NMR assignment through linear programming

被引：1

作者：

Bravo-Ferreira, Jose F. S. ^{[1
]}

Cowburn, David ^{[2
,3
,4
]}

Khoo, Yuehaw ^{[5
]}

Singer, Amit ^{[1
,6
]}

机构：

[1] Princeton Univ, PACM, Princeton, NJ 08540 USA

[2] Albert Einstein Coll Med, Dept Biochem, New York, NY 10461 USA

[3] Albert Einstein Coll Med, Dept Physiol, New York, NY 10461 USA

[4] Albert Einstein Coll Med, Dept Biophys, New York, NY 10461 USA

[5] Univ Chicago, Dept Stat, Chicago, IL 60637 USA

[6] Princeton Univ, Dept Math, Princeton, NJ 08540 USA

来源：

JOURNAL OF GLOBAL OPTIMIZATION | 2022年 / 83卷 / 01期

关键词：

NMR spectroscopy; Shortest path problem; Resonance assignment problem; Linear programming relaxation; RESONANCE ASSIGNMENT; BACKBONE ASSIGNMENT; SEQUENTIAL ASSIGNMENT; AUTOMATED-ANALYSIS; PROTEINS; ALGORITHM;

D O I：

10.1007/s10898-021-01004-3

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

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. This paper introduces LIAN (LInear programming Assignment for NMR), a novel linear programming formulation of the problem which yields state-of-the-art results in simulated and experimental datasets.

引用

页码：3 / 28

页数：26

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