Abstract
The effects of macromolecular crowding on the thermodynamic properties of test proteins are determined by the latter's transfer free energies from a dilute solution to a crowded solution. The transfer free energies in turn are determined by effective protein-crowder interactions. When these interactions are modeled at the all-atom level, the transfer free energies may defy simple predictions. Here we investigated the dependence of the transfer free energy (Δμ) on crowder concentration. We represented both the test protein and the crowder proteins atomistically, and used a general interaction potential consisting of hard-core repulsion, non-polar attraction, and solvent-screened electrostatic terms. The chemical potential was rigorously calculated by FMAP (Qin and Zhou, 2014), which entails expressing the protein-crowder interaction terms as correlation functions and evaluating them via fast Fourier transform (FFT). To high accuracy, the transfer free energy can be decomposed into an excluded-volume component (Δμe−v), arising from the hard-core repulsion, and a soft-attraction component (Δμs−a), arising from non-polar and electrostatic interactions. The decomposition provides physical insight into crowding effects, in particular why such effects are very modest on protein folding stability. Further decomposition of Δμs−a into non-polar and electrostatic components does not work, because these two types of interactions are highly correlated in contributing to Δμs−a. We found that Δμe−v fits well to the generalized fundamental measure theory (Qin and Zhou, 2010), which accounts for atomic details of the test protein but approximates the crowder proteins as spherical particles. Most interestingly, Δμs−a has a nearly linear dependence on crowder concentration. The latter result can be understood within a perturbed virial expansion of Δμ (in powers of crowder concentration), with Δμe−v as reference. Whereas the second virial coefficient deviates strongly from that of the reference system, higher virial coefficients are close to their reference counterparts, thus leaving the linear term to make the dominant contribution to Δμs−a.
Highlights
It is well-recognized that “bystander” macromolecules in cellular milieus may significantly influence the biophysical properties of proteins (Zhou et al, 2008; Zhou, 2013; Gnutt and Ebbinghaus, 2016)
As shown by equation (4), the transfer free energy μ is given by the average of the Boltzmann factor of the protein-crowder interaction energy Uint; the average needs to be taken over the position R of a fictitious placement of the test protein into the crowder box, the orientation of the test protein, and the configuration c of the crowders
The excluded-volume component is given by the clashfree fraction, exp − μe−v/kBT =< exp(−Ust/kBT) >R
Summary
It is well-recognized that “bystander” macromolecules in cellular milieus may significantly influence the biophysical properties of proteins (Zhou et al, 2008; Zhou, 2013; Gnutt and Ebbinghaus, 2016) Such influences can be detected by many experimental observables, including equilibrium sedimentation gradient (Rivas et al, 1999), protein folding and binding stability (Batra et al, 2009a,b; Miklos et al, 2011, 2013; Phillip et al, 2012; Wang et al, 2012; Sarkar et al, 2013), light scattering intensity (Wu and Minton, 2013), small-angle neutron scattering profile (Goldenberg and Argyle, 2014; Banks et al, 2018), and fluorescence resonance energy transfer (FRET) efficiency (Soranno et al, 2014).
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