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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Computational Assignment of Protein Backbone NMR Peaks by Efficient Bounding and Filtering
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Computational Assignment of Protein Backbone NMR Peaks by Efficient Bounding and Filtering

机译：通过有效的边界和过滤对蛋白质骨干NMR峰进行计算分配

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摘要

NMR resonance assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and inter-residue spatial relationships between atoms. The assignment process is tedious and time-consuming, which could take many weeks. Though there exist a number of computer programs to assist the assignment process, many NMR labs are still doing the assignments manually to ensure quality. This paper presents a new computational method based on the combination of a suite of algorithms for automating the assignment process, particularly the process of backbone resonance peak assignment. We formulate the assignment problem as a constrained weighted bipartite matching problem. While the problem, in the most general situation, is NP-hard, we present an efficient solution based on a branch-and-bound algorithm with effective bounding techniques using two recently introduced approximation algorithms. We also devise a greedy filtering algorithm for reducing the search space. Our experimental results on 70 instances of (pseudo) real NMR data derived from 14 proteins demonstrate that the new solution runs much faster than a recently introduced (exhaustive) two-layer algorithm and recovers more correct peak assignments than the two-layer algorithm. Our result demonstrates that integrating different algorithms can achieve a good tradeoff between backbone assignment accuracy and computation time.
机译：NMR共振分配是解决NMR蛋白质结构的关键步骤之一。分配过程将共振峰链接到目标蛋白序列的各个残基，为在原子之间建立残基和残基之间的空间关系提供了先决条件。分配过程繁琐且耗时，可能需要数周的时间。尽管存在许多计算机程序来协助分配过程，但许多NMR实验室仍在手动进行分配以确保质量。本文提出了一种基于算法组合的新计算方法，用于自动完成分配过程，尤其是骨干共振峰分配过程。我们将分配问题公式化为约束加权二分匹配问题。虽然问题在最一般的情况下是NP难题，但我们提出了一种基于分支定界算法的有效解决方案，并使用了两种最新引入的近似算法，采用了有效的边界技术。我们还设计了一种贪婪过滤算法来减少搜索空间。我们对来自14种蛋白质的70个（伪）真实NMR数据实例的实验结果表明，新解决方案的运行速度比最近引入的（穷举性）两层算法快得多，并且比两层算法可恢复更多正确的峰分配。我们的结果表明，集成不同的算法可以在骨干分配精度和计算时间之间取得良好的折衷。

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