Speaker
Description
When analyzing spectroscopic data of complex systems, a key challenge is identifying the features that most effectively distinguish their differences, thereby facilitating the study of physical properties and their evolution. A common strategy involves combining multiple measurements to acquire more detailed information; however, this approach does not always enhance analysis quality, especially when additional noise is present. To address this, we developed an unsupervised machine learning (ML) protocol. As a case study, we applied it to multi-component spectral data—including UV Resonance Raman, Circular Dichroism, and UV absorbance—from nanoparticles (NPs) interacting with proteins to form a corona. Understanding the structural evolution of proteins within the corona across different temperatures is crucial for assessing the safety and toxicity of nanotechnology, yet the impact of NP properties on protein conformation remains poorly understood. We focused on fibrinogen, an essential blood plasma protein, at physiological concentrations and examined its interactions with hydrophobic carbon and hydrophilic silicon dioxide NPs. Our method uncovered significant differences in how protein structure responds to temperature in these two scenarios. The unsupervised ML protocol we developed (a) overcomes the challenges posed by the curse of dimensionality, and (b) effectively handles spectral data from diverse sources. It provides a quantitative assessment of protein structural changes upon adsorption, improving understanding of the relationship between protein conformation and NP interactions. This insight could support the development of nanomedical tools for various therapeutic applications. Furthermore, our method is sufficiently general and can be applied to other spectroscopic analyses from different sources. Details are given in [A. Martinez-Serra, G. Marchetti, F. D’Amico, I. Fenoglio, B. Rossi, M. P. Monopoli, and G. Franzese. A machine learning tool to analyze spectroscopic changes in high-dimensional data. International Journal of Biological Macromolecules, 330:148095, 2025].