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Herlah B, Goričan T, Benedik NS, Grdadolnik SG, Sosič I, Perdih A. Simulation- and AI-directed optimization of 4,6-substituted 1,3,5-triazin-2(1 H)-ones as inhibitors of human DNA topoisomerase IIα. Comput Struct Biotechnol J 2024; 23:2995-3018. [PMID: 39135887 PMCID: PMC11318567 DOI: 10.1016/j.csbj.2024.06.037] [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: 05/03/2024] [Revised: 06/29/2024] [Accepted: 06/30/2024] [Indexed: 08/15/2024] Open
Abstract
The 4,6-substituted-1,3,5-triazin-2(1H)-ones are promising inhibitors of human DNA topoisomerase IIα. To further develop this chemical class targeting the enzyme´s ATP binding site, the triazin-2(1H)-one substitution position 6 was optimized. Inspired by binding of preclinical substituted 9H-purine derivative, bicyclic substituents were incorporated at position 6 and the utility of this modification was validated by a combination of molecular simulations, dynamic pharmacophores, and free energy calculations. Considering also predictions of Deepfrag, a software developed for structure-based lead optimization based on deep learning, compounds with both bicyclic and monocyclic substitutions were synthesized and investigated for their inhibitory activity. The SAR data showed that the bicyclic substituted compounds exhibited good inhibition of topo IIα, comparable to their mono-substituted counterparts. Further evaluation on a panel of human protein kinases showed selectivity for the inhibition of topo IIα. Mechanistic studies indicated that the compounds acted predominantly as catalytic inhibitors, with some exhibiting topo IIα poison effects at higher concentrations. Integration of STD NMR experiments and molecular simulations, provided insights into the binding model and highlighted the importance of the Asn120 interaction and hydrophobic interactions with substituents at positions 4 and 6. In addition, NCI-60 screening demonstrated cytotoxicity of the compounds with bicyclic substituents and identified sensitive human cancer cell lines, underlining the translational relevance of our findings for further preclinical development of this class of compounds. The study highlights the synergy between simulation and AI-based approaches in efficiently guiding molecular design for drug optimization, which has implications for further preclinical development of this class of compounds.
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Affiliation(s)
- Barbara Herlah
- National Institute of Chemistry, Hajdrihova 19, SI 1000 Ljubljana, Slovenia
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva 7, SI 1000 Ljubljana, Slovenia
| | - Tjaša Goričan
- National Institute of Chemistry, Hajdrihova 19, SI 1000 Ljubljana, Slovenia
| | - Nika Strašek Benedik
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva 7, SI 1000 Ljubljana, Slovenia
| | | | - Izidor Sosič
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva 7, SI 1000 Ljubljana, Slovenia
| | - Andrej Perdih
- National Institute of Chemistry, Hajdrihova 19, SI 1000 Ljubljana, Slovenia
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva 7, SI 1000 Ljubljana, Slovenia
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2
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Islam S, Amin MA, Rengasamy KR, Mohiuddin AKM, Mahmud S. Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach. Cancer Med 2024; 13:e70074. [PMID: 39101505 PMCID: PMC11299079 DOI: 10.1002/cam4.70074] [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: 05/22/2024] [Revised: 07/04/2024] [Accepted: 07/24/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. OBJECTIVES/AIMS This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model. METHODS This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP. RESULTS The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis. CONCLUSION Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.
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Affiliation(s)
- Sirajul Islam
- Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
| | - Md. Al Amin
- Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
| | - Kannan R.R. Rengasamy
- Laboratory of Natural Products and Medicinal Chemistry (LNPMC), Center for Global Health Research, Saveetha Medical College and HospitalSaveetha Institute of Medical and Technical Sciences (SIMATS)ThandalamChennai602105India
| | - A. K. M. Mohiuddin
- Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
| | - Shahin Mahmud
- Department of Biotechnology and Genetic EngineeringMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh
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Puls K, Olivé-Marti AL, Hongnak S, Lamp D, Spetea M, Wolber G. Discovery of Novel, Selective, and Nonbasic Agonists for the Kappa-Opioid Receptor Determined by Salvinorin A-Based Virtual Screening. J Med Chem 2024. [PMID: 39088801 DOI: 10.1021/acs.jmedchem.4c00590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2024]
Abstract
Modulating the kappa-opioid receptor (KOR) is a promising strategy for treating various human diseases. KOR agonists show potential for treating pain, pruritus, and epilepsy, while KOR antagonists show potential for treating depression, anxiety, and addiction. The diterpenoid Salvinorin A (SalA), a secondary metabolite of Salvia divinorum, is a potent and selective KOR agonist. Unlike typical opioids, SalA lacks a basic nitrogen, which encouraged us to search for nonbasic KOR ligands. Through structure-based virtual screening using 3D pharmacophore models based on the binding mode of SalA, we identified novel, nonbasic, potent, and selective KOR agonists. In vitro studies confirmed two virtual hits, SalA-VS-07 and SalA-VS-08, as highly selective for the KOR and showing G protein-biased KOR agonist activity. Both KOR ligands share a novel spiro-moiety and a nonbasic scaffold. Our findings provide novel starting points for developing therapeutics aimed at treating pain and other conditions in which KOR is a central player.
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Affiliation(s)
- Kristina Puls
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2-4, 14195 Berlin, Germany
| | - Aina-Leonor Olivé-Marti
- Department of Pharmaceutical Chemistry, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Siriwat Hongnak
- Department of Pharmaceutical Chemistry, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - David Lamp
- Department of Pharmaceutical Chemistry, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Mariana Spetea
- Department of Pharmaceutical Chemistry, Institute of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Gerhard Wolber
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2-4, 14195 Berlin, Germany
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4
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Du J, Li J, Wen J, Liu J, Xiao H, Zhang A, Yang D, Sun P, Zhou H, Xu J. A Systematic Hierarchical Virtual Screening Model for RhlR Inhibitors Based on PCA, Pharmacophore, Docking, and Molecular Dynamics. Int J Mol Sci 2024; 25:8000. [PMID: 39063243 PMCID: PMC11276863 DOI: 10.3390/ijms25148000] [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: 06/17/2024] [Revised: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024] Open
Abstract
RhlR plays a key role in the quorum sensing of Pseudomonas aeruginosa. The current structure-activity relationship (SAR) studies of RhlR inhibitors mainly focus on elucidating the functional groups. Based on a systematic review of previous research on RhlR inhibitors, this study aims to establish a systematic, hierarchical screening model for RhlR inhibitors. We initially established a database and utilized principal component analysis (PCA) to categorize the inhibitors into two classes. Based on the training set, pharmacophore models were established to elucidate the structural characteristics of ligands. Subsequently, molecular docking, molecular dynamics simulations, and the calculation of binding free energy and strain energy were performed to validate the crucial interactions between ligands and receptors. Then, the screening criteria for RhlR inhibitors were established hierarchically based on ligand structure characteristics, ligand-receptor interaction, and receptor affinity. Test sets were finally employed to validate the hierarchical virtual screening model by comparing it with the current SAR studies of RhlR inhibitors. The hierarchical screening model was confirmed to possess higher accuracy and a true positive rate, which holds promise for subsequent screening and the discovery of active RhlR inhibitors.
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Affiliation(s)
- Jiarui Du
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
| | - Jiahao Li
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
| | - Juqi Wen
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
| | - Jun Liu
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
| | - Haichuan Xiao
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
| | - Antian Zhang
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
| | - Dongdong Yang
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
| | - Pinghua Sun
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
- Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832003, China
| | - Haibo Zhou
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
| | - Jun Xu
- College of Pharmacy, Jinan University, Guangzhou 511436, China; (J.D.); (J.L.); (J.W.); (J.L.); (H.X.); (A.Z.); (D.Y.); (P.S.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
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5
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Rianjongdee F, Palmer D, Pickett SD, Pogány P, Tomkinson NCO, Green DVS. bbSelect - An Open-Source Tool for Performing a 3D Pharmacophore-Driven Diverse Selection of R-Groups. J Chem Inf Model 2024; 64:4687-4699. [PMID: 38822782 DOI: 10.1021/acs.jcim.4c00453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2024]
Abstract
The design of compounds during hit-to-lead often seeks to explore a vector from a core scaffold to form additional interactions with the target protein. A rational approach to this is to probe the region of a protein accessed by a vector with a systematic placement of pharmacophore features in 3D, particularly when bound structures are not available. Herein, we present bbSelect, an open-source tool built to map the placements of pharmacophore features in 3D Euclidean space from a library of R-groups, employing partitioning to drive a diverse and systematic selection to a user-defined size. An evaluation of bbSelect against established methods exemplified the superiority of bbSelect in its ability to perform diverse selections, achieving high levels of pharmacophore feature placement coverage with selection sizes of a fraction of the total set and without the introduction of excess complexity. bbSelect also reports visualizations and rationale to enable users to understand and interrogate results. This provides a tool for the drug discovery community to guide their hit-to-lead activities.
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Affiliation(s)
| | - David Palmer
- Department for Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, U.K
| | - Stephen D Pickett
- GSK Medicines Research Centre, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Peter Pogány
- GSK Medicines Research Centre, Stevenage, Hertfordshire SG1 2NY, U.K
| | - Nicholas C O Tomkinson
- Department for Pure and Applied Chemistry, University of Strathclyde, 295 Cathedral Street, Glasgow G1 1XL, U.K
| | - Darren V S Green
- GSK Medicines Research Centre, Stevenage, Hertfordshire SG1 2NY, U.K
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6
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Liu H, Chen P, Hu B, Wang S, Wang H, Luan J, Wang J, Lin B, Cheng M. FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening. Comput Biol Chem 2024; 110:108057. [PMID: 38581840 DOI: 10.1016/j.compbiolchem.2024.108057] [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: 12/23/2023] [Revised: 03/06/2024] [Accepted: 03/20/2024] [Indexed: 04/08/2024]
Abstract
Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.
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Affiliation(s)
- Haihan Liu
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Peiying Chen
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Baichun Hu
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Shizun Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Hanxun Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Jiasi Luan
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Medical Devices, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China
| | - Jian Wang
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Bin Lin
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
| | - Maosheng Cheng
- Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.
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7
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Herlah B, Janežič M, Ogris I, Grdadolnik SG, Kološa K, Žabkar S, Žegura B, Perdih A. Nature-inspired substituted 3-(imidazol-2-yl) morpholines targeting human topoisomerase IIα: Dynophore-derived discovery. Biomed Pharmacother 2024; 175:116676. [PMID: 38772152 DOI: 10.1016/j.biopha.2024.116676] [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: 03/12/2024] [Revised: 04/22/2024] [Accepted: 04/29/2024] [Indexed: 05/23/2024] Open
Abstract
The molecular nanomachine, human DNA topoisomerase IIα, plays a crucial role in replication, transcription, and recombination by catalyzing topological changes in the DNA, rendering it an optimal target for cancer chemotherapy. Current clinical topoisomerase II poisons often cause secondary tumors as side effects due to the accumulation of double-strand breaks in the DNA, spurring the development of catalytic inhibitors. Here, we used a dynamic pharmacophore approach to develop catalytic inhibitors targeting the ATP binding site of human DNA topoisomerase IIα. Our screening of a library of nature-inspired compounds led to the discovery of a class of 3-(imidazol-2-yl) morpholines as potent catalytic inhibitors that bind to the ATPase domain. Further experimental and computational studies identified hit compound 17, which exhibited selectivity against the human DNA topoisomerase IIα versus human protein kinases, cytotoxicity against several human cancer cells, and did not induce DNA double-strand breaks, making it distinct from clinical topoisomerase II poisons. This study integrates an innovative natural product-inspired chemistry and successful implementation of a molecular design strategy that incorporates a dynamic component of ligand-target molecular recognition, with comprehensive experimental characterization leading to hit compounds with potential impact on the development of more efficient chemotherapies.
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Affiliation(s)
- Barbara Herlah
- National Institute of Chemistry, Hajdrihova 19, Ljubljana SI 1000, Slovenia; University of Ljubljana, Faculty of Pharmacy, Aškerčeva 7, Ljubljana SI 1000, Slovenia
| | - Matej Janežič
- National Institute of Chemistry, Hajdrihova 19, Ljubljana SI 1000, Slovenia
| | - Iza Ogris
- National Institute of Chemistry, Hajdrihova 19, Ljubljana SI 1000, Slovenia; University of Ljubljana, Faculty of Medicine, Vrazov trg 2, Ljubljana SI 1000, Slovenia
| | | | - Katja Kološa
- National institute of Biology, Department of Genetic Toxicology and Cancer Biology, Večna pot 121, Ljubljana SI 1000, Slovenia
| | - Sonja Žabkar
- National institute of Biology, Department of Genetic Toxicology and Cancer Biology, Večna pot 121, Ljubljana SI 1000, Slovenia
| | - Bojana Žegura
- National institute of Biology, Department of Genetic Toxicology and Cancer Biology, Večna pot 121, Ljubljana SI 1000, Slovenia
| | - Andrej Perdih
- National Institute of Chemistry, Hajdrihova 19, Ljubljana SI 1000, Slovenia; University of Ljubljana, Faculty of Pharmacy, Aškerčeva 7, Ljubljana SI 1000, Slovenia.
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8
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De Vita S, Colarusso E, Chini MG, Bifulco G, Lauro G. PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes. J Chem Inf Model 2024; 64:4263-4276. [PMID: 38728062 DOI: 10.1021/acs.jcim.3c01920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2024]
Abstract
In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 MPro, confirming the robustness of PharmaCore.
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Affiliation(s)
- Simona De Vita
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy
| | - Ester Colarusso
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy
| | - Maria Giovanna Chini
- Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone, Pesche, Isernia 86090, Italy
| | - Giuseppe Bifulco
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy
| | - Gianluigi Lauro
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II, 132, 84084 Fisciano, Italy
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9
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De-la-Torre P, Martínez-García C, Gratias P, Mun M, Santana P, Akyuz N, González W, Indzhykulian AA, Ramírez D. Identification of Druggable Binding Sites and Small Molecules as Modulators of TMC1. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583611. [PMID: 38826329 PMCID: PMC11142246 DOI: 10.1101/2024.03.05.583611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Our ability to hear and maintain balance relies on the proper functioning of inner ear sensory hair cells, which translate mechanical stimuli into electrical signals via mechano-electrical transducer (MET) channels, composed of TMC1/2 proteins. However, the therapeutic use of ototoxic drugs, such as aminoglycosides and cisplatin, which can enter hair cells through MET channels, often leads to profound auditory and vestibular dysfunction. Despite extensive research on otoprotective compounds targeting MET channels, our understanding of how small molecule modulators interact with these channels remains limited, hampering the discovery of novel compounds. Here, we propose a structure-based screening approach, integrating 3D-pharmacophore modeling, molecular simulations, and experimental validation. Our pipeline successfully identified several novel compounds and FDA-approved drugs that reduced dye uptake in cultured cochlear explants, indicating MET modulation activity. Molecular docking and free-energy estimations for binding allowed us to identify three potential drug binding sites within the channel pore, phospholipids, and key amino acids involved in modulator interactions. We also identified shared ligand-binding features between TMC and structurally related TMEM16 protein families, providing novel insights into their distinct inhibition, while potentially guiding the rational design of MET-channel-specific modulators. Our pipeline offers a broad application to discover small molecule modulators for a wide spectrum of mechanosensitive ion channels.
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Affiliation(s)
- Pedro De-la-Torre
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School and Mass Eye and Ear, Boston, MA, USA
| | | | - Paul Gratias
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School and Mass Eye and Ear, Boston, MA, USA
| | - Matthew Mun
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School and Mass Eye and Ear, Boston, MA, USA
| | - Paula Santana
- Facultad de Ingeniería, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Santiago, Chile
| | - Nurunisa Akyuz
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Wendy González
- Center for Bioinformatics and Molecular Simulations (CBSM), University of Talca, Talca 3460000, Chile
| | - Artur A. Indzhykulian
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School and Mass Eye and Ear, Boston, MA, USA
| | - David Ramírez
- Department of Pharmacology, Faculty of Biological Sciences, University of Concepción, Chile
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10
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Aziz UBA, Saoud A, Bermudez M, Mieth M, Atef A, Rudolf T, Arkona C, Trenkner T, Böttcher C, Ludwig K, Hoelzemer A, Hocke AC, Wolber G, Rademann J. Targeted small molecule inhibitors blocking the cytolytic effects of pneumolysin and homologous toxins. Nat Commun 2024; 15:3537. [PMID: 38670939 PMCID: PMC11053136 DOI: 10.1038/s41467-024-47741-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Pneumolysin (PLY) is a cholesterol-dependent cytolysin (CDC) from Streptococcus pneumoniae, the main cause for bacterial pneumonia. Liberation of PLY during infection leads to compromised immune system and cytolytic cell death. Here, we report discovery, development, and validation of targeted small molecule inhibitors of PLY (pore-blockers, PB). PB-1 is a virtual screening hit inhibiting PLY-mediated hemolysis. Structural optimization provides PB-2 with improved efficacy. Cryo-electron tomography reveals that PB-2 blocks PLY-binding to cholesterol-containing membranes and subsequent pore formation. Scaffold-hopping delivers PB-3 with superior chemical stability and solubility. PB-3, formed in a protein-templated reaction, binds to Cys428 adjacent to the cholesterol recognition domain of PLY with a KD of 256 nM and a residence time of 2000 s. It acts as anti-virulence factor preventing human lung epithelial cells from PLY-mediated cytolysis and cell death during infection with Streptococcus pneumoniae and is active against the homologous Cys-containing CDC perfringolysin (PFO) as well.
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Affiliation(s)
- Umer Bin Abdul Aziz
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195, Berlin, Germany
| | - Ali Saoud
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195, Berlin, Germany
| | - Marcel Bermudez
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195, Berlin, Germany
- Institute for Pharmaceutical and Medicinal Chemistry, University of Münster, Corrensstr. 48, 48149, Münster, Germany
| | - Maren Mieth
- Department of Infectious Diseases, Respiratory Medicine, and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Amira Atef
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195, Berlin, Germany
- Department of Medicinal Chemistry, Faculty of Pharmacy, Assuit University, Assiut, 71526, Egypt
| | - Thomas Rudolf
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195, Berlin, Germany
| | - Christoph Arkona
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195, Berlin, Germany
| | - Timo Trenkner
- Leibniz Institute of Virology, Hamburg, 20251, Germany
| | - Christoph Böttcher
- Institute of Chemistry and Biochemistry, Research Center of Electron Microscopy (FZEM), Freie Universität Berlin, Fabeckstraße 36A, 14195, Berlin, Germany
| | - Kai Ludwig
- Institute of Chemistry and Biochemistry, Research Center of Electron Microscopy (FZEM), Freie Universität Berlin, Fabeckstraße 36A, 14195, Berlin, Germany
| | - Angelique Hoelzemer
- Leibniz Institute of Virology, Hamburg, 20251, Germany
- First Department of Medicine, University Medical Center Hamburg-Eppendorf (UKE), 20251, Hamburg, Germany
| | - Andreas C Hocke
- Department of Infectious Diseases, Respiratory Medicine, and Critical Care, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Gerhard Wolber
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195, Berlin, Germany
| | - Jörg Rademann
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, 14195, Berlin, Germany.
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11
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Ruiz-Moreno AJ, Cedillo-González R, Cordova-Bahena L, An Z, Medina-Franco JL, Velasco-Velázquez MA. Consensus Pharmacophore Strategy For Identifying Novel SARS-Cov-2 M pro Inhibitors from Large Chemical Libraries. J Chem Inf Model 2024; 64:1984-1995. [PMID: 38472094 PMCID: PMC10966741 DOI: 10.1021/acs.jcim.3c01439] [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] [Received: 09/10/2023] [Revised: 02/24/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main Protease (Mpro) is an enzyme that cleaves viral polyproteins translated from the viral genome and is critical for viral replication. Mpro is a target for anti-SARS-CoV-2 drug development, and multiple Mpro crystals complexed with competitive inhibitors have been reported. In this study, we aimed to develop an Mpro consensus pharmacophore as a tool to expand the search for inhibitors. We generated a consensus model by aligning and summarizing pharmacophoric points from 152 bioactive conformers of SARS-CoV-2 Mpro inhibitors. Validation against a library of conformers from a subset of ligands showed that our model retrieved poses that reproduced the crystal-binding mode in 77% of the cases. Using models derived from a consensus pharmacophore, we screened >340 million compounds. Pharmacophore-matching and chemoinformatics analyses identified new potential Mpro inhibitors. The candidate compounds were chemically dissimilar to the reference set, and among them, demonstrating the relevance of our model. We evaluated the effect of 16 candidates on Mpro enzymatic activity finding that seven have inhibitory activity. Three compounds (1, 4, and 5) had IC50 values in the midmicromolar range. The Mpro consensus pharmacophore reported herein can be used to identify compounds with improved activity and novel chemical scaffolds against Mpro. The method developed for its generation is provided as an open-access code (https://github.com/AngelRuizMoreno/ConcensusPharmacophore) and can be applied to other pharmacological targets.
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Affiliation(s)
- Angel J. Ruiz-Moreno
- School
of Medicine, Universidad Nacional Autónoma
de México, Mexico
City 04510, Mexico
| | - Raziel Cedillo-González
- School
of Medicine, Universidad Nacional Autónoma
de México, Mexico
City 04510, Mexico
- Graduate
Program in Biochemical Sciences, Universidad
Nacional Autónoma de México, Mexico City 04510, Mexico
- DIFACQUIM
Research Group, School of Chemistry, Universidad
Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Luis Cordova-Bahena
- School
of Medicine, Universidad Nacional Autónoma
de México, Mexico
City 04510, Mexico
- Consejo
Nacional de Humanidades, Ciencias y Tecnología, Mexico City 03940, Mexico
| | - Zhiqiang An
- Texas
Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas 77030, United States
| | - José L. Medina-Franco
- DIFACQUIM
Research Group, School of Chemistry, Universidad
Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Marco A. Velasco-Velázquez
- School
of Medicine, Universidad Nacional Autónoma
de México, Mexico
City 04510, Mexico
- Texas
Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas 77030, United States
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12
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Maniam S, Maniam S. Screening Techniques for Drug Discovery in Alzheimer's Disease. ACS OMEGA 2024; 9:6059-6073. [PMID: 38371787 PMCID: PMC10870277 DOI: 10.1021/acsomega.3c07046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/22/2023] [Accepted: 12/25/2023] [Indexed: 02/20/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.
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Affiliation(s)
- Sandra Maniam
- Department
of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia
| | - Subashani Maniam
- School
of Science, STEM College, RMIT University, Melbourne, Victoria 3001, Australia
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13
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Khan MZI, Khan D, Akbar MY, Wang H, Haq IU, Chen JZ. 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, MD simulations, in vitro and in vivo studies to identify potential anti-hyperplasia drugs. Biotechnol J 2024; 19:e2300437. [PMID: 38403464 DOI: 10.1002/biot.202300437] [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: 08/28/2023] [Revised: 12/13/2023] [Accepted: 01/02/2024] [Indexed: 02/27/2024]
Abstract
Psoriasis is a common immune-mediated skin condition characterized by aberrant keratinocytes and cell proliferation. The purpose of this study was to explore the FDA-approved drugs by 3D-QSAR pharmacophore model and evaluate their efficiency by in-silico, in vitro, and in vivo psoriasis animal model. A 3D-QSAR pharmacophore model was developed by utilizing HypoGen algorithm using the structural features of 48 diaryl derivatives with diverse molecular patterns. The model was validated by a test set of 27 compounds, by cost analysis method, and Fischer's randomization test. The correlation coefficient of the best model (Hypo2) was 0.9601 for the training set while it was 0.805 for the test set. The selected model was taken as a 3D query for the virtual screening of over 3000 FDA-approved drugs. Compounds mapped with the pharmacophore model were further screened through molecular docking. The hits that showed the best docking results were screened through in silico skin toxicity approach. Top five hits were selected for the MD simulation studies. Based on MD simulations results, the best two hit molecules, that is, ebastine (Ebs) and mebeverine (Mbv) were selected for in vitro and in vivo antioxidant studies performed in mice. TNF-α and COX pro-inflammatory mediators, biochemical assays, histopathological analyses, and immunohistochemistry observations confirmed the anti-inflammatory response of the selected drugs. Based on these findings, it appeared that Ebs can effectively treat psoriasis-like skin lesions and down-regulate inflammatory responses which was consistent with docking predictions and could potentially be employed for further research on inflammation-related skin illnesses such as psoriasis.
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Affiliation(s)
| | - Dildar Khan
- Faculty of Biological Sciences, Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan
| | - Muhammad Yasir Akbar
- Computational Biology Lab, National Centre for Bioinformatics Quaid-i-Azam University, Islamabad, Pakistan
| | - Hao Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Ihsan-Ul Haq
- Faculty of Biological Sciences, Department of Pharmacy, Quaid-i-Azam University, Islamabad, Pakistan
| | - Jian-Zhong Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
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14
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Saha B, Das A, Jangid K, Kumar A, Kumar V, Jaitak V. Identification of coumarin derivatives targeting acetylcholinesterase for Alzheimer's disease by field-based 3D-QSAR, pharmacophore model-based virtual screening, molecular docking, MM/GBSA, ADME and MD Simulation study. Curr Res Struct Biol 2024; 7:100124. [PMID: 38292820 PMCID: PMC10826614 DOI: 10.1016/j.crstbi.2024.100124] [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: 11/01/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/01/2024] Open
Abstract
Alzheimer's disease (AD) leads to gradual memory loss including other compromised cognitive abilities. Acetylcholinesterase (AChE), an important biochemical enzyme from the cholinesterase (ChE) family, is recognized as primary pharmacological target for treating AD. Currently marketed drugs for AD treatment are primarily AChE inhibitors and coumarin derivatives comprising a wide variety of pharmacological activities have proved their efficacy towards AChE inhibition. Ensaculin (KA-672 HCl), a compound that belong to the coumarin family, is a clinical trial candidate for AD treatment. Therefore, a ligand library was prepared with 60 reported coumarin derivatives for field-based 3D-QSAR and pharmacophore modelling. The field-based 3D-QSAR model obtained at partial least square (PLS) factor 7, was the best validated model that predicted activity closer to original activity for each ligand introduced. The contour maps demonstrated spatial distribution of favourable and unfavorable steric, hydrophobic, electrostatic and H-bond donor and acceptor contours around coumarin nucleus. The best pharmacophore model, ADHRR_1 exhibited five essential pharmacophoric features of four different traits for optimum AChE inhibition. Virtual screening through ADHRR_1 accompanied with molecular docking and MM/GBSA identified 10 HITs from a 4,00,000 coumarin derivatives from PubChem database. HITs comprised docking scores ranging from -12.096 kcal/mol to -8.271 kcal/mol and compared with the reference drug Donepezil (-8.271 kcal/mol). ADME properties analysis led into detecting two leads (HIT 1 and HIT 2) among these 10 HITs. Molecular Dynamics Simulation indicated thermodynamic stability of the complex of lead compounds with AChE protein. Finally, thorough survey of the experimental results from 3D-QSAR modelling, pharmacophore modelling and molecular docking interactions led us to develop the lead formula I for future advancements in treating AD through AChE inhibitors.
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Affiliation(s)
- Bikram Saha
- Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Ghudda, Bathinda, 151401, India
| | - Agnidipta Das
- Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Ghudda, Bathinda, 151401, India
| | - Kailash Jangid
- Department of Chemistry, Central University of Punjab, Ghudda, Bathinda, 151401, India
| | - Amit Kumar
- Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Ghudda, Bathinda, 151401, India
| | - Vinod Kumar
- Department of Chemistry, Central University of Punjab, Ghudda, Bathinda, 151401, India
| | - Vikas Jaitak
- Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Ghudda, Bathinda, 151401, India
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15
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Melancon K, Pliushcheuskaya P, Meiler J, Künze G. Targeting ion channels with ultra-large library screening for hit discovery. Front Mol Neurosci 2024; 16:1336004. [PMID: 38249296 PMCID: PMC10796734 DOI: 10.3389/fnmol.2023.1336004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.
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Affiliation(s)
- Kortney Melancon
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | | | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
| | - Georg Künze
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
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16
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Evbuomwan IO, Alejolowo OO, Elebiyo TC, Nwonuma CO, Ojo OA, Edosomwan EU, Chikwendu JI, Elosiuba NV, Akulue JC, Dogunro FA, Rotimi DE, Osemwegie OO, Ojo AB, Ademowo OG, Adeyemi OS, Oluba OM. In silico modeling revealed phytomolecules derived from Cymbopogon citratus (DC.) leaf extract as promising candidates for malaria therapy. J Biomol Struct Dyn 2024; 42:101-118. [PMID: 36974933 DOI: 10.1080/07391102.2023.2192799] [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: 01/02/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023]
Abstract
The emergence of varying levels of resistance to currently available antimalarial drugs significantly threatens global health. This factor heightens the urgency to explore bioactive compounds from natural products with a view to discovering and developing newer antimalarial drugs with novel mode of actions. Therefore, we evaluated the inhibitory effects of sixteen phytocompounds from Cymbopogon citratus leaf extract against Plasmodium falciparum drug targets such as P. falciparum circumsporozoite protein (PfCSP), P. falciparum merozoite surface protein 1 (PfMSP1) and P. falciparum erythrocyte membrane protein 1 (PfEMP1). In silico approaches including molecular docking, pharmacophore modeling and 3D-QSAR were adopted to analyze the inhibitory activity of the compounds under consideration. The molecular docking results indicated that a compound swertiajaponin from C. citratus exhibited a higher binding affinity (-7.8 kcal/mol) to PfMSP1 as against the standard artesunate-amodiaquine (-6.6 kcal/mol). Swertiajaponin also formed strong hydrogen bond interactions with LYS29, CYS30, TYR34, ASN52, GLY55 and CYS28 amino acid residues. In addition, quercetin another compound from C. citratus exhibited significant binding energies -6.8 and -8.3 kcal/mol with PfCSP and PfEMP1, respectively but slightly lower than the standard artemether-lumefantrine with binding energies of -7.4 kcal/mol against PfCSP and -8.7 kcal/mol against PfEMP1. Overall, the present study provides evidence that swertiajaponin and other phytomolecules from C. citratus have modulatory properties toward P. falciparum drug targets and thus may warrant further exploration in early drug discovery efforts against malaria. Furthermore, these findings lend credence to the folkloric use of C. citratus for malaria treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ikponmwosa Owen Evbuomwan
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
- Department of Food Science and Microbiology, Landmark University, Omu-Aran, Nigeria
| | - Omokolade Oluwaseyi Alejolowo
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | | | - Charles Obiora Nwonuma
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | - Oluwafemi Adeleke Ojo
- Phytomedicine, Molecular Toxicology and Computational Biochemistry Research Group, Department of Biochemistry, Bowen University, Iwo, Nigeria
| | - Evelyn Uwa Edosomwan
- Department of Animal and Environmental Biology, University of Benin, Benin City, Nigeria
| | | | | | | | | | - Damilare Emmanuel Rotimi
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
| | | | | | - Olusegun George Ademowo
- Department of Pharmacology and Therapeutics, Faculty of Basic Medical Sciences, University of Ibadan, Ibadan, Nigeria
- Drug Research Laboratory, Institute of Advanced Medical Research and Training (IMRAT), College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oluyomi Stephen Adeyemi
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
- Laboratory of Sustainable Animal Environment, Graduate School of Agricultural Science, Tohoku University, Osaki, Miyagi, Japan
| | - Olarewaju Michael Oluba
- SDG #03 Group - Good Health and Well-Being Research Cluster, Landmark University, Omu-Aran, Nigeria
- Department of Biochemistry, Landmark University, Omu-Aran, Nigeria
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17
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Verburgt J, Jain A, Kihara D. Recent Deep Learning Applications to Structure-Based Drug Design. Methods Mol Biol 2024; 2714:215-234. [PMID: 37676602 PMCID: PMC10578466 DOI: 10.1007/978-1-0716-3441-7_13] [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] [Indexed: 09/08/2023]
Abstract
Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning-based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.
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Affiliation(s)
- Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Anika Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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18
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Lavrentaki V, Kousaxidis A, Theodosis-Nobelos P, Papagiouvannis G, Koutsopoulos K, Nicolaou I. Design, synthesis, and pharmacological evaluation of indazole carboxamides of N-substituted pyrrole derivatives as soybean lipoxygenase inhibitors. Mol Divers 2023:10.1007/s11030-023-10775-8. [PMID: 38145424 DOI: 10.1007/s11030-023-10775-8] [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: 07/31/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023]
Abstract
In this paper, we attempted to develop a novel class of compounds against lipoxygenase, a key enzyme in the biosynthesis of leukotrienes implicated in a series of inflammatory diseases. Given the absence of appropriate human 5-lipoxygenase crystallographic data, solved soybean lipoxygenase-1 and -3 structures were used as a template to generate an accurate pharmacophore model which was further used for virtual screening purposes. Eight compounds (1-8) have been derived from the in-house library consisting of N-substituted pyrroles conjugated with 5- or 6-indazole moieties through a carboxamide linker. This study led to the discovery of hit molecule 8 bearing a naphthyl group with the IC50 value of 22 μM according to soybean lipoxygenase in vitro assay. Isosteric replacement of naphthyl ring with quinoline moieties and reduction of carbonyl carboxamide group resulted in compounds 9-12 and 13, respectively. Compound 12 demonstrated the most promising enzyme inhibition. In addition, compounds 8 and 12 were found to reduce the carrageenan-induced paw edema in vivo by 52.6 and 49.8%, respectively. In view of the encouraging outcomes concerning their notable in vitro and in vivo anti-inflammatory activities, compounds 8 and 12 could be further optimized for the discovery of novel 5-lipoxygenase inhibitors in future. A structure-based 3D pharmacophore model was used in the virtual screening of in-house library to discover novel potential 5-lipoxygenase inhibitors.
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Affiliation(s)
- Vasiliki Lavrentaki
- Department of Pharmaceutical Chemistry, School of Pharmacy, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Antonios Kousaxidis
- Department of Pharmaceutical Chemistry, School of Pharmacy, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | | | - Georgios Papagiouvannis
- Department of Pharmacy, School of Health Sciences, Frederick University, 1036, Nicosia, Cyprus
| | | | - Ioannis Nicolaou
- Department of Pharmaceutical Chemistry, School of Pharmacy, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece.
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19
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Baillif B, Cole J, Giangreco I, McCabe P, Bender A. Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations. J Cheminform 2023; 15:124. [PMID: 38129933 PMCID: PMC10740246 DOI: 10.1186/s13321-023-00794-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023] Open
Abstract
Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.
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Affiliation(s)
- Benoit Baillif
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK
| | - Jason Cole
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
- Exscientia plc, The Schrödinger Building, Oxford Science Park, Oxford, OX4 4GE, UK
| | - Patrick McCabe
- Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK
| | - Andreas Bender
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, Cambridge, CB2 1EW, UK.
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20
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Zaib S, Rana N, Ali HS, Ur Rehman M, Awwad NS, Ibrahium HA, Khan I. Identification of potential inhibitors targeting yellow fever virus helicase through ligand and structure-based computational studies. J Biomol Struct Dyn 2023:1-18. [PMID: 38109183 DOI: 10.1080/07391102.2023.2294839] [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: 08/14/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Yellow fever is a flavivirus having plus-sensed RNA which encodes a single polyprotein. Host proteases cut this polyprotein into seven nonstructural proteins including a vital NS3 protein. The present study aims to identify the most effective inhibitor against the helicase (NS3) using different advanced ligand and structure-based computational studies. A set of 300 ligands was selected against helicase by chemical structural similarity model, which are similar to S-adenosyl-l-cysteine using infiniSee. This tool screens billions of compounds through a similarity search from in-built chemical spaces (CHEMriya, Galaxi, KnowledgeSpace and REALSpace). The pharmacophore was designed from ligands in the library that showed same features. According to the sequence of ligands, six compounds (29, 87, 99, 116, 148, and 208) were taken for pharmacophore designing against helicase protein. Subsequently, compounds from the library which showed the best pharmacophore shared-features were docked using FlexX functionality of SeeSAR and their optibrium properties were analyzed. Afterward, their ADME was improved by replacing the unfavorable fragments, which resulted in the generation of new compounds. The selected best compounds (301, 302, 303 and 304) were docked using SeeSAR and their pharmacokinetics and toxicological properties were evaluated using SwissADME. The optimal inhibitor for yellow fever helicase was 2-amino-N-(4-(dimethylamino)thiazol-2-yl)-4-methyloxazole-5-carboxamide (302), which exhibits promising potential for drug development.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sumera Zaib
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Nehal Rana
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Hafiz Saqib Ali
- Chemistry Research Laboratory, Department of Chemistry and the INEOS Oxford Institute for Antimicrobial Research, University of Oxford, Oxford, UK
| | - Mujeeb Ur Rehman
- Department of Basic and Applied Chemistry, Faculty of Science and Technology, University of Central Punjab, Lahore, Pakistan
| | - Nasser S Awwad
- Department of Chemistry, King Khalid University, Abha, Saudi Arabia
| | - Hala A Ibrahium
- Biology Department, Faculty of Science, King Khalid University, Abha, Saudi Arabia
| | - Imtiaz Khan
- Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
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21
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Hawas SS, El-Sayed SM, Elzahhar PA, Moustafa MA. New 2-alkoxycyanopyridine derivatives as inhibitors of EGFR, HER2, and DHFR: Synthesis, anticancer evaluation, and molecular modeling studies. Bioorg Chem 2023; 141:106874. [PMID: 37769524 DOI: 10.1016/j.bioorg.2023.106874] [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: 08/06/2023] [Revised: 09/07/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023]
Abstract
New series of substituted 2-alkoxycyanopyridine derivatives were synthesized and evaluated for their in vitro and in vivo anticancer activities. Comparing the evaluated activities against cancer cell lines to the broad-spectrum anticancer doxorubicin, and the kinase inhibitor sorafenib, compounds 3a, 4b, 4c, 7a, and 8d demonstrated superior anticancer efficacy with elevated safety profiles and selectivity indices, particularly against MCF7 breast cancer. For exploration of their mechanism of action, assays for inhibition of EGFR, HER2 kinase, and DHFR were performed. The promising synthesized compounds exhibited potent dual kinase EGFR/HER2 inhibitory activity with IC50values of 0.248/0.156 μM for 4b and 0.138/0.092 μM for 4c. Additionally, with IC50 values of 0.138 and 0.193 M, respectively, 4b and 4c had the greatest DHFR inhibitory activity that was comparable to methotrexate. In the MCF7 breast cancer cell line, they caused arrest at the S phase of the cell cycle and exhibited apoptosis induction activity. With restored caspase-3 immunoexpression, the anti-breast cancer assay performed in vivo of 4b and 4c demonstrated a substantial decrease in tumor volume. Results from molecular modeling were in agreement with biological assays proving the importance of the 3-caynopyridine, two substituted phenyl rings attached to central pyridine ring, and propoxy side chain moieties for binding with the receptors. As 4c works by inhibiting both EGFR/HER2 kinase, DHFR enzymes, in addition to cellular apoptosis, it could be viewed as a model of compounds possessing a multi-targeting anticancer activity. Collectively, compounds 4b and 4c might represent prototypes for further development as anticancer molecules.
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Affiliation(s)
- Samia S Hawas
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt; Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Horus University, New Damietta, Egypt
| | - Selwan M El-Sayed
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt.
| | - Perihan A Elzahhar
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Alexandria University, Alexandria 21521, Egypt
| | - Mohamed A Moustafa
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt.
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22
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Wunsch F, Nguyen TN, Wolber G, Bermudez M. Structural determinants of sphingosine-1-phosphate receptor selectivity. Arch Pharm (Weinheim) 2023; 356:e2300387. [PMID: 37806764 DOI: 10.1002/ardp.202300387] [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: 07/15/2023] [Revised: 09/19/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Abstract
Fingolimod, the prodrug of fingolimod-1-phosphate (F1P), was the first sphingosine-1-phosphate receptor (S1PR) modulator approved for multiple sclerosis. F1P unselectively targets all five S1PR subtypes. While agonism (functional antagonism via receptor internalization) at S1PR1 leads to the desired immune modulatory effects, agonism at S1PR3 is associated with cardiac adverse effects. This motivated the development of S1PR3 -sparing compounds and led to a second generation of S1PR1,5 -selective ligands like siponimod and ozanimod. Our method combines molecular dynamics simulations and three-dimensional pharmacophores (dynophores) and enables the elucidation of S1PR subtype-specific binding site characteristics, visualizing also subtle differences in receptor-ligand interactions. F1P and the endogenous ligand sphingosine-1-phosphate bind to the orthosteric pocket of all S1PRs, but show different binding mode dynamics, uncovering potential starting points for the development of subtype-specific ligands. Our study contributes to the mechanistic understanding of the selectivity profile of approved drugs like ozanimod and siponimod and pharmaceutical tool compounds like CYM5541.
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Affiliation(s)
- Friederike Wunsch
- Faculty of Chemistry and Pharmacy, Institue for Pharmaceutical and Medical Chemistry, University of Münster, Münster, Germany
| | - Trung Ngoc Nguyen
- Department of Biology, Chemistry and Pharmacy, Institute for Pharmacy, Freie Universität Berlin, Berlin, Germany
| | - Gerhard Wolber
- Department of Biology, Chemistry and Pharmacy, Institute for Pharmacy, Freie Universität Berlin, Berlin, Germany
| | - Marcel Bermudez
- Faculty of Chemistry and Pharmacy, Institue for Pharmaceutical and Medical Chemistry, University of Münster, Münster, Germany
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23
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Calvo-Barreiro L, Talagayev V, Pach S, Abdel-Rahman SA, Wolber G, Gabr MT. Discovery of ICOS-Targeted Small Molecules Using Pharmacophore-Based Screening. ChemMedChem 2023; 18:e202300305. [PMID: 37845178 DOI: 10.1002/cmdc.202300305] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023]
Abstract
There are currently no small molecules clinically approved as immune checkpoint modulators. Besides possessing oral bioavailability, cell-penetrating capabilities and enhanced tumor penetration compared to monoclonal antibodies (mAbs), small molecules are amenable to pharmacokinetic optimization, which allows adopting flexible dosage regimens that may avoid immune-related adverse events associated with mAbs. The interaction of inducible co-stimulator (ICOS) with its ligand (ICOS-L) plays key roles in T-cell differentiation and activation of T-cell to B-cell functions. This study represents the development and validation of a virtual screening strategy to identify small molecules that bind a novel druggable binding pocket in human ICOS. We used a lipophilic canyon in the apo-structure of ICOS and the ICOS/ICOS-L interface individually as templates for molecular dynamics simulation to generate 3D pharmacophores subsequently used for virtual screening campaigns. Our strategy was successful finding a first-in-class small molecule ICOS binder (5P, KD value=108.08±26.76 μM) and validating biophysical screening platforms for ICOS-targeted small molecules. We anticipate that future structural optimization of 5P will result in the discovery of high affinity chemical ligands for ICOS.
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Affiliation(s)
- Laura Calvo-Barreiro
- Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY-10065, USA
| | - Valerij Talagayev
- Molecular Design Lab, Department of Chemistry, Biology and Pharmacy, Institute of Pharmacy, Freie Universität Berlin, Königin-Luisestr. 2+4, 14195, Berlin, Germany
| | - Szymon Pach
- Molecular Design Lab, Department of Chemistry, Biology and Pharmacy, Institute of Pharmacy, Freie Universität Berlin, Königin-Luisestr. 2+4, 14195, Berlin, Germany
| | - Somaya A Abdel-Rahman
- Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY-10065, USA
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura, 35516, Egypt
| | - Gerhard Wolber
- Molecular Design Lab, Department of Chemistry, Biology and Pharmacy, Institute of Pharmacy, Freie Universität Berlin, Königin-Luisestr. 2+4, 14195, Berlin, Germany
| | - Moustafa T Gabr
- Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY-10065, USA
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24
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Barbosa DB, do Bomfim MR, de Oliveira TA, da Silva AM, Taranto AG, Cruz JN, de Carvalho PB, Campos JM, Santos CBR, Leite FHA. Development of Potential Multi-Target Inhibitors for Human Cholinesterases and Beta-Secretase 1: A Computational Approach. Pharmaceuticals (Basel) 2023; 16:1657. [PMID: 38139784 PMCID: PMC10748024 DOI: 10.3390/ph16121657] [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: 08/22/2023] [Revised: 11/04/2023] [Accepted: 11/13/2023] [Indexed: 12/24/2023] Open
Abstract
Alzheimer's disease causes chronic neurodegeneration and is the leading cause of dementia in the world. The causes of this disease are not fully understood but seem to involve two essential cerebral pathways: cholinergic and amyloid. The simultaneous inhibition of AChE, BuChE, and BACE-1, essential enzymes involved in those pathways, is a promising therapeutic approach to treat the symptoms and, hopefully, also halt the disease progression. This study sought to identify triple enzymatic inhibitors based on stereo-electronic requirements deduced from molecular modeling of AChE, BuChE, and BACE-1 active sites. A pharmacophore model was built, displaying four hydrophobic centers, three hydrogen bond acceptors, and one positively charged nitrogen, and used to prioritize molecules found in virtual libraries. Compounds showing adequate overlapping rates with the pharmacophore were subjected to molecular docking against the three enzymes and those with an adequate docking score (n = 12) were evaluated for physicochemical and toxicological parameters and commercial availability. The structure exhibiting the greatest inhibitory potential against all three enzymes was subjected to molecular dynamics simulations (100 ns) to assess the stability of the inhibitor-enzyme systems. The results of this in silico approach indicate ZINC1733 can be a potential multi-target inhibitor of AChE, BuChE, and BACE-1, and future enzymatic assays are planned to validate those results.
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Affiliation(s)
- Deyse B. Barbosa
- Laboratório de Modelagem Molecular, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BA, Brazil; (D.B.B.); (M.R.d.B.); (F.H.A.L.)
| | - Mayra R. do Bomfim
- Laboratório de Modelagem Molecular, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BA, Brazil; (D.B.B.); (M.R.d.B.); (F.H.A.L.)
| | - Tiago A. de Oliveira
- Departamento de Informática, Gestão e Desenho, Centro Federal de Educação Tecnológica de Minas Gerais, Divinópolis 30575-180, MG, Brazil;
| | - Alisson M. da Silva
- Laboratório de Bioinformática e Desenho de Fármacos, Universidade Federal de São João del-Rei, São João del-Rei 36307-352, MG, Brazil; (A.M.d.S.); (A.G.T.)
| | - Alex G. Taranto
- Laboratório de Bioinformática e Desenho de Fármacos, Universidade Federal de São João del-Rei, São João del-Rei 36307-352, MG, Brazil; (A.M.d.S.); (A.G.T.)
| | - Jorddy N. Cruz
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e de Saúde, Universidade Federal do Amapá, Macapá 68903-419, AP, Brazil;
| | - Paulo B. de Carvalho
- Feik School of Pharmacy, University of the Incarnate Word, San Antonio, TX 78209, USA;
| | - Joaquín M. Campos
- Departamento de Química Orgánica Farmacéutica, Facultad de Farmacia, Campus de la Cartuja, Universidad de Granada, 18012 Granada, Spain;
| | - Cleydson B. R. Santos
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e de Saúde, Universidade Federal do Amapá, Macapá 68903-419, AP, Brazil;
- Programa de Pós-Graduação em Biodiversidade e Biotecnologia—Rede BIONORTE, Universidade Federal do Amapá, Macapá 68903-419, AP, Brazil
| | - Franco H. A. Leite
- Laboratório de Modelagem Molecular, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BA, Brazil; (D.B.B.); (M.R.d.B.); (F.H.A.L.)
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25
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Stevenson GA, Kirshner D, Bennion BJ, Yang Y, Zhang X, Zemla A, Torres MW, Epstein A, Jones D, Kim H, Bennett WFD, Wong SE, Allen JE, Lightstone FC. Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method. J Chem Inf Model 2023; 63:6655-6666. [PMID: 37847557 PMCID: PMC10647021 DOI: 10.1021/acs.jcim.3c00722] [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] [Received: 05/11/2023] [Indexed: 10/18/2023]
Abstract
Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.
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Affiliation(s)
- Garrett A. Stevenson
- Computational
Engineering Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Dan Kirshner
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Brian J. Bennion
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Yue Yang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Xiaohua Zhang
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Adam Zemla
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Marisa W. Torres
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Aidan Epstein
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Derek Jones
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
- Department
of Computer Science and Engineering, University
of California, San Diego, La Jolla, California 92093, United States
| | - Hyojin Kim
- Center
for Applied Scientific Computing, Lawrence
Livermore National Laboratory, Livermore, California 94550, United States
| | - W. F. Drew Bennett
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Sergio E. Wong
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
| | - Jonathan E. Allen
- Global
Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - Felice C. Lightstone
- Biosciences
and Biotechnology Division, Lawrence Livermore
National Laboratory, Livermore, California 94550, United States
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26
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McNutt A, Bisiriyu F, Song S, Vyas A, Hutchison GR, Koes DR. Conformer Generation for Structure-Based Drug Design: How Many and How Good? J Chem Inf Model 2023; 63:6598-6607. [PMID: 37903507 PMCID: PMC10647020 DOI: 10.1021/acs.jcim.3c01245] [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] [Received: 08/07/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 11/01/2023]
Abstract
Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel "common knowledge" well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.
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Affiliation(s)
- Andrew
T. McNutt
- Department
of Computational and Systems Biology, University
of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States
| | - Fatimah Bisiriyu
- The
Neighborhood Academy, Pittsburgh, Pennsylvania 15206, United States
| | - Sophia Song
- Upper
St. Clair High School, Pittsburgh, Pennsylvania 15241, United States
| | - Ananya Vyas
- Taylor
Allderdice High School, Pittsburgh, Pennsylvania 15217, United States
| | - Geoffrey R. Hutchison
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States
- Department
of Chemical and Petroleum Engineering, University
of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States
| | - David Ryan Koes
- Department
of Computational and Systems Biology, University
of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States
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27
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Wang Z, Zhong H, Zhang J, Pan P, Wang D, Liu H, Yao X, Hou T, Kang Y. Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data Sets. J Chem Inf Model 2023; 63:6525-6536. [PMID: 37883143 DOI: 10.1021/acs.jcim.3c01519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Small-molecule conformer generation (SMCG) is an extremely important task in both ligand- and structure-based computer-aided drug design, especially during the hit discovery phase. Recently, a multitude of artificial intelligence (AI) models tailored for SMCG have emerged. Despite developers typically furnishing performance evaluation data upon releasing their AI models, a comprehensive and equitable performance comparison between AI models and conventional methods is still lacking. In this study, we curated a new benchmarking data set comprising 3354 high-quality ligand bioactive conformations. Subsequently, we conducted a systematic assessment of the performance of four widely adopted traditional methods (i.e., ConfGenX, Conformator, OMEGA, and RDKit ETKDG) and five AI models (i.e., ConfGF, DMCG, GeoDiff, GeoMol, and torsional diffusion) in the tasks of reproducing bioactive and low-energy conformations of small molecules. In the former task, the AI models have no advantage, particularly with a maximum ensemble size of 1. Even the best-performing AI model GeoMol is still worse than any of the tested traditional methods. Conversely, in the latter task, the torsional diffusion model shows obvious advantages, surpassing the best-performing traditional method ConfGenX by 26.09 and 12.97% on the COV-R and COV-P metrics, respectively. Furthermore, the influence of force field-based fine-tuning on the quality of the generated conformers was also discussed. Finally, a user-friendly Web server called fastSMCG was developed to enable researchers to rapidly and flexibly generate small-molecule conformers using both traditional and AI methods. We anticipate that our work will offer valuable practical assistance to the scientific community in this field.
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Affiliation(s)
- Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Haiyang Zhong
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jintu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao SAR 999078, China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao SAR 999078, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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28
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Vu LP, Diehl CJ, Casement R, Bond AG, Steinebach C, Strašek N, Bricelj A, Perdih A, Schnakenburg G, Sosič I, Ciulli A, Gütschow M. Expanding the Structural Diversity at the Phenylene Core of Ligands for the von Hippel-Lindau E3 Ubiquitin Ligase: Development of Highly Potent Hypoxia-Inducible Factor-1α Stabilizers. J Med Chem 2023; 66:12776-12811. [PMID: 37708384 PMCID: PMC10544018 DOI: 10.1021/acs.jmedchem.3c00434] [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] [Received: 03/10/2023] [Indexed: 09/16/2023]
Abstract
Hypoxia-inducible factor-1α (HIF-1α) constitutes the principal mediator of cellular adaptation to hypoxia in humans. The HIF-1α protein level and activity are tightly regulated by the ubiquitin E3 ligase von Hippel-Lindau (VHL). Here, we performed a structure-guided and bioactivity-driven design of new VHL inhibitors. Our iterative and combinatorial strategy focused on chemical variability at the phenylene unit and encompassed further points of diversity. The exploitation of tailored phenylene fragments and the stereoselective installation of the benzylic methyl group provided potent VHL ligands. Three high-resolution structures of VHL-ligand complexes were determined, and bioactive conformations of these ligands were explored. The most potent inhibitor (30) exhibited dissociation constants lower than 40 nM, independently determined by fluorescence polarization and surface plasmon resonance and an enhanced cellular potency, as evidenced by its superior ability to induce HIF-1α transcriptional activity. Our work is anticipated to inspire future efforts toward HIF-1α stabilizers and new ligands for proteolysis-targeting chimera (PROTAC) degraders.
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Affiliation(s)
- Lan Phuong Vu
- Pharmaceutical
Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, An der Immenburg 4, D-53121 Bonn, Germany
- Centre
for Targeted Protein Degradation, School of Life Sciences, University of Dundee, 1 James Lindsay Place, Dundee, Scotland DD1 5JJ, U.K.
| | - Claudia J. Diehl
- Centre
for Targeted Protein Degradation, School of Life Sciences, University of Dundee, 1 James Lindsay Place, Dundee, Scotland DD1 5JJ, U.K.
| | - Ryan Casement
- Centre
for Targeted Protein Degradation, School of Life Sciences, University of Dundee, 1 James Lindsay Place, Dundee, Scotland DD1 5JJ, U.K.
| | - Adam G. Bond
- Centre
for Targeted Protein Degradation, School of Life Sciences, University of Dundee, 1 James Lindsay Place, Dundee, Scotland DD1 5JJ, U.K.
| | - Christian Steinebach
- Pharmaceutical
Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, An der Immenburg 4, D-53121 Bonn, Germany
| | - Nika Strašek
- Faculty
of Pharmacy, University of Ljubljana, Aškerčeva 7, SI-1000 Ljubljana, Slovenia
| | - Aleša Bricelj
- Faculty
of Pharmacy, University of Ljubljana, Aškerčeva 7, SI-1000 Ljubljana, Slovenia
| | - Andrej Perdih
- Faculty
of Pharmacy, University of Ljubljana, Aškerčeva 7, SI-1000 Ljubljana, Slovenia
- National
Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Gregor Schnakenburg
- Institute
of Inorganic Chemistry, University of Bonn, Gerhard-Domagk-Straße 1, D-53121 Bonn, Germany
| | - Izidor Sosič
- Faculty
of Pharmacy, University of Ljubljana, Aškerčeva 7, SI-1000 Ljubljana, Slovenia
| | - Alessio Ciulli
- Centre
for Targeted Protein Degradation, School of Life Sciences, University of Dundee, 1 James Lindsay Place, Dundee, Scotland DD1 5JJ, U.K.
| | - Michael Gütschow
- Pharmaceutical
Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, An der Immenburg 4, D-53121 Bonn, Germany
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29
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Jia Y, Schroeder B, Pfeifer Y, Fröhlich C, Deng L, Arkona C, Kuropka B, Sticht J, Ataka K, Bergemann S, Wolber G, Nitsche C, Mielke M, Leiros HKS, Werner G, Rademann J. Kinetics, Thermodynamics, and Structural Effects of Quinoline-2-Carboxylates, Zinc-Binding Inhibitors of New Delhi Metallo-β-lactamase-1 Re-sensitizing Multidrug-Resistant Bacteria for Carbapenems. J Med Chem 2023; 66:11761-11791. [PMID: 37585683 DOI: 10.1021/acs.jmedchem.3c00171] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Carbapenem resistance mediated by metallo-β-lactamases (MBL) such as New Delhi metallo-β-lactamase-1 (NDM-1) has become a major factor threatening the efficacy of essential β-lactam antibiotics. Starting from hit fragment dipicolinic acid (DPA), 8-hydroxy- and 8-sulfonamido-quinoline-2-carboxylic acids were developed as inhibitors of NDM-1 with highly improved inhibitory activity and binding affinity. The most active compounds formed reversibly inactive ternary protein-inhibitor complexes with two zinc ions as proven by native protein mass spectrometry and bio-layer interferometry. Modification of the NDM-1 structure with remarkable entropic gain was shown by isothermal titration calorimetry and NMR spectroscopy of isotopically labeled protein. The best compounds were potent inhibitors of NDM-1 and other representative MBL with no or little inhibition of human zinc-binding enzymes. These inhibitors significantly reduced the minimum inhibitory concentrations (MIC) of meropenem for multidrug-resistant bacteria recombinantly expressing blaNDM-1 as well as for several multidrug-resistant clinical strains at concentrations non-toxic to human cells.
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Affiliation(s)
- Yuwen Jia
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, Berlin 14195, Germany
| | - Barbara Schroeder
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, Berlin 14195, Germany
| | - Yvonne Pfeifer
- FG13 Nosocomial Pathogens and Antibiotic Resistances, Department of Infectious Diseases, Robert Koch Institute, Burgstraße 37, Wernigerode 38855, Germany
| | - Christopher Fröhlich
- Department of Chemistry, Faculty of Science and Technology, UiT The Arctic University of Norway, Tromsø 9037, Norway
| | - Lihua Deng
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, Berlin 14195, Germany
| | - Christoph Arkona
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, Berlin 14195, Germany
| | - Benno Kuropka
- Core Facility BioSupraMol, Institute for Chemistry and Biochemistry, Freie Universität Berlin, Thielallee 63, Berlin 14195, Germany
| | - Jana Sticht
- Core Facility BioSupraMol, Institute for Chemistry and Biochemistry, Freie Universität Berlin, Thielallee 63, Berlin 14195, Germany
| | - Kenichi Ataka
- Department of Physics, Freie Universität Berlin, Arnimallee 14, Berlin 14195, Germany
| | - Silke Bergemann
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, Berlin 14195, Germany
| | - Gerhard Wolber
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, Berlin 14195, Germany
| | - Christoph Nitsche
- Research School of Chemistry, Australian National University, Canberra 2601, Australian Capital Territory, Australia
| | - Martin Mielke
- Department of Infectious Diseases, Robert Koch Institute, Nordufer 20, Berlin 13353, Germany
| | - Hanna-Kirsti S Leiros
- Department of Chemistry, Faculty of Science and Technology, UiT The Arctic University of Norway, Tromsø 9037, Norway
| | - Guido Werner
- FG13 Nosocomial Pathogens and Antibiotic Resistances, Department of Infectious Diseases, Robert Koch Institute, Burgstraße 37, Wernigerode 38855, Germany
| | - Jörg Rademann
- Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 2+4, Berlin 14195, Germany
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Bhowmik R, Kant R, Manaithiya A, Saluja D, Vyas B, Nath R, Qureshi KA, Parkkila S, Aspatwar A. Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study. Front Pharmacol 2023; 14:1265573. [PMID: 37705534 PMCID: PMC10495588 DOI: 10.3389/fphar.2023.1265573] [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: 07/23/2023] [Accepted: 08/10/2023] [Indexed: 09/15/2023] Open
Abstract
Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.
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Affiliation(s)
- Ratul Bhowmik
- Medicinal Chemistry and Molecular Modelling Lab, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Ravi Kant
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research, Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| | - Ajay Manaithiya
- Medicinal Chemistry and Molecular Modelling Lab, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Daman Saluja
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research, Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| | - Bharti Vyas
- Department of Bioinformatics, School of Interdisciplinary Studies, Jamia Hamdard, New Delhi, India
| | - Ranajit Nath
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India
| | - Kamal A. Qureshi
- Department of Pharmaceutics, Unaizah College of Pharmacy, Qassim University, Unaizah, Al-Qassim, Saudi Arabia
| | - Seppo Parkkila
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Fimlab Ltd., Tampere University Hospital, Tampere, Finland
| | - Ashok Aspatwar
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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31
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Alemany-Chavarria M, Rodríguez-Guerra J, Maréchal JD. TALAIA: a 3D visual dictionary for protein structures. Bioinformatics 2023; 39:btad476. [PMID: 37549048 PMCID: PMC10423020 DOI: 10.1093/bioinformatics/btad476] [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] [Received: 02/07/2023] [Revised: 04/30/2023] [Accepted: 08/04/2023] [Indexed: 08/09/2023] Open
Abstract
MOTIVATION Graphical analysis of the molecular structure of proteins can be very complex. Full-atom representations retain most geometric information but are generally crowded, and key structural patterns can be challenging to identify. Non-full-atom representations could be more instructive on physicochemical aspects but be insufficiently detailed regarding shapes (e.g. entity beans-like models in coarse grain approaches) or simple properties of amino acids (e.g. representation of superficial electrostatic properties). In this work, we present TALAIA a visual dictionary that aims to provide another layer of structural representations.TALAIA offers a visual grammar that combines simple representations of amino acids while retaining their general geometry and physicochemical properties. It uses unique objects, with differentiated shapes and colors to represent amino acids. It makes easier to spot crucial molecular information, including patches of amino acids or key interactions between side chains. Most conventions used in TALAIA are standard in chemistry and biochemistry, so experimentalists and modelers can rapidly grasp the meaning of any TALAIA depiction. RESULTS We propose TALAIA as a tool that renders protein structures and encodes structure and physicochemical aspects as a simple visual grammar. The approach is fast, highly informative, and intuitive, allowing the identification of possible interactions, hydrophobic patches, and other characteristic structural features at first glance. The first implementation of TALAIA can be found at https://github.com/insilichem/talaia.
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Affiliation(s)
- Mercè Alemany-Chavarria
- Insilichem, Department of chemistry, Universitat Autònoma de Barcelona, Bellaterra 08193, Barcelona, Spain
| | - Jaime Rodríguez-Guerra
- Insilichem, Department of chemistry, Universitat Autònoma de Barcelona, Bellaterra 08193, Barcelona, Spain
| | - Jean-Didier Maréchal
- Insilichem, Department of chemistry, Universitat Autònoma de Barcelona, Bellaterra 08193, Barcelona, Spain
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Pavlin M, Herlah B, Valjavec K, Perdih A. Unveiling the interdomain dynamics of type II DNA topoisomerase through all-atom simulations: Implications for understanding its catalytic cycle. Comput Struct Biotechnol J 2023; 21:3746-3759. [PMID: 37602233 PMCID: PMC10436251 DOI: 10.1016/j.csbj.2023.07.019] [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: 04/23/2023] [Revised: 07/01/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
Type IIA DNA topoisomerases are complex molecular nanomachines that manage topological states of the DNA molecule in the cell and play a crucial role in cellular processes such as cell division and transcription. They are also established targets of cancer chemotherapy. Starting from the available crystal structure of a fully catalytic topoisomerase IIA homodimer from Saccharomyces cerevisiae, we constructed three states of this molecular motor primarily changing the configurations of the DNA segment bound in the DNA gate and performed μs-long all-atom molecular simulations. A comprehensive analysis revealed a sliding motion within the DNA gate and a teamwork between the N-gate and DNA gate that may be associated with the necessary molecular events that allow passage of the T-segment of DNA. The observed movement of the ATPase dimer relative to the DNA domain was reflected in different interaction patterns between the K-loops of the transducer domain and the B-A-B form of the bound DNA. Based on the obtained results, we mapped simulated configurations to the structures in the proposed catalytic cycle through which type IIA topoisomerases exert their function and discussed the possible transition events. The results extend our understanding of the mechanism of action of type IIA topoisomerases and provide an atomistic interpretation of some of the observed features of these molecular motors.
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Affiliation(s)
- Matic Pavlin
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Barbara Herlah
- Theory Department, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva 7, 1000 Ljubljana, Slovenia
| | - Katja Valjavec
- Theory Department, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
| | - Andrej Perdih
- Theory Department, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva 7, 1000 Ljubljana, Slovenia
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Shehzadi K, Saba A, Yu M, Liang J. Structure-Based Drug Design of RdRp Inhibitors against SARS-CoV-2. Top Curr Chem (Cham) 2023; 381:22. [PMID: 37318607 DOI: 10.1007/s41061-023-00432-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/15/2023] [Indexed: 06/16/2023]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a worldwide pandemic since 2019, spreading rapidly and posing a significant threat to human health and life. With over 6 billion confirmed cases of the virus, the need for effective therapeutic drugs has become more urgent than ever before. RNA-dependent RNA polymerase (RdRp) is crucial in viral replication and transcription, catalysing viral RNA synthesis and serving as a promising therapeutic target for developing antiviral drugs. In this article, we explore the inhibition of RdRp as a potential treatment for viral diseases, analysing the structural information of RdRp in virus proliferation and summarizing the reported inhibitors' pharmacophore features and structure-activity relationship profiles. We hope that the information provided by this review will aid in structure-based drug design and aid in the global fight against SARS-CoV-2 infection.
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Affiliation(s)
- Kiran Shehzadi
- Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 10081, China
| | - Afsheen Saba
- Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 10081, China
| | - Mingjia Yu
- Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 10081, China.
| | - Jianhua Liang
- Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing, 10081, China.
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314019, China.
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Lunghini F, Fava A, Pisapia V, Sacco F, Iaconis D, Beccari AR. ProfhEX: AI-based platform for small molecules liability profiling. J Cheminform 2023; 15:60. [PMID: 37296454 DOI: 10.1186/s13321-023-00728-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Off-target drug interactions are a major reason for candidate failure in the drug discovery process. Anticipating potential drug's adverse effects in the early stages is necessary to minimize health risks to patients, animal testing, and economical costs. With the constantly increasing size of virtual screening libraries, AI-driven methods can be exploited as first-tier screening tools to provide liability estimation for drug candidates. In this work we present ProfhEX, an AI-driven suite of 46 OECD-compliant machine learning models that can profile small molecules on 7 relevant liability groups: cardiovascular, central nervous system, gastrointestinal, endocrine, renal, pulmonary and immune system toxicities. Experimental affinity data was collected from public and commercial data sources. The entire chemical space comprised 289'202 activity data for a total of 210'116 unique compounds, spanning over 46 targets with dataset sizes ranging from 819 to 18896. Gradient boosting and random forest algorithms were initially employed and ensembled for the selection of a champion model. Models were validated according to the OECD principles, including robust internal (cross validation, bootstrap, y-scrambling) and external validation. Champion models achieved an average Pearson correlation coefficient of 0.84 (SD of 0.05), an R2 determination coefficient of 0.68 (SD = 0.1) and a root mean squared error of 0.69 (SD of 0.08). All liability groups showed good hit-detection power with an average enrichment factor at 5% of 13.1 (SD of 4.5) and AUC of 0.92 (SD of 0.05). Benchmarking against already existing tools demonstrated the predictive power of ProfhEX models for large-scale liability profiling. This platform will be further expanded with the inclusion of new targets and through complementary modelling approaches, such as structure and pharmacophore-based models. ProfhEX is freely accessible at the following address: https://profhex.exscalate.eu/ .
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Affiliation(s)
- Filippo Lunghini
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Anna Fava
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
| | - Vincenzo Pisapia
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Francesco Sacco
- Professional Service Department, SAS Institute, Via Darwin 20/22, 20143, Milan, Italy
| | - Daniela Iaconis
- EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy
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Al-Sha’er MA, Taha M, Alelaimat MA. Development of phosphoinositide 3-kinase delta (PI3Kδ) inhibitors as potential anticancer agents through the generation of ligand-based pharmacophores and biological screening. Med Chem Res 2023; 32:1109-1121. [DOI: 10.1007/s00044-023-03057-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/27/2023] [Indexed: 07/10/2024]
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36
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Pliushcheuskaya P, Künze G. Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels. Int J Mol Sci 2023; 24:ijms24119226. [PMID: 37298178 DOI: 10.3390/ijms24119226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.
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Affiliation(s)
- Palina Pliushcheuskaya
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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37
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Rugard M, Audouze K, Tromelin A. Combining the Classification and Pharmacophore Approaches to Understand Homogeneous Olfactory Perceptions at Peripheral Level: Focus on Two Aroma Mixtures. Molecules 2023; 28:molecules28104028. [PMID: 37241770 DOI: 10.3390/molecules28104028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/20/2023] [Accepted: 05/03/2023] [Indexed: 05/28/2023] Open
Abstract
The mechanisms involved in the homogeneous perception of odorant mixtures remain largely unknown. With the aim of enhancing knowledge about blending and masking mixture perceptions, we focused on structure-odor relationships by combining the classification and pharmacophore approaches. We built a dataset of about 5000 molecules and their related odors and reduced the multidimensional space defined by 1014 fingerprints representing the structures to a tridimensional 3D space using uniform manifold approximation and projection (UMAP). The self-organizing map (SOM) classification was then performed using the 3D coordinates in the UMAP space that defined specific clusters. We explored the allocating in these clusters of the components of two aroma mixtures: a blended mixture (red cordial (RC) mixture, 6 molecules) and a masking binary mixture (isoamyl acetate/whiskey-lactone [IA/WL]). Focusing on clusters containing the components of the mixtures, we looked at the odor notes carried by the molecules belonging to these clusters and also at their structural features by pharmacophore modeling (PHASE). The obtained pharmacophore models suggest that WL and IA could have a common binding site(s) at the peripheral level, but that would be excluded for the components of RC. In vitro experiments will soon be carried out to assess these hypotheses.
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Affiliation(s)
- Marylène Rugard
- T3S, Inserm UMR S-1124, Université Paris Cité, F-75006 Paris, France
| | - Karine Audouze
- T3S, Inserm UMR S-1124, Université Paris Cité, F-75006 Paris, France
| | - Anne Tromelin
- Centre des Sciences du Goût et de l'Alimentation, CNRS, INRAE, Institut Agro, Université de Bourgogne, F-21000 Dijon, France
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38
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Yang Y, Hsieh CY, Kang Y, Hou T, Liu H, Yao X. Deep Generation Model Guided by the Docking Score for Active Molecular Design. J Chem Inf Model 2023; 63:2983-2991. [PMID: 37163364 DOI: 10.1021/acs.jcim.3c00572] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
A deep generation model, as a novel drug design and discovery tool, shows obvious advantages in generating compounds with novel backbones and has been applied successfully in the field of drug discovery. However, it is still a challenge to generate molecules with expected properties, especially high activity. Here, to obtain compounds both with novelty and high activity to a target, we proposed a conditional molecular generation model COMG by considering the docking score and 3D pharmacophore matching during molecular generation. The proposed model was based on the conditional variational autoencoder architecture constrained by the pharmacophore matching score. During Bayesian optimization, the docking score was applied to enhance the target relevance of generated compounds. Furthermore, to overcome the problem of high structural similarity caused by Bayesian optimization, the idea of the scaffold memory unit was also introduced. The evaluation results of COMG show that our model not only can improve the structural diversity of generated molecules but also can effectively improve the proportion of target-related drug-active molecules. The obtained results indicate that our proposed model COMG is a useful drug design tool.
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Affiliation(s)
- Yuwei Yang
- Faculty of Applied Sciences, Macao Polytechnic University, Macao (SAR) 999078, P. R. China
- School of Pharmacy, Lanzhou University, Lanzhou 730000, Gansu, P. R. China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Huanxiang Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao (SAR) 999078, P. R. China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, 999078 Macau (SAR), P. R. China
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Heinz CS, Bermudez M, Jaiswal N, Große C, Kauk M, Hoffmann C, Holzgrabe U. Hybridization into a Bitopic Ligand Increased Muscarinic Receptor Activation for Isopilocarpine but Not for Pilocarpine Derivatives. JOURNAL OF NATURAL PRODUCTS 2023; 86:869-881. [PMID: 37042802 DOI: 10.1021/acs.jnatprod.2c01079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Pilocarpine (1), a secondary metabolite of several Pilocarpus species, is a therapeutically used partial agonist of muscarinic acetylcholine receptors (mAChRs). The available pharmacological data and structure-activity relationships do not provide comparable data for all five receptor subtypes. In this study, pilocarpine (1), its epimer isopilocarpine (2), racemic analogues pilosinine (3) and desmethyl pilosinine (4), and the respective hybrid ligands with a naphmethonium fragment (5-C6 to 8-C6) were synthesized and analyzed in mini-G nano-BRET assays at the five mAChRs. In line with earlier studies, pilocarpine was the most active compound among the orthosteric ligands 1-4. Computational docking of pilocarpine and isopilocarpine to the active M2 receptor suggests that the trans-configuration of isopilocarpine leads to a loss of the hydrogen bond from the lactone carbonyl to N6.52, explaining the lower activity of isopilocarpine. Hybrid formation of pilocarpine (1) and isopilocarpine (2) led to an inverted activity rank, with the trans-configured isopilocarpine hybrid (6-C6) being more active. The hydrogen bond of interest is formed by the isopilocarpine hybrid (6-C6) but not by the pilocarpine hybrid (5-C6). Hybridization thus leads to a modified binding mode of the orthosteric moiety, as the binding mode of the hybrid is dominated by the high-affinity allosteric moiety.
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Affiliation(s)
- Christine S Heinz
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
| | - Marcel Bermudez
- Institute of Pharmaceutical and Medicinal Chemistry, Westfälische Wilhelms-Universität, Corrensstraße 48, 48149 Muenster, Germany
| | - Natasha Jaiswal
- Institute for Molecular Cell Biology, CMB-Center for Molecular Biomedicine, University Hospital Jena, Friedrich-Schiller University Jena, Hans-Knöll-Straße 2, 07745 Jena, Germany
| | - Carolin Große
- Institute for Molecular Cell Biology, CMB-Center for Molecular Biomedicine, University Hospital Jena, Friedrich-Schiller University Jena, Hans-Knöll-Straße 2, 07745 Jena, Germany
| | - Michael Kauk
- Institute for Molecular Cell Biology, CMB-Center for Molecular Biomedicine, University Hospital Jena, Friedrich-Schiller University Jena, Hans-Knöll-Straße 2, 07745 Jena, Germany
| | - Carsten Hoffmann
- Institute for Molecular Cell Biology, CMB-Center for Molecular Biomedicine, University Hospital Jena, Friedrich-Schiller University Jena, Hans-Knöll-Straße 2, 07745 Jena, Germany
| | - Ulrike Holzgrabe
- Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
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40
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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: 158] [Impact Index Per Article: 158.0] [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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Zhang X, Bai Y, Tang Q, Liu M, Nan L, Wen K, Yu X, Yu W, Shen J, Wang Z. Development of epitopephore-based rational hapten design strategy: A combination of theoretical evidence and experimental validation. JOURNAL OF HAZARDOUS MATERIALS 2023; 445:130615. [PMID: 37056019 DOI: 10.1016/j.jhazmat.2022.130615] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/06/2022] [Accepted: 12/13/2022] [Indexed: 06/19/2023]
Abstract
Antibody is the key biomolecule that governing the sensitivity and specificity of an immunoassay for chemical compound, also named hapten molecule. Obviously, predication of hapten effectiveness before chemical synthesis is beneficial to boost success, save cost and improve controllability. Here, we proposed and evaluated an epitopephore based rational hapten design (ERHD) to assist antibody production to chemical compound, combining theoretical evidence and then experimental validation by using dinitrocarbanilide (DNC) as a model analyte. Briefly, epitopephores of DNC were firstly generated by HipHop algorithm after features mapping. A homemade drug database also containing reported fragment haptens (HFR) and new designed full hapten (HFU) were constructed, and then was virtually screened by using generated epitopephore followed by structural analysis and visual inspection. The DNC haptens based on the selected hits were further identified by Density Functional Theory before total synthesis. To prove and clarify the usability of the ERHD, two retrieved HFU haptens, one non-retrieved HFU hapten and three non-retrieved HFR haptens were all selected to produce monoclonal antibodies (mAbs) for comparison purpose. A maximal 6000-fold increased affinity of mAb from retrieved HFU than HFR was observed, while, non-retrieved HFU failed to produce antibody to DNC. More importantly, mAbs from HFU haptens provided highly specificity to DNC, while, mAbs from HFR haptens could recognize 15 others analogues. We then constructed antibody structure and investigated molecular recognition of the mAbs to DNC, well supporting the rationality of the ERHD. Lastly, an icELISA was developed for DNC with an IC50 value as low as 0.19 ng mL-1 with high specificity, which has never achieved before.
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Affiliation(s)
- Xiya Zhang
- College of Food Science and Technology, Henan Agricultural University, 450002 Zhengzhou, People's Republic of China
| | - Yuchen Bai
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Qianqian Tang
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Minggang Liu
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Li Nan
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Kai Wen
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Xuezhi Yu
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Wenbo Yu
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Jianzhong Shen
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Zhanhui Wang
- Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory of Food Quality and Safety, College of Veterinary Medicine, China Agricultural University, Beijing 100193, China.
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Phenotypic Discovery of Thiocarbohydrazone with Anticancer Properties and Catalytic Inhibition of Human DNA Topoisomerase IIα. Pharmaceuticals (Basel) 2023; 16:ph16030341. [PMID: 36986441 PMCID: PMC10054454 DOI: 10.3390/ph16030341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/06/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Phenotypic screening of α-substituted thiocarbohydrazones revealed promising activity of 1,5-bis(salicylidene)thiocarbohydrazide against leukemia and breast cancer cells. Supplementary cell-based studies indicated an impairment of DNA replication via the ROS-independent pathway. The structural similarity of α-substituted thiocarbohydrazone to previously published thiosemicarbazone catalytic inhibitors targeting the ATP-binding site of human DNA topoisomerase IIα prompted us to investigate the inhibition activity on this target. Thiocarbohydrazone acted as a catalytic inhibitor and did not intercalate the DNA molecule, which validated their engagement with this cancer target. A comprehensive computational assessment of molecular recognition for a selected thiosemicarbazone and thiocarbohydrazone provided useful information for further optimization of this discovered lead compound for chemotherapeutic anticancer drug discovery.
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Haj Hasan A, Preet G, Milne BF, Ebel R, Jaspars M. Arabinofuranosyl Thymine Derivatives-Potential Candidates against Cowpox Virus: A Computational Screening Study. Int J Mol Sci 2023; 24:ijms24021751. [PMID: 36675269 PMCID: PMC9864678 DOI: 10.3390/ijms24021751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/09/2023] [Accepted: 01/15/2023] [Indexed: 01/18/2023] Open
Abstract
Cowpox is caused by a DNA virus known as the cowpox virus (CPXV) belonging to the Orthopoxvirus genus in the family Poxviridae. Cowpox is a zoonotic disease with the broadest host range among the known poxviruses. The natural reservoir hosts of CPXV are wild rodents. Recently, the cases of orthopoxviral infections have been increasing worldwide, and cowpox is considered the most common orthopoxviral infection in Europe. Cowpox is often a self-limiting disease, although cidofovir or anti-vaccinia gammaglobulin can be used in severe and disseminated cases of human cowpox. In this computational study, a molecular docking analysis of thymine- and arabinofuranosyl-thymine-related structures (1-21) on two cowpox-encoded proteins was performed with respect to the cidofovir standard and a 3D ligand-based pharmacophore model was generated. Three chemical structures (PubChem IDs: 123370001, 154137224, and 90413364) were identified as potential candidates for anti-cowpox agents. Further studies combining in vitro and in silico molecular dynamics simulations to test the stability of these promising compounds could effectively improve the future design of cowpox virus inhibitors, as molecular docking studies are not sufficient to consider a ligand a potential drug.
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Affiliation(s)
- Ahlam Haj Hasan
- Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK
- Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Gagan Preet
- Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK
| | - Bruce Forbes Milne
- Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK
- CFisUC, Department of Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal
| | - Rainer Ebel
- Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK
| | - Marcel Jaspars
- Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK
- Correspondence:
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Annan A, Raiss N, Elmir EH, Filali-Maltouf A, Medraoui L, Oumzil H. Revolutionizing antiretroviral therapy for human immunodeficiency virus/AIDS: A computational approach using molecular docking, virtual screening, and 3D pharmacophore building to address therapeutic failure and propose highly effective candidates. Int J Immunopathol Pharmacol 2023; 37:3946320231207514. [PMID: 37850462 PMCID: PMC10585989 DOI: 10.1177/03946320231207514] [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: 07/30/2023] [Accepted: 09/25/2023] [Indexed: 10/19/2023] Open
Abstract
OBJECTIVES In the context of human immunodeficiency virus (HIV) treatment, the emergence of therapeutic failures with existing antiretroviral drugs presents a significant challenge. This study aims to employ advanced molecular modeling techniques to identify potential alternatives to current antiretroviral agents. METHODS The study focuses on three essential classes of antiretroviral drugs: nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs). Computational analyses were performed on a database of 3,343,652 chemical molecules to evaluate their binding affinities, pharmacokinetic properties, and interactions with viral reverse transcriptase and protease enzymes. Molecular docking, virtual screening, and 3D pharmacophore modeling were utilized to identify promising candidates. RESULTS Molecular docking revealed compounds with high binding energies and strong interactions at the active sites of target enzymes. Virtual screening narrowed down potential candidates with favorable pharmacological profiles. 3D pharmacophore modeling identified crucial structural features for effective binding. Overall, two molecules for class 1, 7 molecules for class 2, and 2 molecules for class 3 were selected. These compounds exhibited robust binding affinities, interactions with target enzymes, and improved pharmacokinetic properties, showing promise for more effective HIV treatments in cases of therapeutic failures. CONCLUSION The combination of molecular docking, virtual screening, and 3D pharmacophore modeling yielded lead compounds that hold potential for addressing HIV therapeutic failures. Further experimental investigations are essential to validate the efficacy and safety of these compounds, with the ultimate goal of advancing toward clinical applications in HIV management.
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Affiliation(s)
- Azzeddine Annan
- Research Center of Plant and Microbial Biotechnologies, Biodiversity and Environment, Faculty of Sciences, Mohammed V University, Rabat, Morocco
- National Reference Laboratory for HIV, Virology Department, Institute National of Hygiene, Rabat, Morocco
| | - Noureddine Raiss
- Research Center of Plant and Microbial Biotechnologies, Biodiversity and Environment, Faculty of Sciences, Mohammed V University, Rabat, Morocco
- National Reference Laboratory for HIV, Virology Department, Institute National of Hygiene, Rabat, Morocco
| | - El Harti Elmir
- National Reference Laboratory for HIV, Virology Department, Institute National of Hygiene, Rabat, Morocco
| | - Abdelkarim Filali-Maltouf
- Research Center of Plant and Microbial Biotechnologies, Biodiversity and Environment, Faculty of Sciences, Mohammed V University, Rabat, Morocco
| | - Leila Medraoui
- Research Center of Plant and Microbial Biotechnologies, Biodiversity and Environment, Faculty of Sciences, Mohammed V University, Rabat, Morocco
| | - Hicham Oumzil
- National Reference Laboratory for HIV, Virology Department, Institute National of Hygiene, Rabat, Morocco
- Laboratory of Biotechnology, Medical and Pharmacy School, Mohammed V University, Rabat, Morocco
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45
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Yu W, Weber DJ, MacKerell AD. Computer-Aided Drug Design: An Update. Methods Mol Biol 2023; 2601:123-152. [PMID: 36445582 PMCID: PMC9838881 DOI: 10.1007/978-1-0716-2855-3_7] [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] [Indexed: 11/30/2022]
Abstract
Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.
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Affiliation(s)
- Wenbo Yu
- Department of Pharmaceutical Sciences, Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA.
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA.
| | - David J Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, Computer-Aided Drug Design Center, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD, USA.
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD, USA.
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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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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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Spiegel J, Senderowitz H. Towards an Enrichment Optimization Algorithm (EOA)-based Target Specific Docking Functions for Virtual Screening. Mol Inform 2022; 41:e2200034. [PMID: 35790469 PMCID: PMC9786651 DOI: 10.1002/minf.202200034] [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: 02/10/2022] [Accepted: 07/05/2022] [Indexed: 12/30/2022]
Abstract
Docking-based virtual screening (VS) is a common starting point in many drug discovery projects. While ligand-based approaches may sometimes provide better results, the advantage of docking lies in its ability to provide reliable ligand binding modes and approximated binding free energies, two factors that are important for hit selection and optimization. Most docking programs were developed to be as general as possible and consequently their performances on specific targets may be sub-optimal. With this in mind, in this work we present a method for the development of target-specific scoring functions using our recently reported Enrichment Optimization Algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations by optimizing an enrichment-like metric. Since EOA requires target-specific active and inactive (or decoy) compounds, we retrieved such data for six targets from the DUD-E database, and used them to re-derive the weights associated with the components that make up GOLD's ChemPLP scoring function yielding target-specific, modified functions. We then used the original ChemPLP function in small-scale VS experiments on the six targets and subsequently rescored the resulting poses with the modified functions. In addition, we used the modified functions for compounds re-docking. We found that in many although not all cases, either rescoring the original ChemPLP poses or repeating the entire docking process with the modified functions, yielded better results in terms of AUC and EF1% , two metrics, common for the evaluation of VS performances. While work on additional datasets and docking tools is clearly required, we propose that the results obtained thus far hint to the potential benefits in using EOA-based optimization for the derivation of target-specific functions in the context of virtual screening. To this end, we discuss the downsides of the methods and how it could be improved.
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Affiliation(s)
- Jacob Spiegel
- Department of ChemistryBar-Ilan UniversityRamat-Gan5290002Israel
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Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence. Pharmaceuticals (Basel) 2022; 15:ph15111304. [DOI: 10.3390/ph15111304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/15/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.
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50
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Jack KS, Asaruddin MRB, Bhawani SA. Pharmacophore study, molecular docking and molecular dynamic simulation of virgin coconut oil derivatives as anti-inflammatory agent against COX-2. CHEMICAL AND BIOLOGICAL TECHNOLOGIES IN AGRICULTURE 2022; 9:73. [PMID: 37520584 PMCID: PMC9579622 DOI: 10.1186/s40538-022-00340-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/27/2022] [Indexed: 05/30/2023]
Abstract
Background Virgin coconut oil is mostly made up of saturated fatty acids in which approximately 72% are medium chain triglycerides. Medium chain triglycerides can be digested into medium chain fatty acids and medium chain monoglycerides which are bioactive components. Therefore, it is very important to study the in-silico ability of some Virgin coconut oil derivatives, namely, medium chain fatty acids and medium chain monoglycerides to inhibit Cyclooxygenase 2 (COX-2) protein for prevention of excessive inflammatory response. Results Pharmacophore study displayed monolaurin with two hydrogen bond donor, three hydrogen bond acceptor and five hydrophobic interactions, while lauric acid presented two hydrogen bond acceptor, five hydrophobic interactions and a negative ion interaction. Molecular docking underlined the ability of monolaurin in the inhibition of COX-2 protein which causes inflammatory action with a decent result of energy binding affinity of - 7.58 kcal/mol and 15 interactions out of which 3 are strong hydrogen bond with TYR385 (3.00 Å), PHE529 (2.77 Å), and GLY533 (3.10 Å) residues of the protein. Monolaurin was employed as hydrogen bond acceptor to the side of residue TYR385 of COX-2 protein with an occupancy of 67.03% and was observed to be long-living during the entire 1000 frames of the molecular dynamic simulation. The analysis of RMSD score of the Monolaurin-COX-2 complex backbone was calculated to be low (1.137 ± 0.153 Å) and was in a stable range of 0.480 to 1.520 Å. Redocking of this complex still maintained a strong hydrogen bond (2.87 Å) with the main residue TYR385. AMDET results where promising for medium chain fatty acids and medium chain monoglycerides with good physicochemical drug scores. Conclusions This can be concluded from the results obtained that the monolaurin has strong interactions with COX-2 protein to disrupt its function due to significant hydrogen bonds and hydrophobic interactions with amino acid residues present in the target protein's active site. These results displayed a very significant anti-inflammatory potential of monolaurin and a new promising drug candidates as anti-inflammatory agent. Graphical Abstract
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Affiliation(s)
- Kho Swen Jack
- Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak Malaysia
| | - Mohd Razip Bin Asaruddin
- Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak Malaysia
| | - Showkat Ahmad Bhawani
- Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak Malaysia
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