The Influence of Different Treatments of Electrostatic Interactions on the Thermodynamics of Folding of Peptides

Andrij Baumketner and Joan-Emma Shea*
Department of Chemistry and Biochemistry, University of California at Santa Barbara, Santa Barbara, California 93106
J. Phys. Chem. B, 2005, 109 (45), pp 21322–21328
DOI: 10.1021/jp051325a
Publication Date (Web): June 28, 2005
Copyright © 2005 American Chemical Society

 Part of the special issue “Irwin Oppenheim Festschrift”.

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 Permanent address:  Institute for Condensed Matter Physics, 1 Svientsitsky Str., Lviv 79011, Ukraine. E-mail:  andrij@icmp.lviv.ua.

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 To whom correspondence should be addressed:  E-mail:  shea@ chem.ucsb.edu.

Abstract

Replica exchange molecular dynamics simulations were performed to investigate the effects of different electrostatic treatments on the structure and thermodynamics of a small β-hairpin forming peptide. Three different electrostatic schemes were considered:  regular cutoffs, generalized reaction field (GRF), and particle mesh Ewald (PME), with the peptide modeled using OPLS/AA all-atom force field with explicit TIP3P water. Both the GRF and PME methods yielded results consistent with experiment, with free energy surfaces displaying a single minimum corresponding to the native β-hairpin structure. In contrast, use of straight cutoffs led to the population of an additional local minimum corresponding to nonhairpin conformations that compete with the formation of the native β-hairpin at low temperatures. This extra minimum would not be apparent in conventional constant-temperature molecular dynamics simulations run for a few nanoseconds. This result points to the critical need of careful sampling of conformational space to assess the quality of different numerical treatments of long-range forces. While differences emerged in the nature of the unfolded states populated using PME and GRF approaches, simulations on the β-hairpin forming peptide and on two additional control peptides indicate that the GRF treatment of electrostatics offers a satisfactory compromise between accuracy and computational speed for the identification of low-energy conformations. A GRF-based approach emerges as a viable means for treating larger biological systems that would be prohibitively costly to simulate using PME methods.

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History

  • Published In Issue November 17, 2005
  • Received March 14, 2005

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