Klepeis, J. L.; Pieja, M. J.; Floudas, C. A. A new class of hybrid global optimization algorithms for peptide structure prediction: integrated hybrids. (English) Zbl 1196.90134 Comput. Phys. Commun. 151, No. 2, 121-140 (2003). Summary: A novel class of hybrid global optimization methods for application to the structure prediction in protein-folding problem is introduced. These optimization methods take the form of a hybrid between a deterministic global optimization algorithm, the \(\alpha \)BB, and a stochastically based method, conformational space annealing (CSA), and attempt to combine the beneficial features of these two algorithms. The \(\alpha \)BB method as previously extant exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from improvements in the computational front. Computational studies for met-enkephalin demonstrate the promise for the proposed hybrid global optimization method. Cited in 9 Documents MSC: 90C90 Applications of mathematical programming 92E99 Chemistry Software:CHARMM; alphaBB × Cite Format Result Cite Review PDF Full Text: DOI References: [1] Adjiman, C.; Androulakis, I.; Floudas, C. A., A global optimization method, \(α\) BB, for general twice-differential constrained npls. I. Theoretical advances, Comput. Chem. Engrg., 22, 1137-1158 (1998) [2] Adjiman, C.; Androulakis, I.; Floudas, C. A., A global optimization method, \(α\) BB, for general twice-differentiable constrained nlps. II. Implementation and computational results, Comput. Chem. Engrg., 22, 1159-1179 (1998) [3] Adjiman, C.; Androulakis, I.; Floudas, C. A., Global optimization of mixed-integer nonlinear problems, AiChE J., 46, 1769-1797 (2000) [4] Klepeis, J. L.; Floudas, C. A.; Morikis, D.; Lambris, J., Predicting peptide structures using nmr data and deterministic global optimization, J. Comput. Chem., 20, 1354-1370 (1999) [5] Klepeis, J. L.; Floudas, C. A., Free energy calculations for peptides via deterministic global optimization, J. Chem. Phys., 110, 7491-7512 (1999) [6] Klepeis, J. L.; Androulakis, I.; Ierapetritou, M.; Floudas, C. A., Predicting solvated peptide conformations via global minimization of energetic atom-to-atom interations, Comput. Chem. Engrg., 22, 765-788 (1998) [7] Floudas, C. A., Deterministic Global Optimization: Theory, Algorithms, and Applications (2000), Kluwer Academic Publications · Zbl 1037.90002 [8] Klepeis, J. L.; Schafroth, H. D.; Westerberg, K. M.; Floudas, C. A., Deterministic global optimization and ab initio approaches for the structure prediction of polypeptides, dynamics of protein folding and protein-protein interaction, (Advances in Chemical Physics, 120 (2002), Wiley), 254-457 [9] Lee, J.; Scheraga, H.; Rackovsky, S., Conformational analysis of the 20-residue membrane-bound portion of melittin by conformational space annealing, Biopolymers, 46, 103-115 (1998) [10] Lee, J.; Scheraga, H.; Rackovsky, S., New optimization method for conformational energy calculations on polypeptides: Conformational space annealing, J. Comp. Chem., 18, 1222-1232 (1997) [11] Lee, J.; Scheraga, H., Conformational space annealing by parallel computations: Extensive conformational search of met-enkephalin and the 20-residue membrane-bound portion of melittin, Intl. J. Quantum Chem., 75, 255-265 (1999) [12] Ripoll, D.; Liwo, A.; Scheraga, H., New developments of the electrostatically driven Monte Carlo method: Tests on the membrane-bound portion of melittin, Biopolymers, 46, 117-126 (1998) [13] Lee, J.; Pillardy, J.; Czaplewski, C.; Arnautova, Y.; Ripoll, D. R.; Liwo, A.; Gibson, K. D.; Wawak, R. J.; Scheraga, H., Efficient parallel algorithms in global optimization of potential energy functions for peptides, proteins and crystals, Comput. Phys. Comm., 128, 399-411 (2000) · Zbl 0953.92003 [14] Anfinsen, C.; Haber, E.; Sela, M.; White, F., The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain, Proc. Natl. Acad. Sci. USA, 47, 1309-1314 (1961) [15] Anfinsen, C., Principles that govern the folding of protein chains, Science, 181, 223-229 (1973) [16] Floudas, C. A.; Klepeis, J. L.; Pardalos, P., Global optimization approaches in protein folding and peptide docking, DIMACS Ser. Discrete Math. Theoret. Comput. Sci., 47, 141-171 (1999) · Zbl 0931.92014 [17] Weiner, S.; Kollman, P.; Case, D.; Singh, U.; Ghio, C.; Alagona, G.; Profeta, S.; Weiner, P., A new force field for molecular mechanical simulation of nucleic acids and proteins, J. Amer. Chem. Soc., 106, 765-784 (1984) [18] Weiner, S.; Kollman, P.; Nguyen, D.; Case, D., An all-atom force field for simulations of proteins and nucleic acids, J. Comp. Chem., 7, 230-252 (1986) [19] Brooks, B.; Bruccoleri, R.; Olafson, B.; States, D.; Swaminathan, S.; Karplus, M., Charmm: A program for macromolecular energy minimization and dynamics calculations, J. Comp. Chem., 4, 187-217 (1983) [20] Nemethy, G.; Gibson, K.; Palmer, K.; Yoon, C.; Paterlini, G.; Zagari, A.; Rumsey, S.; Scharaga, H., Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ecepp/3 algorithm with application to proline-containing peptides, J. Phys. Chem., 96, 6472-6484 (1992) [21] Levitt, M., Protein folding by restrained energy minimization and molecular dynamics, J. Mol. Biol., 170, 723-764 (1983) [22] W. van Gunsteren, H. Berendsen, Gromos, Groningen Molecular Simulation, Groningen, The Netherlands; W. van Gunsteren, H. Berendsen, Gromos, Groningen Molecular Simulation, Groningen, The Netherlands [23] Lii, J.-H.; Allinger, N., Molecular mechanics: The mm3 force field for hydrocarbons. 2: Vibrational frequencies and thermodynamics, J. Amer. Chem. Soc., 111, 8566-8575 (1989) [24] Lii, J.-H.; Allinger, N., Molecular mechanics: The mm3 force field for hydrocarbons. 3: The van der Waals potentials and crystal data for aliphatic and aromatic hydrocarbons, J. Amer. Chem. Soc., 111, 8576-8582 (1989) [25] Li, Z.; Scheraga, H., Structure and free energy of complex thermodynamic systems, J. Mol. Struct. (Theochem.), 179, 333-352 (1988) [26] Forrest, S., Genetic algorithms: Principles of natural selection applied to computation, Science, 261, 872-878 (1993) [27] Sun, S., Reduced representation model of protein structure prediction: Stastical potential and genetic algorithms, Protein Sci., 2, 762-785 (1993) [28] LeGrand, S.; Merz, K., The application of the genetic algorithm to the minimization of potential energy functions, J. Global Optim., 3, 49-66 (1993) · Zbl 0766.90090 [29] Kirkpatrick, S.; Gelatt, C.; Vecchi, M., Optimization by simulated annealing, Science, 220, 671-679 (1983) · Zbl 1225.90162 [30] Floudas, C. A., Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications (1995), Oxford University Press · Zbl 0886.90106 [31] Maranas, C.; Floudas, C. A., Global minimum potential energy conformations of small molecules, J. Global Optim., 4, 135-170 (1994) · Zbl 0797.90114 [32] Androulakis, I. P.; Maranas, C. D.; Floudas, C. A., \(α\) BB: A global optimization method for general constrained nonconvex problems, J. Global. Optim., 7, 337-363 (1995) · Zbl 0846.90087 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.