An improved hybrid global optimization method for protein tertiary structure prediction

First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization...

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Veröffentlicht in:Computational optimization and applications Jg. 45; H. 2; S. 377 - 413
Hauptverfasser: McAllister, Scott R., Floudas, Christodoulos A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.03.2010
Springer Nature B.V
Schlagworte:
Algorithms
Convex and Discrete Geometry
Energy
Hypotheses
Management Science
Mathematics
Mathematics and Statistics
Methods
Operations Research
Operations Research/Decision Theory
Optimization
Optimization algorithms
Optimization techniques
Physics
Protein folding
Proteins
Statistics
Studies
Protein tertiary structure prediction
Hybrid global optimization algorithm
ISSN:0926-6003, 1573-2894
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Zusammenfassung:First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the α BB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations.
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ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-009-9277-y