A Machine Learning Tool to Analyse Spectroscopic Changes in High-Dimensional Data

TL;DR

This paper introduces a machine learning approach for analyzing spectroscopic data to understand how nanoparticles influence protein structures, aiding nanomedicine development.

Contribution

A novel unsupervised machine learning method for multi-component spectral data analysis, addressing high-dimensionality and multi-source integration challenges.

Findings

Differences in temperature-dependent protein structures with different NPs

Method effectively handles high-dimensional spectral data

Provides quantitative insights into protein-NP interactions

Abstract

When nanoparticles (NPs) are introduced into a biological solution, layers of biomolecules form on their surface, creating a corona. Understanding how the structure of the protein 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 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 and enhances the understanding of the correlation between protein structure and NP interactions, which could support the development of nanomedical tools to treat various conditions.