Phase Separation: Restricting the sizes of condensates
Computer simulations of model proteins with sticker-and-spacer architectures shed light on the formation of biomolecular condensates in cells.
- Department of Physics, Washington University in St. Louis, United States
- Center for Science and Engineering of Living Systems, Washington University in St. Louis, United States
- Department of Biomedical Engineering, Washington University in St. Louis, United States
Many of the organelles found inside cells, including the nucleus and mitochondria, are enclosed within a membrane and have been closely studied for decades. However, there is growing interest in organelles that can form and dissolve reversibly because they are not surrounded by a membrane. In particular, the physics and chemistry of membraneless organelles – also known as biomolecular condensates – is the focus of much research (Banani et al., 2017; Shin and Brangwynne, 2017; Choi et al., 2020).
Biomolecular condensates form when a mixture of proteins, nucleic acids and solvents separate into a phase that is rich in proteins and nucleic acids, and a dilute phase that contains relatively few of these macromolecules. Basic thermodynamics suggests that this process of 'phase separation' should result in a single large condensate that co-exists with a dilute phase because the energy needed to maintain the interface between a single large condensate and a dilute phase is lower than the interfacial energy for a system of smaller condensates. A process known as Ostwald ripening establishes this equilibrium by allowing a single large condensate to incorporate smaller ones (Lifshitz and Slyozov, 1961).
Systems containing a single large condensate, as predicted by basic thermodynamics, have been observed in in vitro studies (Elbaum-Garfinkle et al., 2015). However, there have also been reports of living cells that contain multiple condensates that do not grow beyond a certain size (Brangwynne et al., 2009; Berry et al., 2018). The form of phase separation that yields multiple droplets or condensates – a process known as emulsification – is thought to arise from the active production and degradation of macromolecules (Wurtz and Lee, 2018). However, there have also been reports of emulsification happening in the absence of these active mechanisms. How can one explain emulsification when such processes are not at work?
Now, in eLife, Srivastav Ranganathan and Eugene Shakhnovich from Harvard University report the results of simulations modelling the phase behavior of model polymers made up of multiple 'stickers' and 'spacers' that help to answer this question (Ranganathan and Shakhnovich, 2020; Figure 1A). These simulations show that the sizes of condensates are determined by two timescales: the time it takes for macromolecules to come into contact via diffusion; and the time it takes to form and break physical bonds between pairs of ‘stickers’ (Figure 1A,B).
Figure 1
Two timescales determine the size of condensates.
(A) Models in which macromolecules are composed of stickers and spacers can be used to predict the phase behavior of proteins (Choi et al., 2019). This schematic shows the interactions between two such macromolecules, with the stickers in one macromolecule (red shapes) forming bonds (reversible, non-covalent crosslinks) with the stickers in the other macromolecule (blue shapes); the spacers are shown as grey and black circles. Bonds between the stickers are made and broken on a time scale of tbond. (B) Free macromolecules (small orange spheres) diffuse and collide on a timescale of tdifffusion, sometimes sticking together to form condensates (large orange spheres). (C) When these two timescales are roughly equal, a phenomenon known as Ostwald ripening leads to the formation of a dominant condensate that continues to grow by absorbing smaller condensates. (D) Ranganathan and Shakhnovich predict that when the timescale for diffusion is much faster than the timescale for making and breaking bonds, condensates cannot grow beyond a certain size, which results in a large number of small- and medium-sized condensates.
If these two timescales are similar to one another, larger condensates will consume smaller condensates until there is just one dominant condensate (Figure 1C). However, if the timescale for diffusion is orders of magnitude faster than the timescale for bond formation, as is more often the case, most of the inter-sticker bonds will form among molecules that are part of smaller condensates. Moreover, new molecules will not be able to join the condensate because most sticker regions will already be tied up in existing connections. In the absence of stickers to bind to, molecules that are not already in the condensate will diffuse away. As a result, while it is relatively easy to grow a condensate up to a certain size, the lack of available molecules to form bonds with limits further growth, resulting in a roughly homogeneous distribution of smaller condensates (Figure 1D).
There has been much debate over how emulsification arises in cells. In addition to active processes controlling the size of condensates, another possibility is that some cellular components act as surfactants to decrease the energy of the interface between condensates and the solvent (Cuylen et al., 2016). Ranganathan and Shakhnovich now offer a third possible explanation. A fourth possibility is that proteins with block copolymeric architectures (a chain with blocks of two or more distinct monomers) form condensates via micellization (Ruff et al., 2015).
Biology seems to find a way to leverage all aspects of physically feasible scenarios in order to achieve desired outcomes. This is clearly the case with regards to the size distribution and apparent emulsification of condensates. However, it remains unclear how these different modes of emulsification interact with one another and to what extent each of these modes is used by different cell types. Theory and computations have offered elegant, testable predictions that have paved the way for designing experiments that can answer these questions.
References
-
Biomolecular condensates: organizers of cellular biochemistryNature Reviews Molecular Cell Biology 18:285–298.https://doi.org/10.1038/nrm.2017.7
-
Physical principles of intracellular organization via active and passive phase transitionsReports on Progress in Physics 81:046601.https://doi.org/10.1088/1361-6633/aaa61e
-
LASSI: a lattice model for simulating phase transitions of multivalent proteinsPLOS Computational Biology 15:e1007028.https://doi.org/10.1371/journal.pcbi.1007028
-
Physical principles underlying the complex biology of intracellular phase transitionsAnnual Review of Biophysics 49:107–133.https://doi.org/10.1146/annurev-biophys-121219-081629
-
The kinetics of precipitation from supersaturated solid solutionsJournal of Physics and Chemistry of Solids 19:35–50.https://doi.org/10.1016/0022-3697(61)90054-3
-
CAMELOT: a machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequencesThe Journal of Chemical Physics 143:243123.https://doi.org/10.1063/1.4935066
Article and author information
Author details
Publication history
Copyright
© 2020, Dar and Pappu
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 4,436
- views
-
- 558
- downloads
-
- 26
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Phase Separation: Restricting the sizes of condensates
eLife 9:e59663.
https://doi.org/10.7554/eLife.59663
Further reading
-
- Physics of Living Systems
Many proteins have been recently shown to undergo a process of phase separation that leads to the formation of biomolecular condensates. Intriguingly, it has been observed that some of these proteins form dense droplets of sizeable dimensions already below the critical concentration, which is the concentration at which phase separation occurs. To understand this phenomenon, which is not readily compatible with classical nucleation theory, we investigated the properties of the droplet size distributions as a function of protein concentration. We found that these distributions can be described by a scale-invariant log-normal function with an average that increases progressively as the concentration approaches the critical concentration from below. The results of this scaling analysis suggest the existence of a universal behaviour independent of the sequences and structures of the proteins undergoing phase separation. While we refrain from proposing a theoretical model here, we suggest that any model of protein phase separation should predict the scaling exponents that we reported here from the fitting of experimental measurements of droplet size distributions. Furthermore, based on these observations, we show that it is possible to use the scale invariance to estimate the critical concentration for protein phase separation.
-
- Computational and Systems Biology
- Physics of Living Systems
Explaining biodiversity is a fundamental issue in ecology. A long-standing puzzle lies in the paradox of the plankton: many species of plankton feeding on a limited variety of resources coexist, apparently flouting the competitive exclusion principle (CEP), which holds that the number of predator (consumer) species cannot exceed that of the resources at a steady state. Here, we present a mechanistic model and demonstrate that intraspecific interference among the consumers enables a plethora of consumer species to coexist at constant population densities with only one or a handful of resource species. This facilitated biodiversity is resistant to stochasticity, either with the stochastic simulation algorithm or individual-based modeling. Our model naturally explains the classical experiments that invalidate the CEP, quantitatively illustrates the universal S-shaped pattern of the rank-abundance curves across a wide range of ecological communities, and can be broadly used to resolve the mystery of biodiversity in many natural ecosystems.
