Skip to main content
SpringerLink
Log in

Computing Potential of the Mean Force Profiles for Ion Permeation Through Channelrhodopsin Chimera, C1C2

  • Protocol
  • First Online:
Channelrhodopsin

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2191))

Abstract

Umbrella sampling, coupled with a weighted histogram analysis method (US-WHAM), can be used to construct potentials of mean force (PMFs) for studying the complex ion permeation pathways of membrane transport proteins. Despite the widespread use of US-WHAM, obtaining a physically meaningful PMF can be challenging. Here, we provide a protocol to resolve that issue. Then, we apply that protocol to compute a meaningful PMF for sodium ion permeation through channelrhodopsin chimera, C1C2, for illustration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ding Y, Li Y, Yu G (2016) Exploring bio-inspired quinone-based organic redox flow batteries: a combined experimental and computational study. Chem 1:790–801

    Article  CAS  Google Scholar 

  2. Young DC (2009) Computational drug design: a guide for computational and medicinal chemists. John Wiley & Sons, Hoboken, NJ

    Book  Google Scholar 

  3. Chan WK, Lorenzi PL, Anishkin A, Purwaha P, Rogers DM, Sukharev S, Rempe SB, Weinstein J (2014) The glutaminase activity of L-asparaginase is not required for anticancer activity against ASNS-negative cells. Blood 123:3596–3606

    Article  CAS  Google Scholar 

  4. Anishkin A, Vanegas JM, Rogers DM, Lorenzi PL, Chan WK, Purwaha P, Weinstein JN, Sukharev S, Rempe SB (2015) Catalytic role of the substrate defines specificity of therapeutic L-asparaginase. J Mol Biol 427:2867–2885

    Article  CAS  Google Scholar 

  5. Ghasemlou M, Daver F, Ivanova EP, Adhikari B (2019) Bio-inspired sustainable and durable superhydrophobic materials: from nature to market. J Mater Chem A 7:16643–16670

    Article  CAS  Google Scholar 

  6. Rempe LS, Brinker CJ, Rogers DM, Jiang YB, Yang S (2016) National Technology and Engineering Solutions of Sandia LLC. Biomimetic membranes and methods of making biomimetic membranes. US Patent 9,486,742

    Google Scholar 

  7. Fu Y, Croissant JG, Cecchi, JL, Rempe, SB, Brinker CJ (2018) Ultra-thin enzymatic liquid membrane for CO2 separation and capture. Nat Commun 9:990

    Google Scholar 

  8. Tang C, Wang Z, Petrinić I, Fane AG, Hélix-Nielsen C (2015) Biomimetic aquaporin membranes coming of age. Desalination 368:89–105

    Article  CAS  Google Scholar 

  9. Kim CK, Adhikari A, Deisseroth K (2017) Integration of optogenetics with complementary methodologies in systems neuroscience. Nat Rev Neurosci 18:222–235

    Article  CAS  Google Scholar 

  10. Fenno L, Yizhar O, Deisseroth K (2011) The development and application of optogenetics. Annu Rev Neurosci 34:389–412

    Article  CAS  Google Scholar 

  11. Percival SJ, Small LJ, Spoerke ED, Rempe SB (2018) Polyelectrolyte layer-by-layer deposition on nanoporous supports for ion selective membranes. RSC Adv 8:32992–32999

    Article  CAS  Google Scholar 

  12. VanGordon MR, Gyawali G, Rick SW, Rempe SB (2017) Atomistic study of intramolecular interactions in the closed-state channelrhodopsin chimera, C1C2. Biophys J 112:943–952

    Article  CAS  Google Scholar 

  13. Lórenz-Fonfría VA, Joachim H (2014) Channelrhodopsin unchained: structure and mechanism of a light-gated cation channel. Biochim Biophys Acta 1837:626–642

    Article  Google Scholar 

  14. Kato HE, Zhang F, Yizhar O, Ramakrishnan C, Nishizawa T, Hirata K, Ito J, Aita Y, Tsukazaki T, Hayashi S, Hegemann P (2012) Crystal structure of the channelrhodopsin light-gated cation channel. Nature 482:369–374

    Article  CAS  Google Scholar 

  15. Bruegmann T, Van Bremen T, Vogt CC, Send T, Fleischmann BK, Sasse P (2015) Optogenetic control of contractile function in skeletal muscle. Nat Commun 6:7153

    Article  CAS  Google Scholar 

  16. VanGordon MR, Prignano LA, Dempski RE, Rick SW, Rempe SB (2019) Channelrhodopsin C1C2: photocycle kinetics and interactions near the central gate. bioRxiv:807–909

    Google Scholar 

  17. Cheng C, Kamiya M, Takemoto M, Ishitani R, Nureki O, Yoshida N, Hayashi S (2018) An atomistic model of a precursor state of light-induced channel opening of channelrhodopsin. Biophys J 115:1281–1291

    Article  CAS  Google Scholar 

  18. Takemoto M, Kato HE, Koyama M, Ito J, Kamiya M, Hayashi S, Maturana AD, Deisseroth K, Ishitani R, Nureki O (2015) Molecular dynamics of channelrhodopsin at the early stages of channel opening. PLoS One 10:e0131094

    Article  Google Scholar 

  19. Ardevol A, Hummer G (2018) Retinal isomerization and water-pore formation in channelrhodopsin-2. Proc Natl Acad Sci U S A 115:3557–3562

    Article  CAS  Google Scholar 

  20. Sprik M, Ciccotti G (1998) Free energy from constrained molecular dynamics. J Chem Phys 109:7737–7744

    Article  CAS  Google Scholar 

  21. den Otter WK, Briels WJ (1998) The calculation of free-energy differences by constrained molecular-dynamics simulations. J Chem Phys 109:4139–4146

    Article  Google Scholar 

  22. Zwanzig RW (1954) High-temperature equation of state by a perturbation method. I. Nonpolar gases. J Chem Phys 22:1420–1426

    Article  CAS  Google Scholar 

  23. Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci USA 99:12562–12566

    Article  CAS  Google Scholar 

  24. Chaudhari MI, Vanegas JM, Pratt LR, Muralidharan A, & Rempe SB (2020) Hydration mimicry by membrane ion channels. Annu Rev Phys Chem 71:1–24

    Google Scholar 

  25. Basdogan Y, Groenenboom MC, Henderson E, De S, Rempe SB, Keith JA (2020) Machine learning-guided approach for studying solvation environments. J Chem Theory Comput 16:633–642

    Article  Google Scholar 

  26. Baştuğ T, Chen PC, Patra SM, Kuyucak S (2008) Potential of mean force calculations of ligand binding to ion channels from Jarzynski’s equality and umbrella sampling. J Chem Phys 128:155104

    Article  Google Scholar 

  27. Kumar S, Rosenberg JM, Bouzida D, Swendsen RH, Kollman PA (1992) The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comp Chem 13:1011–1021

    Article  CAS  Google Scholar 

  28. Berneche S, Roux B (2001) Energetics of ion conduction through the K+ channel. Nature 414:73

    Article  CAS  Google Scholar 

  29. Portella G, Hub JS, Vesper MD (2008) Not only enthalpy: large entropy contribution to ion permeation barriers in single-file channels. Biophys J 95:2275–2282

    Article  CAS  Google Scholar 

  30. Linder T, De Groot BL, Stary-Weinzinger A (2013) Probing the energy landscape of activation gating of the bacterial potassium channel KcsA. PLoS Comput Biol 9:1003058

    Article  Google Scholar 

  31. Jagger BR, Lee CT, McCammon JA, Amaro RE (2019) Computational predictions of drug-protein binding kinetics with a hybrid molecular dynamics, Brownian dynamics, and milestoning approach. Biophys J 11:562a

    Article  Google Scholar 

  32. Jo S, Kim T, Lyer VG, Im W (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29:1859–1865

    Article  CAS  Google Scholar 

  33. Tajkhorshid E, Paizs B, Suhai S (1997) Conformational effects on the proton affinity of the Schiff base in bacteriorhodopsin: a density functional study. J Phys Chem B 101:8021–8028

    Article  CAS  Google Scholar 

  34. Tajkhorshid E, Suhai S (1999) Influence of the methyl groups on the structure, charge distribution, and proton affinity of the retinal Schiff base. J Phys Chem B 103:5581–5590

    Article  CAS  Google Scholar 

  35. Tajkhorshid E, Baudry J, Schulten K, Suhai S (2000) Molecular dynamics study of the nature and origin of retinal’s twisted structure in bacteriorhodopsin. Biophys J 78:683–693

    Article  CAS  Google Scholar 

  36. Nina M, Roux B, Smith JC (1995) Functional interactions in bacteriorhodopsin: a theoretical analysis of retinal hydrogen bonding with water. Biophys J 68:25–39

    Article  CAS  Google Scholar 

  37. Baudry J, Crouzy S, Roux B, Smith JC (1997) Quantum chemical and free energy simulation analysis of retinal conformational energetics. J Chem Inf Comput Sci 37:1018–1024

    Article  CAS  Google Scholar 

  38. Best RB, Zhu X, Shim J, Lopes PE, Mittal J, Feig M, MacKerell AD (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angle. J Chem Theory Comput 8:3257–3273

    Article  CAS  Google Scholar 

  39. Phillips JC, Braun R, Wang W, Gumbart J (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802

    Article  CAS  Google Scholar 

  40. Grossfield A (2008) An implementation of WHAM: the weighted histogram analysis method. http://membrane.urmc.rochester.edu/Software/WHAM/WHAM.html

  41. Feller SE, Zhang Y, Brooks BR (1995) Constant pressure molecular dynamics simulation: the Langevin piston method. J Chem Phys 103:4613–4621

    Article  CAS  Google Scholar 

  42. Kästner J (2011) Umbrella sampling. Wiley Interdiscip Rev Comput Mol 1:932–942

    Article  Google Scholar 

  43. Beutler TC, van Gunsteren WF (1994) The computation of a potential of mean force: choice of the biasing potential in the umbrella sampling technique. J Chem Phys 100:1492–1497

    Article  CAS  Google Scholar 

  44. Humphrey W, Dalke A, Schulten K (1996) VMD—visual molecular dynamics. J Mol Graph 14:33–38

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. DOE’s National Nuclear Security Administration under contract DE-NA-0003525. The views expressed in the article do not necessarily represent the views of the U.S. DOE or the US government. The authors have no conflicts of interest.

Author information

Authors and Affiliations

  1. Sandia National Laboratories, Albuquerque, NM, USA

    Chad Priest, Caroline Rempe, Mangesh I. Chaudhari, Mark J. Stevens & Susan B. Rempe

  2. Department of Chemistry, University of New Orleans, New Orleans, LA, USA

    Monika R. VanGordon & Steve Rick

Authors
  1. Chad Priest
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Monika R. VanGordon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Caroline Rempe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mangesh I. Chaudhari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mark J. Stevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Steve Rick
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Susan B. Rempe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susan B. Rempe .

Editor information

Editors and Affiliations

  1. Department of Chemistry and Biochemistry, Worcester Polytechnic Institute, Worcester, MA, USA

    Robert E. Dempski

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Priest, C. et al. (2021). Computing Potential of the Mean Force Profiles for Ion Permeation Through Channelrhodopsin Chimera, C1C2. In: Dempski, R. (eds) Channelrhodopsin. Methods in Molecular Biology, vol 2191. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0830-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0830-2_2

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0829-6

  • Online ISBN: 978-1-0716-0830-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation