Conformational space annealing and an off-lattice frustrated model protein

A global optimization method, conformational space annealing (CSA), is applied to study a 46-residue protein with the sequence B9N3(LB)4N3B9N3(LB)5L, where B, L, and N designate hydrophobic, hydrophilic, and neutral residues, respectively. The 46-residue BLN protein is folded into the native state of a four-stranded β barrel. It has been a challenging problem to locate the global minimum of the 46-residue BLN protein since the system is highly frustrated and consequently its energy landscape is quite rugged. The CSA successfully located the global minimum of the 46-mer for all 100 independent runs. The CPU time for CSA is about seventy times less than that for simulated annealing (SA), and its success rate (100%) to find the global minimum is about eleven times higher. The amount of computational effort used for CSA is also about ten times less than that of the best global optimization method yet applied to the 46-residue BLN protein, the quantum thermal annealing with renormalization. The 100 separate CS...

[1]  Quantum Thermal Annealing with Renormalization: Application to a Frustrated Model Protein , 2001 .

[2]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[3]  D. Thirumalai,et al.  Kinetics of protein folding: Nucleation mechanism, time scales, and pathways , 1995 .

[4]  B. Berne,et al.  A renormalization approach to quantum thermal annealing , 2000, Annalen der Physik.

[5]  S. Rackovsky,et al.  Conformational analysis of the 20-residue membrane-bound portion of melittin by conformational space annealing. , 1998, Biopolymers.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Bruce J. Berne,et al.  Global Optimization : Quantum Thermal Annealing with Path Integral Monte Carlo , 1999 .

[8]  H. Scheraga,et al.  Monte Carlo-minimization approach to the multiple-minima problem in protein folding. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[9]  C. Brooks,et al.  Exploring the space of protein folding Hamiltonians: The balance of forces in a minimalist β-barrel model , 1998 .

[10]  A. Liwo,et al.  Calculation of protein conformation by global optimization of a potential energy function , 1999, Proteins.

[11]  D. Thirumalai,et al.  Protein folding kinetics: timescales, pathways and energy landscapes in terms of sequence-dependent properties. , 1996, Folding & design.

[12]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[13]  Zhuyan Guo,et al.  Nucleation mechanism for protein folding and theoretical predictions for hydrogen‐exchange labeling experiments , 1995 .

[14]  A. Liwo,et al.  Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[15]  J. Onuchic,et al.  Folding funnels and frustration in off-lattice minimalist protein landscapes. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Bernd Hartke,et al.  Global cluster geometry optimization by a phenotype algorithm with Niches: Location of elusive minima, and low‐order scaling with cluster size , 1999 .

[17]  R S Berry,et al.  Linking topography of its potential surface with the dynamics of folding of a protein model. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[18]  New optimization method for conformational energy calculations on polypeptides: Conformational space annealing , 1997 .

[19]  Harold A. Scheraga,et al.  Conformational space annealing by parallel computations: Extensive conformational search of Met‐enkephalin and of the 20‐residue membrane‐bound portion of melittin , 1999 .

[20]  D. Thirumalai,et al.  The nucleation-collapse mechanism in protein folding: evidence for the non-uniqueness of the folding nucleus. , 1997, Folding & design.

[21]  Adam Liwo,et al.  Hierarchical energy-based approach to protein-structure prediction: Blind-test evaluation with CASP3 targets , 2000 .

[22]  David J. Wales,et al.  Energy Landscape of a Model Protein , 1999, cond-mat/9904304.

[23]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[24]  C. Brooks,et al.  Thermodynamics of protein folding: A statistical mechanical study of a small all-β protein , 1997 .

[25]  A. Liwo,et al.  Protein structure prediction by global optimization of a potential energy function. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Huafeng Xu,et al.  Multicanonical jump walk annealing: An efficient method for geometric optimization , 2000 .

[27]  R. Berry,et al.  Principal coordinate analysis on a protein model , 1999 .

[28]  D. Thirumalai,et al.  Folding kinetics of proteins : a model study , 1992 .

[29]  John E. Straub,et al.  FOLDING MODEL PROTEINS USING KINETIC AND THERMODYNAMIC ANNEALING OF THE CLASSICAL DENSITY DISTRIBUTION , 1995 .

[30]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[31]  D Thirumalai,et al.  The nature of folded states of globular proteins , 1992, Biopolymers.

[32]  D. Thirumalai,et al.  Metastability of the folded states of globular proteins. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[33]  David J. Wales,et al.  Free energy landscapes of model peptides and proteins , 2003 .

[34]  Choi,et al.  Optimization by multicanonical annealing and the traveling salesman problem. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.