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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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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.