Large a polynomial-time nuclear vector replacement algorithm for automated NMR resonance assignments
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
An Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
High-Throughput 3D Structural Homology Detection via NMR Resonance Assignment
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
An improved ant colony optimisation algorithm for the 2D HP protein folding problem
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
The Computer Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Tabu Search Approach for the NMR Protein Structure-Based Assignment Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Nuclear Magnetic Resonance (NMR) Spectroscopy is an important technique that allows determining protein structure in solution. An important problem in protein structure determination using NMR spectroscopy is the mapping of peaks to corresponding amino acids. Structure Based Assignment (SBA) is an approach to solve this problem using a template structure that is homologous to the target. Our previously developed approach NVR-BIP computed the optimal solution for small proteins, but was unable to solve the assignments of large proteins. NVR-TS extended the applicability of the NVR approach for such proteins, however the accuracies varied significantly from run to run. In this paper, we propose NVR-ACO, an Ant Colony Optimization (ACO) based approach to this problem. NVRACO is similar to other ACO algorithms in a way that it also consists of three phases: the construction phase, an optional local search phase and a pheromone update phase. But it has some important differences from other ACO algorithms in terms of solution construction and pheromone update functions and convergence rules. We studied the data set used in NVR-BIP and NVR-TS. Our new method finds optimal solutions for small proteins and achieves perfect assignment on EIN and higher accuracy on MBP compared to NVR-TS. It is also more robust.