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DOI 10.1002/smtd.202501820

Медицинская библиотека · Small methods · 2025

Characterization of Ligand Binding in Human Serum Albumin from Atomistic Energy Transfer Simulations.

Ergün Özge, Bertran-Mostazo Andrea, Cubero Elena, Galdeano Carles, Curutchet Carles

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

Förster resonance energy transfer (FRET) is a key biophysical method for probing nanometer‑scale distances in biomolecular systems, but its direct application to protein-ligand complexes suffers from substantial biases due to restricted chromophore orientations and the limited validity of the point‑dipole approximation. This study introduces a protocol for identifying binding sites and characterizing ligand coordination modes in situ by combining fluorescence spectroscopy with efficient atomistic simulations based on the TrESP‑MMPol model. The protocol integrates electrostatic potential‑fitted transition charges with a polarizable classical environment, thereby overcoming the orientation and dielectric-screening assumptions inherent to Förster theory. The protocol has been applied to human serum albumin (HSA) and a library of fluorescent small molecules, including known binders of the HSA, accurately reproducing the binding sites of naproxen, carprofen, and indomethacin, and revealing novel binding scenarios for other molecules. The results show that direct comparison of experimental FRET data with atomistically simulated observables enables discrimination of plausible binding models - including the site and binding mode - and avoids systematic errors in distance estimation. The protocol is particularly attractive to examine targets with a single tryptophan, and can also be extended to other targets of interest in drug discovery via site-labelling with unnatural amino acids.

Для цитирования: Ergün Özge, Bertran-Mostazo Andrea, Cubero Elena, Galdeano Carles, Curutchet Carles. Characterization of Ligand Binding in Human Serum Albumin from Atomistic Energy Transfer Simulations.. Small methods. 2025. DOI: 10.1002/smtd.202501820.

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