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Ab initio prediction of the solution structures and populations of a cyclic pentapeptide in DMSO based on an implicit solvation model

โœ Scribed by Canan Baysal; Hagai Meirovitch


Publisher
Wiley (John Wiley & Sons)
Year
2000
Tongue
English
Weight
98 KB
Volume
53
Category
Article
ISSN
0006-3525

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โœฆ Synopsis


Using a recently developed statistical mechanics methodology, the solution structures and populations of the cyclic pentapeptide cyclo(D-Pro 1 -Ala 2 -Ala 3 -Ala 4 -Ala 5 ) in DMSO are obtained ab initio, i.e., without using experimental restraints. An important ingredient of this methodology is a novel optimization of implicit solvation parameters, which in our previous publication [Baysal, C.; Meirovitch, H. J Am Chem Soc 1998, 120, 800 -812] has been applied to a cyclic hexapeptide in DMSO. The molecule has been described by the simplified energy function

, where E GRO is the GROMOS force-field energy, k and A k are the atomic solvation parameter (ASP) and the solvent accessible surface area of atom k. This methodology, which relies on an extensive conformational search, Monte Carlo simulations, and free energy calculations, is applied here with E tot based on the ASPs derived in our previous work, and for comparison also with E GRO alone. For both models, entropy effects are found to be significant. For E tot , the theoretical values of proton-proton distances and 3 J coupling constants agree very well with the NMR results [Mierke, D. F.; Kurz, M.; Kessler, H. J Am Chem Soc 1994, 116, 1042-1049], while the results for E GRO are significantly worse. This suggests that our ASPs might be transferrable to other cyclic peptides in DMSO as well, making our methodology a reliable tool for an ab initio structure prediction; obviously, if necessary, parts of this methodology can also be incorporated in a best-fit analysis where experimental restraints are used.


๐Ÿ“œ SIMILAR VOLUMES


On the transferability of atomic solvati
โœ Canan Baysal; Hagai Meirovitch ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› Wiley (John Wiley & Sons) ๐ŸŒ English โš– 123 KB

A statistical mechanics methodology for predicting the solution structures and populations of peptides developed recently is based on a novel method for optimizing implicit solvation models, which was applied initially to a cyclic hexapeptide in DMSO (C. Baysal and H. Meirovitch, Journal of American