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Denaturation of proteins: electrostatic effects vs. hydration.

Matthias Ballauff
Published in: RSC advances (2022)
The unfolding transition of proteins in aqueous solution containing various salts or uncharged solutes is a classical subject of biophysics. In many cases, this transition is a well-defined two-stage equilibrium process which can be described by a free energy of transition Δ G u and a transition temperature T m . For a long time, it has been known that solutes can change T m profoundly. Here we present a phenomenological model that describes the change of T m with the solute concentration c s in terms of two effects: (i) the change of the number of correlated counterions Δ n ci and (ii) the change of hydration expressed through the parameter Δ w and its dependence on temperature expressed through the parameter dΔ c p /d c s . Proteins always carry charges and Δ n ci describes the uptake or release of counterions during the transition. Likewise, the parameter Δ w measures the uptake or release of water during the transition. The transition takes place in a reservoir with a given salt concentration c s that defines also the activity of water. The parameter Δ n ci is a measure for the gain or loss of free energy because of the release or uptake of ions and is related to purely entropic effects that scale with ln  c s . Δ w describes the effect on Δ G u through the loss or uptake of water molecules and contains enthalpic as well as entropic effects that scale with c s . It is related to the enthalpy of transition Δ H u through a Maxwell relation: the dependence of Δ H u on c s is proportional to the dependence of Δ w on temperature. While ionic effects embodied in Δ n ci are independent of the kind of salt, the hydration effects described through Δ w are directly related to Hofmeister effects of the various salt ions. A comparison with literature data underscores the general validity of the model.
Keyphrases
  • aqueous solution
  • systematic review
  • molecular dynamics
  • electronic health record
  • deep learning
  • solid state