2016
DOI: 10.1039/c6sm01513a
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Modeling phase transitions in mixtures of β–γ lens crystallins

Abstract: We analyze experimentally determined phase diagram of γD–βB1 crystallin mixture. Proteins are described as dumbbells decorated with attractive sites to allow inter–particle interaction. We use thermodynamic perturbation theory to calculate the free energy of such mixtures and, by applying equilibrium conditions, also the compositions and concentrations of the co–existing phases. Initially we fit the Tcloud versus packing fraction η measurements for pure (x2 = 0) γD solution in 0.1 M phosphate buffer at pH = 7.… Show more

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Cited by 32 publications
(33 citation statements)
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References 66 publications
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“…1a ). These particles, with attractive patches decorated on a hard core, which in our case is spherical, have been used to model proteins in LLPS 30 – 33 .
Figure 1 Illustration of the patchy particle model for protein-regulator mixtures and its two phases.
…”
Section: Resultsmentioning
confidence: 99%
“…1a ). These particles, with attractive patches decorated on a hard core, which in our case is spherical, have been used to model proteins in LLPS 30 – 33 .
Figure 1 Illustration of the patchy particle model for protein-regulator mixtures and its two phases.
…”
Section: Resultsmentioning
confidence: 99%
“…These particles, with attractive patches decorated on a hard core, which in our case is spherical, have been used to model proteins in LLPS. [30][31][32][33] The details of the protein and regulator models were chosen based on a number of observations regarding LLPS of protein-regulator mixtures. First of all, in many experimental studies where LLPS has been demonstrated, there exists a single "driver" protein that can form droplets on its own, whereas RNA or other regulatory components can modulate the phase boundaries.…”
Section: Resultsmentioning
confidence: 99%
“…[138,139] These studies indicate that LLPS is sensitiven ot only to the net electric charge of an IDP buta lso the pattern of charged istribution along its sequence. [140][141][142] Mean-field and more coarse-grained approaches that do not address sequence dependence at the residue level have also provided useful insights into the LLPS of globular proteins, [143,144] IDPs, and IDRs [21,63,[145][146][147] such as the role of cation-p interactions [61,63] and RNA. [148] Possible physicalo rigins of LLPS with LCSTsw ere addressed in some of these studies, [33,138] but the theoretical basis of pressure dependenceo fb iomolecular LLPS [42,44,98] has not been quantitatively explored.…”
Section: At Entative Rationalization Of T- P- and Tmao-dependentpromentioning
confidence: 99%
“…[35] Folded proteins are here represented in an abstract fashion similar to that employed in patchy-sphere models of liquid mixtures. [144,148,191] LLPS of folded proteins is envisioned to be driven by transienti nteractions of all types between protein surfaces. The association of differentr esidue types with different LLPS scenarios in Figure 13 is only an indication of general behavioral tendency.F or real proteins, all interaction types contribute, although some may have am ore dominant impact than the others, dependingo nt he amino acid sequence.…”
Section: At Entative Rationalization Of T- P- and Tmao-dependentpromentioning
confidence: 99%