Multivalent biopolymers phase separate into membrane-less organelles (MLOs) which exhibit liquid-like behavior. Here, we explore formation of prototypical MOs from multivalent proteins on various time and length scales and show that the kinetically arrested metastable multi-droplet state is a dynamic outcome of the interplay between two competing processes: a diffusion-limited encounter between proteins, and the exhaustion of available valencies within smaller clusters. Clusters with satisfied valencies cannot coalesce readily, resulting in metastable, long-living droplets. In the regime of dense clusters akin to phase-separation, we observe co-existing assemblies, in contrast to the single, large equilibrium-like cluster. A system-spanning network encompassing all multivalent proteins was only observed at high concentrations and large interaction valencies. In the regime favoring large clusters, we observe a slow-down in the dynamics of the condensed phase, potentially resulting in loss of function. Therefore, metastability could be a hallmark of dynamic functional droplets formed by sticker-spacer proteins.

Background: Peptide/protein hormones are stored as amyloids within endocrine secretory granules. Results: Disulfide bond cleavage enhances conformational dynamics and aggregation kinetics in somatostatin-14, resulting in amyloid fibrils with increased resistance to denaturing conditions and decreased reversibility. Conclusion: Disulfide bond could be a key modulating factor in somatostatin-14 amyloid formation associated with secretory granule biogenesis. Significance: Defective disulfide bonding might cause dysregulation of hormone storage/secretion.

Protein aggregation and amyloid formation are known to play a role both in diseases and in biological functions. Transcription factor p53 plays a major role in tumor suppression by maintaining genomic stability. Recent studies have suggested that amyloid formation of p53 could lead to its loss of physiological function as a tumor suppressor. Here, we investigated the intrinsic amyloidogenic nature of wild-type p53 using sequence analysis. We used bioinformatics and aggregation prediction algorithms to establish the evolutionarily conserved nature of aggregation-prone sequences in wild-type p53. Further, we analyzed the amyloid forming capacity of conserved and aggregation-prone p53-derived peptides PILTIITL and YFTLQI in vitro using various biophysical techniques, including all atom molecular dynamics simulation. Finally, we probed the seeding ability of the PILTIITL peptide on p53 aggregation in vitro and in cells. Our data demonstrate the intrinsic amyloid forming ability of a sequence stretch of the p53 DNA binding domain (DBD) and its aggregation templating behavior on full-length and p53 core domain. Therefore, p53 aggregation, instigated through an amyloidogenic segment in its DBD, could be a putative driving force for p53 aggregation in vivo.

Glycosaminoglycans (GAGs) have been reported to play a significant role in amyloid formation of a wide range of proteins/peptides either associated with diseases or native biological functions. The exact mechanism by which GAGs influence amyloid formation is not clearly understood. Here, we studied two closely related peptides, glucagon-like peptide 1 (GLP1) and glucagon-like peptide 2 (GLP2), for their amyloid formation in the presence and absence of the representative GAG heparin using various biophysical and computational approaches. We show that the aggregation and amyloid formation by these peptides follow distinct mechanisms: GLP1 follows nucleation-dependent aggregation, whereas GLP2 forms amyloids without any significant lag time. Investigating the role of heparin, we also found that heparin interacts with GLP1, accelerates its aggregation, and gets incorporated within its amyloid fibrils. In contrast, heparin neither affects the aggregation kinetics of GLP2 nor gets embedded within its fibrils. Furthermore, we found that heparin preferentially influences the stability of the GLP1 fibrils over GLP2 fibrils. To understand the specific nature of the interaction of heparin with GLP1 and GLP2, we performed all-atom MD simulations. Our in silico results show that the basic-nonbasic-basic (B-X-B) motif of GLP1 (K28-G29-R30) facilitates the interaction between heparin and peptide monomers. However, the absence of such a motif in GLP2 could be the reason for a significantly lower strength of interaction between GLP2 and heparin. Our study not only helps to understand the role of heparin in inducing protein aggregation but also provides insight into the nature of heparin-protein interaction.

Self-assembly of proteins into ordered, fibrillar structures is a commonly observed theme in biology. It has been observed that diverse set of proteins (e.g., alpha-synuclein, insulin, TATA-box binding protein, Sup35, p53), independent of their sequence, native structure, or function could self-assemble into highly ordered structures known as amyloids. What are the crucial features underlying amyloidogenesis that make it so generic? Using coarse-grained simulations of peptide self-assembly, we argue that variation in two physical parametersbending stiffness of the polypeptide and strength of intermolecular interactionscan give rise to many of the structural features typically associated with amyloid self-assembly. We show that the interplay between these two factors gives rise to a rich phase diagram displaying high diversity in aggregated states. For certain parameters, we find a bimodal distribution for the order parameter implying the coexistence of ordered and disordered aggregates. Our findings may explain the experimentally observed variability including the "off-pathway" aggregated structures. Further, we demonstrate that sequence-dependence and protein-specific signatures could be mapped to our coarse-grained framework to study self-assembly behavior of realistic systems such as the STVIIE peptide and Aβ42. The work also provides certain guiding principles that could be used to design novel peptides with desired self-assembly properties, by tuning a few physical parameters.

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Membraneless organelles (MLOs) are spatiotemporally regulated structures that concentrate multivalent proteins or RNA, often in response to stress. The proteins enriched within MLOs are often classified as high-valency "scaffolds" or lowvalency "clients", with the former being associated with a phaseseparation promoting role. In this study, we employ a minimal model for P-body components, with a defined protein−protein interaction network, to study their phase separation at biologically realistic low protein concentrations. Without RNA, multivalent proteins can assemble into solid-like clusters only in the regime of high concentration and stable interactions. RNA molecules promote cluster formation in an RNA-length-dependent manner, even in the regime of weak interactions and low protein volume fraction. Our simulations reveal that long RNA chains act as superscaffolds that stabilize large RNA−protein clusters by recruiting low-valency proteins within them while also ensuring functional "liquid-like" turnover of components. Our results suggest that RNAmediated phase separation could be a plausible mechanism for spatiotemporally regulated phase separation in the cell.

Aggregation of α-synuclein (α-Syn) into neurotoxic oligomers and amyloid fibrils is suggested to be the pathogenic mechanism for Parkinson’s disease (PD). Recent studies have indicated that oligomeric species of α-Syn are more cytotoxic than their mature fibrillar counterparts, which are responsible for dopaminergic neuronal cell death in PD. Therefore, the effective therapeutic strategies for tackling aggregation-associated diseases would be either to prevent aggregation or to modulate the aggregation process to minimize the formation of toxic oligomers during aggregation. In this work, we showed that arginine-substituted α-Syn ligands, based on the most aggregation-prone sequence of α-Syn, accelerate the protein aggregation in a concentration-dependent manner. To elucidate the mechanism by which Arg-substituted peptides could modulate α-Syn aggregation kinetics, we performed surface plasmon resonance (SPR) spectroscopy, nuclear magnetic resonance (NMR) studies, and all-atom molecular dynamics (MD) simulation. The SPR analysis showed a high binding potency of these peptides with α-Syn but one that was nonspecific in nature. The two-dimensional NMR studies suggest that a large stretch within the C-terminus of α-Syn displays a chemical shift perturbation upon interacting with Arg-substituted peptides, indicating C-terminal residues of α-Syn might be responsible for this class of peptide binding. This is further supported by MD simulation studies in which the Arg-substituted peptide showed the strongest interaction with the C-terminus of α-Syn. Overall, our results suggest that the binding of Arg-substituted ligands to the highly acidic C-terminus of α-Syn leads to reduced charge density and flexibility, resulting in accelerated aggregation kinetics. This may be a potentially useful strategy while designing peptides, which act as α-Syn aggregation modulators.