Replica Exchange Molecular Dynamics: A Practical Application Protocol with Solutions to Common Problems and a Peptide Aggregation and Self-Assembly Example

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1777)

Abstract

Protein aggregation is associated with many human diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and type II diabetes (T2D). Understanding the molecular mechanism of protein aggregation is essential for therapy development. Molecular dynamics (MD) simulations have been shown as powerful tools to study protein aggregation. However, conventional MD simulations can hardly sample the whole conformational space of complex protein systems within acceptable simulation time as it can be easily trapped in local minimum-energy states. Many enhanced sampling methods have been developed. Among these, the replica exchange molecular dynamics (REMD) method has gained great popularity. By combining MD simulation with the Monte Carlo algorithm, the REMD method is capable of overcoming high energy-barriers easily and of sampling sufficiently the conformational space of proteins. In this chapter, we present a brief introduction to REMD method and a practical application protocol with a case study of the dimerization of the 11–25 fragment of human islet amyloid polypeptide (hIAPP(11–25)), using the GROMACS software. We also provide solutions to problems that are often encountered in practical use, and provide some useful scripts/commands from our research that can be easily adapted to other systems.

Key words

Replica exchange method Molecular dynamics simulations Free energy landscape Protein aggregation Human islet amyloid polypeptide GROMACS REMD 

Notes

Acknowledgments

This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under contract number HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This research was supported (in part) by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

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Copyright information

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

Authors and Affiliations

  1. 1.Department of Physics, State Key Laboratory of Surface Physics, Key Laboratory for Computational Physical Sciences (MOE)Fudan UniversityShanghaiP.R. China
  2. 2.Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation ProgramNational Cancer InstituteFrederickUSA
  3. 3.Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine Sackler School of MedicineTel Aviv UniversityTel AvivIsrael

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