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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
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Infrared spectroscopy is primarily used to determine the types of bonds and functional groups. In carboxylic acid derivatives, a typical carbonyl bond absorption is observed around 1650–1850 cm−1. For esters, the absorption is recorded at around 1740 cm−1, while acid halides show the absorption at about 1800 cm−1. Another acid derivative, the acid anhydrides, exhibit two carbonyl absorption around 1760 cm−1 and 1820 cm−1, arising from the symmetrical and...
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A Multimodal Wide-Field Fourier-Transform Raman Microscope
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A machine learning tool to analyze spectroscopic changes in high-dimensional data.

Alberto Martinez-Serra1, Gionni Marchetti2, Francesco D'Amico3

  • 1Department of Chemistry, Royal College of Surgeons in Ireland, RCSI University of Medicine and Health Sciences, 123 St Stephen's Green, Dublin, D02 YN77, Ireland.

International Journal of Biological Macromolecules
|October 9, 2025
PubMed
Summary
This summary is machine-generated.

Understanding nanoparticle-protein interactions is key for nanotechnology safety. This study uses machine learning to analyze spectral data, revealing how protein structure changes on different nanoparticle surfaces.

Keywords:
Biomolecular coronaClusteringHydrophilic nanoparticlesHydrophobic nanoparticlesManifold reductionProtein structureSimilarity metricsSpectroscopyUnsupervised machine learning

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Area of Science:

  • Biophysics
  • Nanotechnology
  • Materials Science

Background:

  • Nanoparticle (NP) interactions with biological systems lead to the formation of a biomolecular corona on the NP surface.
  • Understanding protein structure evolution within this corona is critical for assessing NP safety and toxicity.
  • The impact of NP properties on protein conformation remains poorly understood.

Purpose of the Study:

  • To develop and apply a novel machine learning (ML) method for analyzing multi-component spectral data to understand NP-protein interactions.
  • To quantitatively analyze protein structural changes upon adsorption to different types of NPs.
  • To investigate the influence of NP surface properties (hydrophobic vs. hydrophilic) on protein structure.

Main Methods:

  • Utilized a combination of UV Resonance Raman, Circular Dichroism, and UV absorbance spectroscopy.
  • Applied an unsupervised machine learning approach to analyze the multi-component spectral data.
  • Investigated the interaction of fibrinogen with hydrophobic carbon and hydrophilic silicon dioxide NPs at physiological concentrations.

Main Results:

  • The ML method effectively analyzed complex spectral data, overcoming challenges like the curse of dimensionality.
  • Striking differences were observed in the temperature dependence of fibrinogen's protein structure when interacting with hydrophobic carbon versus hydrophilic silicon dioxide NPs.
  • The study provides a quantitative analysis of protein structural alterations induced by NP adsorption.

Conclusions:

  • The developed ML method offers a robust approach for studying NP-protein corona formation and protein structural dynamics.
  • Findings highlight the significant impact of NP surface chemistry on protein conformation, crucial for nanomedicine safety.
  • This research advances the understanding of NP-protein interactions, potentially aiding the development of nanomedical tools.