Computer simulations of model proteins with sticker-and-spacer architectures shed light on the formation of biomolecular condensates in cells.
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).
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.
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
Downloads (link to download the article as PDF)
Download citations (links to download the citations from this article in formats compatible with various reference manager tools)
Open citations (links to open the citations from this article in various online reference manager services)
The design principles dictating the spatio-temporal organisation of eukaryotic cells, and in particular the mechanisms controlling the self-organisation and dynamics of membrane-bound organelles such as the Golgi apparatus, remain elusive. Although this organelle was discovered 120 years ago, such basic questions as whether vesicular transport through the Golgi occurs in an anterograde (from entry to exit) or retrograde fashion are still strongly debated. Here, we address these issues by studying a quantitative model of organelle dynamics that includes: de-novo compartment generation, inter-compartment vesicular exchange, and biochemical conversion of membrane components. We show that anterograde or retrograde vesicular transports are asymptotic behaviors of a much richer dynamical system. Indeed, the structure and composition of cellular compartments and the directionality of vesicular exchange are intimately linked. They are emergent properties that can be tuned by varying the relative rates of vesicle budding, fusion and biochemical conversion.
Previously, in (Hermundstad et al., 2014), we showed that when sampling is limiting, the efficient coding principle leads to a 'variance is salience' hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The 'variance is salience' hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error < 0:13).