Carregant...
Fitxers
Tipus de document
ArticleVersió
Versió publicadaData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/227833
A machine learning tool to analyze spectroscopic changes in high-dimensional data
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
When nanoparticles (NPs) are introduced into a biological solution, layers of biomolecules form on their surface, creating a corona. Understanding how the protein’s structure evolves into the corona is essential for evaluating the safety and toxicity of nanotechnology. However, the influence of NP properties on protein conformation is not well understood. In this study, we propose a new method that addresses this issue by analyzing multi-component spectral data (UV Resonance Raman, Circular Dichroism, and UV absorbance) using machine learning (ML). We apply the method to fibrinogen, a crucial protein in human blood plasma, at physiological concentrations while interacting with hydrophobic carbon or hydrophilic silicon dioxide NPs, revealing striking differences in the temperature dependence of the protein structure between the two cases. Our unsupervised ML method (a) does not suffer from the challenges associated with the curse of dimensionality, and (b) simultaneously handles spectral data from various sources. The method offers a quantitative analysis of protein structural changes upon adsorption. It enhances the understanding of the correlation between protein structure and NP interactions, which could support the development of nanomedical tools to treat various conditions.
Matèries
Matèries (anglès)
Citació
Citació
MARTÍNEZ SERRA, Alberto, MARCHETTI, Gionni, D’AMICO, Francesco, FENOGLIO, Ivana, ROSSI, Barbara, MONOPOLI, Marco p., FRANZESE, Giancarlo. A machine learning tool to analyze spectroscopic changes in high-dimensional data. _International Journal of Biological Macromolecules_. 2025. Vol. 330. [consulta: 6 de març de 2026]. ISSN: 0141-8130. [Disponible a: https://hdl.handle.net/2445/227833]