Next: Chapter 6
Up: Dynamic and Stochastic Protein
Previous: The C Forcefield
Probability Grid Monte Carlo provides a new method for predicting
all-atom protein conformations from C
coordinates. Most of the
previous methods [85][83][78] use database searches
to find conformations for several consecutive residues which match the
configuration of the C coordinates being used as a template. The
PGMC method, in contrast, uses probabilities for individual
residues to guide Monte Carlo searches. The method produces results
as good as or better than the previously published methods
for the protein flavodoxin. In general, backbone conformations are modeled
accurately to within 0.6 Å rms deviation from the crystal structure.
Most of the error comes at the C-terminal ends and in turns, while the
extended secondary structures, 
helices and 
sheets are modeled much better,
with a typical rms deviation of 0.3 Å or better. Sidechain conformations
are not modeled as accurately. Sidechain rms deviations over 2.0 Å can be
expected for large proteins where the computational cost of optimizing
all sidechains concurrently is very large. The sidechain deviation for
the small protein crambin was much better, averaging 1.87 Å for 25
models. Overall rms deviations are typically better than 2.0 Å, and
depend primarily upon the amount of time spent optimizing the sidechain
conformations.
The PGMC C Builder is an extremely fast, automatic method. For proteins
the size of crambin, both the backbone and sidechain can be modeled accurately
in less than 20 minutes on a standard workstation. This may enable the
method to be used for evaluating numerous possible C conformations,
such as those generated from a lattice-base protein folding simulation.
To this end, a simple C forcefield has been developed which enables
lattice conformations to be smoothed, thereby providing a template for
the C Builder.
References
- 101
- I.T. Weber et al., Science, 243, 928-931 (1989).
- 102
- R.S. Struthers, D.H. Kitson, and A.T. Hagler, Proteins: Structure, Function, Genet.,
9, 1-11 (1991).
- 103
- J.F. Leszczynski and G.D. Rose, Science,
234, 849-855 (1986).
- 104
- C. Chothia and A.M. Lesk, J. Mol. Biol., 196, 901-917 (1987).
- 105
- T.A. Jones and S. Thirup, EMBO J., 5, 819-822
(1986).
- 106
- R.E. Bruccoleri and M. Karplus, Biopolymers, 26,
137-168 (1987).
- 107
- P.S. Shenkin et al., Biopolymers, 26, 2053-2085
(1987).
- 108
- A.C.R. Martin, J.C. Cheetham, and A.R. Rees,
Proc. Natl. Acad. Sci., USA, 86, 9268-9272 (1989).
- 109
- S.L. Mayo, B.D. Olafson, and W.A. Goddard III,
J. Phys. Chem., 94, 8897-8909 (1990).
- 110
- BIOGRAF/POLYGRAF. Copyright by Molecular
Simulations, Inc. (1992).
- 111
- N. Go and H.A. Scheraga, Macromolecules, 3, 178-187 (1970).
- 112
- N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth,
and A.H. Teller, J. Chem. Phys., 21, 1087-1092 (1953).
- 113
- D. Eisenberg and A.D. McLachlan, Nature,
319, 199-203 (1986).
- 114
- Y. Satow et al., J. Mol. Biol., 190, 593-604
(1986).
- 115
- S. Sheriff et al., Proc. Natl. Acad. Sci., USA, 84, 8075-8079 (1987).
- 116
- C. Chothia et al., Nature, 342, 887-883 (1989).
- 117
- R.M. Fine et al., Proteins: Structure, Function, Genet., 1, 342-362 (1986).
- 118
- R.E. Bruccoleri, E. Haber, and J. Novotný,
Nature, 335, 564-568 (1988).
- 119
- D.R. Davies, E.A. Padlan, and S. Sheriff, Annu. Rev. Biochem., 59, 439-473, (1990).
- 120
- E.A. Padlan, G.H. Cohen, and D.R. Davies, Ann.
Inst. Pasteur/Immunol., 136C, 259-294 (1985).