Vanin-1 (VNN1) is a glycosylphosphatidylinositol (GPI)-anchored ectoenzyme which hydrolyzes pantetheine to pantothenic acid and cysteamine. It has emerged as a promising drug target for many human diseases associated with oxidative stress and inflammatory pathways. In the present study we used structure-based virtual screening approach for the identification of small molecule inhibitors of vanin-1. A chemical library consisting of natural compounds, synthetic compounds and RRV analogs were screened for drug-like molecules. The filtered molecules were subjected to molecular docking studies. Three potential hits-ZINC04073864 (Natural compound), CID227017 (synthetic compound) and CID129558381 (RRV analog)-were identified for the target enzyme. The molecules form good number of hydrogen bonds with the catalytic residues such as Glu79, Lys178 and Cys211. The apo-VNN1 and VNN1-ligand complexes were subjected to molecular dynamics (MD) simulation for 30 ns. The geometric properties such as root mean square deviation, radius of gyration, solvent accessible surface area, number of hydrogen bonds and the distance between the catalytic triad residues-Glu79, Lys178 and Cys211 were altered upon binding of the compounds. Essential dynamics and entropic studies further confirmed that the fluctuations in VNN1 decrease upon binding of the compounds. The lead molecules were stable throughout the simulation time period. Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) studies showed that Van der Waals interaction energy contributes significantly to the total binding free energy. Thus, our study reveals three lead molecules-ZINC04073864, CID227017 and CID129558381 as potential inhibitors of Vanin-1 which can be validated through further studies. Communicated by Ramaswamy H. Sarma.
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http://dx.doi.org/10.1080/07391102.2020.1854857 | DOI Listing |
J Infect Public Health
December 2024
Department of Pharmaceutical Sciences, College of Pharmacy, QU Health, Qatar University, P.O. Box 2713, Doha, Qatar. Electronic address:
Mol Inform
January 2025
Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties.
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January 2025
Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.
Cyclooxygenase-2 (COX-2) is an enzyme that plays a crucial role in inflammation by converting arachidonic acid into prostaglandins. The overexpression of enzyme is associated with conditions such as cancer, arthritis, and Alzheimer's disease (AD), where it contributes to neuroinflammation. In silico virtual screening is pivotal in early-stage drug discovery; however, the absence of coding or machine learning expertise can impede the development of reliable computational models capable of accurately predicting inhibitor compounds based on their chemical structure.
View Article and Find Full Text PDFMil Med
January 2025
Division of Gynecologic Oncology, Department of Gynecologic Surgery & Obstetrics, Tripler Army Medical Center, Honolulu, HI 96859, USA.
Endometrial cancer is the most prevalent gynecologic cancer in the United States and has rising incidence and mortality. Endometrial intraepithelial neoplasia or atypical endometrial hyperplasia (EIN-AEH), a precancerous neoplasm, is surgically managed with hysterectomy in patients who have completed childbearing because of risk of progression to cancer. Concurrent endometrial carcinoma (EC) is also present on hysterectomy specimens in up to 50% of cases.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
The fuel system serves as the core component of marine diesel engines, and timely and effective fault diagnosis is the prerequisite for the safe navigation of ships. To address the challenge of current data-driven fault-diagnosis-based methods, which have difficulty in feature extraction and low accuracy under small samples, this paper proposes a fault diagnosis method based on digital twin (DT), Siamese Vision Transformer (SViT), and K-Nearest Neighbor (KNN). Firstly, a diesel engine DT model is constructed by integrating the mathematical, mechanism, and three-dimensional physical models of the Medium-speed diesel engines of 6L21/31 Marine, completing the mapping from physical entity to virtual entity.
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