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Applications Of NMR In Biology

Applications Of NMR In Biology

Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.

NMR Spectroscopy and Mass Spectrometry of Aldehydes and Ketones

NMR Spectroscopy and Mass Spectrometry of Aldehydes and Ketones

In aldehydes, the hydrogen atom connected to the carbonyl carbon helps distinguish aldehydes from other carbonyl compounds using ¹H NMR spectroscopy. The closeness of aldehydic hydrogen to the electrophilic carbonyl carbon highly deshields the hydrogen atom causing its signal to appear around 10 ppm in the ¹H NMR spectra. α hydrogens split the aldehydic proton signal, which helps identify the number of α hydrogens in the molecule. For instance, one α hydrogen creates a...

¹H NMR: Complex Splitting

¹H NMR: Complex Splitting

A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...

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Nuclear Magnetic Resonance Spectroscopy for the Identification of Multiple Phosphorylations of Intrinsically Disordered Proteins

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Prediction of S-nitrosylation modification sites based on kernel sparse representation classification and mRMR

Guohua Huang¹, Lin Lu², Kaiyan Feng³

¹Institute of Systems Biology, Shanghai University, Shanghai 200444, China ; Department of Mathematics, Shaoyang University, Shaoyang, Hunan 422000, China.

Biomed Research International

|September 4, 2014

Summary

Predicting protein S-nitrosylation sites is challenging. This study introduces a computational framework using kernel sparse representation classification, improving prediction accuracy and identifying key features for S-nitrosylation site identification.

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Area of Science:

Biochemistry
Computational Biology
Proteomics

Background:

Protein S-nitrosylation is crucial for cellular functions.
Accurate prediction of S-nitrosylation sites remains a significant challenge in bioinformatics.

Purpose of the Study:

To develop a computational framework for predicting protein S-nitrosylation sites.
To identify optimal features for accurate S-nitrosylation site prediction.

Main Methods:

Utilized kernel sparse representation classification (KSRC) and Minimum Redundancy Maximum Relevance (mRMR) algorithm.
Engineered 666 features from amino acid properties and protein structure for protein representation.
Trained and tested the predictor on 529 protein sequences from public databases and literature.

Main Results:

Achieved Matthews' correlation coefficients (MCC) of 0.1634 (training) and 0.2919 (testing).
Demonstrated superior performance compared to k-nearest neighbor, random forest, and standard SRC algorithms.
Identified 134 optimal features that outperformed the original 666 features for S-nitrosylation prediction.
Validated robustness on an independent test set, yielding an MCC of 0.2239.

Conclusions:

The proposed KSRC-based framework effectively predicts protein S-nitrosylation sites.
Feature selection using mRMR significantly enhances prediction accuracy and efficiency.
The developed predictor offers a valuable tool for studying protein S-nitrosylation in biological systems.