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Zeng Q, Hu H, Huang Z, Guo A, Lu S, Tong W, Zhang Z, Shen T. Active and machine learning-enhanced discovery of new FGFR3 inhibitor, Rhapontin, through virtual screening of receptor structures and anti-cancer activity assessment. Front Mol Biosci 2024; 11:1413214. [PMID: 38919748 PMCID: PMC11196408 DOI: 10.3389/fmolb.2024.1413214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 05/23/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction: This study bridges traditional remedies and modern pharmacology by exploring the synergy between natural compounds and Ceritinib in treating Non-Small Cell Lung Cancer (NSCLC), aiming to enhance efficacy and reduce toxicities. Methods: Using a combined approach of computational analysis, machine learning, and experimental procedures, we identified and analyzed PD173074, Isoquercitrin, and Rhapontin as potential inhibitors of fibroblast growth factor receptor 3 (FGFR3). Machine learning algorithms guided the initial selection, followed by Quantitative Structure-Activity Relationship (QSAR) modeling and molecular dynamics simulations to evaluate the interaction dynamics and stability of Rhapontin. Physicochemical assessments further verified its drug-like properties and specificity. Results: Our experiments demonstrate that Rhapontin, when combined with Ceritinib, significantly suppresses tumor activity in NSCLC while sparing healthy cells. The molecular simulations and physicochemical evaluations confirm Rhapontin's stability and favorable interaction with FGFR3, highlighting its potential as an effective adjunct in NSCLC therapy. Discussion: The integration of natural compounds with established cancer therapies offers a promising avenue for enhancing treatment outcomes in NSCLC. By combining the ancient wisdom of natural remedies with the precision of modern science, this study contributes to evolving cancer treatment paradigms, potentially mitigating the side effects associated with current therapies.
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Affiliation(s)
- Qingxin Zeng
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haichuan Hu
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhengwei Huang
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Aotian Guo
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sheng Lu
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenbin Tong
- Department of Thoracic Surgery, Longyou County People’s Hospital, Hangzhou, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Hangzhou, China
| | - Tao Shen
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Sahoo S, Lee HK, Shin D. Structure-based virtual screening and molecular dynamics studies to explore potential natural inhibitors against 3C protease of foot-and-mouth disease virus. Front Vet Sci 2024; 10:1340126. [PMID: 38298458 PMCID: PMC10827980 DOI: 10.3389/fvets.2023.1340126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 12/27/2023] [Indexed: 02/02/2024] Open
Abstract
Foot-and-mouth disease (FMD) is a highly infectious animal disease caused by foot-and-mouth disease virus (FMDV) and primarily infects cloven-hoofed animals such as cattle, sheep, goats, and pigs. It has become a significant health concern in global livestock industries because of diverse serotypes, high mutation rates, and contagious nature. There is no specific antiviral treatment available for FMD. Hence, based on the importance of 3C protease in FMDV viral replication and pathogenesis, we have employed a structure-based virtual screening method by targeting 3C protease with a natural compounds dataset (n = 69,040) from the InterBioScreen database. Virtual screening results identified five potential compounds, STOCK1N-62634, STOCK1N-96109, STOCK1N-94672, STOCK1N-89819, and STOCK1N-80570, with a binding affinity of -9.576 kcal/mol, -8.1 kcal/mol, -7.744 kcal/mol, -7.647 kcal/mol, and - 7.778 kcal/mol, respectively. The compounds were further validated through physiochemical properties and density functional theory (DFT). Subsequently, the comparative 300-ns MD simulation of all five complexes exhibited overall structural stability from various MD analyses such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), H-bonds, principal component analysis (PCA), and free energy landscape (FEL). Furthermore, MM-PBSA calculation suggests that all five compounds, particularly STOCK1N-62634, STOCK1N-96109, and STOCK1N-94672, can be considered as potential inhibitors because of their strong binding affinity toward 3C protease. Thus, we hope that these identified compounds can be studied extensively to develop natural therapeutics for the better management of FMD.
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Affiliation(s)
- Sthitaprajna Sahoo
- Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Hak-Kyo Lee
- Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju, Republic of Korea
- Department of Animal Biotechnology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Donghyun Shin
- Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju, Republic of Korea
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Kim MJ, Martin CA, Kim J, Jablonski MM. Computational methods in glaucoma research: Current status and future outlook. Mol Aspects Med 2023; 94:101222. [PMID: 37925783 PMCID: PMC10842846 DOI: 10.1016/j.mam.2023.101222] [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: 08/01/2023] [Revised: 10/06/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Advancements in computational techniques have transformed glaucoma research, providing a deeper understanding of genetics, disease mechanisms, and potential therapeutic targets. Systems genetics integrates genomic and clinical data, aiding in identifying drug targets, comprehending disease mechanisms, and personalizing treatment strategies for glaucoma. Molecular dynamics simulations offer valuable molecular-level insights into glaucoma-related biomolecule behavior and drug interactions, guiding experimental studies and drug discovery efforts. Artificial intelligence (AI) technologies hold promise in revolutionizing glaucoma research, enhancing disease diagnosis, target identification, and drug candidate selection. The generalized protocols for systems genetics, MD simulations, and AI model development are included as a guide for glaucoma researchers. These computational methods, however, are not separate and work harmoniously together to discover novel ways to combat glaucoma. Ongoing research and progresses in genomics technologies, MD simulations, and AI methodologies project computational methods to become an integral part of glaucoma research in the future.
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Affiliation(s)
- Minjae J Kim
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Cole A Martin
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Jinhwa Kim
- Graduate School of Artificial Intelligence, Graduate School of Metaverse, Department of Management Information Systems, Sogang University, 1 Shinsoo-Dong, Mapo-Gu, Seoul, South Korea.
| | - Monica M Jablonski
- Department of Ophthalmology, The Hamilton Eye Institute, The University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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Shahab M, Zulfat M, Zheng G. Structure-based virtual screening, molecular simulation and free energy calculations of traditional Chinese medicine, ZINC database revealed potent inhibitors of estrogen-receptor α (ERα). J Biomol Struct Dyn 2023:1-14. [PMID: 37904521 DOI: 10.1080/07391102.2023.2275174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 09/07/2023] [Indexed: 11/01/2023]
Abstract
Breast Cancer, a heterogeneous disease at the molecular level, is the most common cause of woman mortality worldwide. We used molecular screening and simulation approaches to target nuclear receptor protein-estrogen receptor alpha (Erα) protein to design and develop of specific and compelling drugs from traditional Chinese medicine (TCM), and ZINC database against pathophysiology of breast cancer. Using virtual screening, only six hits TCM22717, TCM23524, TCM31953, while ZINC05632920, ZINC05773243, and ZINC12780336 demonstrated better pharmacological potential than the 4-hydroxytamoxifen (OHT) taken as control. Binding mode of each of the top hit revealed that these compounds could block the main active site residues and block the function of Erα protein. Moreover, molecular simulation revealed that the identified compounds exhibit stable dynamics and may induce stronger therapeutic effects in experimental setup. All the complexes reported tighter structural packing and less flexible behaviour. We found that the average hydrogen bonds in the identified complexes remained higher than the control drug. Finally, the total binding free energy demonstrated the best hits among the all. The BF energy results revealed -30.4525 ± 3.3565 for the 4-hydroxytamoxifen (OHT)/Erα complex, for the TCM22717/Erα -57.0597 ± 3.4852 kcal/mol, for the TCM23524/Erα complex the BF energy was -56.9084 ± 3.3737 kcal/mol, for the TCM31953/Erα the BF energy was -32.4191 ± 3.8864 kcal/mol while for the ZINC05632920/Erα complex -46.3182 ± 2.7380, ZINC05773243/Erα complex -38.3690 ± 2.8240, and ZINC12780336/Erα complex the BF energy was calculated to be -35.8048 ± 4.1571 kcal/mol.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Muhammad Shahab
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, China
| | - Maryam Zulfat
- Department of Biochemistry, Abdul Wali Khan University, Mardan, Pakistan
| | - Guojun Zheng
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, China
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Pieroni M, Madeddu F, Di Martino J, Arcieri M, Parisi V, Bottoni P, Castrignanò T. MD-Ligand-Receptor: A High-Performance Computing Tool for Characterizing Ligand-Receptor Binding Interactions in Molecular Dynamics Trajectories. Int J Mol Sci 2023; 24:11671. [PMID: 37511429 PMCID: PMC10380688 DOI: 10.3390/ijms241411671] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/15/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand-receptor binding interactions (lrbi) to be studied. In this study, we present MD-ligand-receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand-receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand-receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.
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Affiliation(s)
- Michele Pieroni
- Department of Computer Science, "Sapienza" University of Rome, V. le Regina Elena 295, 00161 Rome, Italy
| | - Francesco Madeddu
- Department of Computer Science, "Sapienza" University of Rome, V. le Regina Elena 295, 00161 Rome, Italy
| | - Jessica Di Martino
- Department of Ecological and Biological Sciences, Tuscia University, Viale dell'Università s.n.c., 01100 Viterbo, Italy
| | - Manuel Arcieri
- Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - Valerio Parisi
- Department of Physics, "Sapienza" University of Rome, P. le Aldo Moro, 5, 00185 Rome, Italy
| | - Paolo Bottoni
- Department of Computer Science, "Sapienza" University of Rome, V. le Regina Elena 295, 00161 Rome, Italy
| | - Tiziana Castrignanò
- Department of Ecological and Biological Sciences, Tuscia University, Viale dell'Università s.n.c., 01100 Viterbo, Italy
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