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Modern quantum-based methods are employed to model interaction of the flavin-dependent enzyme RutA with the uracil and oxygen molecules. This complex presents the structure of reactants for the chain of chemical reactions of monooxygenation in the enzyme active site, which is important in drug metabolism. In this case, application of quantum-based approaches is an essential issue, unlike conventional modeling of protein-ligand interaction with force fields using molecular mechanics and classical molecular dynamics methods. We focus on two difficult problems to characterize the structure of reactants in the RutA-FMN-O -uracil complex, where FMN stands for the flavin mononucleotide species. First, location of a small O molecule in the triplet spin state in the protein cavities is required. Second, positions of both ligands, O and uracil, must be specified in the active site with a comparable accuracy. We show that the methods of molecular dynamics with the interaction potentials of quantum mechanics/molecular mechanics theory (QM/MM MD) allow us to characterize this complex and, in addition, to surmise possible reaction mechanism of uracil oxygenation by RutA.
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http://dx.doi.org/10.1002/minf.202200175 | DOI Listing |
Biotechnol Appl Biochem
September 2025
Emergency Intensive Care Medicine Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.
Background: Differentially expressed genes (DEGs) have been known to provide important information on disease mechanisms and potential therapeutic targets. The traditional Chinese medicine (TCM) offers a large reservoir of bioactive compounds that could modulate at these targets. This study is an attempt to investigate the biomarkers in Sepsis and COVID-19 using gene expression analysis and molecular modeling validation of TCM-derived candidate compounds targeting key DEGs associated with sepsis.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Department of Biosciences, Università degli Studi di Milano, Via Celoria 26, 20133 Milan, Italy.
This study introduces a novel computational approach based on ratchet-and-pawl molecular dynamics (rMD) for accurately estimating ligand dissociation kinetics in protein-ligand complexes. By integrating Kramers's theory with Bell's equation, our method systematically investigates the relationship between the effective biasing force applied during simulations and the ligand residence times. The proposed technique is demonstrated through extensive simulations of the benzamidine-trypsin complex, employing first an implicit solvent model (multi-eGO) to set up the approach parameters and then an explicit solvent model.
View Article and Find Full Text PDFJ Mol Graph Model
September 2025
College of General Education, Kookmin University, Seoul, 02707, Republic of Korea. Electronic address:
Green fluorescent proteins (GFPs) are optical markers that are widely used in molecular and cell biology studies to track the location and function of biomolecules. Elucidating their structures will facilitate further engineering of these fluorescent proteins (FPs) to enhance their properties. AlphaFold3 (AF3) is a recently developed prediction tool that exhibits higher accuracy compared with other prediction tools, particularly in predicting protein-ligand interactions with state-of-the-art docking tools.
View Article and Find Full Text PDFCurr Pharm Des
August 2025
King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.
Introduction: Ovarian cancer (OC) is a malignancy of the female reproductive system for which cisplatin chemotherapy is one of the first-line treatments. Despite the initial response to chemotherapy, such patients eventually develop resistance, which poses a major obstacle to treatment, along with potential side effects. Phytochemicals function as chemosensitizers, offering novel therapies in OC patients by targeting drug resistance, and are perceived to be less toxic.
View Article and Find Full Text PDFChem Sci
August 2025
Xiangya School of Pharmaceutical Sciences, Central South University Changsha 410013 Hunan P.R. China
Structure-based molecular docking, a cornerstone of computational drug design, is undergoing a paradigm shift fueled by deep learning (DL) innovations. However, the rapid proliferation of DL-driven docking methods has created uncharted challenges in translating predictions to biomedical reality. Here, we delve into the performance and prospects of traditional methods and state-of-the-art DL docking paradigms-encompassing generative diffusion models, regression-based architectures, and hybrid frameworks-across five critical dimensions: pose prediction accuracy, physical plausibility, interaction recovery, virtual screening (VS) efficacy, and generalization across diverse protein-ligand landscapes.
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