NMR Assignment through Linear Programming

08/09/2020

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Nuclear Magnetic Resonance (NMR) Spectroscopy is the second most used technique (after X-ray crystallography) for structural determination of proteins. A computational challenge in this technique involves solving a discrete optimization problem that assigns the resonance frequency to each atom in the protein. We present a novel linear programming formulation of the problem which gives state-of-the-art results in simulated and experimental datasets.

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NMR Assignment through Linear Programming

Leveraging deep learning for fully automated NMR protein structure determination

Parallel parametric linear programming solving, and application to polyhedral computations

Automated Assignment of Backbone Resonances Using Residual Dipolar Couplings Acquired from a Protein with Known Structure

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Automated Assignment of Backbone NMR Data using Artificial Intelligence

Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions

Towards fully automated protein structure elucidation with NMR spectroscopy

NMR Assignment through Linear Programming

Related Research

Leveraging deep learning for fully automated NMR protein structure determination

Parallel parametric linear programming solving, and application to polyhedral computations

Automated Assignment of Backbone Resonances Using Residual Dipolar Couplings Acquired from a Protein with Known Structure

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Automated Assignment of Backbone NMR Data using Artificial Intelligence

Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions

Towards fully automated protein structure elucidation with NMR spectroscopy