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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

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The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
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Raman Spectroscopy Instrumentation: Overview01:26

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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Updated: Nov 10, 2025

Ultrafast Time-resolved Near-IR Stimulated Raman Measurements of Functional π-conjugate Systems
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Hot spots localization in proteins by optimized short time Ramanujan Fourier transform.

Yashpal Yadav1, Sanjeev Narayan Sharma1, Devendra Kumar Shakya1

  • 1Department of Electronics and Instrumentation Engineering, Samrat Ashok Technological Institute, Vidisha M.P., India.

Journal of Bioinformatics and Computational Biology
|April 5, 2021
PubMed
Summary

This study introduces a novel computational method to identify protein hotspots using only sequence data, bypassing the need for experimental structures or training. The approach enhances protein engineering and drug discovery by efficiently pinpointing critical residues.

Keywords:
Bioinformaticscomputational biologydiscrete Fourier transformprotein interaction

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Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Protein-protein interactions are crucial for biological functions.
  • Hot spot residues are key determinants of these interactions, with applications in protein engineering and drug discovery.
  • Existing experimental and computational methods for hotspot identification are often resource-intensive or require structural data and training.

Purpose of the Study:

  • To develop a computational method for identifying protein hot spots using only sequence information.
  • To introduce a novel approach based on the Resonant Recognition Model (RRM) utilizing characteristic periods instead of frequencies.
  • To provide a model-independent and training-free method for hotspot identification.

Main Methods:

  • Utilized the Resonant Recognition Model (RRM) with characteristic periods.
  • Employed the Ramanujan Fourier Transform (RFT) to extract characteristic periods from protein family consensus spectra.
  • Generated position-period plots using Short Time RFT (ST-RFT) with an optimized Gaussian window, identifying hot spots via signal thresholding.

Main Results:

  • Successfully identified hot spots using sequence information alone.
  • The proposed method demonstrated improved sensitivity compared to existing RRM-based approaches.
  • The optimization of the Gaussian window shape parameter using a concentration measure enhanced ST-RFT performance.

Conclusions:

  • The developed sequence-based method offers an efficient and accessible tool for identifying protein hot spots.
  • This approach eliminates the need for protein structures or prior training, making it broadly applicable.
  • The method holds significant potential for advancing protein engineering and drug discovery efforts.