Fullerene and its derivatives have potential antiviral activity due to their specific binding interactions with biological molecules. In this study fullerene derivatives were investigated by the synergic combination of three approaches: quantum-mechanical calculations, protein-ligand docking and quantitative structure-activity relationship methods. The protein-ligand docking studies and improved structure-activity models have been able both to predict binding affinities for the set of fullerene-C60 derivatives and to help in finding mechanisms of fullerene derivative interactions with human immunodeficiency virus type 1 aspartic protease, HIV-1 PR. Protein-ligand docking revealed several important molecular fragments that are responsible for the interaction with HIV-1 PR. In addition, a density functional theory method has been utilized to identify the optimal geometries and predict physico-chemical parameters of the studied compounds. The 5-variable GA-MLRA based model showed the best predictive ability (r(2)training = 0.882 and r(2)test = 0.738), with high internal and external correlation coefficients.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1039/c3ob40878g | DOI Listing |
Sci Rep
January 2025
Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, 59064-741, RN, Brazil.
The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a major challenge to global health. Targeting the main protease of the virus (Mpro), which is essential for viral replication and transcription, offers a promising approach for therapeutic intervention. In this study, advanced computational techniques such as molecular docking and molecular dynamics simulations were used to screen a series of antiviral compounds for their potential inhibitory effect on the SARS-CoV-2 Mpro.
View Article and Find Full Text PDFProteins
January 2025
Department of Statistics, Florida State University, Tallahassee, Florida, USA.
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a metalloprotein binds and the binding structure are still not readily available, even with the predicted protein structure.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
During the last 20 years, the fragment-based drug discovery approach gained popularity in both industrial and academic settings due to its efficient exploration of the chemical space. This bottom-up approach relies on identifying high-efficiency small ligands (fragments) that bind to a target binding site and then rationally evolve them into mature druglike compounds. To achieve such a task, researchers rely on accurate information about the ligand binding mode, usually obtained through experimental techniques, such as X-ray crystallography or computer simulations.
View Article and Find Full Text PDFNaunyn Schmiedebergs Arch Pharmacol
January 2025
Department of Chemistry, Faculty of Science, Cairo University, Giza, 12613, Egypt.
Piperazine-based compounds have garnered significant attention due to their notable biological and pharmacological activities, making them essential in fine chemical and pharmaceutical applications. In this study, we managed to synthesize a novel hybrid bis-cyanoacrylamide bearing the piperazine core via phenoxymethyl linker and incorporating sulphamethoxazole moiety. The novel compound was fully characterized using different spectral data including 1H-NMR, C-NMR, and FTIR spectroscopy.
View Article and Find Full Text PDFFront Pharmacol
January 2025
Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, United States.
Introduction: Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!