Decoding the Conformational Binding of Drug Mixtures on Ovalbumin: An Integrated Multimodal Network
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Elsevier
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This research addresses the crucial necessity for a deeper understanding of the binding interactions between surfactants and proteins, with a specific focus on ovalbumin. Considering ovalbumin's role in diverse biochemical processes, it remains a subject of significant interest for drug discovery and design. To fill existing knowledge gaps, we investigated the binding interaction between dicloxacillin and cetyltrimethylammonium bromide (CTAB) on ovalbumin, employing a comprehensive approach that combines computational modeling with experimental validations. Using the ezPocket tool, the computational phase predicted ten relevant binding sites on ovalbumin's surface. The isobologram combination index (CI) heatmap strongly suggested a complex interplay of antagonistic and synergistic effects. Besides, a conformational drug-drug interaction network was proposed to explore the stability of the surfactant mixture within specific binding sites of ovalbumin, revealing a dynamic landscape of suggested antagonist effects. Experimental validations through UV–vis, Fluorescence, and circular dichroism (CD) spectroscopy further corroborated the computational findings, confirming the formation of stable complexes. Finally, this study not only advances our comprehension of ovalbumin's interactions with surfactants but also offers a multidimensional perspective and an advanced methodological framework for efficient therapeutic strategies, opening new avenues for future applications in drug development and applied biochemistry.
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International Journal of Biological Macromolecules Volume 261, Part 2, March 2024, 129866
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https://doi.org/10.1016/j.ijbiomac.2024.129866Sponsors
M.G.D. thanks European Union's H2020 project Sinfonia (N.857253). R.R. and J.M.R. thank Xunta de Galicia for support (ED431B 2022/36). R.R. is granted by the Program for the requalification, international mobility, and attraction of talent in the Spanish university system, modality Margarita Salas (grant UP2021-042).
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Attribution-NonCommercial-NoDerivatives 4.0 International








