Can a continuum solvent model reproduce the free energy landscape of a beta -hairpin folding in water?
The folding free energy landscape of the C-terminal beta-hairpin of protein G is explored using the surface-generalized Born (SGB) implicit solvent model, and the results are compared with the landscape from an earlier study with explicit solvent model. The OPLSAA force field is used for the beta-hairpin in both implicit and explicit solvent simulations, and the conformational space sampling is carried out with a highly parallel replica-exchange method. Surprisingly, we find from exhaustive conformation space sampling that the free energy landscape from the implicit solvent model is quite different from that of the explicit solvent model. In the implicit solvent model some nonnative states are heavily overweighted, and more importantly, the lowest free energy state is no longer the native beta-strand structure. An overly strong salt-bridge effect between charged residues (E42, D46, D47, E56, and K50) is found to be responsible for this behavior in the implicit solvent model. Despite this, we find that the OPLSAA/SGB energies of all the nonnative structures are higher than that of the native structure; thus the OPLSAA/SGB energy is still a good scoring function for structure prediction for this beta-hairpin. Furthermore, the beta-hairpin population at 282 K is found to be less than 40% from the implicit solvent model, which is much smaller than the 72% from the explicit solvent model and approximately equal 80% from experiment. On the other hand, both implicit and explicit solvent simulations with the OPLSAA force field exhibit no meaningful helical content during the folding process, which is in contrast to some very recent studies using other force fields.
Zhou
Ruhong
R
IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA. ruhongz@us.ibm.com
Berne
Bruce J
BJ
D000818:Animals; D055592:Biophysical Phenomena; D001703:Biophysics; D003198:Computer Simulation; D008958:Models, Molecular; D015233:Models, Statistical; D008962:Models, Theoretical; D009419:Nerve Tissue Proteins / Q000737:chemistry; D011485:Protein Binding; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D013816:Thermodynamics; D014867:Water / Q000737:chemistry
C029152:G-substrate; D009419:Nerve Tissue Proteins; D014867:Water