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The Journal of Membrane Biology

, Volume 251, Issue 3, pp 345–356 | Cite as

Computed Free Energies of Peptide Insertion into Bilayers are Independent of Computational Method

  • James C. Gumbart
  • Martin B. Ulmschneider
  • Anthony Hazel
  • Stephen H. White
  • Jakob P. Ulmschneider
Article
Part of the following topical collections:
  1. Lipid Membranes and Reactions at Lipid Interfaces: Theory, experiments, and applications

Abstract

We show that the free energy of inserting hydrophobic peptides into lipid bilayer membranes from surface-aligned to transmembrane inserted states can be reliably calculated using atomistic models. We use two entirely different computational methods: high temperature spontaneous peptide insertion calculations as well as umbrella sampling potential-of-mean-force (PMF) calculations, both yielding the same energetic profiles. The insertion free energies were calculated using two different protein and lipid force fields (OPLS protein/united-atom lipids and CHARMM36 protein/all-atom lipids) and found to be independent of the simulation parameters. In addition, the free energy of insertion is found to be independent of temperature for both force fields. However, we find major difference in the partitioning kinetics between OPLS and CHARMM36, likely due to the difference in roughness of the underlying free energy surfaces. Our results demonstrate not only a reliable method to calculate insertion free energies for peptides, but also represent a rare case where equilibrium simulations and PMF calculations can be directly compared.

Keywords

Peptide partitioning Transfer free energy Translocon Membrane Molecular dynamics 

Notes

Acknowledgements

J. C. G. acknowledges funding support from NSF (MCB-1452464) and NIH (R01-GM123169) and S. H. W acknowledges NIH support (RO1-GM74639). J. P. U. was supported by a China 1000 Plan's Program for Young Talents (13Z127060001). Simulation resources were provided by the Center for High Performance Computing, Shanghai Jiao Tong University and the Maryland Advanced Research Computing Center (MARCC). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Anton computer time was provided by the Pittsburgh Supercomputing Center (PSC) through Grant R01GM116961 from the National Institutes of Health. The Anton machine at PSC was generously made available by D. E. Shaw Research.

Compliance with Ethical Standards

Conflict of interest

All authors declare they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of PhysicsGeorgia TechAtlantaUSA
  2. 2.Department of ChemistryKing’s CollegeLondonUK
  3. 3.Department of Physiology & BiophysicsUniversity of California at IrvineIrvineUSA
  4. 4.Department of Physics and the Institute of Natural SciencesShanghai Jiao Tong UniversityShanghaiChina

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