HOPE: A homotopy optimization method for protein structure prediction

Journal of Computational Biology

Volumn 12, Issue 10, 2005, Pages 1275-1288

  Dunlavy, Daniel M ^a,e   O'Leary, Dianne P ^b   Klimov, Dmitri ^c   Thirumalai, D ^d  

^a UNIVERSITY OF MARYLAND   (United States)

^b Institute of Advanced Computer Science   (United States)

^c GEORGE MASON UNIVERSITY   (United States)

^d University of Maryland   (United States)

^e Applied Mathematics and Scientific Computation Program   (United States)

Author keywords

Energy minimization; Global optimization; Homotopy method; Protein structure prediction; Simulated annealing

Indexed keywords

PROTEIN;

AMINO ACID SEQUENCE; ANALYTIC METHOD; ARTICLE; ENERGY; EVALUATION; HOMOTOPY OPTIMIZATION USING PERTUBATION AND ENSEMBLE; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MOLECULAR MODEL; PREDICTIVE VALIDITY; PRIORITY JOURNAL; PROTEIN CONFORMATION; PROTEIN STRUCTURE; STATISTICAL ANALYSIS; STRUCTURE ANALYSIS; ALGORITHM; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; COMPARATIVE STUDY; PROTEIN DATABASE; THERMODYNAMICS;

ALGORITHMS; AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; MODELS, MOLECULAR; MOLECULAR STRUCTURE; PROTEIN CONFORMATION; PROTEINS; THERMODYNAMICS;

EID: 33644995453     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2005.12.1275     Document Type: Article

References (26)

1. Ackermann, S., and Kliesch, W. 1998. Computation of stationary points via a homotopy method. Theor. Chain. Acc. 99, 255-264.
2. Allgower, E.L., and Georg, K. 2003. Introduction to Numerical Continuation Methods, SIAM, Philadelphia, PA.
3. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., and Bourne, P.E. 2000. The protein data bank. Nucl. Acids Res. 28, 235-242.
4. Brodmeier, T., and Pretsch, E. 1994. Application of genetic algorithms in molecular modeling. J. Comput. Chem. 15, 588-595.
5. Coleman, T.F., and Li, Y. 1994. On the convergence of reflective Newton methods for large-scale nonlinear minimization subject to bounds. Math. Program. 67, 189-224.
6. Coleman, T.F., and Li, Y. 1996. An interior, trust region approach for nonlinear minimization subject to bounds. SlAM J. Optim. 6, 418-445.
7. Coleman, T.F., and Wu, Z. 1994. Parallel continuation-based global optimization for molecular conformation and protein folding. Technical report MCS-P443-0694, Argonne National Laboratory.
8. Das, B., Meirovitch, H., and Navon, I. M. 2003. Performance of enriched methods for large unconstrained optimization as applied to models of proteins. J. Comput. Chem. 24, 1222-1231.
9. Dunlavy, D.M., and O'Leary, O.P. 2005. HOPE: A homotopy method for unconstrained minimization. In preparation.
10. Fiser, A., and Sali, A. 2003. Comparative protein structure modeling. In Chasman, D., ed., Protein Structure: Determination, Analysis, and Applications for Drug Discovery, 167-206. Marcel Dekker, New York, NY.
11. Griewank, A., Juedes, D., and Utke, J. 1996. Algorithm 755: ADOL-C: A package for the automatic differentiation of algorithms written in C/C++. ACM Trans. Math. Software 22, 131-167.
12. Hunjan, J.S., Sarkar, S., and Ramaswamy, R. 2002. Global optimization on an evolving energy landscape. Phys. Rev. E 66, 046704.
13. IUPAC-IUB, Commission on Biochemical Nomenclature (CBN). 1970. Abbreviations and symbols for the description of the conformation of polypeptide chains. Biochemistry 9, 3471-3479.
14. Kawai, H., Kikuchi, T., and Okamoto, Y. 1989. A prediction of tertiary structures of peptide by the Monte Carlo simulated annealing method. Protein Eng. 3, 85-94.
15. Le Grand, S.M., and Merz, Jr., K.M. 1993. The application of the genetic algorithm to the minimization of potential energy functions. J. Global Optim. 3, 49-66.
16. Li, Z., and Scheraga, H. A. 1987. Monte Carlo-minimization approach to the multiple-minima problem in protein folding. Proc. Natl. Acad. Sci. USA 84, 6611-6615.
17. Ngo, J.T., and Marks, J. 1992. Computational complexity of a problem in molecular structure prediction. Protein Eng. 5, 313-321.
18. Salamon, P., Sibani, P., and Frost, R. 2002. Facts, Conjectures, and Improvements for Simulated Annealing, SIAM, Philadelphia, PA.
19. Schelstraete, S., Schepens, W., and Verscheide, H. 1998. Energy minimization by smoothing techniques: A survey. In Balbuena, P.B., and Seminario, J.M., eds., Molecular Dynamics. From Classical to Quantum Methods, 129-185. Elsevier, New York.
20. Shin, J.K., and Jhon, M.S. 1991. High directional Monte Carlo procedure coupled with the temperature heating and annealing as a method to obtain the global energy minimum structure of polypeptides and proteins. Biopolymers 31, 177-185.
21. Veitshans, T., Klimov, D., and Thirumalai, D. 1997. Protein folding kinetics: Timescales, pathways, and energy landscapes in terms of sequence-dependent properties. Fold. Des. 2, 1-22.
22. Wales, D.J., and Scheraga, H.A. 1999. Global optimization of clusters, crystals, and biomolecules. Science 284, 1368-1372.
23. Watson, L.T., and Haftka, R.T. 1989. Modern homotopy methods in optimization. Comput. Methods Appl. Mech. Eng. 74, 289-304.
24. Wilson, S.R., and Cui, W. 1990. Applications of simulated annealing to peptides. Biopolymers 29, 225-235.
25. Wilson, S.R., Cui, W., Moskowitz, J.W., and Schmidt, K.E. 1988. Conformational analysis of flexible molecules: Location of the global minimum energy conformation by the simulated annealing method. Tetrahedron Lett. 29, 4373-4376.
26. Xie, D., and Schlick, T. 1999. Efficient implementation of the truncated-Newton algorithm for large-scale chemistry applications. SIAM J. Optim. 10, 132-154.