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The embedding problem for predistance matrices.

W Glunt¹, T L Hayden, W M Liu

¹Department of Mathematics, University of Kentucky, Lexington 40506.

Bulletin of Mathematical Biology

|January 1, 1991

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This study addresses macromolecule conformation determination using nuclear magnetic resonance (NMR) data. New algorithms improve the distance geometry embedding process, finding more local minima faster than existing methods.

Area of Science:

Molecular Biology
Biophysics
Computational Chemistry

Background:

Determining macromolecule conformation from NMR data is a key challenge.
Existing distance geometry programs like DISGEO face difficulties with the embedding problem.

Purpose of the Study:

To address the embedding problem in distance geometry for NMR data analysis.
To develop novel algorithms for more efficient and comprehensive conformation determination.

Main Methods:

Utilizing the geometric properties inherent in the distance geometry approach.
Developing new algorithms to solve the embedding problem more effectively.

Main Results:

Demonstrated unique geometric insights applicable to locating local minima.

Related Experiment Videos

Developed algorithms that are faster and identify more local minima than current methods.

Conclusions:

The distance geometry approach offers unique geometric advantages for conformation analysis.
Novel algorithms enhance the efficiency and completeness of finding macromolecule conformations from NMR data.