhibited antifungal activity against C. tropicalis and its persist cells; they also inhibited biofilm formation and eradicated preformed biofilms. Compared with fluconazole, they displayed higher activities against the growth of persister cells and more effective preformed biofilm eradication. Among them, CGA-N12-0801, CGA-N12-0902 and CGA-N12-1002 displayed much higher activity and anti-proteinase digestion stability than CGA-N12. Specifically, CGA-N12-0801 was the optimal analogue, with a minimum inhibitory concentration of 3.46 μg/mL and a therapeutic index of 158.07. The results of electronic microscopy observations and KRE9 activity inhibition assays showed that CGA-N12 and its analogues killed C. tropicalis by disrupting the architecture of the cell wall and the integrity of the cell membrane. In conclusion, for the first time, we provide a simple and reliable method for the rational design of antimicrobial peptides and ideal candidates for treating Candida infections that not effectively eliminated by azole drugs.
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Affiliation(s)
- Ruifang Li
- College of Biological Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China; Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 450001 Zhengzhou, Henan, PR China.
| | - Jiasha Wu
- College of Biological Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China; Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Fuyang He
- School of Artificial Intelligence and Big Data, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Qingpeng Xu
- College of Information Science and Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Kedong Yin
- College of Information Science and Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Shang Li
- College of Biological Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Weitong Li
- College of Biological Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Ao Wei
- College of Biological Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Lan Zhang
- College of Biological Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China; Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Xin-Hui Zhang
- College of Biological Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China; Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 450001 Zhengzhou, Henan, PR China
| | - Beibei Zhang
- College of Biological Engineering, Henan University of Technology, 450001 Zhengzhou, Henan, PR China; Key Laboratory of Functional Molecules for Biomedical Research, Henan University of Technology, 450001 Zhengzhou, Henan, PR China.
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113
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Petinrin OO, Saeed F, Toseef M, Liu Z, Basurra S, Muyide IO, Li X, Lin Q, Wong KC. Machine Learning in Metastatic Cancer Research: Potentials, Possibilities, and Prospects. Comput Struct Biotechnol J 2023; 21:2454-2470. [PMID: 37077177 PMCID: PMC10106342 DOI: 10.1016/j.csbj.2023.03.046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detection of cancer is essential, the timely detection of metastasis, the identification of biomarkers, and treatment choice are valuable for improving the quality of life for metastatic cancer patients. This study reviews the existing studies on classical machine learning (ML) and deep learning (DL) in metastatic cancer research. Since the majority of metastatic cancer research data are collected in the formats of PET/CT and MRI image data, deep learning techniques are heavily involved. However, its black-box nature and expensive computational cost are notable concerns. Furthermore, existing models could be overestimated for their generality due to the non-diverse population in clinical trial datasets. Therefore, research gaps are itemized; follow-up studies should be carried out on metastatic cancer using machine learning and deep learning tools with data in a symmetric manner.
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114
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Zhu Z, Yao Z, Qi G, Mazur N, Yang P, Cong B. Associative learning mechanism for drug‐target interaction prediction. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023. [DOI: 10.1049/cit2.12194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Affiliation(s)
- Zhiqin Zhu
- College of Automation Chongqing University of Posts and Telecommunications Chongqing China
| | - Zheng Yao
- College of Automation Chongqing University of Posts and Telecommunications Chongqing China
| | - Guanqiu Qi
- Computer Information Systems Department State University of New York at Buffalo State Buffalo New York USA
| | - Neal Mazur
- Computer Information Systems Department State University of New York at Buffalo State Buffalo New York USA
| | - Pan Yang
- Department of Cardiovascular Surgery Chongqing General Hospital University of Chinese Academy of Sciences Chongqing China
- Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China
| | - Baisen Cong
- Data Scientist Diagnostics Digital DH (Shanghai) Diagnostics Co., Ltd. Danaher Company Shanghai China
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115
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Fereshteh S, Noori Goodarzi N, Kalhor H, Rahimi H, Barzi SM, Badmasti F. Identification of Putative Drug Targets in Highly Resistant Gram-Negative Bacteria; and Drug Discovery Against Glycyl-tRNA Synthetase as a New Target. Bioinform Biol Insights 2023; 17:11779322231152980. [PMID: 36798081 PMCID: PMC9926382 DOI: 10.1177/11779322231152980] [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: 10/19/2022] [Accepted: 12/24/2022] [Indexed: 02/17/2023] Open
Abstract
Background Gram-negative bacterial infections are on the rise due to the high prevalence of multidrug-resistant bacteria, and efforts must be made to identify novel drug targets and then new antibiotics. Methods In the upstream part, we retrieved the genome sequences of 4 highly resistant Gram-negative bacteria (e.g., Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter cloacae). The core proteins were assessed to find common, cytoplasmic, and essential proteins with no similarity to the human proteome. Novel drug targets were identified using DrugBank, and their sequence conservancy was evaluated. Protein Data Bank files and STRING interaction networks were assessed. Finally, the aminoacylation cavity of glycyl-tRNA synthetase (GlyQ) was virtually screened to identify novel inhibitors using AutoDock Vina and the StreptomeDB library. Ligands with high binding affinity were clustered, and then the pharmacokinetics properties of therapeutic agents were investigated. Results A total of 6 common proteins (e.g., RP-L28, RP-L30, RP-S20, RP-S21, Rnt, and GlyQ) were selected as novel and widespread drug targets against highly resistant Gram-negative superbugs based on different criteria. In the downstream analysis, virtual screening revealed that Rimocidin, Flavofungin, Chaxamycin, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin, and Platensimycin were promising hit compounds against GlyQ protein. Finally, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin was identified as the best potential inhibitor of GlyQ protein. This compound showed high absorption capacity in the human intestine. Conclusion The results of this study provide 6 common putative new drug targets against 4 highly resistant and Gram-negative bacteria. Moreover, we presented 5 different hit compounds against GlyQ protein as a novel therapeutic target. However, further in vitro and in vivo studies are needed to explore the bactericidal effects of proposed hit compounds against these superbugs.
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Affiliation(s)
| | - Narjes Noori Goodarzi
- Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Hourieh Kalhor
- Cellular and Molecular Research Center, Qom University of Medical Sciences, Qom, Iran
| | - Hamzeh Rahimi
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
- Texas Biomedical Research Institute, San Antonio, TX, USA
| | | | - Farzad Badmasti
- Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
- Farzad Badmasti, Department of Bacteriology, Pasteur Institute of Iran, Tehran Province, Tehran, 12 Farvardin St, Tehran 1316943551, Iran.
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116
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You Y, Liu H, Zhu Y, Zheng H. Rational design of stapled antimicrobial peptides. Amino Acids 2023; 55:421-442. [PMID: 36781451 DOI: 10.1007/s00726-023-03245-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 01/30/2023] [Indexed: 02/15/2023]
Abstract
The global increase in antimicrobial drug resistance has dramatically reduced the effectiveness of traditional antibiotics. Structurally diverse antibiotics are urgently needed to combat multiple-resistant bacterial infections. As part of innate immunity, antimicrobial peptides have been recognized as the most promising candidates because they comprise diverse sequences and mechanisms of action and have a relatively low induction rate of resistance. However, because of their low chemical stability, susceptibility to proteases, and high hemolytic effect, their usage is subject to many restrictions. Chemical modifications such as D-amino acid substitution, cyclization, and unnatural amino acid modification have been used to improve the stability of antimicrobial peptides for decades. Among them, a side-chain covalent bridge modification, the so-called stapled peptide, has attracted much attention. The stapled side-chain bridge stabilizes the secondary structure, induces protease resistance, and increases cell penetration and biological activity. Recent progress in computer-aided drug design and artificial intelligence methods has also been used in the design of stapled antimicrobial peptides and has led to the successful discovery of many prospective peptides. This article reviews the possible structure-activity relationships of stapled antimicrobial peptides, the physicochemical properties that influence their activity (such as net charge, hydrophobicity, helicity, and dipole moment), and computer-aided methods of stapled peptide design. Antimicrobial peptides under clinical trial: Pexiganan (NCT01594762, 2012-05-07). Omiganan (NCT02576847, 2015-10-13).
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Affiliation(s)
- YuHao You
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, People's Republic of China
| | - HongYu Liu
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, People's Republic of China
| | - YouZhuo Zhu
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, People's Republic of China
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing, 210009, People's Republic of China.
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117
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Azmi MB, Khan F, Asif U, Khurshid B, Wadood A, Qureshi SA, Ahmed SDH, Mudassir HA, Sheikh SI, Feroz N. In Silico Characterization of Withania coagulans Bioactive Compounds as Potential Inhibitors of Hydroxymethylglutaryl (HMG-CoA) Reductase of Mus musculus. ACS OMEGA 2023; 8:5057-5071. [PMID: 36777558 PMCID: PMC9909811 DOI: 10.1021/acsomega.2c07893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Hypercholesterolemia is a mediator for the etiology of cardiovascular diseases, which are characterized as the global leading cause of mortality. We aimed to investigate the inhibitory activity of Withania coagulans compounds against 3-hydroxy-3-methylglutaryl-coenzyme A reductase (Hmgcr) of Mus musculus using an extensive in silico approach. The 3D structure of the Hmgcr protein is not yet known, so we performed the homology modeling using MODELLER and SWISS-MODEL tools, followed with structural validation and assessment. The PROCHECK web server showed that the top-ranked homology model from SWISS-MODEL has 93.4% of residues in the most-favorable region, the quality factor was 98%, and the Verify3D score was 91.43%, compared to the other generated models. The druggable protein-binding cavities in a 3D model of Hmgcr were investigated with the aid of commonly prescribed statin compounds using the CB-dock approach. We compiled a 3D compound library of W. coagulans, followed by drug-likeness evaluation, and found 20 eligible compounds. The pattern of consensus residues obtained from the CB-dock procedure was then used for grid-box docking of W. coagulans compounds and statin drugs using AutoDock 4.2, respectively. The results showed that withanolide R (-10.77 kcal/mol), withanolide Q (-10.56 kcal/mol), withanolide J (-10.52 kcal/mol), atorvastatin (-8.99 kcal/mol), simvastatin (-8.66 kcal/mol), and rosuvastatin (-8.58 kcal/mol) were promising candidates that bind Hmgcr protein. The key residues involved in protein-ligand (withanolide R) interactions were Y516, C526, V529, I530, M533, I535, and V537, and the formation of a H-bond was at C526, M533, and I535 residues. M533 was the consensus residue having a tendency to form a H-bond with withanolide Q, too. Molecular dynamics simulations were used to validate the top-ranked docked complexes for the stability of the modeled protein. We also predicted the pharmacokinetic properties of binding affinity-based top-ranked compounds and concluded that they could be used as potential inhibitors of Hmgcr. However, further in vitro and in vivo studies are essential to completing the drug development process.
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Affiliation(s)
- Muhammad Bilal Azmi
- Department
of Biochemistry, Dow Medical College, Dow
University of Health Sciences, Karachi 74200, Pakistan
| | - Fearoz Khan
- Department
of Biochemistry, University of Karachi, Karachi 75270, Pakistan
- Rahman
Medical College, Peshawar 25000, Pakistan
| | - Uzma Asif
- Department
of Biochemistry, Medicine Program, Batterjee
Medical College, Jeddah 21442, Saudi Arabia
| | - Beenish Khurshid
- Department
of Biochemistry, Abdul Wali Khan University
Mardan, Mardan 23200, Pakistan
| | - Abdul Wadood
- Department
of Biochemistry, Abdul Wali Khan University
Mardan, Mardan 23200, Pakistan
| | | | - Syed Danish Haseen Ahmed
- Department
of Biochemistry, Dow Medical College, Dow
University of Health Sciences, Karachi 74200, Pakistan
| | - Hina Akram Mudassir
- Department
of Biochemistry, Federal Urdu University
of Arts, Science and Technology, Karachi 75300, Pakistan
| | - Sadia Ikhlaque Sheikh
- Department
of Biochemistry, Dow Medical College, Dow
University of Health Sciences, Karachi 74200, Pakistan
| | - Nazia Feroz
- Department
of Biochemistry, Dow Medical College, Dow
University of Health Sciences, Karachi 74200, Pakistan
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118
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Computational Approaches to the Rational Design of Tubulin-Targeting Agents. Biomolecules 2023; 13:biom13020285. [PMID: 36830654 PMCID: PMC9952983 DOI: 10.3390/biom13020285] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/27/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.
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119
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Jaziri E, Louis H, Gharbi C, Unimuke TO, Agwamba EC, Mathias GE, Fugita W, Nasr CB, Khedhiri L. Antispasmodic activity of novel 2,4-dichloroanilinium perchlorate hybrid material: X-ray crystallography, DFT studies and molecular docking approach. J Mol Struct 2023. [DOI: 10.1016/j.molstruc.2022.134440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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120
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Csizi K, Reiher M. Universal
QM
/
MM
approaches for general nanoscale applications. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
| | - Markus Reiher
- Laboratorium für Physikalische Chemie ETH Zürich Zürich Switzerland
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121
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Şahin S, Can NN. A Schiff Base with Polymorphic Structure ( Z′ = 2): Investigations with Computational Techniques and in Silico Predictions. Polycycl Aromat Compd 2023. [DOI: 10.1080/10406638.2022.2161585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Songül Şahin
- Department of Chemistry, Faculty of Art and Sciences, Ondokuz Mayis University, Samsun, Turkey
| | - Nisa Nur Can
- Department of Neuroscience, Institute of Health Sciences, Ondokuz Mayis University, Samsun, Turkey
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122
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Anti-HIV Potential of Beesioside I Derivatives as Maturation Inhibitors: Synthesis, 3D-QSAR, Molecular Docking and Molecular Dynamics Simulations. Int J Mol Sci 2023; 24:ijms24021430. [PMID: 36674943 PMCID: PMC9867151 DOI: 10.3390/ijms24021430] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/04/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
HIV-1 maturation is the final step in the retroviral lifecycle that is regulated by the proteolytic cleavage of the Gag precursor protein. As a first-in-class HIV-1 maturation inhibitor (MI), bevirimat blocks virion maturation by disrupting capsid-spacer peptide 1 (CA-SP1) cleavage, which acts as the target of MIs. Previous alterations of beesioside I (1) produced (20S,24S)-15ꞵ,16ꞵ-diacetoxy-18,24; 20,24-diepoxy-9,19-cyclolanostane-3ꞵ,25-diol 3-O-3′,3′-dimethylsuccinate (3, DSC), showing similar anti-HIV potency compared to bevirimat. To ascertain the binding modes of this derivative, further modification of compound 1 was conducted. Three-dimensional quantitative structure−activity relationship (3D-QSAR) analysis combined with docking simulations and molecular dynamics (MD) were conducted. Five new derivatives were synthesized, among which compound 3b showed significant activity against HIV-1NL4-3 with an EC50 value of 0.28 µM. The developed 3D-QSAR model resulted in great predictive ability with training set (r2 = 0.99, q2 = 0.55). Molecular docking studies were complementary to the 3D-QSAR analysis, showing that DSC was differently bound to CA-SP1 with higher affinity than that of bevirimat. MD studies revealed that the complex of the ligand and the protein was stable, with root mean square deviation (RMSD) values <2.5 Å. The above results provided valuable insights into the potential of DSC as a prototype to develop new antiviral agents.
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123
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Lu G, Ou K, Zhang Y, Zhang H, Feng S, Yang Z, Sun G, Liu J, Wei S, Pan S, Chen Z. Structural Analysis, Multi-Conformation Virtual Screening and Molecular Simulation to Identify Potential Inhibitors Targeting pS273R Proteases of African Swine Fever Virus. MOLECULES (BASEL, SWITZERLAND) 2023; 28:molecules28020570. [PMID: 36677630 PMCID: PMC9866604 DOI: 10.3390/molecules28020570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/26/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
The African Swine Fever virus (ASFV) causes an infectious viral disease in pigs of all ages. The development of antiviral drugs primarily aimed at inhibition of proteases required for the proteolysis of viral polyproteins. In this study, the conformation of the pS273R protease in physiological states were investigated, virtually screened the multi-protein conformation of pS273R target proteins, combined various molecular docking scoring functions, and identified five potential drugs from the Food and Drug Administration drug library that may inhibit pS273R. Subsequent validation of the dynamic interactions of pS273R with the five putative inhibitors was achieved using molecular dynamics simulations and binding free energy calculations using the molecular mechanics/Poison-Boltzmann (Generalized Born) (MM/PB(GB)SA) surface area. These findings demonstrate that the arm domain and Thr159-Lys167 loop region of pS273R are significantly more flexible compared to the core structural domain, and the Thr159-Lys167 loop region can serve as a "gatekeeper" in the substrate channel. Leucovorin, Carboprost, Protirelin, Flavin Mononucleotide, and Lovastatin Acid all have Gibbs binding free energies with pS273R that were less than -20 Kcal/mol according to the MM/PBSA analyses. In contrast to pS273R in the free energy landscape, the inhibitor and drug complexes of pS273R showed distinct structural group distributions. These five drugs may be used as potential inhibitors of pS273R and may serve as future drug candidates for treating ASFV.
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Affiliation(s)
- Gen Lu
- Key Laboratory of Livestock Infectious Diseases, Ministry of Education, Shenyang Agricultural University, No. 120, Dongling Road, Shenhe District, Shenyang 110866, China
| | - Kang Ou
- Key Laboratory of Livestock Infectious Diseases, Ministry of Education, Shenyang Agricultural University, No. 120, Dongling Road, Shenhe District, Shenyang 110866, China
| | - Yihan Zhang
- Key Laboratory of Livestock Infectious Diseases, Ministry of Education, Shenyang Agricultural University, No. 120, Dongling Road, Shenhe District, Shenyang 110866, China
| | - Huan Zhang
- Key Laboratory of Livestock Infectious Diseases, Ministry of Education, Shenyang Agricultural University, No. 120, Dongling Road, Shenhe District, Shenyang 110866, China
| | - Shouhua Feng
- Key Laboratory of Livestock Infectious Diseases, Ministry of Education, Shenyang Agricultural University, No. 120, Dongling Road, Shenhe District, Shenyang 110866, China
| | - Zuofeng Yang
- The Preventive and Control Center of Animal Disease of Liaoning Province, Liaoning Agricultural Development Service Center, No. 95, Renhe Road, Shenbei District, Shenyang 110164, China
| | - Guo Sun
- Qianyuanhao Biological Co., Ltd., Building 20, District 11, No. 188 South Fourth Ring West Road, Fengtai District, Beijing 100070, China
| | - Jinling Liu
- Key Laboratory of Livestock Infectious Diseases, Ministry of Education, Shenyang Agricultural University, No. 120, Dongling Road, Shenhe District, Shenyang 110866, China
- Correspondence: (J.L.); (S.W.); (S.P.); (Z.C.); Tel.: +86-13022453165 (J.L.); Fax: +86-24-88487156 (J.L.)
| | - Shu Wei
- The Preventive and Control Center of Animal Disease of Liaoning Province, Liaoning Agricultural Development Service Center, No. 95, Renhe Road, Shenbei District, Shenyang 110164, China
- Correspondence: (J.L.); (S.W.); (S.P.); (Z.C.); Tel.: +86-13022453165 (J.L.); Fax: +86-24-88487156 (J.L.)
| | - Shude Pan
- Key Laboratory of Livestock Infectious Diseases, Ministry of Education, Shenyang Agricultural University, No. 120, Dongling Road, Shenhe District, Shenyang 110866, China
- Correspondence: (J.L.); (S.W.); (S.P.); (Z.C.); Tel.: +86-13022453165 (J.L.); Fax: +86-24-88487156 (J.L.)
| | - Zeliang Chen
- Key Laboratory of Livestock Infectious Diseases, Ministry of Education, Shenyang Agricultural University, No. 120, Dongling Road, Shenhe District, Shenyang 110866, China
- Correspondence: (J.L.); (S.W.); (S.P.); (Z.C.); Tel.: +86-13022453165 (J.L.); Fax: +86-24-88487156 (J.L.)
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Yang Z, Cai X, Ye Q, Zhao Y, Li X, Zhang S, Zhang L. High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior. Curr Drug Targets 2023; 24:532-545. [PMID: 36876836 DOI: 10.2174/1389450124666230306141725] [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: 06/18/2022] [Revised: 11/09/2022] [Accepted: 01/11/2023] [Indexed: 03/07/2023]
Abstract
Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.
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Affiliation(s)
- Zhiwei Yang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Xinhui Cai
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Qiushi Ye
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Yizhen Zhao
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Xuhua Li
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Shengli Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
| | - Lei Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, School of Physics, Xi'an Jiaotong University, Xi'an710049, China
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125
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Quantum control of optoelectronic and thermodynamic properties of dopamine molecule in external electric field : A DFT and TD-DFT study. COMPUT THEOR CHEM 2023. [DOI: 10.1016/j.comptc.2023.114051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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126
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Kapoor S, Chatterjee DR, Chowdhury MG, Das R, Shard A. Roadmap to Pyruvate Kinase M2 Modulation - A Computational Chronicle. Curr Drug Targets 2023; 24:464-483. [PMID: 36998144 DOI: 10.2174/1389450124666230330103126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/14/2023] [Accepted: 02/10/2023] [Indexed: 04/01/2023]
Abstract
Pyruvate kinase M2 (PKM2) has surfaced as a potential target for anti-cancer therapy. PKM2 is known to be overexpressed in the tumor cells and is a critical metabolic conduit in supplying the augmented bioenergetic demands of the recalcitrant cancer cells. The presence of PKM2 in structurally diverse tetrameric as well as dimeric forms has opened new avenues to design novel modulators. It is also a truism to state that drug discovery has advanced significantly from various computational techniques like molecular docking, virtual screening, molecular dynamics, and pharmacophore mapping. The present review focuses on the role of computational tools in exploring novel modulators of PKM2. The structural features of various isoforms of PKM2 have been discussed along with reported modulators. An extensive analysis of the structure-based and ligand- based in silico methods aimed at PKM2 modulation has been conducted with an in-depth review of the literature. The role of advanced tools like QSAR and quantum mechanics has been established with a brief discussion of future perspectives.
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Affiliation(s)
- Saumya Kapoor
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
| | - Deep Rohan Chatterjee
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
| | - Moumita Ghosh Chowdhury
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
| | - Rudradip Das
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
| | - Amit Shard
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Ahmedabad, Opposite Air force Station Palaj, Gandhinagar-382355, Gujarat, India
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127
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Naseem S, Shafiq Z, Taslimi P, Hussain S, Taskin-Tok T, Kisa D, Saeed A, Temirak A, Tahir MN, Rauf K, El-Gokha A. Synthesis and evaluation of novel xanthene-based thiazoles as potential antidiabetic agents. Arch Pharm (Weinheim) 2023; 356:e2200356. [PMID: 36220614 DOI: 10.1002/ardp.202200356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/30/2022] [Accepted: 09/16/2022] [Indexed: 01/04/2023]
Abstract
A series of xanthene-based thiazoles was synthesized and characterized by different scpectroscopic methods, i.e. Proton nuclear magnetic resonance (1 H NMR), carbon nuclear magnetic resonance (13 C NMR), infrared spectroscopy, carbon hydrogen nitrogen analysis, and X-ray crystallography. The inhibition potencies of 18 newly synthesized thiazole derivatives were investigated on the activities of acetylcholinesterase (AChE), butyrylcholinesterase (BChE), α-amylase (α-Amy), and α-glycosidase (α-Gly) enzymes in accordance with their antidiabetic and anticholinesterase ability. The synthesized compounds have the highest inhibition potential against the enzymes at low nanomolar concentrations. Among the 18 newly synthesized molecules, 3b and 3p were superior to the known commercial inhibitors of the enzymes and have a much more effective inhibitory potential, with IC50 : 2.37 and 1.07 nM for AChE, 0.98 and 0.59 nM for BChE, 56.47 and 61.34 nM for α-Gly, and 152.48 and 124.84 nM for α-Amy, respectively. Finally, the optimized 18 compounds were subjected to molecular docking to describe the interaction between thiazole derivatives and AChE, BChE, α-Amy, and α-Gly enzymes in which important interactions were monitored with amino acid residues of each target enzyme.
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Affiliation(s)
- Saira Naseem
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, Pakistan
| | - Zahid Shafiq
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, Pakistan.,Department of Pharmaceutical & Medicinal Chemistry, University of Bonn, Bonn, Germany
| | - Parham Taslimi
- Department of Biotechnology, Faculty of Science, Bartin University, Bartin, Turkey.,Department of Chemistry, Faculty of Science, Istinye University, Istanbul, Turkey
| | - Saghir Hussain
- Institute of Chemical Sciences, Bahauddin Zakariya University, Multan, Pakistan
| | - Tugba Taskin-Tok
- Department of Chemistry, Faculty of Arts and Sciences, Gaziantep University, Gaziantep, Turkey.,Department of Bioinformatics and Computational Biology, Institute of Health Sciences, Gaziantep University, Gaziantep, Turkey
| | - Dursun Kisa
- Department of Molecular Biology and Genetics, Faculty of Science, Bartin University, Bartin, Turkey
| | - Aamer Saeed
- Department of Chemistry, Quaid-i-Azam University, Islamabad, Pakistan
| | - Ahmed Temirak
- Chemistry of Natural and Microbial Products Department, Pharmaceutical and Drug Industries Research Institute, National Research Centre, Dokki, Cairo, Egypt
| | - Muhammad N Tahir
- Department of Physics, University of Sargodha, Sargodha, Pakistan
| | - Khawar Rauf
- Department of Chemistry, Govt. Post-Graduate Gordon College, Rawalpindi, Pakistan
| | - Ahmed El-Gokha
- Chemistry Department, Faculty of Science, Menoufia University, Menoufia, Egypt
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128
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Holcomb M, Chang Y, Goodsell DS, Forli S. Evaluation of AlphaFold2 structures as docking targets. Protein Sci 2023; 32:e4530. [PMID: 36479776 PMCID: PMC9794023 DOI: 10.1002/pro.4530] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/28/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
AlphaFold2 is a promising new tool for researchers to predict protein structures and generate high-quality models, with low backbone and global root-mean-square deviation (RMSD) when compared with experimental structures. However, it is unclear if the structures predicted by AlphaFold2 will be valuable targets of docking. To address this question, we redocked ligands in the PDBbind datasets against the experimental co-crystallized receptor structures and against the AlphaFold2 structures using AutoDock-GPU. We find that the quality measure provided during structure prediction is not a good predictor of docking performance, despite accurately reflecting the quality of the alpha carbon alignment with experimental structures. Removing low-confidence regions of the predicted structure and making side chains flexible improves the docking outcomes. Overall, despite high-quality prediction of backbone conformation, fine structural details limit the naive application of AlphaFold2 models as docking targets.
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Affiliation(s)
- Matthew Holcomb
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Ya‐Ting Chang
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - David S. Goodsell
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA,Research Collaboratory for Structural Bioinformatics Protein Data Bank, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative Biomedicine, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA,Rutgers Cancer Institute of New Jersey, RutgersThe State University of New JerseyNew BrunswickNew JerseyUSA
| | - Stefano Forli
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
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129
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Jain P, Sudandiradoss C. Andrographolide-based potential anti-inflammatory transcription inhibitors against nuclear factor NF-kappa-B p50 subunit (NF-κB p50): an integrated molecular and quantum mechanical approach. 3 Biotech 2023; 13:15. [PMID: 36540414 PMCID: PMC9759609 DOI: 10.1007/s13205-022-03431-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
The unregulated activation of nuclear factor-κB (NF-κB) is a critical event in the progression of various inflammatory diseases such as ulcerative colitis, asthma, rheumatoid arthritis, bacterial induced gastritis, etc. Hence, blocking the transcriptional activity of NF-κB is a promising strategy towards the development of an anti-inflammatory agent. In this study, an integrated molecular and quantum mechanical approach was carried out to find a new potent andrographolide (AGP)-based analog that can inhibit DNA binding to NF-κB p50 and manifest anti-inflammatory activity. Our approach includes multiple sequence alignment, virtual screening, molecular docking (protein-ligand and protein-DNA), in silico site-directed mutagenesis, ADMET prediction, DFT (HOMO, LUMO, HLG, and EPM energy) analysis, MD simulation, and MM/GBSA rescoring. The virtual screening analysis of 237 AGP analogs yielded the five lead compounds based on the binding affinity. Further, molecular interactive docking and ADMET prediction of hit analogs revealed that Ana2 ((3Z,4S)-3-[2-[(4aR,6aS,7R,10aS,10bR)-3,3,6a,10b-tetramethyl-8-methylidene-1,4a,5,6,7,9,10,10a-octahydronaphtho[2,1-d][1,3]dioxin-7-yl]ethylidene]-4-hydroxyoxolan-2-one) is the most potent moiety as it displays the strongest binding affinity and better molecular/pharmacokinetic features. Moreover, DFT, MD simulation, and MM/GBSA studies corroborated the docking results and demonstrated better chemical and dynamic stability with the least binding free energy (- 29.99 kcal/mol) for the Ana2. Site-directed mutagenesis investigation (Cys62Ala) establishes the importance of the Cys62 amino acid residue towards the binding interaction and stability of Ana2 with NF-κB p50. Overall, the identified NF-κB p50 inhibitor opens up a new research horizon towards the development of plant-based anti-inflammatory drugs to combat progressive inflammatory diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-022-03431-9.
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Affiliation(s)
- Priyanka Jain
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014 India
| | - C Sudandiradoss
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014 India
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130
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Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3–d]pyrimidines as potential active site inhibitors and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro. Bioorg Chem 2023; 135:106390. [PMID: 37037129 PMCID: PMC9883075 DOI: 10.1016/j.bioorg.2023.106390] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/29/2023]
Abstract
In this paper, an environmentally benign, convenient, and efficient one-pot three-component reaction has been developed for the regioselective synthesis of novel 5-aroyl(or heteroaroyl)-6-(alkylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-diones (4a‒n) through the sequential condensation of aryl(or heteroaryl)glyoxal monohydrates (1a‒g), 1,3-dimethylbarbituric acid (2), and alkyl(viz. cyclohexyl or tert-butyl)isocyanides (3a or 3b) catalyzed by ultra-low loading ZrOCl2•8H2O (just 2 mol%) in water at 50 ˚C. After synthesis and characterization of the mentioned furo[2,3-d]pyrimidines (4a‒n), their multi-targeting inhibitory properties were investigated against the active site and putative allosteric hotspots of both SARS-CoV-2 main protease (MPro) and papain-like protease (PLPro) based on molecular docking studies and compare the attained results with various medicinal compounds which approximately in three past years were used, introduced, and or repurposed to fight against COVID-19. Furthermore, drug-likeness properties of the mentioned small heterocyclic frameworks (4a‒n) have been explored using in silico ADMET analyses. Interestingly, the molecular docking studies and ADMET-related data revealed that the novel series of furo[2,3-d]pyrimidines (4a‒n), especially 5-(3,4-methylendioxybenzoyl)-6-(cyclohexylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-dione (4g) as hit one is potential COVID-19 drug candidate, can subject to further in vitro and in vivo studies. It is worthwhile to note that the protein-ligand-type molecular docking studies on the human body temperature-dependent MPro protein that surprisingly contains zincII (ZnII) ion between His41/Cys145 catalytic dyad in the active site, which undoubtedly can make new plans for designing novel SARS-CoV-2 MPro inhibitors, is performed for the first time in this paper, to the best of our knowledge.
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131
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Traditional Machine and Deep Learning for Predicting Toxicity Endpoints. MOLECULES (BASEL, SWITZERLAND) 2022; 28:molecules28010217. [PMID: 36615411 PMCID: PMC9822478 DOI: 10.3390/molecules28010217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022]
Abstract
Molecular structure property modeling is an increasingly important tool for predicting compounds with desired properties due to the expensive and resource-intensive nature and the problem of toxicity-related attrition in late phases during drug discovery and development. Lately, the interest for applying deep learning techniques has increased considerably. This investigation compares the traditional physico-chemical descriptor and machine learning-based approaches through autoencoder generated descriptors to two different descriptor-free, Simplified Molecular Input Line Entry System (SMILES) based, deep learning architectures of Bidirectional Encoder Representations from Transformers (BERT) type using the Mondrian aggregated conformal prediction method as overarching framework. The results show for the binary CATMoS non-toxic and very-toxic datasets that for the former, almost equally balanced, dataset all methods perform equally well while for the latter dataset, with an 11-fold difference between the two classes, the MolBERT model based on a large pre-trained network performs somewhat better compared to the rest with high efficiency for both classes (0.93-0.94) as well as high values for sensitivity, specificity and balanced accuracy (0.86-0.87). The descriptor-free, SMILES-based, deep learning BERT architectures seem capable of producing well-balanced predictive models with defined applicability domains. This work also demonstrates that the class imbalance problem is gracefully handled through the use of Mondrian conformal prediction without the use of over- and/or under-sampling, weighting of classes or cost-sensitive methods.
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132
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Qin GF, Zhang X, Zhu F, Huo ZQ, Yao QQ, Feng Q, Liu Z, Zhang GM, Yao JC, Liang HB. MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication. MOLECULES (BASEL, SWITZERLAND) 2022; 28:molecules28010157. [PMID: 36615351 PMCID: PMC9822519 DOI: 10.3390/molecules28010157] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022]
Abstract
Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.
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Affiliation(s)
- Guo-Fei Qin
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China
- Correspondence: (G.-F.Q.); (J.-C.Y.); (H.-B.L.); Tel.: +86-539-503-0319 (G.-F.Q.)
| | - Xiao Zhang
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Feng Zhu
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China
| | - Zong-Qing Huo
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China
| | | | - Qun Feng
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China
| | - Zhong Liu
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China
| | - Gui-Min Zhang
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Jing-Chun Yao
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China
- Correspondence: (G.-F.Q.); (J.-C.Y.); (H.-B.L.); Tel.: +86-539-503-0319 (G.-F.Q.)
| | - Hong-Bao Liang
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Lunan Pharmaceutical Group Co., Ltd., Linyi 273400, China
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
- Correspondence: (G.-F.Q.); (J.-C.Y.); (H.-B.L.); Tel.: +86-539-503-0319 (G.-F.Q.)
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133
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Chang Y, Hawkins BA, Du JJ, Groundwater PW, Hibbs DE, Lai F. A Guide to In Silico Drug Design. Pharmaceutics 2022; 15:pharmaceutics15010049. [PMID: 36678678 PMCID: PMC9867171 DOI: 10.3390/pharmaceutics15010049] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022] Open
Abstract
The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.
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Affiliation(s)
- Yiqun Chang
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Bryson A. Hawkins
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Jonathan J. Du
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Paul W. Groundwater
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - David E. Hibbs
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Felcia Lai
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence:
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134
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Martins LS, Kruger HG, Naicker T, Alves CN, Lameira J, Araújo Silva JR. Computational insights for predicting the binding and selectivity of peptidomimetic plasmepsin IV inhibitors against cathepsin D. RSC Adv 2022; 13:602-614. [PMID: 36605626 PMCID: PMC9773328 DOI: 10.1039/d2ra06246a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Plasmepsins (Plms) are aspartic proteases involved in the degradation of human hemoglobin by P. falciparum and are essential for the survival and growth of the parasite. Therefore, Plm enzymes are reported as an important antimalarial drug target. Herein, we have applied molecular docking, molecular dynamics (MD) simulations, and binding free energy with the Linear Interaction Energy (LIE) approach to investigate the binding of peptidomimetic PlmIV inhibitors with a particular focus on understanding their selectivity against the human Asp protease cathepsin D (CatD). The residual decomposition analysis results suggest that amino acid differences in the subsite S3 of PlmIV and CatD are responsible for the higher selectivity of the 5a inhibitor. These findings yield excellent agreement with experimental binding data and provide new details regarding van der Waals and electrostatic interactions of subsite residues as well as structural properties of the PlmIV and CatD systems.
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Affiliation(s)
- Lucas Sousa Martins
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do ParáBelémPará 66075-110Brazil
| | | | - Tricia Naicker
- Catalysis and Peptide Research Unit, University of KwaZulu-NatalDurban 4000South Africa
| | - Cláudio Nahum Alves
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do ParáBelémPará 66075-110Brazil
| | - Jerônimo Lameira
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do ParáBelémPará 66075-110Brazil
| | - José Rogério Araújo Silva
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do ParáBelémPará 66075-110Brazil
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135
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The Impact of Software Used and the Type of Target Protein on Molecular Docking Accuracy. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27249041. [PMID: 36558174 PMCID: PMC9788237 DOI: 10.3390/molecules27249041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/05/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
The modern development of computer technology and different in silico methods have had an increasing impact on the discovery and development of new drugs. Different molecular docking techniques most widely used in silico methods in drug discovery. Currently, the time and financial costs for the initial hit identification can be significantly reduced due to the ability to perform high-throughput virtual screening of large compound libraries in a short time. However, the selection of potential hit compounds still remains more of a random process, because there is still no consensus on what the binding energy and ligand efficiency (LE) of a potentially active compound should be. In the best cases, only 20-30% of compounds identified by molecular docking are active in biological tests. In this work, we evaluated the impact of the docking software used as well as the type of the target protein on the molecular docking results and their accuracy using an example of the three most popular programs and five target proteins related to neurodegenerative diseases. In addition, we attempted to determine the "reliable range" of the binding energy and LE that would allow selecting compounds with biological activity in the desired concentration range.
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136
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Sharma S, Sharma BK, Jain S, Gulyani P. A Combined QSAR and Molecular Docking Approach for Identifying
Pyrimidine Derivatives as Penicillin Binding Protein Inhibitors. LETT DRUG DES DISCOV 2022. [DOI: 10.2174/1570180819666220427101322] [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
Background:
Antimicrobial resistance has been rising continuously in the past few years due
to the overuse and exploitation of existing antimicrobials. This has motivated the search for a novel scaffold
that has the capability of rapid antimicrobial action. The hybridized pyrimidines have attracted us due
to their widespread biological activities, such as anti-bacterial and antifungal activities.
Objective:
The present study incorporates a series of pyrimidine-based antimicrobial agents for the 2D
quantitative structure-activity relationship analysis (2D QSAR) and docking analysis.
Methods:
The exploration of the chemical structures in combination with the biological activity in CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model has been validated using combinatorial protocol in multiple linear regression (CP-MLR) and partial least squares (PLS) analysis.
Methods:
The exploration of the chemical structures in combination with the biological activity in
CPMLR led to the detection of six descriptors (Constitutional descriptors, Topological descriptors, Modified
Burden Eigenvalues and 2D autocorrelations) for modeling the activity. The resulted QSAR model
has been validated using a combinatorial protocol in multiple linear regression (CP-MLR) and partial
least squares (PLS) analysis.
Results:
The best QSAR model displays the r2
t
value of 0.594, Q2
LOO value of 0.779, Q2
L5O value of
0.767. Further docking study was executed using Autodock Vina against Penicillin-binding protein
(PBP2a).
Conclusion:
From the results, Compounds 4, 11and 24 were found to possess a good binding affinity
towards PBP2a.
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Affiliation(s)
- Smriti Sharma
- Amity Institute of Pharmacy, Amity University, Sector-125, Noida-201313, India
| | - Brij K. Sharma
- Department of Chemistry, Government
College, Bundi-323 001, Rajasthan, India
| | - Surabhi Jain
- Faculty of Pharmacy, B. Pharmacy College Rampura-kakanpur, (Gujarat
Technological University), Panchmahals, Gujarat, India
| | - Puja Gulyani
- Amity Institute of Pharmacy, Amity University, Sector-125, Noida-201313, India
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137
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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138
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Putt KS, Du Y, Fu H, Zhang ZY. High-throughput screening strategies for space-based radiation countermeasure discovery. LIFE SCIENCES IN SPACE RESEARCH 2022; 35:88-104. [PMID: 36336374 DOI: 10.1016/j.lssr.2022.07.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 06/13/2022] [Accepted: 07/19/2022] [Indexed: 06/16/2023]
Abstract
As humanity begins to venture further into space, approaches to better protect astronauts from the hazards found in space need to be developed. One particular hazard of concern is the complex radiation that is ever present in deep space. Currently, it is unlikely enough spacecraft shielding could be launched that would provide adequate protection to astronauts during long-duration missions such as a journey to Mars and back. In an effort to identify other means of protection, prophylactic radioprotective drugs have been proposed as a potential means to reduce the biological damage caused by this radiation. Unfortunately, few radioprotectors have been approved by the FDA for usage and for those that have been developed, they protect normal cells/tissues from acute, high levels of radiation exposure such as that from oncology radiation treatments. To date, essentially no radioprotectors have been developed that specifically counteract the effects of chronic low-dose rate space radiation. This review highlights how high-throughput screening (HTS) methodologies could be implemented to identify such a radioprotective agent. Several potential target, pathway, and phenotypic assays are discussed along with potential challenges towards screening for radioprotectors. Utilizing HTS strategies such as the ones proposed here have the potential to identify new chemical scaffolds that can be developed into efficacious radioprotectors that are specifically designed to protect astronauts during deep space journeys. The overarching goal of this review is to elicit broader interest in applying drug discovery techniques, specifically HTS towards the identification of radiation countermeasures designed to be efficacious towards the biological insults likely to be encountered by astronauts on long duration voyages.
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Affiliation(s)
- Karson S Putt
- Institute for Drug Discovery, Purdue University, West Lafayette IN 47907 USA
| | - Yuhong Du
- Department of Pharmacology and Chemical Biology and Emory Chemical Biology Discovery Center, Emory University School of Medicine, Atlanta, GA 30322 USA
| | - Haian Fu
- Department of Pharmacology and Chemical Biology and Emory Chemical Biology Discovery Center, Emory University School of Medicine, Atlanta, GA 30322 USA
| | - Zhong-Yin Zhang
- Institute for Drug Discovery, Purdue University, West Lafayette IN 47907 USA; Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette IN 47907 USA.
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139
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Maghsoudi S, Taghavi Shahraki B, Rameh F, Nazarabi M, Fatahi Y, Akhavan O, Rabiee M, Mostafavi E, Lima EC, Saeb MR, Rabiee N. A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery. Chem Biol Drug Des 2022; 100:699-721. [PMID: 36002440 PMCID: PMC9539342 DOI: 10.1111/cbdd.14136] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/07/2022] [Accepted: 08/21/2022] [Indexed: 11/29/2022]
Abstract
Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.
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Affiliation(s)
- Saeid Maghsoudi
- Faculty of Medicine, Department of Physiology and PathophysiologyUniversity of ManitobaWinnipegManitobaCanada
- Biology of Breathing Group, Children's Hospital Research Institute of Manitoba (CHRIM), University of ManitobaWinnipegManitobaCanada
| | | | | | - Masoomeh Nazarabi
- Faculty of Organic Chemistry, Department of ChemistryUniversity of KashanKashanIran
| | - Yousef Fatahi
- Department of Pharmaceutical Nanotechnology, Faculty of PharmacyTehran University of Medical SciencesTehranIran
- Nanotechnology Research Center, Faculty of PharmacyTehran University of Medical SciencesTehranIran
| | - Omid Akhavan
- Department of PhysicsSharif University of TechnologyTehranIran
| | - Mohammad Rabiee
- Biomaterials Group, Department of Biomedical EngineeringAmirkabir University of TechnologyTehranIran
| | - Ebrahim Mostafavi
- Stanford Cardiovascular Institute, Stanford University School of MedicineStanfordCaliforniaUSA
- Department of MedicineStanford University School of MedicineStanfordCaliforniaUSA
| | - Eder C. Lima
- Institute of Chemistry, Federal University of Rio Grande Do Sul (UFRGS)Porto AlegreBrazil
| | - Mohammad Reza Saeb
- Department of Polymer Technology, Faculty of ChemistryGdańsk University of TechnologyGdańskPoland
| | - Navid Rabiee
- Department of PhysicsSharif University of TechnologyTehranIran
- School of EngineeringMacquarie UniversitySydneyNew South WalesAustralia
- Department of Materials Science and EngineeringPohang University of Science and Technology (POSTECH)PohangSouth Korea
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140
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Inferring Therapeutic Targets in Candida albicans and Possible Inhibition through Natural Products: A Binding and Physiological Based Pharmacokinetics Snapshot. Life (Basel) 2022; 12:life12111743. [DOI: 10.3390/life12111743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/12/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
Despite being responsible for invasive infections, fungal pathogens have been underrepresented in computer aided therapeutic target mining and drug design. Excess of Candida albicans causes candidiasis, causative of thrush and vaginal infection due to off-balance. In this study, we attempted to mine drug targets (n = 46) using a subtractive proteomic approach in this pathogenic yeast and screen natural products with inhibition potential against fructose-bisphosphate aldolase (FBA) of the C. albicans. The top compound selected on the basis of best docking score from traditional Indian medicine/Ayurvedic library was (4-Hydroxybenzyl)thiocarbamic acid, from the ZINC FBA inhibitor library was ZINC13507461 (IUPAC name: [(2R)-2-hydroxy-3-phosphonooxypropyl] (9E,12E)-octadeca-9,12-dienoate), and from traditional Tibetan medicine/Sowa rigpa was Chelerythrine (IUPAC name: 1,2-Dimethoxy-12-methyl-9H-[1,3]benzodioxolo[5,6-c]phenanthridin-12-ium), compared to the control (2E)-1-(4-nitrophenyl)-2-[(4-nitrophenyl)methylidene]hydrazine. No Ames toxicity was predicted for prioritized compounds while control depicted this toxicity. (4-Hydroxybenzyl)thiocarbamic acid showed hepatotoxicity, while Chelerythrine depicted hERG inhibition, which can lead to QT syndrome, so we recommend ZINC13507461 for further testing in lab. Pharmacological based pharmacokinetic modeling revealed that it has low bioavailability and hence, absorption in healthy state. In cirrhosis and renal impairment, absorption and plasma accumulation increased so we recommend further investigation into this occurrence and recommend high dosage in further tests to increase bioavailability.
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141
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Rodríguez-Arana N, Jiménez-Aliaga K, Intiquilla A, León JA, Flores E, Zavaleta AI, Izaguirre V, Solis-Calero C, Hernández-Ledesma B. Protection against Oxidative Stress and Metabolic Alterations by Synthetic Peptides Derived from Erythrina edulis Seed Protein. Antioxidants (Basel) 2022; 11:2101. [PMID: 36358473 PMCID: PMC9686657 DOI: 10.3390/antiox11112101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/14/2022] [Accepted: 10/20/2022] [Indexed: 06/21/2024] Open
Abstract
The ability of multifunctional food-derived peptides to act on different body targets make them promising alternatives in the prevention/management of chronic disorders. The potential of Erythrina edulis (pajuro) protein as a source of multifunctional peptides was proven. Fourteen selected synthetic peptides identified in an alcalase hydrolyzate from pajuro protein showed in vitro antioxidant, anti-hypertensive, anti-diabetic, and/or anti-obesity effects. The radical scavenging properties of the peptides could be responsible for the potent protective effects observed against the oxidative damage caused by FeSO4 in neuroblastoma cells. Moreover, their affinity towards the binding cavity of angiotensin-converting enzyme (ACE) and dipeptidyl peptidase IV (DPP-IV) were predicted by molecular modeling. The results demonstrated that some peptides such as YPSY exhibited promising binding at both enzymes, supporting the role of pajuro protein as a novel ingredient of functional foods or nutraceuticals for prevention/management of oxidative stress, hypertension, and metabolic-alteration-associated chronic diseases.
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Affiliation(s)
- Nathaly Rodríguez-Arana
- Laboratorio de Biología Molecular, Grupo de Investigación BIOMIAS, Facultad de Farmacia y Bioquímica, Universidad Nacional Mayor de San Marcos, Jr. Puno N° 1002, Lima 4559, Peru
| | - Karim Jiménez-Aliaga
- Laboratorio de Biología Molecular, Grupo de Investigación BIOMIAS, Facultad de Farmacia y Bioquímica, Universidad Nacional Mayor de San Marcos, Jr. Puno N° 1002, Lima 4559, Peru
| | - Arturo Intiquilla
- Laboratorio de Biología Molecular, Grupo de Investigación BIOMIAS, Facultad de Farmacia y Bioquímica, Universidad Nacional Mayor de San Marcos, Jr. Puno N° 1002, Lima 4559, Peru
- Departamento de Ciencia de los Alimentos y Tecnología Química, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santos Dumont 964, Independencia, Santiago 8380494, Chile
| | - José A. León
- Laboratorio de Biología Molecular, Grupo de Investigación BIOMIAS, Facultad de Farmacia y Bioquímica, Universidad Nacional Mayor de San Marcos, Jr. Puno N° 1002, Lima 4559, Peru
| | - Eduardo Flores
- Laboratorio de Biología Molecular, Grupo de Investigación BIOMIAS, Facultad de Farmacia y Bioquímica, Universidad Nacional Mayor de San Marcos, Jr. Puno N° 1002, Lima 4559, Peru
| | - Amparo Iris Zavaleta
- Laboratorio de Biología Molecular, Grupo de Investigación BIOMIAS, Facultad de Farmacia y Bioquímica, Universidad Nacional Mayor de San Marcos, Jr. Puno N° 1002, Lima 4559, Peru
| | - Víctor Izaguirre
- Laboratorio de Biología Molecular, Grupo de Investigación BIOMIAS, Facultad de Farmacia y Bioquímica, Universidad Nacional Mayor de San Marcos, Jr. Puno N° 1002, Lima 4559, Peru
| | - Christian Solis-Calero
- Laboratorio de Biología Molecular, Grupo de Investigación BIOMIAS, Facultad de Farmacia y Bioquímica, Universidad Nacional Mayor de San Marcos, Jr. Puno N° 1002, Lima 4559, Peru
| | - Blanca Hernández-Ledesma
- Department of Bioactivity and Food Analysis, Institute of Food Science Research (CIAL, CSIC-UAM, CEI UAM+CSIC), Nicolás Cabrera 9, 28049 Madrid, Spain
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142
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Alsagaby SA, Iqbal D, Ahmad I, Patel H, Mir SA, Madkhali YA, Oyouni AAA, Hawsawi YM, Alhumaydhi FA, Alshehri B, Alturaiki W, Alanazi B, Mir MA, Al Abdulmonem W. In silico investigations identified Butyl Xanalterate to competently target CK2α (CSNK2A1) for therapy of chronic lymphocytic leukemia. Sci Rep 2022; 12:17648. [PMID: 36271116 PMCID: PMC9587039 DOI: 10.1038/s41598-022-21546-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/28/2022] [Indexed: 01/18/2023] Open
Abstract
Chronic lymphocytic leukemia (CLL) is an incurable malignancy of B-cells. In this study, bioinformatics analyses were conducted to identify possible pathogenic roles of CK2α, which is a protein encoded by CSNK2A1, in the progression and aggressiveness of CLL. Furthermore, various computational tools were used to search for a competent inhibitor of CK2α from fungal metabolites that could be proposed for CLL therapy. In CLL patients, high-expression of CSNK2A1 was associated with early need for therapy (n = 130, p < 0.0001) and short overall survival (OS; n = 107, p = 0.005). Consistently, bioinformatics analyses showed CSNK2A1 to associate with/play roles in CLL proliferation and survival-dependent pathways. Furthermore, PPI network analysis identified interaction partners of CK2α (PPI enrichment p value = 1 × 10-16) that associated with early need for therapy (n = 130, p < 0.003) and have been known to heavily impact on the progression of CLL. These findings constructed a rational for targeting CK2α for CLL therapy. Consequently, computational analyses reported 35 fungal metabolites out of 5820 (filtered from 19,967 metabolites) to have lower binding energy (ΔG: - 10.9 to - 11.7 kcal/mol) and better binding affinity (Kd: 9.77 × 107 M-1 to 3.77 × 108 M-1) compared with the native ligand (ΔG: - 10.8, Kd: 8.3 × 107 M--1). Furthermore, molecular dynamics simulation study established that Butyl Xanalterate-CK2α complex continuously remained stable throughout the simulation time (100 ns). Moreover, Butyl Xanalterate interacted with most of the catalytic residues, where complex was stabilized by more than 65% hydrogen bond interactions, and a significant hydrophobic interaction with residue Phe113. Here, high-expression of CSNK2A1 was implicated in the progression and poor prognosis of CLL, making it a potential therapeutic target in the disease. Butyl Xanalterate showed stable and strong interactions with CK2α, thus we propose it as a competitive inhibitor of CK2α for CLL therapy.
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Affiliation(s)
- Suliman A. Alsagaby
- grid.449051.d0000 0004 0441 5633Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952 Kingdom of Saudi Arabia
| | - Danish Iqbal
- grid.449051.d0000 0004 0441 5633Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952 Kingdom of Saudi Arabia
| | - Iqrar Ahmad
- grid.412233.50000 0001 0641 8393Division of Computer Aided Drug Design, Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra 425405 India
| | - Harun Patel
- grid.412233.50000 0001 0641 8393Division of Computer Aided Drug Design, Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra 425405 India
| | - Shabir Ahmad Mir
- grid.449051.d0000 0004 0441 5633Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952 Kingdom of Saudi Arabia
| | - Yahya Awaji Madkhali
- grid.449051.d0000 0004 0441 5633Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952 Kingdom of Saudi Arabia
| | - Atif Abdulwahab A. Oyouni
- grid.440760.10000 0004 0419 5685Department of Biology, Faculty of Sciences, University of Tabuk, Tabuk, Kingdom of Saudi Arabia ,grid.440760.10000 0004 0419 5685Genome and Biotechnology Unit, Faculty of Sciences, University of Tabuk, Tabuk, Kingdom of Saudi Arabia
| | - Yousef M. Hawsawi
- grid.415310.20000 0001 2191 4301Research Center, King Faisal Specialist Hospital and Research Center, P.O. Box 40047, Jeddah, 21499 Kingdom of Saudi Arabia ,grid.411335.10000 0004 1758 7207College of Medicine, Al-Faisal University, P.O. Box 50927, Riyadh, 11533 Kingdom of Saudi Arabia
| | - Fahad A. Alhumaydhi
- grid.412602.30000 0000 9421 8094Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, Kingdom of Saudi Arabia
| | - Bader Alshehri
- grid.449051.d0000 0004 0441 5633Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952 Kingdom of Saudi Arabia
| | - Wael Alturaiki
- grid.449051.d0000 0004 0441 5633Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, AL-Majmaah, 11952 Kingdom of Saudi Arabia
| | - Bader Alanazi
- grid.415277.20000 0004 0593 1832Biomedical Research Administration, Research Center, King Fahad Medical City, Riyadh, Kingdom of Saudi Arabia ,Prince Mohammed bin Abdulaziz Medical City, AlJouf, Kingdom of Saudi Arabia
| | - Manzoor Ahmad Mir
- grid.412997.00000 0001 2294 5433Department of Bioresources, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Waleed Al Abdulmonem
- grid.412602.30000 0000 9421 8094Department of Pathology, College of Medicine, Qassim University, Qassim, Kingdom of Saudi Arabia
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143
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DrugRep: an automatic virtual screening server for drug repurposing. Acta Pharmacol Sin 2022; 44:888-896. [PMID: 36216900 PMCID: PMC9549438 DOI: 10.1038/s41401-022-00996-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 09/02/2022] [Indexed: 12/01/2022] Open
Abstract
Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/.
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144
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Phytochemical profiling, in vitro biological activities, and in-silico molecular docking studies of Typha domingensis. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.104133] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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145
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Liu J, Guo H, Zhou J, Wang Y, Yan H, Jin R, Tang Y. Evodiamine and Rutaecarpine as Potential Anticancer Compounds: A Combined Computational Study. Int J Mol Sci 2022; 23:ijms231911513. [PMID: 36232809 PMCID: PMC9570036 DOI: 10.3390/ijms231911513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/18/2022] [Accepted: 09/26/2022] [Indexed: 12/29/2022] Open
Abstract
Evodiamine (EVO) and rutaecarpine (RUT) are the main active compounds of the traditional Chinese medicinal herb Evodia rutaecarpa. Here, we fully optimized the molecular geometries of EVO and RUT at the B3LYP/6-311++G (d, p) level of density functional theory. The natural population analysis (NPA) charges, frontier molecular orbitals, molecular electrostatic potentials, and the chemical reactivity descriptors for EVO and RUT were also investigated. Furthermore, molecular docking, molecular dynamics simulations, and the analysis of the binding free energies of EVO and RUT were carried out against the anticancer target topoisomerase 1 (TOP1) to clarify their anticancer mechanisms. The docking results indicated that they could inhibit TOP1 by intercalating into the cleaved DNA-binding site to form a TOP1−DNA−ligand ternary complex, suggesting that they may be potential TOP1 inhibitors. Molecular dynamics (MD) simulations evaluated the binding stability of the TOP1−DNA−ligand ternary complex. The calculation of binding free energy showed that the binding ability of EVO with TOP1 was stronger than that of RUT. These results elucidated the structure−activity relationship and the antitumor mechanism of EVO and RUT at the molecular level. It is suggested that EVO and RUT may be potential compounds for the development of new anticancer drugs.
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Affiliation(s)
| | - Hui Guo
- Correspondence: (H.G.); (Y.T.)
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146
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Ghosh A, Panda P, Halder AK, Cordeiro MNDS. In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus. Front Pharmacol 2022; 13:1004255. [PMID: 36225563 PMCID: PMC9548590 DOI: 10.3389/fphar.2022.1004255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
RNA-dependent RNA polymerase (RdRp) is a potential therapeutic target for the discovery of novel antiviral agents for the treatment of life-threatening infections caused by newly emerged strains of the influenza virus. Being one of the most conserved enzymes among RNA viruses, RdRp and its inhibitors require further investigations to design novel antiviral agents. In this work, we systematically investigated the structural requirements for antiviral properties of some recently reported aryl benzoyl hydrazide derivatives through a range of in silico tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations. The 2D-QSAR models developed in the current work achieved high statistical reliability and simultaneously afforded in-depth mechanistic interpretability towards structural requirements. The structure-based pharmacophore model developed with the docked conformation of one of the most potent compounds with the RdRp protein of H5N1 influenza strain was utilized for developing a 3D-QSAR model with satisfactory statistical quality validating both the docking and the pharmacophore modeling methodologies performed in this work. However, it is the atom-based alignment of the compounds that afforded the most statistically reliable 3D-QSAR model, the results of which provided mechanistic interpretations consistent with the 2D-QSAR results. Additionally, molecular dynamics simulations performed with the apoprotein as well as the docked complex of RdRp revealed the dynamic stability of the ligand at the proposed binding site of the receptor. At the same time, it also supported the mechanistic interpretations drawn from 2D-, 3D-QSAR and pharmacophore modeling. The present study, performed mostly with open-source tools and webservers, returns important guidelines for research aimed at the future design and development of novel anti-viral agents against various RNA viruses like influenza virus, human immunodeficiency virus-1, hepatitis C virus, corona virus, and so forth.
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Affiliation(s)
- Abhishek Ghosh
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Durgapur, West Bengal, India
| | - Parthasarathi Panda
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Durgapur, West Bengal, India
- *Correspondence: Parthasarathi Panda, ; Maria Natalia D. S. Cordeiro,
| | - Amit Kumar Halder
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Durgapur, West Bengal, India
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Maria Natalia D. S. Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
- *Correspondence: Parthasarathi Panda, ; Maria Natalia D. S. Cordeiro,
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147
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Sharma T, Saralamma VVG, Lee DC, Imran MA, Choi J, Baig MH, Dong JJ. Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors. Int J Biol Macromol 2022; 222:239-250. [PMID: 36130643 DOI: 10.1016/j.ijbiomac.2022.09.151] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/13/2022] [Accepted: 09/16/2022] [Indexed: 11/05/2022]
Abstract
Bruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 μM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 μM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.
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Affiliation(s)
- Tanuj Sharma
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul 120-752, Republic of Korea
| | - Venu Venkatarame Gowda Saralamma
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul 120-752, Republic of Korea
| | - Duk Chul Lee
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul 120-752, Republic of Korea
| | - Mohammad Azhar Imran
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul 120-752, Republic of Korea
| | - Jaehyuk Choi
- BNJBiopharma, 2nd floor Memorial Hall, 85, Songdogwahak-ro, Yeonsu-gu, Incheon 21983, Republic of Korea
| | - Mohammad Hassan Baig
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul 120-752, Republic of Korea.
| | - Jae-June Dong
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Gangnam-gu, Seoul 120-752, Republic of Korea.
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148
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Ma BB, Montgomery AP, Chen B, Kassiou M, Danon JJ. Strategies for targeting the P2Y 12 receptor in the central nervous system. Bioorg Med Chem Lett 2022; 71:128837. [PMID: 35640763 DOI: 10.1016/j.bmcl.2022.128837] [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: 05/10/2022] [Accepted: 05/26/2022] [Indexed: 11/28/2022]
Abstract
The purinergic 2Y type 12 receptor (P2Y12R) is a well-known biological target for anti-thrombotic drugs due to its role in platelet aggregation and blood clotting. While the importance of the P2Y12R in the periphery has been known for decades, much less is known about its expression and roles in the central nervous system (CNS), where it is expressed exclusively on microglia - the first responders to brain insults and neurodegeneration. Several seminal studies have shown that P2Y12 is a robust, translatable biomarker for anti-inflammatory and neuroprotective microglial phenotypes in models of degenerative diseases such as multiple sclerosis and Alzheimer's disease. An enduring problem for studying this receptor in vivo, however, is the lack of selective, high-affinity small molecule ligands that can bypass the blood-brain barrier and accumulate in the CNS. In this Digest, we discuss previous attempts by researchers to target the P2Y12R in the CNS and opine on strategies that may be employed to design and assess the suitability of novel P2Y12 ligands for this purpose going forward.
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Affiliation(s)
- Ben B Ma
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | | | - Biling Chen
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - Michael Kassiou
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
| | - Jonathan J Danon
- School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia.
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149
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Progress and Impact of Latin American Natural Product Databases. Biomolecules 2022; 12:biom12091202. [PMID: 36139041 PMCID: PMC9496143 DOI: 10.3390/biom12091202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 08/27/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element of CADD. This work reviews the progress toward developing compound databases of natural origin, and it surveys computational methods, emphasizing chemoinformatic approaches to profile natural product databases. Furthermore, it reviews the present state of the art in developing Latin American NP databases and their practical applications to the drug discovery area.
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150
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Liu J, Zhang L, Gao J, Zhang B, Liu X, Yang N, Liu X, Liu X, Cheng Y. Discovery of genistein derivatives as potential SARS-CoV-2 main protease inhibitors by virtual screening, molecular dynamics simulations and ADMET analysis. Front Pharmacol 2022; 13:961154. [PMID: 36091808 PMCID: PMC9452787 DOI: 10.3389/fphar.2022.961154] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Due to the constant mutation of virus and the lack of specific therapeutic drugs, the coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still poses a huge threat to the health of people, especially those with underlying diseases. Therefore, drug discovery against the SARS-CoV-2 remains of great significance. Methods: With the main protease of virus as the inhibitor target, 9,614 genistein derivatives were virtually screened by LeDock and AutoDock Vina, and the top 20 compounds with highest normalized scores were obtained. Molecular dynamics simulations were carried out for studying interactions between these 20 compounds and the target protein. The drug-like properties, activity, and ADMET of these compounds were also evaluated by DruLiTo software or online server. Results: Twenty compounds, including compound 11, were screened by normalized molecular docking, which could bind to the target through multiple non-bonding interactions. Molecular dynamics simulation results showed that compounds 2, 4, 5, 11, 13, 14, 17, and 18 had the best binding force with the target protein of SARS-CoV-2, and the absolute values of binding free energies all exceeded 50 kJ/mol. The drug-likeness properties indicated that a variety of compounds including compound 11 were worthy of further study. The results of bioactivity score prediction found that compounds 11 and 12 had high inhibitory activities against protease, which indicated that these two compounds had the potential to be further developed as COVID-19 inhibitors. Finally, compound 11 showed excellent predictive ADMET properties including high absorption and low toxicity. Conclusion: These in silico work results show that the preferred compound 11 (ZINC000111282222), which exhibited strong binding to SARS-CoV-2 main protease, acceptable drug-like properties, protease inhibitory activity and ADMET properties, has great promise for further research as a potential therapeutic agent against COVID-19.
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Affiliation(s)
- Jiawei Liu
- Center for Drug Innovation and Discovery, College of Life Science, Hebei Normal University, Shijiazhuang, China
| | - Ling Zhang
- School of Chemical Technology, Shijiazhuang University, Shijiazhuang, China
| | - Jian Gao
- College of Plant Protection, Southwest University, Chongqing, China
| | - Baochen Zhang
- Center for Drug Innovation and Discovery, College of Life Science, Hebei Normal University, Shijiazhuang, China
| | - Xiaoli Liu
- Center for Drug Innovation and Discovery, College of Life Science, Hebei Normal University, Shijiazhuang, China
| | - Ninghui Yang
- Center for Drug Innovation and Discovery, College of Life Science, Hebei Normal University, Shijiazhuang, China
| | - Xiaotong Liu
- Center for Drug Innovation and Discovery, College of Life Science, Hebei Normal University, Shijiazhuang, China
| | - Xifu Liu
- Center for Drug Innovation and Discovery, College of Life Science, Hebei Normal University, Shijiazhuang, China
- *Correspondence: Xifu Liu, ; Yu Cheng,
| | - Yu Cheng
- Center for Drug Innovation and Discovery, College of Life Science, Hebei Normal University, Shijiazhuang, China
- *Correspondence: Xifu Liu, ; Yu Cheng,
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