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Marciniec K, Nowakowska J, Chrobak E, Bębenek E, Latocha M. Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides-Triazole Hybrids with Anticancer Activity. Molecules 2024; 29:3158. [PMID: 38999109 PMCID: PMC11243625 DOI: 10.3390/molecules29133158] [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: 06/07/2024] [Revised: 06/18/2024] [Accepted: 06/28/2024] [Indexed: 07/14/2024] Open
Abstract
In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed and synthesised. The title compounds were designed using molecular modelling techniques. For this purpose, machine-learning, molecular docking, and molecular dynamics methods were used. Calculations of the pharmacokinetic parameters (connected with absorption, distribution, metabolism, excretion, and toxicity) of the hybrids were also performed. The new compounds were synthesised via a copper-catalysed azide-alkyne cycloaddition reaction (CuAAC). 8-N-Methyl-N-{[1-(7-chloroquinolin-4-yl)-1H-1,2,3-triazol-4-yl]methyl}quinolinesulfonamide was identified in in silico studies as a potential strong inhibitor of Rho-associated protein kinase and as a compound that has an appropriate pharmacokinetic profile. The results obtained from in vitro experiments confirm the cytotoxicity of derivative 9b in four selected cancer cell lines and the lack of cytotoxicity of this derivative towards normal cells. The results obtained from silico and in vitro experiments indicate that the introduction of another quinolinyl fragment into the inhibitor molecule may have a significant impact on increasing the level of cytotoxicity toward cancer cells and indicate a further direction for future research in order to find new substances suitable for clinical applications in cancer treatment.
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Affiliation(s)
- Krzysztof Marciniec
- Department of Organic Chemistry, Medical University of Silesia, Jagiellońska 4, 41-200 Sosnowiec, Poland; (J.N.); (E.C.); (E.B.)
| | - Justyna Nowakowska
- Department of Organic Chemistry, Medical University of Silesia, Jagiellońska 4, 41-200 Sosnowiec, Poland; (J.N.); (E.C.); (E.B.)
| | - Elwira Chrobak
- Department of Organic Chemistry, Medical University of Silesia, Jagiellońska 4, 41-200 Sosnowiec, Poland; (J.N.); (E.C.); (E.B.)
| | - Ewa Bębenek
- Department of Organic Chemistry, Medical University of Silesia, Jagiellońska 4, 41-200 Sosnowiec, Poland; (J.N.); (E.C.); (E.B.)
| | - Małgorzata Latocha
- Department of Molecular Biology, Jagiellońska 4, 41-200 Sosnowiec, Poland;
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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Lester A, Sandman M, Herring C, Girard C, Dixon B, Ramsdell H, Reber C, Poulos J, Mitchell A, Spinney A, Henager ME, Evans CN, Turlington M, Johnson QR. Computational Exploration of Potential CFTR Binding Sites for Type I Corrector Drugs. Biochemistry 2023; 62:2503-2515. [PMID: 37437308 PMCID: PMC10433520 DOI: 10.1021/acs.biochem.3c00165] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/22/2023] [Indexed: 07/14/2023]
Abstract
Cystic fibrosis (CF) is a recessive genetic disease that is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) protein. The recent development of a class of drugs called "correctors", which repair the structure and function of mutant CFTR, has greatly enhanced the life expectancy of CF patients. These correctors target the most common disease causing CFTR mutant F508del and are exemplified by the FDA-approved VX-809. While one binding site of VX-809 to CFTR was recently elucidated by cryo-electron microscopy, four additional binding sites have been proposed in the literature and it has been theorized that VX-809 and structurally similar correctors may engage multiple CFTR binding sites. To explore these five binding sites, ensemble docking was performed on wild-type CFTR and the F508del mutant using a large library of structurally similar corrector drugs, including VX-809 (lumacaftor), VX-661 (tezacaftor), ABBV-2222 (galicaftor), and a host of other structurally related molecules. For wild-type CFTR, we find that only one site, located in membrane spanning domain 1 (MSD1), binds favorably to our ligand library. While this MSD1 site also binds our ligand library for F508del-CFTR, the F508del mutation also opens a binding site in nucleotide binding domain 1 (NBD1), which enables strong binding of our ligand library to this site. This NBD1 site in F508del-CFTR exhibits the strongest overall binding affinity for our library of corrector drugs. This data may serve to better understand the structural changes induced by mutation of CFTR and how correctors bind to the protein. Additionally, it may aid in the design of new, more effective CFTR corrector drugs.
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Affiliation(s)
- Anna Lester
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Madeline Sandman
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Caitlin Herring
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Christian Girard
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Brandon Dixon
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Havanna Ramsdell
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Callista Reber
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Jack Poulos
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Alexis Mitchell
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Allison Spinney
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Marissa E. Henager
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Claudia N. Evans
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Mark Turlington
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
| | - Quentin R. Johnson
- Berry College Department
of Chemistry and Biochemistry, Mount Berry, Georgia 30149, United States
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Chandak T, Wong CF. EDock-ML: A web server for using ensemble docking with machine learning to aid drug discovery. Protein Sci 2021; 30:1087-1097. [PMID: 33733530 DOI: 10.1002/pro.4065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/07/2021] [Accepted: 03/15/2021] [Indexed: 01/08/2023]
Abstract
EDock-ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock-ML takes a bottom-up approach in which machine-learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine-learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324-2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock-ML can be accessed freely at edock-ml.umsl.edu.
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Affiliation(s)
- Tanay Chandak
- Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, USA
| | - Chung F Wong
- Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, USA
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