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IR and UV–Vis Spectroscopy of Aldehydes and Ketones01:29

IR and UV–Vis Spectroscopy of Aldehydes and Ketones

Infrared spectroscopy, also known as vibrational spectroscopy, is mainly used to determine the types of bonds and functional groups in molecules. In aldehydes and ketones, the carbonyl (C=O) bond shows an absorption around 1710 cm-1. The C=O bond vibration of an aldehyde occurs at lower frequencies than that of a ketone. In addition to the C=O absorption in an aldehyde, the aldehydic C–H bond also gives two peaks in the 2700–2800 cm-1 range. This absorption, coupled with the C=O stretching, is...
IR and UV–Vis Spectroscopy of Carboxylic Acids01:28

IR and UV–Vis Spectroscopy of Carboxylic Acids

In IR spectroscopy of carboxylic acids, the C=O bond shows a characteristic band between 1710 and 1760 cm⁻¹, and the O–H bond exhibits a broad band between 2500 and 3300 cm⁻¹.
However, the stretching absorptions for the C=O bond vary depending on the structure of carboxylic acids. The C=O bond of the free carboxylic acids shows a higher stretching frequency, 1760 cm−1, while H-bonded carboxylic acids (dimers) exhibit stretching absorptions at a lower frequency, 1710 cm−1. The C=O bond of the...
UV–Vis Spectroscopy: Woodward–Fieser Rules01:29

UV–Vis Spectroscopy: Woodward–Fieser Rules

UV–Visible absorption spectra of conjugated dienes arise from the lowest energy π → π* transitions. The light-absorbing part of the molecule is called the chromophore, and the substituents directly attached to the chromophore are called auxochromes. A strong correlation exists between the absorption maxima, λmax, and the structure of a conjugated π system. The Woodward–Fieser rules predict the value of λmax for a given structure by adding the contributions...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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 C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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Related Experiment Video

Updated: Jun 30, 2026

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays
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iBhb-Lys: Identify lysine β-hydroxybutyrylation sites using autoencoder feature representation and fuzzy SVM

Zhe Ju1, Qing-Bao Zhang1

  • 1College of Science, Shenyang Aerospace University, 110136, People's Republic of China.

Analytical Biochemistry
|November 9, 2024
PubMed
Summary

A new computational model, iBhb-Lys, accurately identifies lysine β-hydroxybutyrylation (Kbhb) sites in proteins. This advancement aids in understanding Kbhb

Keywords:
AutoencoderFeature extractionFuzzy support vector machinePost-translational modificationβ-hydroxybutyrylation

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Area of Science:

  • Biochemistry
  • Proteomics
  • Computational Biology

Background:

  • Lysine β-hydroxybutyrylation (Kbhb) is a novel histone modification linked to human disease pathogenesis.
  • Accurate identification of Kbhb sites is crucial for understanding its biological significance and molecular mechanisms.

Purpose of the Study:

  • To develop a novel computational model, iBhb-Lys, for accurate identification of Kbhb sites from protein sequences.
  • To improve upon existing prediction methods for Kbhb site identification.

Main Methods:

  • Combined four types of features to create a 3266-dimensional feature vector for each potential Kbhb site.
  • Utilized an autoencoder network for dimensionality reduction of the high-dimensional feature space.
  • Proposed a fuzzy support vector machine algorithm incorporating sample density to enhance classification robustness.

Main Results:

  • The iBhb-Lys model achieved an AUC increase of 2.22% compared to the existing predictor KbhbXG on independent tests.
  • Feature analysis identified the frequency of leucine and histidine residues near Kbhb sites as significant predictors.
  • The developed model demonstrates superior performance in identifying Kbhb sites.

Conclusions:

  • The iBhb-Lys model provides an effective computational tool for identifying lysine β-hydroxybutyrylation sites.
  • Findings offer valuable insights into the molecular mechanisms underlying Kbhb and its role in diseases.
  • This study contributes to the advancement of post-translational modification analysis in proteomics.