1
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Konsue A, Lamtha T, Gleeson D, Jones DJL, Britton RG, Pickering JD, Choowongkomon K, Gleeson MP. Design, preparation and biological evaluation of new Rociletinib-inspired analogs as irreversible EGFR inhibitors to treat non-small-cell-lung cancer. Bioorg Med Chem 2024; 113:117906. [PMID: 39299082 DOI: 10.1016/j.bmc.2024.117906] [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: 07/26/2024] [Revised: 08/30/2024] [Accepted: 08/31/2024] [Indexed: 09/22/2024]
Abstract
Epidermal growth factor receptor (EGFR) kinase has been implicated in the uncontrolled cell growth associated with non-small cell lung cancer (NSCLC). This has prompted the development of 3 generations of EGFR inhibitors over the last 2 decades due to the rapid development of drug resistance issues caused by clinical mutations, including T790M, L858R and the double mutant T790M & L858R. In this work we report the design, preparation and biological assessment of new irreversible 2,4-diaminopyrimidine-based inhibitors of EGFR kinase. Twenty new compounds have been prepared and evaluated which incorporate a range of electrophilic moieties. These include acrylamide, 2-chloroacetamide and (2E)-3-phenylprop-2-enamide, to allow reaction with residue Cys797. In addition, more polar groups have been incorporated to provide a better balance of physical properties than clinical candidate Rociletinib. Inhibitory activities against EGFR wildtype (WT) and EGFR T790M & L858R have been evaluated along with cytotoxicity against EGFR-overexpressing (A549, A431) and normal cell lines (HepG2). Selectivity against JAK3 kinase as well as physicochemical properties determination (logD7.4 and phosphate buffer solubility) have been used to profile the compounds. We have identified 20, 21 and 23 as potent mutant EGFR inhibitors (≤20 nM), with comparable or better selectivity over WT EGFR, and lower activity at JAK3, than Osimertinib or Rociletinib. Compounds 21 displayed the best combination of EGFR mutant activity, JAK3 selectivity, cellular activity and physicochemical properties. Finally, kinetic studies on 21 were performed, confirming a covalent mechanism of action at EGFR.
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Affiliation(s)
- Adchata Konsue
- Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Thomanai Lamtha
- Department of Biochemistry, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
| | - Duangkamol Gleeson
- Department of Chemistry & Applied Computational Chemistry Research Unit, School of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Donald J L Jones
- Leicester Cancer Research Centre, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - Robert G Britton
- Leicester Cancer Research Centre, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - James D Pickering
- School of Chemistry, University of Leicester, Leicester LE1 7RH, United Kingdom
| | - Kiattawee Choowongkomon
- Department of Biochemistry, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
| | - M Paul Gleeson
- Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
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2
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Qian R, Xue J, Xu Y, Huang J. Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery. J Chem Inf Model 2024; 64:7214-7237. [PMID: 39360948 DOI: 10.1021/acs.jcim.4c01024] [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: 10/15/2024]
Abstract
Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.
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Affiliation(s)
- Runtong Qian
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Xue
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - You Xu
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
| | - Jing Huang
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China
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3
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Liu Y, Tan J, Hu S, Hussain M, Qiao C, Tu Y, Lu X, Zhou Y. Dynamics Playing a Key Role in the Covalent Binding of Inhibitors to Focal Adhesion Kinase. J Chem Inf Model 2024; 64:6053-6061. [PMID: 39051776 DOI: 10.1021/acs.jcim.4c00418] [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: 07/27/2024]
Abstract
Covalent kinase inhibitors (CKIs) have recently garnered considerable attention, yet the rational design of CKIs continues to pose a great challenge. In the discovery of CKIs targeting focal adhesion kinase (FAK), it has been observed that the chemical structure of the linkers plays a key role in achieving covalent targeting of FAK. However, the mechanism behind the observation remains elusive. In this work, we employ a comprehensive suite of advanced computational methods to investigate the mechanism of CKIs covalently targeting FAK. We reveal that the linker of an inhibitor influences the contacts between the warhead and residue(s) and the residence time in active conformation, thereby dictating the inhibitor's capability to bind covalently to FAK. This study reflects the complexity of CKI design and underscores the importance of considering the dynamic interactions and residence times for the successful development of covalent drugs.
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Affiliation(s)
- Yiling Liu
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education, Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, #855 Xingye Avenue, Guangzhou 510632, China
| | - Jundong Tan
- School of Management, Jinan University, Guangzhou 511400, China
| | - Shiliang Hu
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education, Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, #855 Xingye Avenue, Guangzhou 510632, China
| | - Muzammal Hussain
- Department of Biochemistry and Molecular Pharmacology, New York University Grossman School of Medicine, New York, New York 10016, United States
| | - Chang Qiao
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education, Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, #855 Xingye Avenue, Guangzhou 510632, China
| | - Yaoquan Tu
- Department of Theoretical Chemistry and Biology, KTH Royal Institute of Technology, Stockholm 114 28, Sweden
| | - Xiaoyun Lu
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education, Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, #855 Xingye Avenue, Guangzhou 510632, China
| | - Yang Zhou
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education, Guangzhou City Key Laboratory of Precision Chemical Drug Development, School of Pharmacy, Jinan University, #855 Xingye Avenue, Guangzhou 510632, China
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4
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Medrano Sandonas L, Van Rompaey D, Fallani A, Hilfiker M, Hahn D, Perez-Benito L, Verhoeven J, Tresadern G, Kurt Wegner J, Ceulemans H, Tkatchenko A. Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules. Sci Data 2024; 11:742. [PMID: 38972891 PMCID: PMC11228031 DOI: 10.1038/s41597-024-03521-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/13/2024] [Indexed: 07/09/2024] Open
Abstract
We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.
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Affiliation(s)
- Leonardo Medrano Sandonas
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg.
- Institute for Materials Science and Max Bergmann Center of Biomaterials, TU Dresden, 01062, Dresden, Germany.
| | - Dries Van Rompaey
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium.
| | - Alessio Fallani
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Mathias Hilfiker
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - David Hahn
- Computational Chemistry, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Laura Perez-Benito
- Computational Chemistry, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Jonas Verhoeven
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Joerg Kurt Wegner
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
- Drug Discovery Data Sciences (D3S), Johnson & Johnson Innovative Medicine, 301 Binney Street, MA 02142, Cambridge, USA
| | - Hugo Ceulemans
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg.
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5
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Danilack AD, Dickson CJ, Soylu C, Fortunato M, Rodde S, Munkler H, Hornak V, Duca JS. Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design. J Comput Aided Mol Des 2024; 38:21. [PMID: 38693331 DOI: 10.1007/s10822-024-00560-6] [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: 12/06/2023] [Accepted: 03/25/2024] [Indexed: 05/03/2024]
Abstract
Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.
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Affiliation(s)
- Aaron D Danilack
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA.
| | - Callum J Dickson
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Cihan Soylu
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Mike Fortunato
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Stephane Rodde
- Biomedical Research, Novartis, Novartis Campus, 4056, Basel, Switzerland
| | - Hagen Munkler
- Technical Research & Development, Novartis Pharma AG, Novartis Campus, 4056, Basel, Switzerland
| | - Viktor Hornak
- Merck Research Laboratories, 33 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Jose S Duca
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA
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6
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Patel D, Huma ZE, Duncan D. Reversible Covalent Inhibition─Desired Covalent Adduct Formation by Mass Action. ACS Chem Biol 2024; 19:824-838. [PMID: 38567529 PMCID: PMC11040609 DOI: 10.1021/acschembio.3c00805] [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: 12/30/2023] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/04/2024]
Abstract
Covalent inhibition has seen a resurgence in the last several years. Although long-plagued by concerns of off-target effects due to nonspecific reactions leading to covalent adducts, there has been success in developing covalent inhibitors, especially within the field of anticancer therapy. Covalent inhibitors can have an advantage over noncovalent inhibitors since the formation of a covalent adduct may serve as an additional mode of selectivity due to the intrinsic reactivity of the target protein that is absent in many other proteins. Unfortunately, many covalent inhibitors form irreversible adducts with off-target proteins, which can lead to considerable side-effects. By designing the inhibitor to form reversible covalent adducts, one can leverage competing on/off kinetics in complex formation by taking advantage of the law of mass action. Although covalent adducts do form with off-target proteins, the reversible nature of inhibition prevents accumulation of the off-target adduct, thus limiting side-effects. In this perspective, we outline important characteristics of reversible covalent inhibitors, including examples and a guide for inhibitor development.
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Affiliation(s)
| | | | - Dustin Duncan
- Department of Chemistry, Brock
University, St. Catharines, Ontario L2S 3A1, Canada
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7
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Nam K, Shao Y, Major DT, Wolf-Watz M. Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development. ACS OMEGA 2024; 9:7393-7412. [PMID: 38405524 PMCID: PMC10883025 DOI: 10.1021/acsomega.3c09084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 02/27/2024]
Abstract
Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.
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Affiliation(s)
- Kwangho Nam
- Department
of Chemistry and Biochemistry, University
of Texas at Arlington, Arlington, Texas 76019, United States
| | - Yihan Shao
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019-5251, United States
| | - Dan T. Major
- Department
of Chemistry and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel
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8
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Mihalovits LM, Kollár L, Bajusz D, Knez D, Bozovičar K, Imre T, Ferenczy GG, Gobec S, Keserű GM. Molecular Mechanism of Labelling Functional Cysteines by Heterocyclic Thiones. Chemphyschem 2024; 25:e202300596. [PMID: 37888491 DOI: 10.1002/cphc.202300596] [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: 08/21/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 10/28/2023]
Abstract
Heterocyclic thiones have recently been identified as reversible covalent warheads, consistent with their mild electrophilic nature. Little is known so far about their mechanism of action in labelling nucleophilic sidechains, especially cysteines. The vast number of tractable cysteines promotes a wide range of target proteins to examine; however, our focus was put on functional cysteines. We chose the main protease of SARS-CoV-2 harboring Cys145 at the active site that is a structurally characterized and clinically validated target of covalent inhibitors. We screened an in-house, cysteine-targeting covalent inhibitor library which resulted in several covalent fragment hits with benzoxazole, benzothiazole and benzimidazole cores. Thione derivatives and Michael acceptors were selected for further investigations with the objective of exploring the mechanism of inhibition of the thiones and using the thoroughly characterized Michael acceptors for benchmarking our studies. Classical and hybrid quantum mechanical/molecular mechanical (QM/MM) molecular dynamics simulations were carried out that revealed a new mechanism of covalent cysteine labelling by thione derivatives, which was supported by QM and free energy calculations and by a wide range of experimental results. Our study shows that the molecular recognition step plays a crucial role in the overall binding of both sets of molecules.
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Affiliation(s)
- Levente M Mihalovits
- Medicinal Chemistry Research Group, HUN-REN Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - Levente Kollár
- Medicinal Chemistry Research Group, HUN-REN Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., 1111, Budapest, Hungary
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, HUN-REN Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - Damijan Knez
- Department of Medicinal Chemistry, Faculty of Pharmacy, University of Ljubljana, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia
| | - Krištof Bozovičar
- Department of Pharmaceutical Biology, Faculty of Pharmacy, University of Ljubljana, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia
| | - Tímea Imre
- Medicinal Chemistry Research Group, HUN-REN Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
- MS Metabolomics Research Group, HUN-REN Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - György G Ferenczy
- Medicinal Chemistry Research Group, HUN-REN Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
| | - Stanislav Gobec
- Department of Medicinal Chemistry, Faculty of Pharmacy, University of Ljubljana, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia
| | - György M Keserű
- Medicinal Chemistry Research Group, HUN-REN Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117, Budapest, Hungary
- Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3., 1111, Budapest, Hungary
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9
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Mehta NV, Degani MS. The expanding repertoire of covalent warheads for drug discovery. Drug Discov Today 2023; 28:103799. [PMID: 37839776 DOI: 10.1016/j.drudis.2023.103799] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 10/04/2023] [Accepted: 10/10/2023] [Indexed: 10/17/2023]
Abstract
The reactive functionalities of drugs that engage in covalent interactions with the enzyme/receptor residue in either a reversible or an irreversible manner are called 'warheads'. Covalent warheads that were previously neglected because of safety concerns have recently gained center stage as a result of their various advantages over noncovalent drugs, including increased selectivity, increased residence time, and higher potency. With the approval of several covalent inhibitors over the past decade, research in this area has accelerated. Various strategies are being continuously developed to tune the characteristics of warheads to improve their potency and mitigate toxicity. Here, we review research progress in warhead discovery over the past 5 years to provide valuable insights for future drug discovery.
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Affiliation(s)
- Namrashee V Mehta
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Nathalal Parekh Marg, Matunga, Mumbai 400019, Maharashtra, India.
| | - Mariam S Degani
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Nathalal Parekh Marg, Matunga, Mumbai 400019, Maharashtra, India.
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10
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Hasan MN, Ray M, Saha A. Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery. J Phys Chem B 2023; 127:9663-9684. [PMID: 37921534 DOI: 10.1021/acs.jpcb.3c04710] [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: 11/04/2023]
Abstract
Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.
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Affiliation(s)
- Md Nazmul Hasan
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
| | - Manisha Ray
- Department of Chemistry and Biochemistry, Loyola University Chicago, Chicago, Illinois 60660, United States
| | - Arjun Saha
- Department of Chemistry and Biochemistry, University of Wisconsin─Milwaukee, Milwaukee, Wisconsin 53211, United States
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11
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Muegge I, Hu Y. Recent Advances in Alchemical Binding Free Energy Calculations for Drug Discovery. ACS Med Chem Lett 2023; 14:244-250. [PMID: 36923913 PMCID: PMC10009785 DOI: 10.1021/acsmedchemlett.2c00541] [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: 01/06/2023] [Accepted: 02/07/2023] [Indexed: 02/18/2023] Open
Abstract
Rigorous physics-based methods to calculate binding free energies of protein-ligand complexes have become a valued component of structure-based drug design. Relative and absolute binding free energy calculations have been deployed prospectively in support of solving diverse drug discovery challenges. Here we review recent applications of binding free energy calculations to fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning. Furthermore, we discuss the merits of using protein models and highlight recent efforts to replace costly binding free energy calculations with predictions from machine learning models trained on a limited number of free energy perturbation or thermodynamic integration calculations thereby allowing for extended chemical space exploration.
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Affiliation(s)
- Ingo Muegge
- Alkermes,
Inc, 852 Winter Street, Waltham, Massachusetts 02451-1420, United States
| | - Yuan Hu
- Frontier
Medicines Corp, 451 D
Street, Suite 207, Boston, Massachusetts 02210, United States
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12
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Shi C, Zhang C, Fu Z, Liu J, Zhou Y, Cheng B, Wang C, Li S, Zhang Y. Antitumor activity of aumolertinib, a third-generation EGFR tyrosine kinase inhibitor, in non-small-cell lung cancer harboring uncommon EGFR mutations. Acta Pharm Sin B 2023. [DOI: 10.1016/j.apsb.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023] Open
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13
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Arafet K, Scalvini L, Galvani F, Martí S, Moliner V, Mor M, Lodola A. Mechanistic Modeling of Lys745 Sulfonylation in EGFR C797S Reveals Chemical Determinants for Inhibitor Activity and Discriminates Reversible from Irreversible Agents. J Chem Inf Model 2023; 63:1301-1312. [PMID: 36762429 PMCID: PMC9976278 DOI: 10.1021/acs.jcim.2c01586] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Targeted covalent inhibitors hold promise for drug discovery, particularly for kinases. Targeting the catalytic lysine of epidermal growth factor receptor (EGFR) has attracted attention as a new strategy to overcome resistance due to the emergence of C797S mutation. Sulfonyl fluoride derivatives able to inhibit EGFRL858R/T790M/C797S by sulfonylation of Lys745 have been reported. However, atomistic details of this process are still poorly understood. Here, we describe the mechanism of inhibition of an innovative class of compounds that covalently engage the catalytic lysine of EGFR, through a sulfur(VI) fluoride exchange (SuFEx) process, with the help of hybrid quantum mechanics/molecular mechanics (QM/MM) and path collective variables (PCVs) approaches. Our simulations identify the chemical determinants accounting for the irreversible activity of agents targeting Lys745 and provide hints for the further optimization of sulfonyl fluoride agents.
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Affiliation(s)
- Kemel Arafet
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I- 43124 Parma, Italy,BioComp
Group, Institute of Advanced Materials (INAM), Universitat Jaume I, 12071 Castelló, Spain
| | - Laura Scalvini
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I- 43124 Parma, Italy
| | - Francesca Galvani
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I- 43124 Parma, Italy
| | - Sergio Martí
- BioComp
Group, Institute of Advanced Materials (INAM), Universitat Jaume I, 12071 Castelló, Spain
| | - Vicent Moliner
- BioComp
Group, Institute of Advanced Materials (INAM), Universitat Jaume I, 12071 Castelló, Spain
| | - Marco Mor
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I- 43124 Parma, Italy,Microbiome
Research Hub, University of Parma, Parco Area delle Scienze 11/A, I-43124 Parma, Italy
| | - Alessio Lodola
- Dipartimento
di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I- 43124 Parma, Italy,. Phone: +39 0521 905062. Fax: +39 0521 905006
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14
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McAulay K, Bilsland A, Bon M. Reactivity of Covalent Fragments and Their Role in Fragment Based Drug Discovery. Pharmaceuticals (Basel) 2022; 15:1366. [PMID: 36355538 PMCID: PMC9694498 DOI: 10.3390/ph15111366] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/30/2022] [Accepted: 11/04/2022] [Indexed: 09/27/2023] Open
Abstract
Fragment based drug discovery has long been used for the identification of new ligands and interest in targeted covalent inhibitors has continued to grow in recent years, with high profile drugs such as osimertinib and sotorasib gaining FDA approval. It is therefore unsurprising that covalent fragment-based approaches have become popular and have recently led to the identification of novel targets and binding sites, as well as ligands for targets previously thought to be 'undruggable'. Understanding the properties of such covalent fragments is important, and characterizing and/or predicting reactivity can be highly useful. This review aims to discuss the requirements for an electrophilic fragment library and the importance of differing warhead reactivity. Successful case studies from the world of drug discovery are then be examined.
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Affiliation(s)
- Kirsten McAulay
- Cancer Research Horizons—Therapeutic Innovation, Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK
- Centre for Targeted Protein Degradation, University of Dundee, Nethergate, Dundee DD1 4HN, UK
| | - Alan Bilsland
- Cancer Research Horizons—Therapeutic Innovation, Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK
| | - Marta Bon
- Cancer Research Horizons—Therapeutic Innovation, Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK
- Exscientia, The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, UK
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15
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Correa GB, Maciel JCSL, Tavares FW, Abreu CRA. A New Formulation for the Concerted Alchemical Calculation of van der Waals and Coulomb Components of Solvation Free Energies. J Chem Theory Comput 2022; 18:5876-5889. [PMID: 36189930 DOI: 10.1021/acs.jctc.2c00563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Alchemical free energy calculations via molecular dynamics have been widely used to obtain thermodynamic properties related to protein-ligand binding and solute-solvent interactions. Although soft-core modeling is the most common approach, the linear basis function (LBF) methodology [Naden, L. N.; et al. J. Chem. Theory Comput.2014, 10 (3), 1128; 2015, 11 (6), 2536] has emerged as a suitable alternative. It overcomes the end-point singularity of the scaling method while maintaining essential advantages such as ease of implementation and high flexibility for postprocessing analysis. In the present work, we propose a simple LBF variant and formulate an efficient protocol for evaluating van der Waals and Coulomb components of an alchemical transformation in tandem, in contrast to the prevalent sequential evaluation mode. To validate our proposal, which results from a careful optimization study, we performed solvation free energy calculations and obtained octanol-water partition coefficients of small organic molecules. Comparisons with results obtained via the sequential mode using either another LBF approach or the soft-core model attest to the effectiveness and correctness of our method. In addition, we show that a reaction field model with an infinite dielectric constant can provide very accurate hydration free energies when used instead of a lattice-sum method to model solute-solvent electrostatics.
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Affiliation(s)
- Gabriela B Correa
- Chemical Engineering Program, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil
| | - Jéssica C S L Maciel
- Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil
| | - Frederico W Tavares
- Chemical Engineering Program, Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa em Engenharia, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil.,Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil
| | - Charlles R A Abreu
- Chemical Engineering Department, Escola de Química, Universidade Federal do Rio de Janeiro, 21941-909Rio de Janeiro, RJ, Brazil
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16
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Awoonor-Williams E. Estimating the binding energetics of reversible covalent inhibitors of the SARS-CoV-2 main protease: an in silico study. Phys Chem Chem Phys 2022; 24:23391-23401. [PMID: 36128834 DOI: 10.1039/d2cp03080b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The main protease (Mpro) of the SARS-CoV-2 virus is an attractive therapeutic target for developing antivirals to combat COVID-19. Mpro is essential for the replication cycle of the SARS-CoV-2 virus, so inhibiting Mpro blocks a vital piece of the cell replication machinery of the virus. A promising strategy to disrupt the viral replication cycle is to design inhibitors that bind to the active site cysteine (Cys145) of the Mpro. Cysteine targeted covalent inhibitors are gaining traction in drug discovery owing to the benefits of improved potency and extended drug-target engagement. An interesting aspect of these inhibitors is that they can be chemically tuned to form a covalent, but reversible bond, with their targets of interest. Several small-molecule cysteine-targeting covalent inhibitors of the Mpro have been discovered-some of which are currently undergoing evaluation in early phase human clinical trials. Understanding the binding energetics of these inhibitors could provide new insights to facilitate the design of potential drug candidates against COVID-19. Motivated by this, we employed rigorous absolute binding free energy calculations and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations to estimate the energetics of binding of some promising reversible covalent inhibitors of the Mpro. We find that the inclusion of enhanced sampling techniques such as replica-exchange algorithm in binding free energy calculations can improve the convergence of predicted non-covalent binding free energy estimates of inhibitors binding to the Mpro target. In addition, our results indicate that binding free energy calculations coupled with multiscale simulations can be a useful approach to employ in ranking covalent inhibitors to their targets. This approach may be valuable in prioritizing and refining covalent inhibitor compounds for lead discovery efforts against COVID-19 and other coronavirus infections.
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Affiliation(s)
- Ernest Awoonor-Williams
- Department of Chemistry, Memorial University of Newfoundland, St. John's, NL, A1B 3X9, Canada.
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17
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Vandermause J, Xie Y, Lim JS, Owen CJ, Kozinsky B. Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt. Nat Commun 2022; 13:5183. [PMID: 36055982 PMCID: PMC9440250 DOI: 10.1038/s41467-022-32294-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/21/2022] [Indexed: 11/08/2022] Open
Abstract
Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous "on-the-fly" training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.
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Affiliation(s)
- Jonathan Vandermause
- Department of Physics, Harvard University, Cambridge, MA, 02138, USA.
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
| | - Yu Xie
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Jin Soo Lim
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Cameron J Owen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Boris Kozinsky
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
- Robert Bosch LLC, Research and Technology Center, Cambridge, MA, 02139, USA.
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18
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Dos Santos AM, Oliveira ARS, da Costa CHS, Kenny PW, Montanari CA, Varela JDJG, Lameira J. Assessment of Reversibility for Covalent Cysteine Protease Inhibitors Using Quantum Mechanics/Molecular Mechanics Free Energy Surfaces. J Chem Inf Model 2022; 62:4083-4094. [PMID: 36044342 DOI: 10.1021/acs.jcim.2c00466] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We have used molecular dynamics (MD) simulations with hybrid quantum mechanics/molecular mechanics (QM/MM) potentials to investigate the reaction mechanism for covalent inhibition of cathepsin K and assess the reversibility of inhibition. The computed free energy profiles suggest that a nucleophilic attack by the catalytic cysteine on the inhibitor warhead and proton transfer from the catalytic histidine occur in a concerted manner. The results indicate that the reaction is more strongly exergonic for the alkyne-based inhibitors, which bind irreversibly to cathepsin K, than for the nitrile-based inhibitor odanacatib, which binds reversibly. Gas-phase energies were also calculated for the addition of methanethiol to structural prototypes for a number of warheads of interest in cysteine protease inhibitor design in order to assess electrophilicity. The approaches presented in this study are particularly applicable to assessment of novel warheads, and computed transition state geometries can be incorporated into molecular models for covalent docking.
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Affiliation(s)
- Alberto M Dos Santos
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Universidade Federal do Pará, Rua Augusto Correa S/N, 66075-110 Belém, PA, Brazil.,Laboratório de Química Quântica Computacional, Universidade Federal do Maranhão, 65080 401 São Luis, MA, Brazil
| | - Amanda Ruslana Santana Oliveira
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Universidade Federal do Pará, Rua Augusto Correa S/N, 66075-110 Belém, PA, Brazil
| | - Clauber H S da Costa
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Universidade Federal do Pará, Rua Augusto Correa S/N, 66075-110 Belém, PA, Brazil
| | - Peter W Kenny
- Medicinal and Biological Chemistry Group, Institute of Chemistry of Sao Carlos, University of Sao Paulo, Avenue Trabalhador Sancarlense 400, 13566-590 São Carlos, SP, Brazil
| | - Carlos A Montanari
- Medicinal and Biological Chemistry Group, Institute of Chemistry of Sao Carlos, University of Sao Paulo, Avenue Trabalhador Sancarlense 400, 13566-590 São Carlos, SP, Brazil
| | - Jaldyr de Jesus G Varela
- Laboratório de Química Quântica Computacional, Universidade Federal do Maranhão, 65080 401 São Luis, MA, Brazil
| | - Jerônimo Lameira
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Universidade Federal do Pará, Rua Augusto Correa S/N, 66075-110 Belém, PA, Brazil
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19
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Rácz A, Mihalovits LM, Bajusz D, Héberger K, Miranda-Quintana RA. Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices. J Chem Inf Model 2022; 62:3415-3425. [PMID: 35834424 PMCID: PMC9326969 DOI: 10.1021/acs.jcim.2c00433] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Molecular dynamics (MD) is a core methodology of molecular
modeling
and computational design for the study of the dynamics and temporal
evolution of molecular systems. MD simulations have particularly benefited
from the rapid increase of computational power that has characterized
the past decades of computational chemical research, being the first
method to be successfully migrated to the GPU infrastructure. While
new-generation MD software is capable of delivering simulations on
an ever-increasing scale, relatively less effort is invested in developing
postprocessing methods that can keep up with the quickly expanding
volumes of data that are being generated. Here, we introduce a new
idea for sampling frames from large MD trajectories, based on the
recently introduced framework of extended similarity indices. Our
approach presents a new, linearly scaling alternative to the traditional
approach of applying a clustering algorithm that usually scales as
a quadratic function of the number of frames. When showcasing its
usage on case studies with different system sizes and simulation lengths,
we have registered speedups of up to 2 orders of magnitude, as compared
to traditional clustering algorithms. The conformational diversity
of the selected frames is also noticeably higher, which is a further
advantage for certain applications, such as the selection of structural
ensembles for ligand docking. The method is available open-source
at https://github.com/ramirandaq/MultipleComparisons.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Levente M Mihalovits
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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20
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Galvani F, Scalvini L, Rivara S, Lodola A, Mor M. Mechanistic Modeling of Monoglyceride Lipase Covalent Modification Elucidates the Role of Leaving Group Expulsion and Discriminates Inhibitors with High and Low Potency. J Chem Inf Model 2022; 62:2771-2787. [PMID: 35580195 PMCID: PMC9198976 DOI: 10.1021/acs.jcim.2c00140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Inhibition of monoglyceride
lipase (MGL), also known as monoacylglycerol
lipase (MAGL), has emerged as a promising approach for treating neurological
diseases. To gain useful insights in the design of agents with balanced
potency and reactivity, we investigated the mechanism of MGL carbamoylation
by the reference triazole urea SAR629 (IC50 = 0.2 nM) and
two recently described inhibitors featuring a pyrazole (IC50 = 1800 nM) or a 4-cyanopyrazole (IC50 = 8 nM) leaving
group (LG), using a hybrid quantum mechanics/molecular mechanics (QM/MM)
approach. Opposite to what was found for substrate 2-arachidonoyl-sn-glycerol (2-AG), covalent modification of MGL by azole
ureas is controlled by LG expulsion. Simulations indicated that changes
in the electronic structure of the LG greatly affect reaction energetics
with triazole and 4-cyanopyrazole inhibitors following a more accessible
carbamoylation path compared to the unsubstituted pyrazole derivative.
The computational protocol provided reaction barriers able to discriminate
between MGL inhibitors with different potencies. These results highlight
how QM/MM simulations can contribute to elucidating structure–activity
relationships and provide insights for the design of covalent inhibitors.
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Affiliation(s)
- Francesca Galvani
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Laura Scalvini
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Silvia Rivara
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Alessio Lodola
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy
| | - Marco Mor
- Dipartimento di Scienze degli Alimenti e del Farmaco, Università degli Studi di Parma, Parco Area delle Scienze 27/A, I-43124 Parma, Italy.,Microbiome Research Hub, University of Parma, Parco Area delle Scienze 11/A, I-43124 Parma, Italy
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21
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Péczka N, Orgován Z, Ábrányi-Balogh P, Keserű GM. Electrophilic warheads in covalent drug discovery: an overview. Expert Opin Drug Discov 2022; 17:413-422. [PMID: 35129005 DOI: 10.1080/17460441.2022.2034783] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Covalent drugs have been used for more than hundred years, but gathered larger interest in the last two decades. There are currently over a 100 different electrophilic warheads used in covalent ligands, and there are several considerations tailoring their reactivity against the target of interest, which is still a challenging task. AREAS COVERED This review aims to give an overview of electrophilic warheads used for protein labeling in chemical biology and medicinal chemistry. The warheads are discussed by targeted residues, mechanism and selectivity, and analyzed through three different datasets including our collection of warheads, the CovPDB database, and the FDA approved covalent drugs. Moreover, the authors summarize general practices that facilitate the selection of the appropriate warhead for the target of interest. EXPERT OPINION In spite of the numerous electrophilic warheads, only a fraction of them is used in current drug discovery projects. Recent studies identified new tractable residues by applying a wider array of warhead chemistries. However, versatile, selective warheads are not available for all targetable amino acids, hence discovery of new warheads for these residues is needed. Broadening the toolbox of the warheads could result in novel inhibitors even for challenging targets developing with significant therapeutic potential.
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Affiliation(s)
- Nikolett Péczka
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - Zoltán Orgován
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - Péter Ábrányi-Balogh
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
| | - György Miklós Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Budapest, Hungary
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22
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Abstract
Covalent drugs offer higher efficacy and longer duration of action than their noncovalent counterparts. Significant advances in computational methods for modeling covalent drugs are poised to shift the paradigm of small molecule therapeutics within the next decade. This viewpoint discusses the advantages of a two-state model for ranking reversible and irreversible covalent ligands and of more complex models for dissecting reaction mechanisms. The relation between these models highlights the complexity and diversity of covalent drug binding and provides opportunities for mechanism-based rational design.
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Affiliation(s)
- Yun Lyna Luo
- Department of Pharmaceutical Sciences, Western University of Health Sciences, Pomona, California 91709, United States
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23
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Abstract
Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients complying with their treatments. Accelerated pharmaceutical development and drug product approval rates can further benefit from the quantum computing (QC) technology, which will ultimately enable larger profits from patent-protected market exclusivity.Key pharma stakeholders are endorsing cutting-edge technologies based on AI and QC , covering drug discovery, preclinical and clinical development, and postapproval activities. Indeed, AI-QC applications are expected to become standard in the pharma operating model over the next 5-10 years. Generalizing scalability to larger pharmaceutical problems instead of specialization is now the main principle for transforming pharmaceutical tasks on multiple fronts, for which systematic and cost-effective solutions have benefited in areas such as molecular screening, synthetic pathway design, and drug discovery and development.The information generated by coupling the life cycle of drugs and AI and/or QC through data-driven analysis, neural network prediction, and chemical system monitoring will enable (1) better understanding of the complexity of process data, (2) streamlining the design of experiments, (3) discovering new molecular targets and materials, and also (4) planning or rethinking upcoming pharmaceutical challenges The power of AI-QC makes accessible a range of different pharmaceutical problems and their rationalization that have not been previously addressed due to a lack of appropriate analytical tools, demonstrating the breadth of potential applications of these emerging multidimensional approaches. In this context, creating the right AI-QC strategy often involves a steep learning path, especially given the embryonic stage of the industry development and the relative lack of case studies documenting success. As such, a comprehensive knowledge of the underlying pillars is imperative to extend the landscape of applications across the drug life cycle.The topics enclosed in this chapter will focus on AI-QC methods applied to drug discovery and development, with emphasis on the most recent advances in this field.
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24
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Mihalovits LM, Ferenczy GG, Keserű GM. Mechanistic and thermodynamic characterization of oxathiazolones as potent and selective covalent immunoproteasome inhibitors. Comput Struct Biotechnol J 2021; 19:4486-4496. [PMID: 34471494 PMCID: PMC8379283 DOI: 10.1016/j.csbj.2021.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 01/20/2023] Open
Abstract
The ubiquitin–proteasome system is responsible for the degradation of proteins and plays a critical role in key cellular processes. While the constitutive proteasome (cPS) is expressed in all eukaryotic cells, the immunoproteasome (iPS) is primarily induced during disease processes, and its inhibition is beneficial in the treatment of cancer, autoimmune disorders and neurodegenerative diseases. Oxathiazolones were reported to selectively inhibit iPS over cPS, and the inhibitory activity of several oxathiazolones against iPS was experimentally determined. However, the detailed mechanism of the chemical reaction leading to irreversible iPS inhibition and the key selectivity drivers are unknown, and separate characterization of the noncovalent and covalent inhibition steps is not available for several compounds. Here, we investigate the chemical reaction between oxathiazolones and the Thr1 residue of iPS by quantum mechanics/molecular mechanics (QM/MM) simulations to establish a plausible reaction mechanism and to determine the rate-determining step of covalent complex formation. The modelled binding mode and reaction mechanism are in line with the selective inhibition of iPS versus cPS by oxathiazolones. The kinact value of several ligands was estimated by constructing the potential of mean force of the rate-determining step by QM/MM simulations coupled with umbrella sampling. The equilibrium constant Ki of the noncovalent complex formation was evaluated by classical force field-based thermodynamic integration. The calculated Ki and kinact values made it possible to analyse the contribution of the noncovalent and covalent steps to the overall inhibitory activity. Compounds with similar intrinsic reactivities exhibit varying selectivities for iPS versus cPS owing to subtle differences in the binding modes that slightly affect Ki, the noncovalent affinity, and importantly alter kinact, the covalent reactivity of the bound compounds. A detailed understanding of the inhibitory mechanism of oxathiazolones is useful in designing iPS selective inhibitors with improved drug-like properties.
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Affiliation(s)
- Levente M Mihalovits
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary
| | - György G Ferenczy
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary
| | - György M Keserű
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok körútja 2, Budapest 1117, Hungary
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25
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Lu W, Kostic M, Zhang T, Che J, Patricelli MP, Jones LH, Chouchani ET, Gray NS. Fragment-based covalent ligand discovery. RSC Chem Biol 2021; 2:354-367. [PMID: 34458789 PMCID: PMC8341086 DOI: 10.1039/d0cb00222d] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/22/2021] [Accepted: 01/20/2021] [Indexed: 12/15/2022] Open
Abstract
Targeted covalent inhibitors have regained widespread attention in drug discovery and have emerged as powerful tools for basic biomedical research. Fueled by considerable improvements in mass spectrometry sensitivity and sample processing, chemoproteomic strategies have revealed thousands of proteins that can be covalently modified by reactive small molecules. Fragment-based drug discovery, which has traditionally been used in a target-centric fashion, is now being deployed on a proteome-wide scale thereby expanding its utility to both the discovery of novel covalent ligands and their cognate protein targets. This powerful approach is allowing ‘high-throughput’ serendipitous discovery of cryptic pockets leading to the identification of pharmacological modulators of proteins previously viewed as “undruggable”. The reactive fragment toolkit has been enabled by recent advances in the development of new chemistries that target residues other than cysteine including lysine and tyrosine. Here, we review the emerging area of covalent fragment-based ligand discovery, which integrates the benefits of covalent targeting and fragment-based medicinal chemistry. We discuss how the two strategies synergize to facilitate the efficient discovery of new pharmacological modulators of established and new therapeutic target proteins. Covalent fragment-based ligand discovery greatly facilitates the discovery of useful fragments for drug discovery and helps unveil chemical-tractable biological targets in native biological systems.![]()
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Affiliation(s)
- Wenchao Lu
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA
| | - Milka Kostic
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA
| | - Tinghu Zhang
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA
| | - Jianwei Che
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA.,Center for Protein Degradation, Dana-Farber Cancer Institute Boston MA 02215 USA
| | | | - Lyn H Jones
- Center for Protein Degradation, Dana-Farber Cancer Institute Boston MA 02215 USA
| | - Edward T Chouchani
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Cell Biology, Harvard Medical School Boston MA 02215 USA
| | - Nathanael S Gray
- Department of Cancer Biology, Dana-Farber Cancer Institute Boston MA 02215 USA .,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School Boston MA 02215 USA
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