Abstract
A smooth empirical potential is constructed for use in off-lattice protein folding studies. Our potential is a function of the ammo acid labels and of the distances between the C α atoms of a protein. The potential is a sum of smooth surface potential terms that model solvent interactions and of pair potentials that are functions of a distance, with a smooth cutoff at 12 Ångström. Techniques include the use of a fully automatic and reliable estimator for smooth densities, of cluster analysis to group together amino acid pairs with similar distance distributions, and of quadratic programming to find appropriate weights with which the various terms enter the total potential. For nine small test proteins, the new potential has local minima within 1.3-4.7Å of the PDB geometry, with one exception that has an error of 8.5Å.
The authors gratefully acknowledge support of this research by the Austrian Fond zur Förderung der wissenschaftlichen Forschung (FWF) under grant P11516-MAT.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A. Bauer and A. Beyer, An improved pair potential to recognize native protein folds, Proteins: Struct. Funct. Gen. 18 (1994), 254–261.
A. Ben-Nairn, Statistical potentials extracted from protein structures: Are these meaningful potentials? J. Chem Phys. 107 (1997), 3698–3706.
F.C. Bernstein, T.F. Koetzle, G.J.B. Williams, E. Meyer, M.D. Bryce, J.R. Rogers, O. Kennard, T. Shikanouchi and M. Tasumi, The protein data bank: A computer-based archival file for macromolecular structures, J. Mol. Biol. 112 (1977), 535–542.
J.D. Bryngelson, When is a potential accurate enough for structure prediction? Theory and application to a random heteropolymer model of protein folding, J. Chem. Phys. 100 (1994), 6038–6045.
P.E. Gill, W. Murray and M.H. Wright, Practical optimization, Acad. Press, London 1981.
J. R. Gunn, A. Monge, R.A. Friesner and C.H. Marshall, Hierarchical algorithm for computer modeling of protein tertiary structure: folding of myoglobin to 6.2A resolution, J. Phys. Chem. 98 (1994), 702–711.
P. Hall and J.S. Marron, Lower bounds for bandwidth selection in density estimation, Probab. Th. Rel. Fields 90 (1991), 149–173.
M. Hendlich, P. Lackner, S. Weitckus, H. Floeckner, R. Froschauer, K. Gottsbacher, G. Casari and M. J. Sippl, Identification of native protein folds amongst a large number of incorrect models, J. Mol. Biol. 216 (1990), 167–180.
U. Hobohm, M. Scharf, R. Schneider and C. Sander, Selection of representative protein data sets, Protein Sci. 1 (1992), 409–417.
J. Holland, Genetic algorithms and the optimal allocation of trials, SIAM J. Computing 2 (1973), 88–105.
L. Holm and C. Sander, Database algorithm for generating protein backbone and side-chain co-ordinates from a Cα trace, J. Mol. Biol. 218 (1991), 183–194.
L. Holm and C. Sander, Fast and simple Monte Carlo algorithm for side chain optimization in proteins. Proteins 14 (1992), 213–223.
T. Huber and A.E. Torda, Protein fold recognition without Boltzmann statistics or explicit physical basis, submitted to Protein Sci. (1997).
M.C. Jones, J.S. Marron and S.J. Sheather, Progress in data-based bandwidth selection for kernel density estimation, Comput. Statist. 11 (1996), 337–381.
S. Kirkpatrick, C.D. Geddat, Jr., and M.P. Vecchi, Optimization by simulated annealing, Science 220 (1983), 671–680.
J. Kostrowicki and H.A. Scheraga, Application of the diffusion equation method for global optimization to oligopeptides, J. Phys. Chem. 96 (1992), 7442–7449.
M. Levitt, A simplified representation of protein confomations for rapid simulation of protein folding, J. Mol. Biol. 104 (1976), 59–107.
M. Levitt and A. Warshel, Computer simulation of protein folding, Nature 253 (1975), 694–698.
O. Lund, J. Hansen, S. Brunak and J. Bohr, Relationship between protein structure and geometrical constraints, Protein Sci. 5 (1996), 2217–2225.
C.D. Maranas, I.P. Androulakis and C.A. Floudas, A deterministic global optimization approach for the protein folding problem, pp. 133–150 in: Global minimization of nonconvex energy functions: molecular conformation and protein folding (P. M. Pardalos et al., eds.), Amer. Math. Soc, Providence, RI, 1996.
J.J. Moré and Z. Wu, Global continuation for distance geometry problems, SIAM J. Optimization 7 (1997), 814–836.
A. Neumaier, Molecular modeling of proteins and mathematical prediction of protein structure, SIAM Rev. 39 (1997), 407–460.
A. Neumaier, A nonuniqueness theorem for empirical protein potentials, in preparation.
M. Oobatake and G.M. Crippen, Residue-residue potential function for conformational analysis of proteins, J. Phys. Chem. 85 (1981), 1187–1197.
T. Schlick and A. Fogelson, TNPACK-A truncated Newton minimization package for large scale problems, ACM Trans. Math. Softw. 18 (1992), 46–70; 71-111.
G. Schwarz, Estimating the dimension of a model, Ann. Statistics 6 (1978), 461–464.
D. Shortle, Y. Wang, J. Gillespie and J.O. Wrabl, Protein folding for realists: a timeless phenomenon, Prot. Sci. 5 (1996), 991–1000.
M.J. Sippl, Boltzmann’s principle, knowledge based mean fields and protein folding, J. Comp. Aided Mol. Design 7 (1993), 473–501.
M.J. Sippl, M. Hendlich and P. Lackner, Assembly of polypeptide and protein backbone conformations from low energy ensembles of short fragments, Protein Sci. 1 (1992), 625–640.
D.R. Stampf, C.E. Felser and J.L. Sussman, PDBBrowse — a graphics interface to the Brookhaven Protein Data Bank, Nature 374 (1995), 572–574.
S. Sun, Reduced representation model of protein structure prediction: statistical potential and genetic algorithms, Protein Sci. 2 (1993), 762–785.
S. Sun, Reduced representation approach to protein tertiary structure prediction: statistical potential and simulated annealing, J. Theor. Biol. 172 (1995), 13–32.
P.D. Thomas and K.A. Dill, Statistical potentials extracted from protein structures: How accurate are they? J. Mol. Biol. 257 (1996), 457–469.
P. Ulrich, W. Scott, W.F. van Gunsteren and A. Torda, Protein structure prediction force fields: parametrization with quasi Newtonian dynamics, Proteins 27 (1997), 367–384.
L.L. Walsh, Navigating the Brookhaven Protein Data Bank, Cabos Communication 10 (1994), 551–557.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Neumaier, A., Dallwig, S., Huyer, W., Schichl, H. (1999). New Techniques for the Construction of Residue Potentials for Protein Folding. In: Deuflhard, P., Hermans, J., Leimkuhler, B., Mark, A.E., Reich, S., Skeel, R.D. (eds) Computational Molecular Dynamics: Challenges, Methods, Ideas. Lecture Notes in Computational Science and Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58360-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-58360-5_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63242-9
Online ISBN: 978-3-642-58360-5
eBook Packages: Springer Book Archive