In the present work, the molecular recognition of the oldest active amphenicols by the most popular renal carrier, lysozyme, was deciphered by using fluorescence, circular dichroism (CD) and molecular modeling at the molecular scale. Steady state fluorescence data showed that the recognition of amphenicol by lysozyme yields a static type of fluorescence quenching. This corroborates time-resolved fluorescence results that lysozyme-amphenicol adduct formation has a moderate affinity of 10(4) M(-1), and the driving forces were found to be chiefly hydrogen bonds, hydrophobic interactions and π stacking. Far-UV CD spectra confirmed that the spatial structure of lysozyme was slightly changed with a distinct reduction of α-helices in the presence of amphenicol, suggesting partial destabilization of the protein. Furthermore, via the extrinsic 8-anilino-1-naphthalenesulfonic acid fluorescence spectral properties and molecular modeling, one could see that the amphenicol binding site was situated at the deep crevice on the protein surface, and the ligand was also near to several crucial amino acid residues, such as Trp-62, Trp-63 and Arg-73. Simultaneously, contrastive studies of protein-amphenicols revealed clearly that some substituting groups, e.g. nitryl in the molecular structure of ligands, may be vitally important for the recognition activity of amphenicols with lysozyme. Due to the connection of amphenicols with fatal detrimental effects and because lysozyme has been applied as a drug carrier for proximal tubular targeting, the discussion herein is necessary for rational antibiotic use, development of safe antibiotics and particularly a better appraisal of the risks associated with human exposure to toxic agrochemicals.
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J Mol Graph Model
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Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Gomtinagar Extension, Lucknow, 226028, India; Research Cell, Amity University Uttar Pradesh, Lucknow Campus, India. Electronic address:
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Molecular docking is a crucial technique for elucidating protein-ligand interactions. Machine learning-based docking methods offer promising advantages over traditional approaches, with significant potential for further development. However, many current machine learning-based methods face challenges in ensuring the physical plausibility of generated docking poses.
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Medicine and Pharmacy Research Center, and Yantai Key Laboratory for Stem Cell Biology and Regenerative Medicine, Binzhou Medical University, 346 Guanhai Road, Yantai, Shandong 264003, China.
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Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.
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Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.
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