Relationship of Sequence and Phase Separation in Protein Low-Complexity Regions

Abstract

Liquid-liquid phase separation seems to play critical roles in the compartmentalization of cells through the formation of biomolecular condensates. Many proteins with low-complexity regions are found in these condensates, and they can undergo phase separation in vitro in response to changes in temperature, pH, and ion concentration. Low-complexity regions are thus likely important players in mediating compartmentalization in response to stress. However, how the phase behavior is encoded in their amino acid composition and patterning is only poorly understood. We discuss here that polymer physics provides a powerful framework for our understanding of the thermodynamics of mixing and demixing and for how the phase behavior is encoded in the primary sequence. We propose to classify low-complexity regions further into subcategories based on their sequence properties and phase behavior. Ongoing research promises to improve our ability to link the primary sequence of low-complexity regions to their phase behavior as well as the emerging miscibility and material properties of the resulting biomolecular condensates, providing mechanistic insight into this fundamental biological process across length scales.

Figure 1.. Co-existence lines with lower and upper critical solution temperatures.

A) Phase separation of biopolymers can be observed macroscopically using differential interference contrast (DIC) microscopy and is manifest as the formation of droplets. B) Biopolymers in solution can be in the one-phase or two-phase regime; in the latter, a dense and a light phase coexist. Whether the phase transitions have an upper or lower critical solution temperature (UCST or LCST, respectively) is defined by their critical temperatures for mixing; if the protein mixes when the temperature is lowered, the phase transition has a LCST; if mixing occurs at increasing temperature, the phase transition has a UCST.

Figure 2.. Low-complexity regions are highly variable in composition and not all undergo phase transitions under physiologically relevant conditions.

A) The frequency of amino acid types in the entire human proteome (grey) is compared with the frequency of amino acid types in statistical low-complexity regions (white) found using the SEG algorithm. The SEG algorithm scores the amino acid frequency within a sliding window (10 residues) versus the probability of that sequence occurring randomly. Regions with complexity scores below a threshold value were extracted as LCRs and the amino acid composition compared to the complete human proteome. B) The frequency of amino acids in statistical low-complexity regions correlates well (R = 0.989) with the frequency in the bulk proteome. The red line represents the ideal correlation. Rare amino acids (W / M / C / H / Y) are underrepresented in LCRs while common amino acids (L / S) appear enriched in LCRs. C) The structure of a leucine-rich repeat (PDB: 4FCG), which is a common LCR and a folded domain, but does not undergo liquid-liquid phase separation under physiological conditions and does not preferentially localize to biomolecular condensates. Leucine residues are represented by grey spheres. D) A schematic representation of the LCRs of hnRNPA1, Arf and Pab1 with their respective enriched amino acid types indicated, i.e. serine/glycine, arginine and hydrophobic residues, respectively. These LCRs mediate phase separation homotypically or in case of Arf with its binding partner Npm1.

Figure 3.. Protein length and interchain interaction potential potentiate phase separation.

A) The combinatoric entropy of mixing is shown as a function of chain length. Increasing chain length decreases the entropic cost of demixing, particularly at lower volume fractions. B) The free energy as a function of the interaction parameter χ. Increasing the strength of protein-protein interactions versus protein-solvent interactions can drive phase separation. The conditions for spinodal demixing occur where the second derivative of the free energy is zero, as noted by blue circles. Under conditions for the green to blue curves, spinodal decomposition does not occur. C) The binodal curve (dashed green line) defines the coexistence of light and dense protein phases, the spinodal curve defines the unstable region below which phase separation must occur. Between the binodal and spinodal curves is the region of metastability, in which phase separation occurs if it is nucleated.

Figure 4.. Polar LCRs are enriched in glycine and polar residues.

FUS, hnRNPA1, DDX4, Laf-1 and atGRP7 (A-E) LCR sequences are shown with polar, glycine, aromatic, hydrophobic, negatively charged and positively charged residues colored green, orange, black, grey, red and blue, respectively. The amino acid fractions for each sequence are displayed as a pie chart with similar coloring.

Figure 5.. The phase behavior of LCRs is encoded in their primary amino acid sequence.

Existing experimental data suggests that whether a LCR is soluble at typical solution conditions or demixes with UCST or LCST behavior depends on the balance of hydrophobic versus polar / charged amino acids. A purely polar LCR (I) is likely soluble over a wide temperature range. The addition of hydrophobic amino acids (II) results in LCST behavior. A mixture of oppositely charged polymers has UCST behavior. If the hydrophobic amino acids are predominately aromatic (IV) the LCR could have either a UCST or LCST transition. Finally, polar LCRs with aromatic amino acids and increasing charge (V) has UCST behavior. Further, the intervening sequence space modulates the properties of the dense protein phase. For example, increasing the fraction of charged residues could result in a dense protein phase that has a higher fraction of solvent and is therefore less dense.