PSICO: Solving protein structures with constraint programming and optimization. In this paper we propose PSICO (Processing Structural Information with Constraint programming and Optimisation) as a constraint-based approach to determining protein structures compatible with distance constraints obtained from Nuclear Magnetic Resonance (NMR) data. We compare the performance of our proposed algorithm with DYANA (“Dynamics algorithm for NMR applications”) an existing commercial application based on simulated annealing. On a test case with experimental data on the dimeric protein Desulforedoxin, the method proposed here supplied similar results in less than 10 minutes compared to approximately 10 hours of computation time for DYANA. Although the quality of results can still be improved, this shows that CP technology can greatly reduce computation time, a major advantage because structural NMR technique generally demands multiple runs of structural computation.
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References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Campeotto, Federico; Dal Palù, Alessandro; Dovier, Agostino; Fioretto, Ferdinando; Pontelli, Enrico: A constraint solver for flexible protein model (2013)
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- Domes, Ferenc; Neumaier, Arnold: Constraint propagation on quadratic constraints (2010)
- Ullah, A.Dayem; Kapsokalivas, L.; Mann, M.; Steinhöfel, K.: Protein folding simulation by two-stage optimization (2009)
- Kamarainen, Olli; El Sakkout, Hani: A framework for constructing complete algorithms based on local search (2007)
- Krippahl, Ludwig; Barahona, Pedro: Applying constraint programming to rigid body protein docking (2005)
- Correia, Marco; Barahona, Pedro: Machine learned heuristics to improve constraint satisfaction (2004)
- Katriel, Irit; Thiel, Sven: Fast bound consistency for the global cardinality constraint (2003)
- Krippahl, Ludwig; Barahona, Pedro: PSICO: Solving protein structures with constraint programming and optimization (2002)