Using neural networks to obtain NMR spectra of both small and macromolecules from blood samples in a single experiment.

Metabolomics plays a crucial role in understanding metabolic processes within biological systems. Using specific pulse sequences, NMR-based metabolomics detects small and macromolecular metabolites that are altered in blood samples. Here we proposed a method called spectral editing neural network, which can effectively edit and separate the spectral signals of small and macromolecules in H NMR spectra of serum and plasma based on the linewidth of the peaks.

Prediction of order parameters based on protein NMR structure ensemble and machine learning.

The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. The most used NMR approach to study fast protein dynamics is the model free method, which uses order parameter S to describe the amplitude of the internal motion of local group. However, to obtain order parameter through NMR experiments is quite complex and lengthy.

Restore High-Resolution Nuclear Magnetic Resonance Spectra from Inhomogeneous Magnetic Fields Using a Neural Network.

High-resolution nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool with wide applications. However, the conventional shim technique may not guarantee the homogeneity of the magnetic field when the experimental conditions are unfavorable. In this study, we proposed a data postprocessing method called Restore High-resolution Unet (RH-Unet), which uses a convolutional neural network to restore distorted NMR spectra that have been acquired in inhomogeneous magnetic fields.

CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts.

Protein secondary structure provides rich structural information, hence the description and understanding of protein structure relies heavily on it. Identification or prediction of secondary structures therefore plays an important role in protein research. In protein NMR studies, it is more convenient to predict secondary structures from chemical shifts as compared to the traditional determination methods based on inter-nuclear distances provided by NOESY experiment.

NMR-based Metabolomics Analysis of Liver from C57BL/6 Mouse Exposed to Ionizing Radiation.

The effects of ionizing radiation to human health are of great concern in the field of space exploration and for patients considering radiotherapy. However, to date, the effect of high-dose radiation on metabolism in the liver has not been clearly defined. In this study, H nuclear magnetic resonance (NMR)-based metabolomics combined with multivariate data analysis was applied to study the changes of metabolism in the liver of C57BL/6 mouse after whole-body gamma (3.