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Darsaraee M, Kaveh S, Mani-Varnosfaderani A, Neiband MS. General structure-activity/selectivity relationship patterns for the inhibitors of the chemokine receptors (CCR1/CCR2/CCR4/CCR5) with application for virtual screening of PubChem database. J Biomol Struct Dyn 2024; 42:8781-8799. [PMID: 37599469 DOI: 10.1080/07391102.2023.2248255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/08/2023] [Indexed: 08/22/2023]
Abstract
CC chemokine receptors (CCRs) form a crucial subfamily of G protein-linked receptors that play a distinct role in the onset and progression of various life-threatening diseases. The main aim of this research is to derive general structure-activity relationship (SAR) patterns to describe the selectivity and activity of CCR inhibitors. To this end, a total of 7332 molecules related to the inhibition of CCR1, CCR2, CCR4, and CCR5 were collected from the Binding Database and analyzed using machine learning techniques. A diverse set of 450 molecular descriptors was calculated for each molecule, and the molecules were classified based on their therapeutic targets and activities. The variable importance in the projection (VIP) approach was used to select discriminatory molecular features, and classification models were developed using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN). The reliability and predictability of the models were estimated using 10-fold cross-validation, an external validation set, and an applicability domain approach. We were able to identify different sets of molecular descriptors for discriminating between active and inactive molecules and model the selectivity of inhibitors towards different CCRs. The sensitivities of the predictions for the external test set for the SKN models ranged from 0.827-0.873. Finally, the developed classification models were used to screen approximately 2 million random molecules from the PubChem database, with average values for areas under the receiver operating characteristic curves ranging from 0.78-0.96 for SKN models and 0.75-0.89 for CPANN models.Communicated by Ramaswamy H. Sarma.
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MESH Headings
- Structure-Activity Relationship
- Humans
- Databases, Chemical
- Receptors, CCR1/antagonists & inhibitors
- Receptors, CCR1/chemistry
- Receptors, CCR1/metabolism
- Receptors, CCR5/chemistry
- Receptors, CCR5/metabolism
- Receptors, CCR/antagonists & inhibitors
- Receptors, CCR/chemistry
- Receptors, CCR/metabolism
- Receptors, CCR2/antagonists & inhibitors
- Receptors, CCR2/chemistry
- Receptors, CCR2/metabolism
- Receptors, Chemokine/antagonists & inhibitors
- Receptors, Chemokine/chemistry
- Receptors, Chemokine/metabolism
- Models, Molecular
- Neural Networks, Computer
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Affiliation(s)
- M Darsaraee
- Chemometrics and Cheminformatics Laboratory, Department of Analytical Chemistry, Tarbiat Modares University, Tehran, Iran
| | - S Kaveh
- Chemometrics and Cheminformatics Laboratory, Department of Analytical Chemistry, Tarbiat Modares University, Tehran, Iran
| | - A Mani-Varnosfaderani
- Chemometrics and Cheminformatics Laboratory, Department of Analytical Chemistry, Tarbiat Modares University, Tehran, Iran
| | - M S Neiband
- Department of Chemistry, Payame Noor University (PNU), Tehran, Iran
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Özil M, Balaydın HT, Dogan B, Şentürk M, Durdagi S. Efficient, rapid, and high-yield synthesis of aryl Schiff base derivatives and their in vitro and in silico inhibition studies of hCA I, hCA II, AChE, and BuChE. Arch Pharm (Weinheim) 2024; 357:e2300266. [PMID: 38593306 DOI: 10.1002/ardp.202300266] [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: 12/24/2023] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 04/11/2024]
Abstract
This study reports a rapid and efficient synthesis of four novel aryl Schiff base derivatives. Biological activity and molecular modeling studies were conducted to evaluate the inhibitory effects of these compounds on human carbonic anhydrases (hCA) and cholinesterases. The results indicate that the triazole-ring-containing compounds have strong inhibitory effects on hCA I, hCA II, acetylcholinesterase (AChE), and butyrylcholinesterase (BuChE) targets. Besides comparing the Schiff bases synthesized in our study to reference molecules, we conducted in silico investigations to examine how these compounds interact with their targets. Our studies revealed that these compounds can occupy binding sites and establish interactions with crucial residues, thus inhibiting the functions of the targets. These findings have significant implications as they can be utilized to develop more potent compounds for treating the diseases that these target proteins play crucial roles in or to obtain drug precursors with enhanced efficacy.
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Affiliation(s)
- Musa Özil
- Department of Chemistry, The Faculty of Arts and Sciences, Recep Tayyip Erdogan University, Rize, Türkiye
| | - Halis T Balaydın
- Education Faculty, Recep Tayyip Erdogan University, Rize, Türkiye
| | - Berna Dogan
- Department of Chemistry, Istanbul Technical University, Istanbul, Türkiye
- Department of Biochemistry, School of Medicine, Bahçeşehir University, Istanbul, Türkiye
| | - Murat Şentürk
- Pharmacy Faculty, Agri Ibrahim Cecen University, Agri, Türkiye
| | - Serdar Durdagi
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, Türkiye
- Molecular Therapy Lab, Department of Pharmaceutical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul, Türkiye
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3
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Sayyah E, Oktay L, Tunc H, Durdagi S. Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis. ChemMedChem 2024; 19:e202300644. [PMID: 38523069 DOI: 10.1002/cmdc.202300644] [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: 11/19/2023] [Revised: 03/12/2024] [Accepted: 03/19/2024] [Indexed: 03/26/2024]
Abstract
Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.
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Affiliation(s)
- Ehsan Sayyah
- Computational Biology and Molecular Simulations Lab, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, Turkey
- Computational Drug Design Center (HITMER), Bahçeşehir University, Istanbul, Turkey
| | - Lalehan Oktay
- Computational Biology and Molecular Simulations Lab, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, Turkey
- Computational Drug Design Center (HITMER), Bahçeşehir University, Istanbul, Turkey
| | - Huseyin Tunc
- Department of Biostatistics and Medical Informatics, School of Medicine, Bahçeşehir University, Istanbul, Turkey
| | - Serdar Durdagi
- Computational Biology and Molecular Simulations Lab, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, Turkey
- Computational Drug Design Center (HITMER), Bahçeşehir University, Istanbul, Turkey
- Molecular Therapy Lab, Department of Pharmaceutical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul, Turkey
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Sayyed SK, Quraishi M, Jobby R, Rameshkumar N, Kayalvizhi N, Krishnan M, Sonawane T. A computational overview of integrase strand transfer inhibitors (INSTIs) against emerging and evolving drug-resistant HIV-1 integrase mutants. Arch Microbiol 2023; 205:142. [PMID: 36966200 PMCID: PMC10039815 DOI: 10.1007/s00203-023-03461-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 03/27/2023]
Abstract
AIDS (Acquired immunodeficiency syndrome) is one of the chronic and potentially life-threatening epidemics across the world. Hitherto, the non-existence of definitive drugs that could completely cure the Human immunodeficiency virus (HIV) implies an urgent necessity for the discovery of novel anti-HIV agents. Since integration is the most crucial stage in retroviral replication, hindering it can inhibit overall viral transmission. The 5 FDA-approved integrase inhibitors were computationally investigated, especially owing to the rising multiple mutations against their susceptibility. This comparative study will open new possibilities to guide the rational design of novel lead compounds for antiretroviral therapies (ARTs), more specifically the structure-based design of novel Integrase strand transfer inhibitors (INSTIs) that may possess a better resistance profile than present drugs. Further, we have discussed potent anti-HIV natural compounds and their interactions as an alternative approach, recommending the urgent need to tap into the rich vein of indigenous knowledge for reverse pharmacology. Moreover, herein, we discuss existing evidence that might change in the near future.
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Affiliation(s)
- Sharif Karim Sayyed
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | - Marzuqa Quraishi
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | - Renitta Jobby
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India
| | | | - Nagarajan Kayalvizhi
- Regenerative Medicine Laboratory, Department of Zoology, School of Life Sciences, Periyar University, Salem, Tamil Nadu, 636011, India
| | | | - Tareeka Sonawane
- Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, 410206, India.
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Oktay L, Erdemoğlu E, Tolu İ, Yumak Y, Özcan A, Acar E, Büyükkiliç Ş, Olkan A, Durdaği S. Binary-QSAR guided virtual screening of FDA approved drugs and compounds in clinical investigation against SARS-CoV-2 main protease. Turk J Biol 2021; 45:459-468. [PMID: 34803447 PMCID: PMC8573836 DOI: 10.3906/biy-2106-61] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 08/06/2021] [Indexed: 12/20/2022] Open
Abstract
With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the antiviral therapeutic activity binary QSAR model of the MetaCore/MetaDrug platform. Among 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 main protease (Mpro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docking scored compounds and following molecular mechanics generalized born surface area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivoxetil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 Mpro.
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Affiliation(s)
- Lalehan Oktay
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey
| | - Ece Erdemoğlu
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey.,School of Medicine, Mersin University, Mersin Turkey
| | - İlayda Tolu
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey
| | - Yeşim Yumak
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey.,Faculty of Science and Letters, Tokat Gaziosmanpaşa University, Tokat Turkey
| | - Ayşenur Özcan
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey.,Faculty of Medicine, İstanbul Medeniyet University, İstanbul Turkey
| | - Elif Acar
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey.,Faculty of Medicine, İstanbul Medeniyet University, İstanbul Turkey
| | - Şehriban Büyükkiliç
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey.,Faculty of Science, Necmettin Erbakan University, Konya Turkey
| | - Alpsu Olkan
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey.,School of Medicine, Bahçeşehir University, İstanbul Turkey
| | - Serdar Durdaği
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, İstanbul Turkey
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Durdagi S, Avsar T, Orhan MD, Serhatli M, Balcioglu BK, Ozturk HU, Kayabolen A, Cetin Y, Aydinlik S, Bagci-Onder T, Tekin S, Demirci H, Guzel M, Akdemir A, Calis S, Oktay L, Tolu I, Butun YE, Erdemoglu E, Olkan A, Tokay N, Işık Ş, Ozcan A, Acar E, Buyukkilic S, Yumak Y. The neutralization effect of montelukaston SARS-CoV-2 is shown by multiscale in silicosimulations and combined in vitro studies. Mol Ther 2021; 30:963-974. [PMID: 34678509 PMCID: PMC8524809 DOI: 10.1016/j.ymthe.2021.10.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/31/2021] [Accepted: 10/15/2021] [Indexed: 12/22/2022] Open
Abstract
Small molecule inhibitors have previously been investigated in different studies as possible therapeutics in the treatment of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In the current drug repurposing study, we identified the leukotriene (D4) receptor antagonist montelukast as a novel agent that simultaneously targets two important drug targets of SARS-CoV-2. We initially demonstrated the dual inhibition profile of montelukast through multiscale molecular modeling studies. Next, we characterized its effect on both targets by different in vitro experiments including the enzyme (main protease) inhibition-based assay, surface plasmon resonance (SPR) spectroscopy, pseudovirus neutralization on HEK293T/hACE2+TMPRSS2, and virus neutralization assay using xCELLigence MP real-time cell analyzer. Our integrated in silico and in vitro results confirmed the dual potential effect of montelukast both on the main protease enzyme inhibition and virus entry into the host cell (spike/ACE2). The virus neutralization assay results showed that SARS-CoV-2 virus activity was delayed with montelukast for 20 h on the infected cells. The rapid use of new small molecules in the pandemic is very important today. Montelukast, whose pharmacokinetic and pharmacodynamic properties are very well characterized and has been widely used in the treatment of asthma since 1998, should urgently be completed in clinical phase studies and, if its effect is proved in clinical phase studies, it should be used against coronavirus disease 2019 (COVID-19).
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Affiliation(s)
- Serdar Durdagi
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey.
| | - Timucin Avsar
- Department of Medical Biology, School of Medicine, Bahçeşehir University, Istanbul, Turkey
| | - Muge Didem Orhan
- Department of Medical Biology, School of Medicine, Bahçeşehir University, Istanbul, Turkey
| | - Muge Serhatli
- The Scientific and Technological Research Council of Turkey (TÜBİTAK) Marmara Research Center (MAM), Genetic Engineering and Biotechnology Institute, 41470 Gebze, Kocaeli
| | - Bertan Koray Balcioglu
- The Scientific and Technological Research Council of Turkey (TÜBİTAK) Marmara Research Center (MAM), Genetic Engineering and Biotechnology Institute, 41470 Gebze, Kocaeli
| | - Hasan Umit Ozturk
- The Scientific and Technological Research Council of Turkey (TÜBİTAK) Marmara Research Center (MAM), Genetic Engineering and Biotechnology Institute, 41470 Gebze, Kocaeli
| | - Alisan Kayabolen
- Brain Cancer Research and Therapy Laboratory, Koç University School of Medicine, 34450 Istanbul, Turkey
| | - Yuksel Cetin
- The Scientific and Technological Research Council of Turkey (TÜBİTAK) Marmara Research Center (MAM), Genetic Engineering and Biotechnology Institute, 41470 Gebze, Kocaeli
| | - Seyma Aydinlik
- The Scientific and Technological Research Council of Turkey (TÜBİTAK) Marmara Research Center (MAM), Genetic Engineering and Biotechnology Institute, 41470 Gebze, Kocaeli
| | - Tugba Bagci-Onder
- Brain Cancer Research and Therapy Laboratory, Koç University School of Medicine, 34450 Istanbul, Turkey; Koç University Research Center for Translational Medicine, 34450 Istanbul, Turkey
| | - Saban Tekin
- The Scientific and Technological Research Council of Turkey (TÜBİTAK) Marmara Research Center (MAM), Genetic Engineering and Biotechnology Institute, 41470 Gebze, Kocaeli; Department of Basic Sciences, Division of Medical Biology, Faculty of Medicine, University of Health Sciences, Istanbul, Turkey
| | - Hasan Demirci
- Department of Molecular Biology and Genetics, Koç University, 34450 Istanbul, Turkey
| | - Mustafa Guzel
- Department of Medical Pharmacology, International School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Atilla Akdemir
- Department of Pharmacology, Computer-aided Drug Discovery Laboratory, Faculty of Pharmacy, Bezmialem Vakif University, Istanbul, Turkey
| | - Seyma Calis
- Department of Medical Biology, School of Medicine, Bahçeşehir University, Istanbul, Turkey; Department of Molecular Biology-Genetics and Biotechnology, Istanbul Technical University, 34485 Istanbul, Turkey
| | - Lalehan Oktay
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey
| | - Ilayda Tolu
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey
| | - Yasar Enes Butun
- Department of Medical Pharmacology, International School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Ece Erdemoglu
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey; Faculty of Medicine, Mersin University, Mersin, Turkey
| | - Alpsu Olkan
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey
| | - Nurettin Tokay
- The Scientific and Technological Research Council of Turkey (TÜBİTAK) Marmara Research Center (MAM), Genetic Engineering and Biotechnology Institute, 41470 Gebze, Kocaeli
| | - Şeyma Işık
- The Scientific and Technological Research Council of Turkey (TÜBİTAK) Marmara Research Center (MAM), Genetic Engineering and Biotechnology Institute, 41470 Gebze, Kocaeli
| | - Aysenur Ozcan
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey; Faculty of Medicine, Istanbul Medeniyet University, Istanbul, Turkey
| | - Elif Acar
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey; Faculty of Medicine, Istanbul Medeniyet University, Istanbul, Turkey
| | - Sehriban Buyukkilic
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey; Faculty of Science, Necmettin Erbakan University, Konya, Turkey
| | - Yesim Yumak
- Department of Biophysics, Computational Biology and Molecular Simulations Laboratory, School of Medicine, Bahçeşehir University, Istanbul, Turkey; Faculty of Science and Letters, Tokat Gaziosmanpaşa University, Tokat, Turkey
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