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Organismal physiology is widely regulated by the molecular circadian clock, a feedback loop composed of protein complexes whose members are enriched in intrinsically disordered regions. These regions can mediate protein-protein interactions via SLiMs, but the contribution of these disordered regions to clock protein interactions had not been elucidated. To determine the functionality of these disordered regions, we applied a synthetic peptide microarray approach to the disordered clock protein FRQ inNeurospora crassa. We identified residues required for FRQ’s interaction with its partner protein FRH, the mutation of which demonstrated FRH is necessary for persistent clock oscillations but not repression of transcriptional activity. Additionally, the microarray demonstrated an enrichment of FRH binding to FRQ peptides with a net positive charge. We found that positively charged residues occurred in significant “blocks” within the amino acid sequence of FRQ and that ablation of one of these blocks affected both core clock timing and physiological clock output. Finally, we found positive charge clusters were a commonly shared molecular feature in repressive circadian clock proteins. Overall, our study suggests a mechanistic purpose for positive charge blocks and yielded insights into repressive arm protein roles in clock function.

 

Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well described by a stable three-dimensional structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means that IDRs are largely absent from the Protein Data Bank, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations and deep learning to develop ALBATROSS, a deep-learning model for predicting ensemble dimensions of IDRs, including the radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences at a proteome-wide scale. ALBATROSS is lightweight, easy to use and accessible as both a locally installable software package and a point-and-click-style interface via Google Colab notebooks. We first demonstrate the applicability of our predictors by examining the generalizability of sequence–ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize the sequence-specific biophysical behavior of IDRs within and between proteomes.

 

Intrinsically disordered regions within human proteins play critical roles in cellular information processing, including signaling, transcription, stress response, DNA repair, genome organization, and RNA processing. Here, we summarize current challenges in the field and propose cutting-edge approaches to address them in normal physiology and disease, with a focus on cancer. 

Denatured, unfolded, and intrinsically disordered proteins (collectively referred to here as unfolded proteins) can be described using analytical polymer models. These models capture various polymeric properties and can be fit to simulation results or experimental data. However, the model parameters commonly require users’ decisions, making them useful for data interpretation but less clearly applicable as stand-alone reference models. Here we use all-atom simulations of polypeptides in conjunction with polymer scaling theory to parameterize an analytical model of unfolded polypeptides that behave as ideal chains (ν = 0.50). The model, which we call the analytical Flory random coil (AFRC), requires only the amino acid sequence as input and provides direct access to probability distributions of global and local conformational order parameters. The model defines a specific reference state to which experimental and computational results can be compared and normalized. As a proof-of-concept, we use the AFRC to identify sequence-specific intramolecular interactions in simulations of disordered proteins. We also use the AFRC to contextualize a curated set of 145 different radii of gyration obtained from previously published small-angle X-ray scattering experiments of disordered proteins. The AFRC is implemented as a stand-alone software package and is also available via a Google Colab notebook. In summary, the AFRC provides a simple-to-use reference polymer model that can guide intuition and aid in interpreting experimental or simulation results. 

Abstract Intracellular phase separation is emerging as a universal principle for organizing biochemical reactions in time and space. It remains incompletely resolved how biological function is encoded in these assemblies and whether this depends on their material state. The conserved intrinsically disordered protein PopZ forms condensates at the poles of the bacterium Caulobacter crescentus , which in turn orchestrate cell-cycle regulating signaling cascades. Here we show that the material properties of these condensates are determined by a balance between attractive and repulsive forces mediated by a helical oligomerization domain and an expanded disordered region, respectively. A series of PopZ mutants disrupting this balance results in condensates that span the material properties spectrum, from liquid to solid. A narrow range of condensate material properties supports proper cell division, linking emergent properties to organismal fitness. We use these insights to repurpose PopZ as a modular platform for generating tunable synthetic condensates in human cells. 

Biomolecular condensates are non-stoichiometric assemblies that are characterized by their capacity to spatially concentrate biomolecules and play a key role in cellular organization. Proteins that drive the formation of biomolecular condensates frequently contain oligomerization domains and intrinsically disordered regions (IDRs), both of which can contribute multivalent interactions that drive higher-order assembly. Our understanding of the relative and temporal contribution of oligomerization domains and IDRs to the material properties of in vivo biomolecular condensates is limited. Similarly, the spatial and temporal dependence of protein oligomeric state inside condensates has been largely unexplored in vivo.

In this study, we combined quantitative microscopy with number and brightness analysis to investigate the aging, material properties, and protein oligomeric state of biomolecular condensates in vivo. Our work is focused on condensates formed by AUXIN RESPONSE FACTOR 19 (ARF19), a transcription factor integral to the auxin signaling pathway in plants. ARF19 contains a large central glutamine-rich IDR and a C-terminal Phox Bem1 (PB1) oligomerization domain and forms cytoplasmic condensates.

Our results reveal that the IDR amino acid composition can influence the morphology and material properties of ARF19 condensates. In contrast the distribution of oligomeric species within condensates appears insensitive to the IDR composition. In addition, we identified a relationship between the abundance of higher- and lower-order oligomers within individual condensates and their apparent fluidity.

IDR amino acid composition affects condensate morphology and material properties. In ARF condensates, altering the amino acid composition of the IDR did not greatly affect the oligomeric state of proteins within the condensate.

 

The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA-binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA-binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction.