Protein Structure Optimization with a "Lamarckian" Ant Colony Algorithm
Authors: 
Mark T. Oakley, Elizabeth Grace Richardson, Harriet Carr, Roy L. JohnstonAuthors Info & Claims
Mark T. Oakley, Elizabeth Grace Richardson, Harriet Carr, Roy L. JohnstonAuthors Info & ClaimsPages 1548 - 1552
Abstract
We describe the LamarckiAnt algorithm: a search algorithm that combines the features of a "Lamarckian†genetic algorithm and ant colony optimization. We have implemented this algorithm for the optimization of BLN model proteins, which have frustrated energy landscapes and represent a challenge for global optimization algorithms. We demonstrate that LamarckiAnt performs competitively with other state-of-the-art optimization algorithms.
References
[1]
N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller, "Equation of State Calculations by Fast Computing Machines," J. Chemical Physics, vol. 21, no. 6 pp. 1087-1092, http://dx.doi.org/10.1063/ 1.1699114, 1953.
[2]
Z. Li and H.A. Scheraga, "Monte Carlo-Minimization Approach to the Multiple-Minima Problem in Protein Folding," Proc. Nat'l Academy of Sciences USA, vol. 84, no. 19, pp. 6611-6615, http://www.pnas.org/content/84/19/6611.abstract, 1987.
[3]
D.J. Wales and J.P.K. Doye, "Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing Up to 110 Atoms," J. Physical Chemistry A, vol. 101, no. 28, pp. 5111-5116, http://dx.doi.org/10.1021/jp970984n, 1997.
[4]
D.J. Wales and H.A. Scheraga, "Global Optimization of Clusters, Crystals, and Biomolecules," Science, vol. 285, no. 5432, pp. 1368-1372, http://dx.doi.org/10.1126/science.285.5432.1368, 1999.
[5]
M.T. Oakley, D.J. Wales, and R.L. Johnston, "Energy Landscape and Global Optimization for a Frustrated Model Protein," J. Physical Chemistry B, vol. 115, no. 39, pp. 11525-11529, http://dx.doi.org/10.1021/jp207246m, 2011.
[6]
M.T. Oakley, D.J. Wales, and R.L. Johnston, "The Effect of Nonnative Interactions on the Energy Landscapes of Frustrated Model Proteins," J. Atomic, Molecular, and Optical Physics, vol. 2012, article 192613, http://dx.doi.org/10.1155/2012/192613, 2012.
[7]
D.M. Deaven, N. Tit, J.R. Morris, and K.M. Ho, "Structural Optimization of Lennard-Jones Clusters by a Genetic Algorithm," Chemical Physics Letters, vol. 256, nos. 1/2, pp. 195-200, http://dx.doi.org/10.1016/0009- 2614(96)00406-x, June 1996.
[8]
R.L. Johnston, "Evolving Better Nanoparticles: Genetic Algorithms for Optimising Cluster Geometries," Dalton Trans., no. 22, pp. 4193-4207, http://dx.doi.org/10.1039/b305686d, 2003.
[9]
R. Unger, "The Genetic Algorithm Approach to Protein Structure Prediction," Applications of Evolutionary Computation in Chemistry, R.L. Johnston, ed., pp. 2697-2699, Springer, http://dx.doi.org/10.1007/b13936, 2004.
[10]
M.J. Vainio and M.S. Johnson, "Generating Conformer Ensembles Using a Multiobjective Genetic Algorithm," J. Chemical Information Modeling, vol. 47, no. 6, pp. 2462-2474, http://dx.doi.org/10.1021/ci6005646, Sept. 2007.
[11]
T. Huber and W.F. van Gunsteren, "SWARM-MD: Searching Conformational Space by Cooperative Molecular Dynamics," J. Physical Chemistry A, vol. 102, no. 29, pp. 5937-5943, http://dx.doi.org/10.1021/jp9806258, June 1998.
[12]
J. Lv, Y. Wang, L. Zhu, and Y. Ma, "Particle-Swarm Structure Prediction on Clusters," J. Chemical Physics, vol. 137, no. 8, p. 084104, http://dx.doi.org/10.1063/1.4746757, 2012.
[13]
A.J. Bennett, R.L. Johnston, E. Turpin, and J.Q. He, "Analysis of an Immune Algorithm for Protein Structure Prediction," Informatica, vol. 32, no. 2, pp. 245-251, 2008.
[14]
H.A. Bahamish, R. Abdullah, and R.A. Salam, "Protein Tertiary Structure Prediction Using Artificial Bee Colony Algorithm," Proc. Third Asia Int'l Conf. Modelling & Simulation, pp. 258-263, http://dx.doi.org/10.1109/ams.2009.47, 2009.
[15]
A. Shmygelska and H. Hoos, "An Ant Colony Optimisation Algorithm for the 2D and 3D Hydrophobic Polar Protein Folding Problem," BMC Bioinformatics, vol. 6, no. 1, article 30, http://dx.doi.org/10.1186/1471- 2105-6-30, 2005.
[16]
S. Fidanova and I. Lirkov, "Ant Colony System Approach for Protein Folding," Proc. Int'l Multiconf. Computer Science and Information Technology, pp. 887-891, http://dx.doi.org/10.1109/IMCSIT.2008.4747347, Oct. 2008.
[17]
F. Daeyaert, M. De Jonge, L. Koymans, and M. Vinkers, "An Ant Algorithm for the Conformational Analysis of Flexible Molecules," J. Computational Chemistry, vol. 28, no. 5, pp. 890-898, http://dx.doi.org/10.1002/jcc.20595, 2007.
[18]
T. Dresselhaus, J. Yang, S. Kumbhar, and M.P. Waller, "A Hybrid Metaheuristic Approach for Non-Local Optimization of Molecular Systems," J. Chemical Theory and Computation, vol. 9, pp. 2137-2149, http://dx.doi.org/10.1021/ct301079m, Feb. 2013.
[19]
M. Dorigo, V. Maniezzo, and A. Colorni, "Ant System: Optimization by a Colony of Cooperating Agents," IEEE Trans. Systems, Man, and Cybernetics --Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, http://dx.doi.org/10.1109/3477.484436, Feb. 1996.
[20]
P.G. Mezey, "Catchment Region Partitioning of Energy Hypersurfaces, I," Theoretica Chimica Acta, vol. 58, no. 4, pp. 309-330, http://dx.doi.org/10.1007/BF00553581, 1981.
[21]
J.D. Honeycutt and D. Thirumalai, "Metastability of the Folded States of Globular Proteins," Proc. Nat'l Academy of Sciences USA, vol. 87, no. 9, pp. 3526-3529, http://www.pnas.org/cgi/content/abstract/87/9/3526, May 1990.
[22]
R.S. Berry, N. Elmaci, J.P. Rose, and B. Vekhter, "Linking Topography of Its Potential Surface with the Dynamics of Folding of a Protein Model," Proc. Nat'l Academy of Sciences USA, vol. 94, no. 18, pp. 9520-9524, http://www.pnas.org/cgi/content/abstract/94/18/9520, 1997.
[23]
M.A. Miller and D.J. Wales, "Energy Landscape of a Model Protein," J. Chemical Physics, vol. 111, no. 14, pp. 6610-6616, http://dx.doi.org/ 10.1063/1.480011, 1999.
[24]
D.J. Wales and P.E.J. Dewsbury, "Effect of Salt Bridges on the Energy Landscape of a Model Protein," J. Chemical Physics, vol. 121, no. 20, pp. 10284-10290, http://dx.doi.org/10.1063/1.1810471, 2004.
[25]
S.A. Larrass, L.M. Pegram, H.L. Gordon, and S.M. Rothstein, "Efficient Generation of Low-Energy Folded States of a Model Protein. II. Automated Histogram Filtering," J. Chemical Physics, vol. 119, no. 24, pp. 13149-13158, http://dx.doi.org/10.1063/1.1628671, 2003.
[26]
P.W. Pan, H.L. Gordon, and S.M. Rothstein, "Local-Structural Diversity and Protein Folding: Application to All-Beta Off-Lattice Protein Models," J. Chemical Physics, vol. 124, no. 2, article 024905, http://dx.doi.org/10.1063/1.2151174, 2006.
[27]
J. Kim, J.E. Straub, and T. Keyes, "Statistical Temperature Molecular Dynamics: Application to Coarse-Grained Beta-Barrel-Forming Protein Models," J. Chemical Physics, vol. 126, no. 13, p. 135101, http://dx.doi.org/10.1063/1.2711812, 2007.
[28]
S.Y. Kim, "An Off-Lattice Frustrated Model Protein with a Six-Stranded Beta-Barrel Structure," J. Chemical Physics, vol. 133, no. 13, p. 135102, http://dx.doi.org/10.1063/1.3494038, 2010.
[29]
D.A. Evans and D.J. Wales, "Free Energy Landscapes of Model Peptides and Proteins," J. Chemical Physics, vol. 118, no. 8, pp. 3891-3897, http://dx.doi.org/10.1063/1.1540099, 2003.
[30]
D. Liu and J. Nocedal, "On the Limited Memory BFGS Method for Large Scale Optimization," Math. Programming, vol. 45, nos. 1-3, pp. 503-528, http://dx.doi.org/10.1007/BF01589116, Aug. 1989.
[31]
D.J. Wales, "GMIN: A Program for Finding Global Minima and Calculating Thermodynamic Properties from Basin-Sampling," http://wwww-ales. ch.cam.ac.uk/GMIN/, 2013.
[32]
T. Stützle and H.H. Hoos, "MAX-MIN Ant System," Future Generation Computer Systems, vol. 16, no. 8, pp. 889-914, http://dx.doi.org/10.1016/s0167-739x(00)00043-1, June 2000.
[33]
R. Salomon-Ferrer, D.A. Case, and R.C. Walker, "An Overview of the Amber Biomolecular Simulation Package," WIREs Computational Molecular Science, vol. 3, no. 2, pp. 198-210, http://dx.doi.org/10.1002/wcms.1121, Mar. 2013.
[34]
B.R. Brooks, C.L. Brooks, A.D. Mackerell, L. Nilsson, R.J. Petrella, B. Roux, Y. Won, G. Archontis, C. Bartels, S. Boresch, A. Caflisch, L. Caves, Q. Cui, A.R. Dinner, M. Feig, S. Fischer, J. Gao, M. Hodoscek, W. Im, K. Kuczera, T. Lazaridis, J. Ma, V. Ovchinnikov, E. Paci, R.W. Pastor, C.B. Post, J.Z. Pu, M. Schaefer, B. Tidor, R.M. Venable, H.L. Woodcock, X. Wu, W. Yang, D.M. York, and M. Karplus, "CHARMM: The Biomolecular Simulation Program," J. Computational Chemistry, vol. 30, no. 10, pp. 1545-1614, http://dx.doi.org/10.1002/jcc.21287, July 2009.
Index Terms
- Protein Structure Optimization with a "Lamarckian" Ant Colony Algorithm
Recommendations
Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures
A heuristic particle swarm ant colony optimization (HPSACO) is presented for optimum design of trusses. The algorithm is based on the particle swarm optimizer with passive congregation (PSOPC), ant colony optimization and harmony search scheme. HPSACO ...
Incorporating psychology model of emotion into ant colony optimization algorithm
MATH'07: Proceedings of the 12th WSEAS International Conference on Applied MathematicsThis paper presents a modification of the ant colony optimization algorithm (ACO) intended to introduce psychology factor of emotion into the algorithm. We define only two emotions ants could have, positive and negative, and correspond to two reaction ...
Hybridization strategies for continuous ant colony optimization and particle swarm optimization applied to data clustering
Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with ...
Comments
Information & Contributors
Information
Published In
Publisher
IEEE Computer Society Press
Washington, DC, United States
Publication History
Published: 01 November 2013
Published in TCBB Volume 10, Issue 6
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 75Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)1
Reflects downloads up to 23 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in