An efficient cost scaling algorithm for the assignment problem
Mathematical Programming: Series A and B
A unified approach to approximating resource allocation and scheduling
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Computing in Science and Engineering
Approximation algorithms for NMR spectral peak assignment
Theoretical Computer Science
Automated Protein NMR Resonance Assignments
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
A random graph approach to NMR sequential assignment
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Statistical evaluation of NMR backbone resonance assignment
International Journal of Bioinformatics Research and Applications
RIBRA–an error-tolerant algorithm for the NMR backbone assignment problem
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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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 our recent work towardsautomating 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 and a greedy filtering algorithm for reducing the search space. Our experimental results on 70instances 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.