AIMC Topic: Nuclear Magnetic Resonance, Biomolecular
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Assisting and accelerating NMR assignment with restrained structure prediction.
Communications biology
Jul 18, 2025
Accurate dynamic protein structures are essential for drug design. NMR experiments can detect protein structures and potential dynamics, but the spectrum assignment and structure determination requires expertise and is time-consuming, while deep-lear...
Loop Nucleotide Chemical Shifts as a Tool to Characterize DNA G-Quadruplexes.
Chemphyschem : a European journal of chemical physics and physical chemistry
May 7, 2025
DNA G-quadruplexes are known to play myriad functional roles in the cellular context and their structural diversity has diverse applications in various fields of science. Solution-state NMR spectroscopy has been instrumental in characterization of DN...
LEGOLAS: A Machine Learning Method for Rapid and Accurate Predictions of Protein NMR Chemical Shifts.
Journal of chemical theory and computation
Apr 10, 2025
This work introduces LEGOLAS, a fully open source TorchANI-based neural network model designed to predict NMR chemical shifts for protein backbone atoms (N, Cα, Cβ, C', HN, Hα). LEGOLAS has been designed to be fast without loss of accuracy, as our mo...
A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins.
Science advances
Dec 20, 2024
Nuclear magnetic resonance (NMR) spectroscopy is an important technique for deriving the dynamics and interactions of macromolecules; however, characterizations of aromatic residues in proteins still pose a challenge. Here, we present a deep neural n...
Analysis of solid-state NMR data facilitated by MagRO_NMRViewJ with Graph_Robot: Application for membrane protein and amyloid.
Biophysical chemistry
Nov 17, 2024
Solid-state NMR (ssNMR) methods have continued to be developed in recent years for the efficient assignment of signals and 3D structure modeling of biomacromolecules. Consequently, we are approaching an era in which vigorous applications of these met...
Ab initio characterization of protein molecular dynamics with AIBMD.
Nature
Nov 6, 2024
Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods...
Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.
Journal of biomolecular NMR
Oct 20, 2024
Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR paramete...
Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.
International journal of molecular sciences
Sep 8, 2024
Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence...
Prediction of order parameters based on protein NMR structure ensemble and machine learning.
Journal of biomolecular NMR
Mar 26, 2024
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. Th...
Biomolecular NMR spectroscopy in the era of artificial intelligence.
Structure (London, England : 1993)
Oct 16, 2023
Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in ac...
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