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Xu J, Zhang Y, Cai Q, Chen L, Sun Y, Liu Q, Gao Y, Chen H. Green Late-Stage Functionalization of Tryptamines. Chemistry 2024; 30:e202401436. [PMID: 38869004 DOI: 10.1002/chem.202401436] [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: 04/13/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/14/2024]
Abstract
An efficient and rapid protocol for the oxidative halogenation of tryptamines with 10 % aqueous NaClO has been developed. This reaction is featured by its operational simplicity, metal-free conditions, no purification, and high yield. Notably, the resulting key intermediates are suitable for further functionalization with various nucleophiles, including amines, N-aromatic heterocycles, indoles and phenols. The overall transformation exhibits broad functional-group tolerance and is applicable to the late-stage functionalization of complex biorelevant molecules.
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Affiliation(s)
- Jiayi Xu
- Key Laboratory of Molecule Synthesis and Function Discovery, College of Chemistry, Fuzhou University, Fuzhou, 350108, China
| | - Yahui Zhang
- Key Laboratory of Molecule Synthesis and Function Discovery, College of Chemistry, Fuzhou University, Fuzhou, 350108, China
| | - Qiling Cai
- Fujian Provincial Key Laboratory of Medical Instrument and Pharmaceutical Technology, College of Biological Science and Technology, Fuzhou University, Fuzhou, 350108, China
| | - Li Chen
- Fujian Provincial Key Laboratory of Medical Instrument and Pharmaceutical Technology, College of Biological Science and Technology, Fuzhou University, Fuzhou, 350108, China
| | - Yang Sun
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Qinying Liu
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Yu Gao
- Key Laboratory of Molecule Synthesis and Function Discovery, College of Chemistry, Fuzhou University, Fuzhou, 350108, China
| | - Haijun Chen
- Key Laboratory of Molecule Synthesis and Function Discovery, College of Chemistry, Fuzhou University, Fuzhou, 350108, China
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2
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Muegge I, Bentzien J, Ge Y. Perspectives on current approaches to virtual screening in drug discovery. Expert Opin Drug Discov 2024:1-11. [PMID: 39132881 DOI: 10.1080/17460441.2024.2390511] [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: 06/29/2024] [Accepted: 08/06/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods. AREAS COVERED The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS. EXPERT OPINION The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.
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Affiliation(s)
- Ingo Muegge
- Research department, Alkermes, Inc, Waltham, MA, USA
| | - Jörg Bentzien
- Research department, Alkermes, Inc, Waltham, MA, USA
| | - Yunhui Ge
- Research department, Alkermes, Inc, Waltham, MA, USA
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3
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Wu Y, Liu F, Glenn I, Fonseca-Valencia K, Paris L, Xiong Y, Jerome SV, Brooks CL, Shoichet BK. Identifying Artifacts from Large Library Docking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.17.603966. [PMID: 39071262 PMCID: PMC11275789 DOI: 10.1101/2024.07.17.603966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
While large library docking has discovered potent ligands for multiple targets, as the libraries have grown, the very top of the hit-lists can become populated with artifacts that cheat our scoring functions. Though these cheating molecules are rare, they become ever-more dominant with library growth. Here, we investigate rescoring top-ranked molecules from docking screens with orthogonal methods to identify these artifacts, exploring implicit solvent models and absolute binding free energy perturbation (AB-FEP) as cross-filters. In retrospective studies, this approach deprioritized high-ranking non-binders for nine targets while leaving true ligands relatively unaffected. We tested the method prospectively against results from large library docking AmpC β-lactamase. From the very top of the docking hit lists, we prioritized 128 molecules for synthesis and experimental testing, a mixture of 39 molecules that rescoring flagged as likely cheaters and another 89 that were plausible true actives. None of the 39 predicted cheating compounds inhibited AmpC up to 200 μ M in enzyme assays, while 57% of the 89 plausible true actives did do so, with 19 of them inhibiting the enzyme with apparentK i values better than 50 μ M . As our libraries continue to grow, a strategy of catching docking artifacts by rescoring with orthogonal methods may find wide use in the field.
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Affiliation(s)
- Yujin Wu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States
| | - Fangyu Liu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States
| | - Isabella Glenn
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States
| | - Karla Fonseca-Valencia
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States
| | - Lu Paris
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States
| | - Yuyue Xiong
- Schrödinger, Inc., 9868 Scranton Road, San Diego, California 92121, United States
| | - Steven V. Jerome
- Schrödinger, Inc., 1540 Broadway, New York, New York, 10036, United States
| | - Charles L. Brooks
- Biophysics Program, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, 94158, United States
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Govor EV, Naumchyk V, Nestorak I, Radchenko DS, Dudenko D, Moroz YS, Kachkovsky OD, Grygorenko OO. Generation of multimillion chemical space based on the parallel Groebke-Blackburn-Bienaymé reaction. Beilstein J Org Chem 2024; 20:1604-1613. [PMID: 39076290 PMCID: PMC11285076 DOI: 10.3762/bjoc.20.143] [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: 04/29/2024] [Accepted: 07/03/2024] [Indexed: 07/31/2024] Open
Abstract
Parallel Groebke-Blackburn-Bienaymé reaction was evaluated as a source of multimillion chemically accessible chemical space. Two most popular classical protocols involving the use of Sc(OTf)3 and TsOH as the catalysts were tested on a broad substrate scope, and prevalence of the first method was clearly demonstrated. Furthermore, the scope and limitations of the procedure were established. A model 790-member library was obtained with 85% synthesis success rate. These results were used to generate a 271-Mln. readily accessible (REAL) heterocyclic chemical space mostly containing unique chemotypes, which was confirmed by comparative analysis with commercially available compound collections. Meanwhile, this chemical space contained 432 compounds that already showed biological activity according to the ChEMBL database.
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Affiliation(s)
- Evgen V Govor
- Enamine Ltd., Winston Churchill Street 78, Kyїv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyїv 01601, Ukraine
| | - Vasyl Naumchyk
- Enamine Ltd., Winston Churchill Street 78, Kyїv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyїv 01601, Ukraine
| | - Ihor Nestorak
- Enamine Ltd., Winston Churchill Street 78, Kyїv 02094, Ukraine
- V. P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, Akademik Kukhar Street 1, Kyїv 02094, Ukraine
| | | | - Dmytro Dudenko
- Enamine Ltd., Winston Churchill Street 78, Kyїv 02094, Ukraine
| | - Yurii S Moroz
- Enamine Ltd., Winston Churchill Street 78, Kyїv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyїv 01601, Ukraine
| | - Olexiy D Kachkovsky
- V. P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, Akademik Kukhar Street 1, Kyїv 02094, Ukraine
| | - Oleksandr O Grygorenko
- Enamine Ltd., Winston Churchill Street 78, Kyїv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyїv 01601, Ukraine
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5
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Liu F, Kaplan AL, Levring J, Einsiedel J, Tiedt S, Distler K, Omattage NS, Kondratov IS, Moroz YS, Pietz HL, Irwin JJ, Gmeiner P, Shoichet BK, Chen J. Structure-based discovery of CFTR potentiators and inhibitors. Cell 2024; 187:3712-3725.e34. [PMID: 38810646 PMCID: PMC11262615 DOI: 10.1016/j.cell.2024.04.046] [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: 09/15/2023] [Revised: 03/19/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024]
Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.
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Affiliation(s)
- Fangyu Liu
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Anat Levit Kaplan
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jesper Levring
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Jürgen Einsiedel
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Stephanie Tiedt
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Katharina Distler
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Natalie S Omattage
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Ivan S Kondratov
- Enamine Ltd., Chervonotkatska Street 78, 02094 Kyïv, Ukraine; V.P. Kukhar Institute of Bioorganic Chemistry & Petrochemistry, National Academy of Sciences of Ukraine, Murmanska Street 1, 02660 Kyïv, Ukraine
| | - Yurii S Moroz
- Chemspace, Chervonotkatska Street 85, 02094 Kyïv, Ukraine; Taras Shevchenko National University of Kyïv, Volodymyrska Street 60, 01601 Kyïv, Ukraine
| | - Harlan L Pietz
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Peter Gmeiner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany.
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - Jue Chen
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
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6
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Liu F, Mailhot O, Glenn IS, Vigneron SF, Bassim V, Xu X, Fonseca-Valencia K, Smith MS, Radchenko DS, Fraser JS, Moroz YS, Irwin JJ, Shoichet BK. The impact of Library Size and Scale of Testing on Virtual Screening. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.602536. [PMID: 39026784 PMCID: PMC11257449 DOI: 10.1101/2024.07.08.602536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC β-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on reanalysis of large-scale results against the σ2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.
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Affiliation(s)
- Fangyu Liu
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Olivier Mailhot
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Isabella S Glenn
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Seth F Vigneron
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Violla Bassim
- Dept. of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco CA 94143, USA
| | - Xinyu Xu
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Karla Fonseca-Valencia
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Matthew S Smith
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | | | - James S Fraser
- Dept. of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco CA 94143, USA
| | - Yurii S Moroz
- Enamine Ltd., Kyiv, 02094, Ukraine
- Chemspace (www.chem-space.com), Chervonotkatska Street 85, Kyїv 02094, Ukraine
- Taras Shevchenko National University of Kyїv, Volodymyrska Street 60, Kyїv 01601, Ukraine
| | - John J Irwin
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Brian K Shoichet
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
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7
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Liu S, Anderson PJ, Rajagopal S, Lefkowitz RJ, Rockman HA. G Protein-Coupled Receptors: A Century of Research and Discovery. Circ Res 2024; 135:174-197. [PMID: 38900852 PMCID: PMC11192237 DOI: 10.1161/circresaha.124.323067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
GPCRs (G protein-coupled receptors), also known as 7 transmembrane domain receptors, are the largest receptor family in the human genome, with ≈800 members. GPCRs regulate nearly every aspect of human physiology and disease, thus serving as important drug targets in cardiovascular disease. Sharing a conserved structure comprised of 7 transmembrane α-helices, GPCRs couple to heterotrimeric G-proteins, GPCR kinases, and β-arrestins, promoting downstream signaling through second messengers and other intracellular signaling pathways. GPCR drug development has led to important cardiovascular therapies, such as antagonists of β-adrenergic and angiotensin II receptors for heart failure and hypertension, and agonists of the glucagon-like peptide-1 receptor for reducing adverse cardiovascular events and other emerging indications. There continues to be a major interest in GPCR drug development in cardiovascular and cardiometabolic disease, driven by advances in GPCR mechanistic studies and structure-based drug design. This review recounts the rich history of GPCR research, including the current state of clinically used GPCR drugs, and highlights newly discovered aspects of GPCR biology and promising directions for future investigation. As additional mechanisms for regulating GPCR signaling are uncovered, new strategies for targeting these ubiquitous receptors hold tremendous promise for the field of cardiovascular medicine.
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Affiliation(s)
- Samuel Liu
- Department of Medicine, Duke University Medical
Center
| | - Preston J. Anderson
- Cell and Molecular Biology (CMB), Duke University, Durham,
NC, 27710, USA
- Duke Medical Scientist Training Program, Duke University,
Durham, NC, 27710, USA
| | - Sudarshan Rajagopal
- Department of Medicine, Duke University Medical
Center
- Cell and Molecular Biology (CMB), Duke University, Durham,
NC, 27710, USA
- Deparment of Biochemistry Duke University, Durham, NC,
27710, USA
| | - Robert J. Lefkowitz
- Department of Medicine, Duke University Medical
Center
- Deparment of Biochemistry Duke University, Durham, NC,
27710, USA
- Howard Hughes Medical Institute, Duke University Medical
Center, Durham, North Carolina 27710, USA
| | - Howard A. Rockman
- Department of Medicine, Duke University Medical
Center
- Cell and Molecular Biology (CMB), Duke University, Durham,
NC, 27710, USA
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8
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Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Glenn IS, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL. AlphaFold2 structures guide prospective ligand discovery. Science 2024; 384:eadn6354. [PMID: 38753765 PMCID: PMC11253030 DOI: 10.1126/science.adn6354] [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: 12/19/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024]
Abstract
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
- The Evnin Family Laboratory of Computational Molecular Discovery, The Rockefeller University, New York, NY 10065, USA
| | - Nicholas Kapolka
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ryan Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Liang Wang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Manish K. Jain
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Kensuke Sakamoto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Yoojoong Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Jeffrey DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Kuglae Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Isabella S. Glenn
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Tia A. Tummino
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Sijie Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Yurii Moroz
- Chemspace LLC, Kyiv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine
- Enamine Ltd., Kyiv 02094, Ukraine
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Andrew C. Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Bryan L. Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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9
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Shi Y, Zhu J, Hou C, Li X, Tong Q. Mining key circadian biomarkers for major depressive disorder by integrating bioinformatics and machine learning. Aging (Albany NY) 2024; 16:10299-10320. [PMID: 38874508 PMCID: PMC11236317 DOI: 10.18632/aging.205930] [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: 12/01/2023] [Accepted: 05/03/2024] [Indexed: 06/15/2024]
Abstract
OBJECTIVE This study aimed to identify key clock genes closely associated with major depressive disorder (MDD) using bioinformatics and machine learning approaches. METHODS Gene expression data of 128 MDD patients and 64 healthy controls from blood samples were obtained. Differentially expressed were identified and weighted gene co-expression network analysis (WGCNA) was first performed to screen MDD-related key genes. These genes were then intersected with 1475 known circadian rhythm genes to identify circadian rhythm genes associated with MDD. Finally, multiple machine learning algorithms were applied for further selection, to determine the most critical 4 circadian rhythm biomarkers. RESULTS Four key circadian rhythm genes (ABCC2, APP, HK2 and RORA) were identified that could effectively distinguish MDD samples from controls. These genes were significantly enriched in circadian pathways and showed strong correlations with immune cell infiltration. Drug target prediction suggested that small molecules like melatonin and escitalopram may target these circadian rhythm proteins. CONCLUSION This study revealed discovered 4 key circadian rhythm genes closely associated with MDD, which may serve as diagnostic biomarkers and therapeutic targets. The findings highlight the important roles of circadian disruptions in the pathogenesis of MDD, providing new insights for precision diagnosis and targeted treatment of MDD.
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Affiliation(s)
- Yuhe Shi
- Department of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
| | - Jue Zhu
- Department of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
| | - Chaowen Hou
- Department of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
| | - Xiaoling Li
- Department of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
| | - Qiaozhen Tong
- Department of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
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10
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Zhou H, Skolnick J. Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening. J Phys Chem B 2024; 128:5363-5370. [PMID: 38783525 PMCID: PMC11163432 DOI: 10.1021/acs.jpcb.4c01875] [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: 03/21/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
In modern drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing and refinement due to its cost-effective nature for large compound libraries. For decades, efforts have been devoted to developing VLS methods with high accuracy. These include the state-of-the-art FINDSITE suite of approaches FINDSITEcomb2.0, FRAGSITE, and FRAGSITE2 and the meta version FRAGSITEcomb that were developed in our lab. These methods combine ligand homology modeling (LHM), traditional ligand similarity methods, and more recently machine learning approaches to rank ligands and have proven to be superior to most recent deep learning and large language model-based approaches. Here, we describe further improvements to our previous best methods by combining the Morgan fingerprint (MF) with the originally used PubChem fingerprint and FP2 fingerprint. We then benchmarked FINDSITEcomb2.0M, FRAGSITEM, FRAGSITE2M, and the composite meta-approach FRAGSITEcombM. On the 102 target DUD-E set, the 1% enrichment factor (EF1%) and area under the precision-recall curve (AUPR) of FRAGSITEcomb increased from 42.0/0.59 to 47.6/0.72. This 0.72 AUPR is significantly better than that of the state-of-the-art deep learning-based method DenseFS's AUPR of 0.443. An independent test on the 81 targets DEKOIS2.0 set shows that EF1%/AUPR increases from 18.3/0.520 to 23.1/0.683. An ablation investigation shows that the MF contributes to most of the improvement of all four approaches. Thus, the MF is a useful addition to structure-based VLS.
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Affiliation(s)
- Hongyi Zhou
- Center for the Study of Systems
Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jeffrey Skolnick
- Center for the Study of Systems
Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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11
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Altomare A, Baron G, Cambiaghi G, Ferrario G, Zoanni B, Della Vedova L, Fumagalli GM, D’Alessandro S, Parapini S, Vittorio S, Vistoli G, Riso P, Carini M, Delbue S, Aldini G. Screening of M pro Protease (SARS-CoV-2) Covalent Inhibitors from an Anthocyanin-Rich Blueberry Extract Using an HRMS-Based Analytical Platform. Molecules 2024; 29:2702. [PMID: 38893578 PMCID: PMC11173886 DOI: 10.3390/molecules29112702] [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: 05/02/2024] [Revised: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND The viral main protease (Mpro) of SARS-CoV-2 has been recently proposed as a key target to inhibit virus replication in the host. Therefore, molecules that can bind the catalytic site of Mpro could be considered as potential drug candidates in the treatment of SARS-CoV-2 infections. Here we proposed the application of a state-of-the-art analytical platform which combines metabolomics and protein structure analysis to fish-out potential active compounds deriving from a natural matrix, i.e., a blueberry extract. METHODS The experiments focus on finding MS covalent inhibitors of Mpro that contain in their structure a catechol/pyrogallol moiety capable of binding to the nucleophilic amino acids of the enzyme's catalytic site. RESULTS Among the potential candidates identified, the delphinidin-3-glucoside showed the most promising results. Its antiviral activity has been confirmed in vitro on Vero E6 cells infected with SARS-CoV-2, showing a dose-dependent inhibitory effect almost comparable to the known Mpro inhibitor baicalin. The interaction of delphinidin-3-glucoside with the Mpro pocket observed was also evaluated by computational studies. CONCLUSIONS The HRMS analytical platform described proved to be effective in identifying compounds that covalently bind Mpro and are active in the inhibition of SARS-CoV-2 replication, such as delphinidin-3-glucoside.
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Affiliation(s)
- Alessandra Altomare
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | - Giovanna Baron
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | - Giulia Cambiaghi
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | - Giulio Ferrario
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | - Beatrice Zoanni
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | - Larissa Della Vedova
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | | | - Sarah D’Alessandro
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Via Carlo Pascal 36, 20133 Milan, Italy;
| | - Silvia Parapini
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Carlo Pascal 36, 20133 Milan, Italy;
| | - Serena Vittorio
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | - Giulio Vistoli
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | - Patrizia Riso
- Department of Food, Environmental and Nutritional Sciences, Università degli Studi di Milano, Via Luigi Mangiagalli 25, 20133 Milan, Italy;
| | - Marina Carini
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
| | - Serena Delbue
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Via Carlo Pascal 36, 20133 Milan, Italy;
| | - Giancarlo Aldini
- Department of Pharmaceutical Sciences (DISFARM), Università degli Studi di Milano, Via Mangiagalli 25, 20133 Milan, Italy; (G.B.); (G.C.); (G.F.); (B.Z.); (L.D.V.); (S.V.); (G.V.); (M.C.); (G.A.)
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Birgül Iyison N, Abboud C, Abboud D, Abdulrahman AO, Bondar AN, Dam J, Georgoussi Z, Giraldo J, Horvat A, Karoussiotis C, Paz-Castro A, Scarpa M, Schihada H, Scholz N, Güvenc Tuna B, Vardjan N. ERNEST COST action overview on the (patho)physiology of GPCRs and orphan GPCRs in the nervous system. Br J Pharmacol 2024. [PMID: 38825750 DOI: 10.1111/bph.16389] [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: 07/31/2023] [Revised: 02/09/2024] [Accepted: 02/24/2024] [Indexed: 06/04/2024] Open
Abstract
G protein-coupled receptors (GPCRs) are a large family of cell surface receptors that play a critical role in nervous system function by transmitting signals between cells and their environment. They are involved in many, if not all, nervous system processes, and their dysfunction has been linked to various neurological disorders representing important drug targets. This overview emphasises the GPCRs of the nervous system, which are the research focus of the members of ERNEST COST action (CA18133) working group 'Biological roles of signal transduction'. First, the (patho)physiological role of the nervous system GPCRs in the modulation of synapse function is discussed. We then debate the (patho)physiology and pharmacology of opioid, acetylcholine, chemokine, melatonin and adhesion GPCRs in the nervous system. Finally, we address the orphan GPCRs, their implication in the nervous system function and disease, and the challenges that need to be addressed to deorphanize them.
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Affiliation(s)
- Necla Birgül Iyison
- Department of Molecular Biology and Genetics, University of Bogazici, Istanbul, Turkey
| | - Clauda Abboud
- Laboratory of Molecular Pharmacology, GIGA-Molecular Biology of Diseases, University of Liege, Liege, Belgium
| | - Dayana Abboud
- Laboratory of Molecular Pharmacology, GIGA-Molecular Biology of Diseases, University of Liege, Liege, Belgium
| | | | - Ana-Nicoleta Bondar
- Faculty of Physics, University of Bucharest, Magurele, Romania
- Forschungszentrum Jülich, Institute for Computational Biomedicine (IAS-5/INM-9), Jülich, Germany
| | - Julie Dam
- Institut Cochin, CNRS, INSERM, Université Paris Cité, Paris, France
| | - Zafiroula Georgoussi
- Laboratory of Cellular Signalling and Molecular Pharmacology, Institute of Biosciences and Applications, National Center for Scientific Research "Demokritos", Athens, Greece
| | - Jesús Giraldo
- Laboratory of Molecular Neuropharmacology and Bioinformatics, Unitat de Bioestadística and Institut de Neurociències, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Unitat de Neurociència Traslacional, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí (I3PT), Institut de Neurociències, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anemari Horvat
- Laboratory of Neuroendocrinology - Molecular Cell Physiology, Institute of Pathophysiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Laboratory of Cell Engineering, Celica Biomedical, Ljubljana, Slovenia
| | - Christos Karoussiotis
- Laboratory of Cellular Signalling and Molecular Pharmacology, Institute of Biosciences and Applications, National Center for Scientific Research "Demokritos", Athens, Greece
| | - Alba Paz-Castro
- Molecular Pharmacology of GPCRs research group, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidade de Santiago de Compostela, Santiago, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago, Spain
| | - Miriam Scarpa
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Hannes Schihada
- Department of Pharmaceutical Chemistry, Philipps-University Marburg, Marburg, Germany
| | - Nicole Scholz
- Rudolf Schönheimer Institute of Biochemistry, Division of General Biochemistry, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Bilge Güvenc Tuna
- Department of Biophysics, School of Medicine, Yeditepe University, Istanbul, Turkey
| | - Nina Vardjan
- Laboratory of Neuroendocrinology - Molecular Cell Physiology, Institute of Pathophysiology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Laboratory of Cell Engineering, Celica Biomedical, Ljubljana, Slovenia
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13
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Meqbil YJ, Aguilar J, Blaine AT, Chen L, Cassell RJ, Pradhan AA, van Rijn RM. Identification of 1,3,8-Triazaspiro[4.5]Decane-2,4-Dione Derivatives as a Novel δ Opioid Receptor-Selective Agonist Chemotype. J Pharmacol Exp Ther 2024; 389:301-309. [PMID: 38621994 PMCID: PMC11125782 DOI: 10.1124/jpet.123.001735] [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: 05/10/2023] [Revised: 03/25/2024] [Accepted: 04/08/2024] [Indexed: 04/17/2024] Open
Abstract
δ opioid receptors (DORs) hold potential as a target for neurologic and psychiatric disorders, yet no DOR agonist has proven efficacious in critical phase II clinical trials. The exact reasons for the failure to produce quality drug candidates for the DOR are unclear. However, it is known that certain DOR agonists can induce seizures and exhibit tachyphylaxis. Several studies have suggested that those adverse effects are more prevalent in delta agonists that share the (+)-4-[(αR)-α-((2S,5R)-4-allyl-2,5-dimethyl-1-piperazinyl)-3-methoxybenzyl]-N,N-diethylbenzamide (SNC80)/4-[(αR*)-α-((2S*,5R*)-4-allyl-2,5-dimethyl-1-piperazinyl)-3-hydroxybenzyl]-N,N-diethylbenzamide chemotype. There is a need to find novel lead candidates for drug development that have improved pharmacological properties to differentiate them from the current failed delta agonists. Our objective in this study was to identify novel DOR agonists. We used a β-arrestin assay to screen a small G-protein coupled receptors (GPCR)-focused chemical library. We identified a novel chemotype of DOR agonists that appears to bind to the orthosteric site based of docking and molecular dynamic simulation. The most potent agonist hit compound is selective for the DOR over a panel of 167 other GPCRs, is slightly biased toward G-protein signaling and has anti-allodynic efficacy in a complete Freund's adjuvant model of inflammatory pain in C57BL/6 male and female mice. The newly discovered chemotype contrasts with molecules like SNC80 that are highly efficacious β-arrestin recruiters and may suggest this novel class of DOR agonists could be expanded on to develop a clinical candidate drug. SIGNIFICANCE STATEMENT: δ opioid receptors are a clinical target for various neurological disorders, including migraine and chronic pain. Many of the clinically tested delta opioid agonists share a single chemotype, which carries risks during drug development. Through a small-scale high-throughput screening assay, this study identified a novel δ opioid receptor agonist chemotype, which may serve as alternative for the current analgesic clinical candidates.
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Affiliation(s)
- Yazan J Meqbil
- Borch Department of Medicinal Chemistry and Molecular Pharmacology (Y.J.M., A.T.B., R.J.C., R.M.v.R.), Computational Interdisciplinary Graduate Programs, Computational Life Sciences (Y.J.M.), and Interdisciplinary Life Science-PULSe (A.T.B.), Purdue University, West Lafayette, Indiana; Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana (R.M.v.R.); Purdue Institute for Drug Discovery, West Lafayette, Indiana (L.C., R.M.v.R.); Septerna Inc., South San Francisco, California (R.M.v.R.); and Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (J.A., A.A.P.)
| | - Jhoan Aguilar
- Borch Department of Medicinal Chemistry and Molecular Pharmacology (Y.J.M., A.T.B., R.J.C., R.M.v.R.), Computational Interdisciplinary Graduate Programs, Computational Life Sciences (Y.J.M.), and Interdisciplinary Life Science-PULSe (A.T.B.), Purdue University, West Lafayette, Indiana; Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana (R.M.v.R.); Purdue Institute for Drug Discovery, West Lafayette, Indiana (L.C., R.M.v.R.); Septerna Inc., South San Francisco, California (R.M.v.R.); and Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (J.A., A.A.P.)
| | - Arryn T Blaine
- Borch Department of Medicinal Chemistry and Molecular Pharmacology (Y.J.M., A.T.B., R.J.C., R.M.v.R.), Computational Interdisciplinary Graduate Programs, Computational Life Sciences (Y.J.M.), and Interdisciplinary Life Science-PULSe (A.T.B.), Purdue University, West Lafayette, Indiana; Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana (R.M.v.R.); Purdue Institute for Drug Discovery, West Lafayette, Indiana (L.C., R.M.v.R.); Septerna Inc., South San Francisco, California (R.M.v.R.); and Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (J.A., A.A.P.)
| | - Lan Chen
- Borch Department of Medicinal Chemistry and Molecular Pharmacology (Y.J.M., A.T.B., R.J.C., R.M.v.R.), Computational Interdisciplinary Graduate Programs, Computational Life Sciences (Y.J.M.), and Interdisciplinary Life Science-PULSe (A.T.B.), Purdue University, West Lafayette, Indiana; Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana (R.M.v.R.); Purdue Institute for Drug Discovery, West Lafayette, Indiana (L.C., R.M.v.R.); Septerna Inc., South San Francisco, California (R.M.v.R.); and Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (J.A., A.A.P.)
| | - Robert J Cassell
- Borch Department of Medicinal Chemistry and Molecular Pharmacology (Y.J.M., A.T.B., R.J.C., R.M.v.R.), Computational Interdisciplinary Graduate Programs, Computational Life Sciences (Y.J.M.), and Interdisciplinary Life Science-PULSe (A.T.B.), Purdue University, West Lafayette, Indiana; Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana (R.M.v.R.); Purdue Institute for Drug Discovery, West Lafayette, Indiana (L.C., R.M.v.R.); Septerna Inc., South San Francisco, California (R.M.v.R.); and Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (J.A., A.A.P.)
| | - Amynah A Pradhan
- Borch Department of Medicinal Chemistry and Molecular Pharmacology (Y.J.M., A.T.B., R.J.C., R.M.v.R.), Computational Interdisciplinary Graduate Programs, Computational Life Sciences (Y.J.M.), and Interdisciplinary Life Science-PULSe (A.T.B.), Purdue University, West Lafayette, Indiana; Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana (R.M.v.R.); Purdue Institute for Drug Discovery, West Lafayette, Indiana (L.C., R.M.v.R.); Septerna Inc., South San Francisco, California (R.M.v.R.); and Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (J.A., A.A.P.)
| | - Richard M van Rijn
- Borch Department of Medicinal Chemistry and Molecular Pharmacology (Y.J.M., A.T.B., R.J.C., R.M.v.R.), Computational Interdisciplinary Graduate Programs, Computational Life Sciences (Y.J.M.), and Interdisciplinary Life Science-PULSe (A.T.B.), Purdue University, West Lafayette, Indiana; Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana (R.M.v.R.); Purdue Institute for Drug Discovery, West Lafayette, Indiana (L.C., R.M.v.R.); Septerna Inc., South San Francisco, California (R.M.v.R.); and Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri (J.A., A.A.P.)
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de Lima Menezes G, Sales Bezerra K, Nobre Oliveira JI, Fontenele Araújo J, Soares Galvão D, Alves da Silva R, Vogel Saivish M, Laino Fulco U. Quantum mechanics insights into melatonin and analogs binding to melatonin MT 1 and MT 2 receptors. Sci Rep 2024; 14:10922. [PMID: 38740789 PMCID: PMC11091226 DOI: 10.1038/s41598-024-59786-x] [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: 02/08/2024] [Accepted: 04/15/2024] [Indexed: 05/16/2024] Open
Abstract
Melatonin receptors MT1 and MT2 are G protein-coupled receptors that mediate the effects of melatonin, a hormone involved in circadian rhythms and other physiological functions. Understanding the molecular interactions between these receptors and their ligands is crucial for developing novel therapeutic agents. In this study, we used molecular docking, molecular dynamics simulations, and quantum mechanics calculation to investigate the binding modes and affinities of three ligands: melatonin (MLT), ramelteon (RMT), and 2-phenylmelatonin (2-PMT) with both receptors. Based on the results, we identified key amino acids that contributed to the receptor-ligand interactions, such as Gln181/194, Phe179/192, and Asn162/175, which are conserved in both receptors. Additionally, we described new meaningful interactions with Gly108/Gly121, Val111/Val124, and Val191/Val204. Our results provide insights into receptor-ligand recognition's structural and energetic determinants and suggest potential strategies for designing more optimized molecules. This study enhances our understanding of receptor-ligand interactions and offers implications for future drug development.
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Affiliation(s)
- Gabriela de Lima Menezes
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande no Norte, Natal, RN, 59072-970, Brazil
- Bioinformatics Multidisciplinary Environment, Programa de Pós Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, 59078-400, Brazil
| | - Katyanna Sales Bezerra
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande no Norte, Natal, RN, 59072-970, Brazil
- Applied Physics Department, University of Campinas, Campinas, São Paulo, 13083-859, Brazil
| | - Jonas Ivan Nobre Oliveira
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande no Norte, Natal, RN, 59072-970, Brazil
| | - John Fontenele Araújo
- Departamento de Fisiologia e Comportamento, Universidade Federal do Rio Grande no Norte, Natal, RN, 59072-970, Brazil
| | - Douglas Soares Galvão
- Applied Physics Department, University of Campinas, Campinas, São Paulo, 13083-859, Brazil
| | - Roosevelt Alves da Silva
- Unidade Especial de Ciências Exatas, Universidade Federal de Jataí, Jataí, GO, 75801-615, Brazil
| | - Marielena Vogel Saivish
- Laboratório de Pesquisas em Virologia, Departamento de Doenças Dermatológicas, Infecciosas e Parasitárias, Faculdade de Medicina de São José Do Rio Preto, São José Do Rio, Preto, SP, 15090-000, Brazil
- Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Brazilian Biosciences National Laboratory, Campinas, SP, 13083-100, Brazil
| | - Umberto Laino Fulco
- Departamento de Biofísica e Farmacologia, Universidade Federal do Rio Grande no Norte, Natal, RN, 59072-970, Brazil.
- Bioinformatics Multidisciplinary Environment, Programa de Pós Graduação em Bioinformática, Universidade Federal do Rio Grande do Norte, Natal, RN, 59078-400, Brazil.
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15
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Cui M, Xing T, Zhao A, Zheng L, Zhang X, Xue H, Wu Z, Wang F, Zhao P. Effects of intraoperative sodium oxybate infusion on post-operative sleep quality in patients undergoing gynecological laparoscopic surgery: A randomized clinical trial. J Clin Anesth 2024; 93:111349. [PMID: 38039631 DOI: 10.1016/j.jclinane.2023.111349] [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: 06/21/2023] [Revised: 11/14/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023]
Abstract
STUDY OBJECTIVE Post-operative sleep quality is an important factor that influences post-operative recovery. Sodium oxybate has been used to treat sleep disturbances associated with various pathological conditions. However, whether intraoperative intravenous infusion of sodium oxybate improves post-operative sleep quality is unknown. This study aimed to examine the effects of sodium oxybate on the post-operative sleep quality of patients who underwent gynecological laparoscopic surgery. DESIGN A single-center, prospective, two-arm, double-blinded randomized controlled trial. SETTING The Shengjing Hospital of China Medical University in Liaoning, China. PATIENTS We enrolled 180 adult patients (90 for each group) undergoing elective gynecological laparoscopic surgery, and 178 patients (89 for each group) were included in the final analysis. INTERVENTIONS Patients were randomly allocated in a 1:1 ratio to receive either sodium oxybate (30 mg kg-1) or an equivalent volume of saline after intubation. The patients, anesthetists, and follow-up staff were blinded to group assignment. MEASUREMENTS The primary outcome was sleep quality measured using the Richards-Campbell Sleep Questionnaire (RCSQ) on post-operative days (PODs) one and three. Secondary outcomes included post-operative pain measured using the visual analog scale, sleep quality at one and three months post-operatively measured using the Pittsburgh Sleep Quality Index, and factors associated with post-operative sleep quality. MAIN RESULTS Analysis with generalized estimating equations showed that sodium oxybate significantly improved post-operative sleep quality, as represented by increased total RCSQ scores (mean difference (95% CI); 9 (2, 16), P = 0.010) over PODs one and three. There was no difference in post-operative pain between the two groups over PODs one and three or in post-operative sleep quality over one and three months post-operatively. Age, surgery type, start time of surgery, and use of sufentanil-based patient-controlled intravenous analgesia were significantly associated with post-operative sleep quality. CONCLUSIONS Intraoperative sodium oxybate infusion improved post-operative sleep in patients who underwent gynecological laparoscopic surgery. TRIAL REGISTRATION Chinese Clinical Trial Registry, Clinical trial number: ChiCTR2200061460.
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Affiliation(s)
- Meiying Cui
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tianyi Xing
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Anqi Zhao
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Lanlan Zheng
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xinping Zhang
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Hang Xue
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ziyi Wu
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Fang Wang
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ping Zhao
- Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
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16
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Bedini A, Boutin JA, Legros C, Zlotos DP, Spadoni G. Industrial and academic approaches to the search for alternative melatonin receptor ligands: An historical survey. J Pineal Res 2024; 76:e12953. [PMID: 38682544 DOI: 10.1111/jpi.12953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 03/05/2024] [Accepted: 03/24/2024] [Indexed: 05/01/2024]
Abstract
The search for melatonin receptor agonists formed the main part of melatonin medicinal chemistry programs for the last three decades. In this short review, we summarize the two main aspects of these programs: the development of all the necessary tools to characterize the newly synthesized ligands at the two melatonin receptors MT1 and MT2, and the medicinal chemist's approaches to find chemically diverse ligands at these receptors. Both strategies are described. It turns out that the main source of tools were industrial laboratories, while the medicinal chemistry was mainly carried out in academia. Such complete accounts are interesting, as they delineate the spirits in which the teams were working demonstrating their strength and innovative character. Most of the programs were focused on nonselective agonists and few of them reached the market. In contrast, discovery of MT1-selective agonists and melatonergic antagonists with proven in vivo activity and MT1 or MT2-selectivity is still in its infancy, despite the considerable interest that subtype selective compounds may bring in the domain, as the physiological respective roles of the two subtypes of melatonin receptors, is still poorly understood. Poly-pharmacology applications and multitarget ligands have also been considered.
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MESH Headings
- Ligands
- Humans
- Animals
- Receptor, Melatonin, MT2/metabolism
- Receptor, Melatonin, MT2/agonists
- Receptor, Melatonin, MT1/metabolism
- Receptor, Melatonin, MT1/agonists
- Receptor, Melatonin, MT1/antagonists & inhibitors
- Receptors, Melatonin/metabolism
- Receptors, Melatonin/agonists
- Melatonin/metabolism
- History, 20th Century
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Affiliation(s)
- Annalida Bedini
- Dipartimento di Scienze Biomolecolari, Università degli Studi di Urbino Carlo Bo, Urbino, Italy
| | - Jean A Boutin
- Laboratory of Neuroendocrine Endocrine and Germinal Differentiation and Communication (NorDiC), Univ Rouen Normandie, Inserm, NorDiC, Rouen, France
| | | | - Darius P Zlotos
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy and Biotechnology, The German University in Cairo, New Cairo City, Egypt
| | - Gilberto Spadoni
- Dipartimento di Scienze Biomolecolari, Università degli Studi di Urbino Carlo Bo, Urbino, Italy
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17
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Kiperman T, Ma K. Circadian Clock in Muscle Disease Etiology and Therapeutic Potential for Duchenne Muscular Dystrophy. Int J Mol Sci 2024; 25:4767. [PMID: 38731986 PMCID: PMC11083552 DOI: 10.3390/ijms25094767] [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: 03/07/2024] [Revised: 04/20/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
Circadian clock and clock-controlled output pathways exert temporal control in diverse aspects of skeletal muscle physiology, including the maintenance of muscle mass, structure, function, and metabolism. They have emerged as significant players in understanding muscle disease etiology and potential therapeutic avenues, particularly in Duchenne muscular dystrophy (DMD). This review examines the intricate interplay between circadian rhythms and muscle physiology, highlighting how disruptions of circadian regulation may contribute to muscle pathophysiology and the specific mechanisms linking circadian clock dysregulation with DMD. Moreover, we discuss recent advancements in chronobiological research that have shed light on the circadian control of muscle function and its relevance to DMD. Understanding clock output pathways involved in muscle mass and function offers novel insights into the pathogenesis of DMD and unveils promising avenues for therapeutic interventions. We further explore potential chronotherapeutic strategies targeting the circadian clock to ameliorate muscle degeneration which may inform drug development efforts for muscular dystrophy.
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Affiliation(s)
| | - Ke Ma
- Department of Diabetes Complications & Metabolism, Arthur Riggs Diabetes & Metabolism Research Institute, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA;
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18
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Grotsch K, Sadybekov AV, Hiller S, Zaidi S, Eremin D, Le A, Liu Y, Smith EC, Illiopoulis-Tsoutsouvas C, Thomas J, Aggarwal S, Pickett JE, Reyes C, Picazo E, Roth BL, Makriyannis A, Katritch V, Fokin VV. Virtual Screening of a Chemically Diverse "Superscaffold" Library Enables Ligand Discovery for a Key GPCR Target. ACS Chem Biol 2024; 19:866-874. [PMID: 38598723 DOI: 10.1021/acschembio.3c00602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
The advent of ultra-large libraries of drug-like compounds has significantly broadened the possibilities in structure-based virtual screening, accelerating the discovery and optimization of high-quality lead chemotypes for diverse clinical targets. Compared to traditional high-throughput screening, which is constrained to libraries of approximately one million compounds, the ultra-large virtual screening approach offers substantial advantages in both cost and time efficiency. By expanding the chemical space with compounds synthesized from easily accessible and reproducible reactions and utilizing a large, diverse set of building blocks, we can enhance both the diversity and quality of the discovered lead chemotypes. In this study, we explore new chemical spaces using reactions of sulfur(VI) fluorides to create a combinatorial library consisting of several hundred million compounds. We screened this virtual library for cannabinoid type II receptor (CB2) antagonists using the high-resolution structure in conjunction with a rationally designed antagonist, AM10257. The top-predicted compounds were then synthesized and tested in vitro for CB2 binding and functional antagonism, achieving an experimentally validated hit rate of 55%. Our findings demonstrate the effectiveness of reliable reactions, such as sulfur fluoride exchange, in diversifying ultra-large chemical spaces and facilitate the discovery of new lead compounds for important biological targets.
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Affiliation(s)
- Katharina Grotsch
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Anastasiia V Sadybekov
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Sydney Hiller
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Saheem Zaidi
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Dmitry Eremin
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Austen Le
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Yongfeng Liu
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Evan Carlton Smith
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Christos Illiopoulis-Tsoutsouvas
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Joice Thomas
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Shubhangi Aggarwal
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Julie E Pickett
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Cesar Reyes
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Elias Picazo
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
| | - Bryan L Roth
- Department of Pharmacology, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill 27599, North Carolina, United States
- Psychoactive Drug Screening Program, National Institute of Mental Health, School of Medicine, University of North Carolina, Chapel Hill 27599, North Carolina, United States
| | - Alexandros Makriyannis
- Department of Pharmaceutical Sciences, Center for Drug Discovery, Boston 02115, Massachusetts, United States
- Department of Chemistry and Chemical Biology, Northeastern University, Boston 02115, Massachusetts, United States
| | - Vsevolod Katritch
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles 90089, California, United States
| | - Valery V Fokin
- Department of Chemistry, the Bridge Institute, University of Southern California, Los Angeles 90089, California, United States
- Loker Hydrocarbon Research Institute, University of Southern California, Los Angeles 90089, California, United States
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19
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Sun Y, Zhong M, Xu N, Zhang X, Sun H, Wang Y, Lu Y, Nie Y, Li Q, Sun Q, Jiang J, Tang YC, Chang HC. High-frequency neural activity dysregulation is associated with sleep and psychiatric disorders in BMAL1-deficient animal models. iScience 2024; 27:109381. [PMID: 38500822 PMCID: PMC10946332 DOI: 10.1016/j.isci.2024.109381] [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: 09/25/2023] [Revised: 01/29/2024] [Accepted: 02/27/2024] [Indexed: 03/20/2024] Open
Abstract
Sleep disturbance led by BMAL1-deficiency has been recognized both in rodent and non-human primate models. Yet it remained unclear how their diurnal brain oscillations were affected upon BMAL1 ablation and what caused the discrepancy in the quantity of sleep between the two species. Here, we investigated diurnal electroencephalographs of BMAL1-deficient mice and cynomolgus monkeys at young adult age and uncovered a shared defect of dysregulated high-frequency oscillations by Kullback-Leibler divergence analysis. We found beta and gamma oscillations were significantly disturbed in a day versus night manner in BMAL1-deficient monkeys, while in mice the beta band difference was less evident. Notably, the dysregulation of beta oscillations was particularly associated with psychiatric behaviors in BMAL1-deficient monkeys, including the occurrence of self-injuring and delusion-like actions. As such psychiatric phenotypes were challenging to uncover in rodent models, our results offered a unique method to study the correlation between circadian clock dysregulation and psychiatric disorders.
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Affiliation(s)
- Yu Sun
- Lingang Laboratory, Shanghai 201203, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingzhu Zhong
- Lingang Laboratory, Shanghai 201203, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Niannian Xu
- Lingang Laboratory, Shanghai 201203, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | | | - Yan Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yong Lu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanhong Nie
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qing Li
- Lingang Laboratory, Shanghai 201203, China
| | - Qiang Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jian Jiang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Hung-Chun Chang
- Lingang Laboratory, Shanghai 201203, China
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201210, China
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20
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Smith S, Cassada JB, Von Bredow L, Erreger K, Webb EM, Trombley TA, Kalbfleisch JJ, Bender BJ, Zagol-Ikapitte I, Kramlinger VM, Bouchard JL, Mitchell SG, Tretbar M, Shoichet BK, Lindsley CW, Meiler J, Hamm HE. Discovery of Protease-Activated Receptor 4 (PAR4)-Tethered Ligand Antagonists Using Ultralarge Virtual Screening. ACS Pharmacol Transl Sci 2024; 7:1086-1100. [PMID: 38633591 PMCID: PMC11020070 DOI: 10.1021/acsptsci.3c00378] [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: 12/19/2023] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 04/19/2024]
Abstract
Here, we demonstrate a structure-based small molecule virtual screening and lead optimization pipeline using a homology model of a difficult-to-drug G-protein-coupled receptor (GPCR) target. Protease-activated receptor 4 (PAR4) is activated by thrombin cleavage, revealing a tethered ligand that activates the receptor, making PAR4 a challenging target. A virtual screen of a make-on-demand chemical library yielded a one-hit compound. From the single-hit compound, we developed a novel series of PAR4 antagonists. Subsequent lead optimization via simultaneous virtual library searches and structure-based rational design efforts led to potent antagonists of thrombin-induced activation. Interestingly, this series of antagonists was active against PAR4 activation by the native protease thrombin cleavage but not the synthetic PAR4 agonist peptide AYPGKF.
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Affiliation(s)
- Shannon
T. Smith
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Jackson B. Cassada
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Lukas Von Bredow
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig 04109, Germany
| | - Kevin Erreger
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Emma M. Webb
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Trevor A. Trombley
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Jacob J. Kalbfleisch
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Brian J. Bender
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Irene Zagol-Ikapitte
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Valerie M. Kramlinger
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Jacob L. Bouchard
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Sidnee G. Mitchell
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Maik Tretbar
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig 04109, Germany
| | - Brian K. Shoichet
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Craig W. Lindsley
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Warren
Center for Neuroscience Drug Discovery, Nashville, Tennessee 37067, United States
| | - Jens Meiler
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Institute
for Drug Discovery, Leipzig University Medical
School, Leipzig 04109, Germany
| | - Heidi E. Hamm
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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21
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Wu Z, Wang C, Li C, Xu N, Cao X, Chen S, Shi Y, He Y, Zhang P, Ji J. Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides. J Phys Chem Lett 2024; 15:3748-3756. [PMID: 38551401 DOI: 10.1021/acs.jpclett.4c00393] [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: 04/12/2024]
Abstract
Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.
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Affiliation(s)
- Zhiyu Wu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Cong Wang
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Chen Li
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Nan Xu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Xiaoyong Cao
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Shengfu Chen
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yao Shi
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
| | - Yi He
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Peng Zhang
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Transvascular Implantation Devices, Qidi Road 456, Hangzhou 310058, China
| | - Jian Ji
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Transvascular Implantation Devices, Qidi Road 456, Hangzhou 310058, China
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22
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Zhao H. The Science and Art of Structure-Based Virtual Screening. ACS Med Chem Lett 2024; 15:436-440. [PMID: 38628791 PMCID: PMC11017385 DOI: 10.1021/acsmedchemlett.4c00093] [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: 02/27/2024] [Accepted: 03/11/2024] [Indexed: 04/19/2024] Open
Abstract
Structure-based virtual screening has gained momentum again as the high attrition rate at every stage of drug discovery drives the need to explore a greater chemical space. From the Bayesian perspective, its shortcomings as a viable strategy for sustainable hit discovery are discussed, with regard to the prior hit rates of screening libraries and the performance of computational methods. Lessons are shared in selecting virtual hits for experimental validation learned from a series of eight successful campaigns, one of which impacted the discovery of a drug candidate currently in clinical trials.
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Affiliation(s)
- Hongtao Zhao
- Medicinal Chemistry, Research and Early
Development, Respiratory and Immunology (R&I), BioPharmaceuticals
R&D, AstraZeneca, Gothenburg 43183, Sweden
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23
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Marin E, Kovaleva M, Kadukova M, Mustafin K, Khorn P, Rogachev A, Mishin A, Guskov A, Borshchevskiy V. Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking. J Chem Inf Model 2024; 64:2612-2623. [PMID: 38157481 PMCID: PMC11005039 DOI: 10.1021/acs.jcim.3c01661] [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: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.
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Affiliation(s)
- Egor Marin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Margarita Kovaleva
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Maria Kadukova
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- University
Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Khalid Mustafin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Polina Khorn
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Andrey Rogachev
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Joint
Institute for Nuclear Research, Dubna 141980, Russian
Federation
| | - Alexey Mishin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Albert Guskov
- Groningen
Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Valentin Borshchevskiy
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Joint
Institute for Nuclear Research, Dubna 141980, Russian
Federation
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24
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Dou X, Huo T, Liu Y, Pang Z, Su L, Zhao X, Peng X, Liu Z, Zhang L, Jiao N. Discovery of novel and selective farnesoid X receptor antagonists through structure-based virtual screening, preliminary structure-activity relationship study, and biological evaluation. Eur J Med Chem 2024; 269:116323. [PMID: 38547735 DOI: 10.1016/j.ejmech.2024.116323] [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: 12/26/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/07/2024]
Abstract
Farnesoid X receptor (FXR) is a bile acids receptor and plays a crucial role in regulating bile acids, lipids, and glucose metabolism. Previous research suggests that inhibiting FXR activation can be beneficial in reducing cholesterol and low-density lipoprotein cholesterol (LDL-C) levels, offering potential treatment options for metabolic syndrome with lipid disorders. Herein, we report p-acetylaminobenzene sulfonate derivatives as a novel scaffold of FXR antagonists by multistage screening. Among these derivatives, compound F44-A13 exhibited a half-maximal inhibitory concentration of 1.1 μM. Furthermore, compound F44-A13 demonstrated effective inhibition of FXR activation in cellular assays and exhibited high selectivity over eleven other nuclear receptors. Besides, compound F44-A13 significantly suppressed the regulation of FXR target genes Shp, Besp, and Cyp7a1, while reducing cholesterol levels in human hepatoma HepG2 cells. Pharmacological studies conducted on C57BL/6 mice further confirmed that compound F44-A13 had beneficial effects in reducing cholesterol, triglycerides, and LDL-C levels. These findings highlight that F44-A13 is a highly selective FXR antagonist that might serve as a useful molecule for further FXR studies as well as the development of FXR antagonists for the potential treatment of metabolic diseases with lipid disorders.
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Affiliation(s)
- Xiaodong Dou
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Tongyu Huo
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Yameng Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China; Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
| | - Zichen Pang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Lingyu Su
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Xinyi Zhao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Xing Peng
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Ning Jiao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China; Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China.
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25
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Okamoto HH, Cecon E, Nureki O, Rivara S, Jockers R. Melatonin receptor structure and signaling. J Pineal Res 2024; 76:e12952. [PMID: 38587234 DOI: 10.1111/jpi.12952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/05/2024] [Accepted: 03/24/2024] [Indexed: 04/09/2024]
Abstract
Melatonin (5-methoxy-N-acetyltryptamine) binds with high affinity and specificity to membrane receptors. Several receptor subtypes exist in different species, of which the mammalian MT1 and MT2 receptors are the best-characterized. They are members of the G protein-coupled receptor superfamily, preferentially coupling to Gi/o proteins but also to other G proteins in a cell-context-depending manner. In this review, experts on melatonin receptors will summarize the current state of the field. We briefly report on the discovery and classification of melatonin receptors, then focus on the molecular structure of human MT1 and MT2 receptors and highlight the importance of molecular simulations to identify new ligands and to understand the structural dynamics of these receptors. We then describe the state-of-the-art of the intracellular signaling pathways activated by melatonin receptors and their complexes. Brief statements on the molecular toolbox available for melatonin receptor studies and future perspectives will round-up this review.
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Affiliation(s)
- Hiroyuki H Okamoto
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Erika Cecon
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
| | - Osamu Nureki
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Silvia Rivara
- Department of Food and Drug, University of Parma, Parma, Italy
| | - Ralf Jockers
- Université Paris Cité, Institut Cochin, INSERM, CNRS, Paris, France
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26
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Sindt F, Seyller A, Eguida M, Rognan D. Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces. ACS CENTRAL SCIENCE 2024; 10:615-627. [PMID: 38559302 PMCID: PMC10979501 DOI: 10.1021/acscentsci.3c01521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 04/04/2024]
Abstract
Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.
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Affiliation(s)
- François Sindt
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | - Anthony Seyller
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | | | - Didier Rognan
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
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27
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Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL. AlphaFold2 structures template ligand discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.20.572662. [PMID: 38187536 PMCID: PMC10769324 DOI: 10.1101/2023.12.20.572662] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
- The Evnin Family Laboratory of Computational Molecular Discovery, The Rockefeller University, New York, NY 10065, USA (present address)
| | - Nicholas Kapolka
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Ryan Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Pharmacology Department, Yale School of Medicine, New Haven, CT 06510, USA (present address)
| | - Liang Wang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Manish K Jain
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kensuke Sakamoto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Yoojoong Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Jeffrey DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
| | - Kuglae Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- Department of Pharmacy, College of Pharmacy, Yonsei University, Incheon 21983, Korea (present address)
| | - Tia A Tummino
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Sijie Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Yurii Moroz
- Chemspace LLC, Kyiv, 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv, 01601, Ukraine
- Enamine Ltd., Kyiv, 02094, Ukraine
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, US
| | - Andrew C Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599-7365, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7360, USA
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28
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Liu F, Kaplan AL, Levring J, Einsiedel J, Tiedt S, Distler K, Omattage NS, Kondratov IS, Moroz YS, Pietz HL, Irwin JJ, Gmeiner P, Shoichet BK, Chen J. Structure-based discovery of CFTR potentiators and inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.09.557002. [PMID: 37745391 PMCID: PMC10515777 DOI: 10.1101/2023.09.09.557002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, while its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify novel CFTR modulators. We docked ~155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered novel mid-nanomolar potentiators as well as inhibitors that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.
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Affiliation(s)
- Fangyu Liu
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Anat Levit Kaplan
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Jesper Levring
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Jürgen Einsiedel
- Dept. of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Stephanie Tiedt
- Dept. of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Katharina Distler
- Dept. of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Natalie S Omattage
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- Current address: Department of Infectious Diseases, Genentech, Inc., South San Francisco, CA 94080, USA
| | - Ivan S Kondratov
- Enamine Ltd. (www.enamine.net), Chervonotkatska Street 78, Kyїv 02094, Ukraine
- V.P. Kukhar Institute of Bioorganic Chemistry & Petrochemistry, National Academy of Sciences of Ukraine, Murmanska Street 1, Kyїv 02660, Ukraine
| | - Yurii S Moroz
- Chemspace (www.chem-space.com), Chervonotkatska Street 85, Kyїv 02094, Ukraine
- Taras Shevchenko National University of Kyїv, Volodymyrska Street 60, Kyїv 01601, Ukraine
| | - Harlan L Pietz
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - John J Irwin
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Peter Gmeiner
- Dept. of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Brian K Shoichet
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Jue Chen
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
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29
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Liu F, Wu CG, Tu CL, Glenn I, Meyerowitz J, Levit Kaplan A, Lyu J, Cheng Z, Tarkhanova OO, Moroz YS, Irwin JJ, Chang W, Shoichet BK, Skiniotis G. Small vs. Large Library Docking for Positive Allosteric Modulators of the Calcium Sensing Receptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.27.573448. [PMID: 38234749 PMCID: PMC10793424 DOI: 10.1101/2023.12.27.573448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Drugs acting as positive allosteric modulators (PAMs) to enhance the activation of the calcium sensing receptor (CaSR) and to suppress parathyroid hormone (PTH) secretion can treat hyperparathyroidism but suffer from side effects including hypocalcemia and arrhythmias. Seeking new CaSR modulators, we docked libraries of 2.7 million and 1.2 billion molecules against transforming pockets in the active-state receptor dimer structure. Consistent with simulations suggesting that docking improves with library size, billion-molecule docking found new PAMs with a hit rate that was 2.7-fold higher than the million-molecule library and with hits up to 37-fold more potent. Structure-based optimization of ligands from both campaigns led to nanomolar leads, one of which was advanced to animal testing. This PAM displays 100-fold the potency of the standard of care, cinacalcet, in ex vivo organ assays, and reduces serum PTH levels in mice by up to 80% without the hypocalcemia typical of CaSR drugs. Cryo-EM structures with the new PAMs show that they induce residue rearrangements in the binding pockets and promote CaSR dimer conformations that are closer to the G-protein coupled state compared to established drugs. These findings highlight the promise of large library docking for therapeutic leads, especially when combined with experimental structure determination and mechanism.
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Affiliation(s)
- Fangyu Liu
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Cheng-Guo Wu
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chia-Ling Tu
- San Francisco VA Medical Center, Dept. of Medicine, University of California, San Francisco, San Francisco CA 94158, USA
| | - Isabella Glenn
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Justin Meyerowitz
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Anat Levit Kaplan
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Jiankun Lyu
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
- Current address: The Rockefeller University, New York, NY, 10065
| | - Zhiqiang Cheng
- San Francisco VA Medical Center, Dept. of Medicine, University of California, San Francisco, San Francisco CA 94158, USA
| | | | - Yurii S. Moroz
- Chemspace LLC, Kyiv, 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv, 01601, Ukraine
- Enamine Ltd., Kyiv, 02094, Ukraine
| | - John J. Irwin
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Wenhan Chang
- San Francisco VA Medical Center, Dept. of Medicine, University of California, San Francisco, San Francisco CA 94158, USA
| | - Brian K. Shoichet
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
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30
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Duran T, Chaudhuri B. Where Might Artificial Intelligence Be Going in Pharmaceutical Development? Mol Pharm 2024; 21:993-995. [PMID: 38376360 DOI: 10.1021/acs.molpharmaceut.4c00112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
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31
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Jia X, Song Y, Li Z, Yang N, Liu T, Han D, Sun Z, Shi C, Zhou Y, Shi J, Liu Y, Guo X. Melatonin regulates the circadian rhythm to ameliorate postoperative sleep disorder and neurobehavioral abnormalities in aged mice. CNS Neurosci Ther 2024; 30:e14436. [PMID: 37736695 PMCID: PMC10916446 DOI: 10.1111/cns.14436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 06/07/2023] [Accepted: 08/16/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Postoperative sleep disorder (PSD) and delirium, which may be associated with surgery and inhalational anesthetics, induce adverse effects in old adults. Emerging evidence indicates that circadian rhythm contributes to various neuropathological diseases, including Alzheimer's disease. Thus, we analyzed the potential role of circadian rhythm in PSD and delirium-like behavior in aged mice and determined whether exogenous melatonin could facilitate entrainment of the circadian rhythm after laparotomy under sevoflurane anesthesia. METHODS We selected old C57BL/6J mice which receiving laparotomy/sevoflurane anesthesia as model animals. We employed buried food, open field, and Y maze test to assess delirium-like behavior, and electroencephalography/electromyography (EEG/EMG) were used to investigate sleep changes. We analyzed the transcription rhythm of clock genes in superchiasmatic nucleus (SCN) to explore the effects of surgery and melatonin pretreatment on the circadian rhythm. Then, we measured melatonin receptor levels in SCN and ERK/CREB pathway-related proteins in hippocampus and prefrontal cortex to assess their role in PSDs and delirium-like behavior. RESULTS Laparotomy under sevoflurane anesthesia had a greater influence than sevoflurane alone, leading to sleep disorder, a shift in sleep-wake rhythm, and delirium-like behavior. Bmal1, Clock, and Cry1 mRNA expression showed a peak shift, MT1 melatonin receptor expression level was increased in the SCN, and p-ERK/ERK and p-CREB/CREB were decreased in hippocampus and prefrontal cortex of aged mice 1 day after laparotomy. Melatonin showed significant efficacy in ameliorating PSD and delirium-like behavior and restoring the circadian rhythm, reversing melatonin receptor and ERK/CREB pathway expression abnormalities. In addition, most of the beneficial effect of melatonin was antagonized by luzindole, a melatonin receptor antagonist. CONCLUSIONS Melatonin receptors in SCN, circadian rhythm, and ERK/CREB signaling pathway participate in the pathophysiological processes of PSD and delirium-like behavior. Melatonin intervention could be a potential preventative approach for PSD and delirium.
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Affiliation(s)
- Xixi Jia
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Yanan Song
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Zhengqian Li
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Ning Yang
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Taotao Liu
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Dengyang Han
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Zhuonan Sun
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Chengmei Shi
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Yang Zhou
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug DependencePeking UniversityBeijingChina
| | - Yajie Liu
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
| | - Xiangyang Guo
- Department of AnesthesiologyPeking University Third HospitalBeijingChina
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Tummino TA, Iliopoulos-Tsoutsouvas C, Braz JM, O'Brien ES, Stein RM, Craik V, Tran NK, Ganapathy S, Liu F, Shiimura Y, Tong F, Ho TC, Radchenko DS, Moroz YS, Rosado SR, Bhardwaj K, Benitez J, Liu Y, Kandasamy H, Normand C, Semache M, Sabbagh L, Glenn I, Irwin JJ, Kumar KK, Makriyannis A, Basbaum AI, Shoichet BK. Large library docking for cannabinoid-1 receptor agonists with reduced side effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.27.530254. [PMID: 38328157 PMCID: PMC10849508 DOI: 10.1101/2023.02.27.530254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Large library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking new agonists for the cannabinoid-1 receptor (CB1R), we docked 74 million tangible molecules, prioritizing 46 high ranking ones for de novo synthesis and testing. Nine were active by radioligand competition, a 20% hit-rate. Structure-based optimization of one of the most potent of these (Ki = 0.7 uM) led to '4042, a 1.9 nM ligand and a full CB1R agonist. A cryo-EM structure of the purified enantiomer of '4042 ('1350) in complex with CB1R-Gi1 confirmed its docked pose. The new agonist was strongly analgesic, with generally a 5-10-fold therapeutic window over sedation and catalepsy and no observable conditioned place preference. These findings suggest that new cannabinoid chemotypes may disentangle characteristic cannabinoid side-effects from their analgesia, supporting the further development of cannabinoids as pain therapeutics.
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Cheng C, Beroza P. Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces. J Chem Inf Model 2024; 64:1251-1260. [PMID: 38335044 DOI: 10.1021/acs.jcim.3c01865] [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: 02/12/2024]
Abstract
Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.
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Affiliation(s)
- Chen Cheng
- Discovery Chemistry, Genentech, South San Francisco, California 94080, United States
| | - Paul Beroza
- Discovery Chemistry, Genentech, South San Francisco, California 94080, United States
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34
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Yao H, Wang X, Chi J, Chen H, Liu Y, Yang J, Yu J, Ruan Y, Xiang X, Pi J, Xu JF. Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures. Molecules 2024; 29:964. [PMID: 38474476 DOI: 10.3390/molecules29050964] [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: 11/17/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.
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Affiliation(s)
- Hanbo Yao
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Xiaodong Wang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaxin Chi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Haorong Chen
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yilin Liu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiayi Yang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaqi Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yongdui Ruan
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
| | - Xufu Xiang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiang Pi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jun-Fa Xu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
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Knight IS, Mailhot O, Tang KG, Irwin JJ. DockOpt: A Tool for Automatic Optimization of Docking Models. J Chem Inf Model 2024; 64:1004-1016. [PMID: 38206771 PMCID: PMC10865354 DOI: 10.1021/acs.jcim.3c01406] [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: 09/02/2023] [Revised: 12/17/2023] [Accepted: 12/26/2023] [Indexed: 01/13/2024]
Abstract
Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.
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Affiliation(s)
- Ian S. Knight
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - Olivier Mailhot
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - Khanh G. Tang
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
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36
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Li H, Sun X, Cui W, Xu M, Dong J, Ekundayo BE, Ni D, Rao Z, Guo L, Stahlberg H, Yuan S, Vogel H. Computational drug development for membrane protein targets. Nat Biotechnol 2024; 42:229-242. [PMID: 38361054 DOI: 10.1038/s41587-023-01987-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/13/2023] [Indexed: 02/17/2024]
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
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Affiliation(s)
- Haijian Li
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Xiaolin Sun
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Wenqiang Cui
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Marc Xu
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junlin Dong
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Babatunde Edukpe Ekundayo
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zhili Rao
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Liwei Guo
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Shuguang Yuan
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
| | - Horst Vogel
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
- Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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Balius TE, Tan YS, Chakrabarti M. DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking. J Comput Chem 2024; 45:47-63. [PMID: 37743732 DOI: 10.1002/jcc.27218] [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: 06/01/2023] [Accepted: 08/17/2023] [Indexed: 09/26/2023]
Abstract
To allow DOCK 6 access to unprecedented chemical space for screening billions of small molecules, we have implemented features from DOCK 3.7 into DOCK 6, including a search routine that traverses precomputed ligand conformations stored in a hierarchical database. We tested them on the DUDE-Z and SB2012 test sets. The hierarchical database search routine is 16 times faster than anchor-and-grow. However, the ability of hierarchical database search to reproduce the experimental pose is 16% worse than that of anchor-and-grow. The enrichment performance is on average similar, but DOCK 3.7 has better enrichment than DOCK 6, and DOCK 6 is on average 1.7 times slower. However, with post-docking torsion minimization, DOCK 6 surpasses DOCK 3.7. A large-scale virtual screen is performed with DOCK 6 on 23 million fragment molecules. We use current features in DOCK 6 to complement hierarchical database calculations, including torsion minimization, which is not available in DOCK 3.7.
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Affiliation(s)
- Trent E Balius
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
| | - Y Stanley Tan
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
| | - Mayukh Chakrabarti
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
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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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Wang D, Guo Q, Wu Z, Li M, He B, Du Y, Zhang K, Tao Y. Molecular mechanism of antihistamines recognition and regulation of the histamine H 1 receptor. Nat Commun 2024; 15:84. [PMID: 38167898 PMCID: PMC10762250 DOI: 10.1038/s41467-023-44477-4] [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: 06/02/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024] Open
Abstract
Histamine receptors are a group of G protein-coupled receptors (GPCRs) that play important roles in various physiological and pathophysiological conditions. Antihistamines that target the histamine H1 receptor (H1R) have been widely used to relieve the symptoms of allergy and inflammation. Here, to uncover the details of the regulation of H1R by the known second-generation antihistamines, thereby providing clues for the rational design of newer antihistamines, we determine the cryo-EM structure of H1R in the apo form and bound to different antihistamines. In addition to the deep hydrophobic cavity, we identify a secondary ligand-binding site in H1R, which potentially may support the introduction of new derivative groups to generate newer antihistamines. Furthermore, these structures show that antihistamines exert inverse regulation by utilizing a shared phenyl group that inserts into the deep cavity and block the movement of the toggle switch residue W4286.48. Together, these results enrich our understanding of GPCR modulation and facilitate the structure-based design of novel antihistamines.
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Affiliation(s)
- Dandan Wang
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, 230027, Hefei, P. R. China
| | - Qiong Guo
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, 230027, Hefei, P. R. China
| | - Zhangsong Wu
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China
| | - Ming Li
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, 230027, Hefei, P. R. China
| | - Binbin He
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, 230027, Hefei, P. R. China
| | - Yang Du
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China
| | - Kaiming Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, 230027, Hefei, P. R. China.
| | - Yuyong Tao
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, 230027, Hefei, P. R. China.
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40
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Dou X, Sun Q, Liu Y, Lu Y, Zhang C, Xu G, Xu Y, Huo T, Zhao X, Su L, Xing Y, Lai L, Jiao N. Discovery of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as SARS-CoV-2 main protease inhibitors through virtual screening and biological evaluation. Bioorg Med Chem Lett 2024; 97:129547. [PMID: 37944867 DOI: 10.1016/j.bmcl.2023.129547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/12/2023]
Abstract
The COVID-19 caused by SARS-CoV-2 has led to a global pandemic that continues to impact societies and economies worldwide. The main protease (Mpro) plays a crucial role in SARS-CoV-2 replication and is an attractive target for anti-SARS-CoV-2 drug discovery. Herein, we report a series of 3-oxo-1,2,3,4-tetrahydropyrido[1,2-a]pyrazin derivatives as non-peptidomimetic inhibitors targeting SARS-CoV-2 Mpro through structure-based virtual screening and biological evaluation. Further similarity search and structure-activity relationship study led to the identification of compound M56-S2 with the enzymatic IC50 value of 4.0 μM. Moreover, the molecular simulation and predicted ADMET properties, indicated that non-peptidomimetic inhibitor M56-S2 might serve as a useful starting point for the further discovery of highly potent inhibitors targeting SARS-CoV-2 Mpro.
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Affiliation(s)
- Xiaodong Dou
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Qi Sun
- BNLMS, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yameng Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China; Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
| | - Yangbin Lu
- BNLMS, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Caifang Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Guofeng Xu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Yue Xu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Tongyu Huo
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Xinyi Zhao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Lingyu Su
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Yihong Xing
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Luhua Lai
- BNLMS, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
| | - Ning Jiao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China; Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China.
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41
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Popov KI, Wellnitz J, Maxfield T, Tropsha A. HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries. Mol Inform 2024; 43:e202300207. [PMID: 37802967 PMCID: PMC11156482 DOI: 10.1002/minf.202300207] [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: 08/16/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 10/08/2023]
Abstract
Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.
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Affiliation(s)
- Konstantin I. Popov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - James Wellnitz
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - Travis Maxfield
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
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42
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Zhou H, Skolnick J. FRAGSITE2: A structure and fragment-based approach for virtual ligand screening. Protein Sci 2024; 33:e4869. [PMID: 38100293 PMCID: PMC10751727 DOI: 10.1002/pro.4869] [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: 08/17/2023] [Revised: 12/06/2023] [Accepted: 12/09/2023] [Indexed: 12/17/2023]
Abstract
Protein function annotation and drug discovery often involve finding small molecule binders. In the early stages of drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing. While our recent ligand homology modeling (LHM)-machine learning VLS method FRAGSITE outperformed approaches that combined traditional docking to generate protein-ligand poses and deep learning scoring functions to rank ligands, a more robust approach that could identify a more diverse set of binding ligands is needed. Here, we describe FRAGSITE2 that shows significant improvement on protein targets lacking known small molecule binders and no confident LHM identified template ligands when benchmarked on two commonly used VLS datasets: For both the DUD-E set and DEKOIS2.0 set and ligands having a Tanimoto coefficient (TC) < 0.7 to the template ligands, the 1% enrichment factor (EF1% ) of FRAGSITE2 is significantly better than those for FINDSITEcomb2.0 , an earlier LHM algorithm. For the DUD-E set, FRAGSITE2 also shows better ROC enrichment factor and AUPR (area under the precision-recall curve) than the deep learning DenseFS scoring function. Comparison with the RF-score-VS on the 76 target subset of DEKOIS2.0 and a TC < 0.99 to training DUD-E ligands, FRAGSITE2 has double the EF1% . Its boosted tree regression method provides for more robust performance than a deep learning multiple layer perceptron method. When compared with the pretrained language model for protein target features, FRAGSITE2 also shows much better performance. Thus, FRAGSITE2 is a promising approach that can discover novel hits for protein targets. FRAGSITE2's web service is freely available to academic users at http://sites.gatech.edu/cssb/FRAGSITE2.
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Affiliation(s)
- Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of TechnologyAtlantaGeorgiaUSA
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43
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Feng Y, Jiang X, Liu W, Lu H. The location, physiology, pathology of hippocampus Melatonin MT 2 receptor and MT 2-selective modulators. Eur J Med Chem 2023; 262:115888. [PMID: 37866336 DOI: 10.1016/j.ejmech.2023.115888] [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: 08/27/2023] [Revised: 10/06/2023] [Accepted: 10/17/2023] [Indexed: 10/24/2023]
Abstract
Melatonin, a neurohormone secreted by the pineal gland and regulated by the suprachiasmatic nucleus (SCN) of the hypothalamus, is synthesized and directly released into the cerebrospinal fluid (CSF) of the third ventricle (3rdv), where it undergoes rapid absorption by surrounding tissues to exert its physiological function. The hippocampus, a vital structure in the limbic system adjacent to the ventricles, plays a pivotal role in emotional response and memory formation. Melatonin MT1 and MT2 receptors are G protein-coupled receptors (GPCRs) that primarily mediate melatonin's receptor-dependent effects. In comparison to the MT1 receptor, the widely expressed MT2 receptor is crucial for mediating melatonin's biological functions within the hippocampus. Specifically, MT2 receptor is implicated in hippocampal synaptic plasticity and memory processes, as well as neurogenesis and axogenesis. Numerous studies have demonstrated the involvement of MT2 receptors in the pathophysiology and pharmacology of Alzheimer's disease, depression, and epilepsy. This review focuses on the anatomical localization of MT2 receptor in the hippocampus, their physiological function in this region, and their signal transduction and pharmacological roles in neurological disorders. Additionally, we conducted a comprehensive review of MT2 receptor ligands used in psychopharmacology and other MT2-selective ligands over recent years. Ultimately, we provide an outlook on future research for selective MT2 receptor drug candidates.
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Affiliation(s)
- Yueqin Feng
- Department of Ultrasound, the First Affiliated Hospital of China Medical University, Shenyang, PR China
| | - Xiaowen Jiang
- School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, PR China
| | - Wenwu Liu
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, PR China
| | - Hongyuan Lu
- Department of Clinical Pharmacology, China Medical University, Shenyang, PR China.
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44
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Gahbauer S, DeLeon C, Braz JM, Craik V, Kang HJ, Wan X, Huang XP, Billesbølle CB, Liu Y, Che T, Deshpande I, Jewell M, Fink EA, Kondratov IS, Moroz YS, Irwin JJ, Basbaum AI, Roth BL, Shoichet BK. Docking for EP4R antagonists active against inflammatory pain. Nat Commun 2023; 14:8067. [PMID: 38057319 PMCID: PMC10700596 DOI: 10.1038/s41467-023-43506-6] [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: 03/23/2023] [Accepted: 11/12/2023] [Indexed: 12/08/2023] Open
Abstract
The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.
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Affiliation(s)
- Stefan Gahbauer
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Chelsea DeLeon
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
| | - Joao M Braz
- Department of Anatomy, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Veronica Craik
- Department of Anatomy, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Hye Jin Kang
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, South Korea
| | - Xiaobo Wan
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Xi-Ping Huang
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
| | - Christian B Billesbølle
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Yongfeng Liu
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
| | - Tao Che
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA
- Center of Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ishan Deshpande
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Madison Jewell
- Department of Anatomy, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Elissa A Fink
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ivan S Kondratov
- Enamine Ltd., Kyiv, Ukraine
- V.P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Yurii S Moroz
- Chemspace LLC, Kyiv, Ukraine
- National Taras Shevchenko University of Kyiv, Kyiv, Ukraine
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Allan I Basbaum
- Department of Anatomy, University of California San Francisco, San Francisco, CA, 94158, USA.
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA.
- National Institute of Mental Health Psychoactive Drug Screening Program, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, 27514, USA.
- Division of Chemical Biology and Medicinal Chemistry, University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, NC, 27514, USA.
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA.
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45
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Moesgaard L, Pedersen ML, Uhd Nielsen C, Kongsted J. Structure-based discovery of novel P-glycoprotein inhibitors targeting the nucleotide binding domains. Sci Rep 2023; 13:21217. [PMID: 38040777 PMCID: PMC10692163 DOI: 10.1038/s41598-023-48281-4] [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: 09/22/2023] [Accepted: 11/24/2023] [Indexed: 12/03/2023] Open
Abstract
P-glycoprotein (P-gp), a membrane transport protein overexpressed in certain drug-resistant cancer cells, has been the target of numerous drug discovery projects aimed at overcoming drug resistance in cancer. Most characterized P-gp inhibitors bind at the large hydrophobic drug binding domain (DBD), but none have yet attained regulatory approval. In this study, we explored the potential of designing inhibitors that target the nucleotide binding domains (NBDs), by computationally screening a large library of 2.6 billion synthesizable molecules, using a combination of machine learning-guided molecular docking and molecular dynamics (MD). 14 of the computationally best-scoring molecules were subsequently tested for their ability to inhibit P-gp mediated calcein-AM efflux. In total, five diverse compounds exhibited inhibitory effects in the calcein-AM assay without displaying toxicity. The activity of these compounds was confirmed by their ability to decrease the verapamil-stimulated ATPase activity of P-gp in a subsequent assay. The discovery of these five novel P-gp inhibitors demonstrates the potential of in-silico screening in drug discovery and provides a new stepping point towards future potent P-gp inhibitors.
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Affiliation(s)
- Laust Moesgaard
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, 5230, Denmark.
| | - Maria L Pedersen
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, 5230, Denmark
| | - Carsten Uhd Nielsen
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, 5230, Denmark
| | - Jacob Kongsted
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, 5230, Denmark
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Jin J, Han W, Yang T, Xu Z, Zhang J, Cao R, Wang Y, Wang J, Hu X, Gu T, He F, Huang J, Li G. Artificial light at night, MRI-based measures of brain iron deposition and incidence of multiple mental disorders. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166004. [PMID: 37544462 DOI: 10.1016/j.scitotenv.2023.166004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/21/2023] [Accepted: 08/01/2023] [Indexed: 08/08/2023]
Abstract
BACKGROUND Epidemiologic evidence on whether iron accumulation in brain modified the association between artificial light at night (ALAN) and incident mental disorders is lacking. The authors aims to investigate modification of brain iron deposition on the associations of ALAN with multiple mental disorders in the middle-aged and older adults. METHODS This prospective study used data from the UK Biobank. ALAN was drawn from satellite datasets. Susceptibility-weighted magnetic resonance imaging was used to ascertain iron content of each brain region. T2* signal loss was used as indices of iron deposition. The main outcomes are impacts of ALAN exposure on onset of wide spectrum of physician-diagnosed mental disorders, which was estimated by time-varying Cox proportional hazard model. The authors further conducted stratified analyses by levels of iron brain deposition to examine the potential modifying effects. RESULTS Among 298,283 participants followed for a median of 10.91 years, higher ALAN exposure was associated with increased risk of mental disorders. An IQR (11.37 nW/cm2/sr) increase in annual levels of ALAN was associated with an HR of 1.050 (95 % CI: 1.034,1.066) for any mental disorder, 1.076 (95 % CI: 1.053,1.099) for substance use disorder, and 1.036 (95 % CI: 1.004,1.069) for depression disorder in fully adjusted models. The exposure-response curves showed steeper trends at lower ALAN levels and a plateau at higher exposures. The associations were stronger in participants with high iron deposition in left hippocampus, left accumbens and left pallidum. CONCLUSIONS ALAN was associated with multiple mental disorders in the middle-aged and older adults, and the findings indicated stricter standards of ALAN is needed and targeted preventive measures are warranted, especially with high brain iron deposition.
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Affiliation(s)
- Jianbo Jin
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Wenxing Han
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Teng Yang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Zhihu Xu
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Jin Zhang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Ru Cao
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Yuxin Wang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Jiawei Wang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Xin Hu
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Tiantian Gu
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China.
| | - Fan He
- Beijing Anding Hospital, Capital Medical University, Beijing, China.
| | - Jing Huang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China; Peking University Institute for Global Health and Development, Beijing, China.
| | - Guoxing Li
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China; Environmental Research Group, School of Public Health, Imperial college London, London, UK.
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Jiao X, Peng X, Jin X, Liu N, Yu Y, Liu R, Li Z. Nano-composite system of traditional Chinese medicine for ocular applications: molecular docking and three-dimensional modeling insight for intelligent drug evaluation. Drug Deliv Transl Res 2023; 13:3132-3144. [PMID: 37355484 DOI: 10.1007/s13346-023-01376-x] [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] [Accepted: 05/30/2023] [Indexed: 06/26/2023]
Abstract
The absorption of drugs was impeded in the posterior part of the eye due to the special structure. In addition, it was crucial to comprehend transport laws of molecules in ocular drug delivery for designing effective strategies. However, the current quality evaluation methods of the eye were backward and lack of dynamic monitoring of drug processes in vivo. Herein, nano-drug delivery system and three-dimensional (3D) model were combined to overcome the problems of low bioavailability and diffusion law. The model drugs were screened by molecular docking. The flexible nano-liposome (FNL) and temperature-sensitive gel (TSG) composite formulation was characterized through comprehensive evaluation. COMSOL software was utilized to build 3D eyeball to predict the bioavailability of drugs. The size of the preparation was about 98.34 nm which is relatively optimal for the enhanced permeability of the eyes. The formulation showed a stronger safety and non-irritant. The pharmacokinetics results of aqueous humor showed that the AUC of two drugs in this system increased by 3.79 and 3.94 times, respectively. The results of 3D calculation model proved that the concentrations of drugs reaching the retina were 1.90×10-5 mol/m3 and 6.37×10-6 mol/m3. In conclusion, the FNL-TSG markedly improved the bioavailability of multiple components in the eye. More importantly, a simplified 3D model was developed to preliminarily forecast the bioavailability of the retina after drug infusion, providing technical support for the accurate evaluation of ocular drug delivery. It provided new pattern for the development of intelligent versatile ophthalmic preparations.
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Affiliation(s)
- Xinyi Jiao
- State Key Laboratory of Component‑Based Chinese Medicine, College of Pharmaceutical Engineering of Traditional Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Xingru Peng
- State Key Laboratory of Component‑Based Chinese Medicine, College of Pharmaceutical Engineering of Traditional Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Xin Jin
- Military Medicine Section, Dongli District, Logistics University of People's Armed Police Force, 1 Huizhihuan Road, Tianjin, 300309, China
| | - Ning Liu
- State Key Laboratory of Component‑Based Chinese Medicine, College of Pharmaceutical Engineering of Traditional Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yang Yu
- State Key Laboratory of Component‑Based Chinese Medicine, College of Pharmaceutical Engineering of Traditional Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Rui Liu
- State Key Laboratory of Component‑Based Chinese Medicine, College of Pharmaceutical Engineering of Traditional Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
| | - Zheng Li
- State Key Laboratory of Component‑Based Chinese Medicine, College of Pharmaceutical Engineering of Traditional Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
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48
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Ahmed F, Brooks CL. FASTDock: A Pipeline for Allosteric Drug Discovery. J Chem Inf Model 2023; 63:7219-7227. [PMID: 37939386 PMCID: PMC10773972 DOI: 10.1021/acs.jcim.3c00895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.
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Affiliation(s)
- Furyal Ahmed
- Biophysics Program, University of Michigan, Ann Arbor, MI 48103
| | - Charles L. Brooks
- Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, MI 48103
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49
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Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. J Chem Theory Comput 2023; 19:7478-7495. [PMID: 37883810 PMCID: PMC10653122 DOI: 10.1021/acs.jctc.3c00814] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.
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Affiliation(s)
- Song Xia
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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50
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Tran-Nguyen VK, Junaid M, Simeon S, Ballester PJ. A practical guide to machine-learning scoring for structure-based virtual screening. Nat Protoc 2023; 18:3460-3511. [PMID: 37845361 DOI: 10.1038/s41596-023-00885-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/03/2023] [Indexed: 10/18/2023]
Abstract
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in size. Artificial intelligence and, more concretely, its machine-learning (ML) branch, including deep learning, have effectively exploited these data sets to build scoring functions (SFs) for SBVS against targets with an atomic-resolution 3D model (e.g., generated by X-ray crystallography or predicted by AlphaFold2). Often outperforming their generic and non-ML counterparts, target-specific ML-based SFs represent the state of the art for SBVS. Here, we present a comprehensive and user-friendly protocol to build and rigorously evaluate these new SFs for SBVS. This protocol is organized into four sections: (i) using a public benchmark of a given target to evaluate an existing generic SF; (ii) preparing experimental data for a target from public repositories; (iii) partitioning data into a training set and a test set for subsequent target-specific ML modeling; and (iv) generating and evaluating target-specific ML SFs by using the prepared training-test partitions. All necessary code and input/output data related to three example targets (acetylcholinesterase, HMG-CoA reductase, and peroxisome proliferator-activated receptor-α) are available at https://github.com/vktrannguyen/MLSF-protocol , can be run by using a single computer within 1 week and make use of easily accessible software/programs (e.g., Smina, CNN-Score, RF-Score-VS and DeepCoy) and web resources. Our aim is to provide practical guidance on how to augment training data to enhance SBVS performance, how to identify the most suitable supervised learning algorithm for a data set, and how to build an SF with the highest likelihood of discovering target-active molecules within a given compound library.
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Affiliation(s)
| | - Muhammad Junaid
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Saw Simeon
- Centre de Recherche en Cancérologie de Marseille, Marseille, France
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