Substrate transport and diffusion through membrane-bound channels are processes that can span a range of time scales, with only the fastest ones being amenable to most atomic-scale equilibrium molecular dynamics (MD) simulations. However, the application of forces within a simulation can greatly accelerate diffusion processes, revealing important structural and energetic features of the channel. Here, we demonstrate the use of two methods for applying biases to a substrate in a simulation, using the ammonia/ammonium transporter AmtB as an example. The first method, steered MD, applies a constant force or velocity constraint to the substrate, permitting the exploration of potential substrate pathways and the barriers encountered, although typically far outside of equilibrium. On the other hand, the second method, adaptive biasing forces, is quasi-equilibrium, permitting the derivation of a potential of mean force, which characterizes the free energy of the substrate during transport.
Steered molecular dynamics Adaptive biasing forces Potential of mean force AmtB Ammonia/ammonium transport
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This work was supported by the National Institutes of Health (P41-RR005969). Simulations were run at the National Center for Supercomputing Applications (MCA93S028).
Khalili-Araghi F et al (2009) Molecular dynamics simulations of membrane channels and transporters. Curr Opin Struct Biol 19:128–137PubMedCrossRefGoogle Scholar
Izrailev S et al (1997) Molecular dynamics study of unbinding of the avidin-biotin complex. Biophys J 72:1568–1581PubMedCrossRefGoogle Scholar
Dehez F, Pebay-Peyroula E, Chipot C (2008) Binding of ADP in the mitochondrial ADP/ATP carrier is driven by an electrostatic funnel. J Am Chem Soc 130:12725–12733PubMedCrossRefGoogle Scholar
Ivanov I et al (2007) Barriers to ion translocation in cationic and anionic receptors from the cys-loop family. J Am Chem Soc 129:8217–8224PubMedCrossRefGoogle Scholar
Bostick DL, Brooks CL III (2007) Deprotonation by dehydration: the origin of ammonium sensing in the AmtB channel. PLoS Comput Biol 3:e22PubMedCrossRefGoogle Scholar
Lamoureux G, Klein ML, Bernèche S (2007) A stable water chain in the hydrophobic pore of the AmtB ammonium transporter. Biophys J 92:L82–L84PubMedCrossRefGoogle Scholar
Lin Y, Cao Z, Mo Y (2006) Molecular dynamics simulations on the Escherichia coli ammonia channel protein AmtB: mechanism of ammonia/ammonium transport. J Am Chem Soc 128:10876–10884PubMedCrossRefGoogle Scholar
Luzhkov VB et al (2006) Computational study of the binding affinity and selectivity of the bacterial ammonium transporter AmtB. Biochemistry 45:10807–10814PubMedCrossRefGoogle Scholar
Nygaard TP et al (2006) Ammonium recruitment and ammonia transport by E. coli ammonia channel AmtB. Biophys J 91:4401–4412PubMedCrossRefGoogle Scholar
Yang H et al (2007) Detailed mechanism for AmtB conducting NH4+/NH3: molecular dynamics simulations. Biophys J 92:877–885PubMedCrossRefGoogle Scholar
Khademi S et al (2004) Mechanism of ammonia transport by Amt/MEP/Rh: structure of AmtB at 1.35 Å. Science 305:1587–1594PubMedCrossRefGoogle Scholar
MacKerell AD Jr et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616CrossRefGoogle Scholar
MacKerell AD Jr, Feig M, Brooks CL III (2004) Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comp Chem 25:1400–1415CrossRefGoogle Scholar
Phillips JC et al (2005) Scalable molecular dynamics with NAMD. J Comp Chem 26:1781–1802CrossRefGoogle Scholar