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Biophysical Journal 84:869-882 (2003)
© 2003 The Biophysical Society

Hybrid Global Optimization Algorithms for Protein Structure Prediction: Alternating Hybrids

J. L. Klepeis, M. J. Pieja and C. A. Floudas

Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263

Correspondence: Address reprint requests to C. A. Floudas, Tel.: 609-258-4595; Fax: 609-258-0211; E-mail: floudas{at}titan.princeton.edu.

Hybrid global optimization methods attempt to combine the beneficial features of two or more algorithms, and can be powerful methods for solving challenging nonconvex optimization problems. In this paper, novel classes of hybrid global optimization methods, termed alternating hybrids, are introduced for application as a tool in treating the peptide and protein structure prediction problems. In particular, these new optimization methods take the form of hybrids between a deterministic global optimization algorithm, the {alpha}BB, and a stochastically based method, conformational space annealing (CSA). The {alpha}BB method, as a theoretically proven global optimization approach, exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from computationally related enhancements. On the other hand, the independent CSA algorithm is highly efficient, though the method lacks theoretical guarantees of convergence. Furthermore, both the {alpha}BB method and the CSA method are found to identify ensembles of low-energy conformers, an important feature for determining the true free energy minimum of the system. The proposed hybrid methods combine the desirable features of efficiency and consistency, thus enabling the accurate prediction of the structures of larger peptides. Computational studies for met-enkephalin and melittin, employing sequential and parallel computing frameworks, demonstrate the promise for these proposed hybrid methods.




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