Mutational scanning of CRX classifies clinical variants and reveals biochemical properties of the transcriptional effector domain.

Cone-Rod Homeobox, encoded by CRX, is a transcription factor (TF) essential for the terminal differentiation and maintenance of mammalian photoreceptors. Structurally, CRX comprises an ordered DNA-binding homeodomain and an intrinsically disordered transcriptional effector domain. Although a handful of human variants in CRX have been shown to cause several different degenerative retinopathies with varying cone and rod predominance, as with most human disease genes the vast majority of observed CRX genetic variants are uncharacterized variants of uncertain significance (VUS). We performed a deep mutational scan (DMS) of nearly all possible single amino acid substitution variants in CRX, using an engineered cell-based transcriptional reporter assay. We measured the ability of each CRX missense variant to transactivate a synthetic fluorescent reporter construct in a pooled fluorescence-activated cell sorting assay and compared the activation strength of each variant to that of wild-type CRX to compute an activity score, identifying thousands of variants with altered transcriptional activity. We calculated a statistical confidence for each activity score derived from multiple independent measurements of each variant marked by unique sequence barcodes, curating a high-confidence list of nearly 2,000 variants with significantly altered transcriptional activity compared to wild-type CRX. We evaluated the performance of the DMS assay as a clinical variant classification tool using gold-standard classified human variants from ClinVar, and determined that activity scores could be used to identify pathogenic variants with high specificity. That this performance could be achieved using a synthetic reporter assay in a foreign cell type, even for a highly cell type-specific TF like CRX, suggests that this approach shows promise for DMS of other TFs that function in cell types that are not easily accessible. Per-position average activity scores closely aligned to a predicted structure of the ordered homeodomain and demonstrated position-specific residue requirements. The intrinsically disordered transcriptional effector domain, by contrast, displayed a qualitatively different pattern of substitution effects, following compositional constraints without specific residue position requirements in the peptide chain. The observed compositional constraints of the effector domain were consistent with the acidic exposure model of transcriptional activation. Together, the results of the CRX DMS identify molecular features of the CRX effector domain and demonstrate clinical utility for variant classification.

Disordered clock protein interactions and charge blocks turn an hourglass into a persistent circadian oscillator.

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 in Neurospora 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.

Phase transition of GvpU regulates gas vesicle clustering in bacteria.

Gas vesicles (GVs) are microbial protein organelles that support cellular buoyancy. GV engineering has multiple applications, including reporter gene imaging, acoustic control and payload delivery. GVs often cluster into a honeycomb pattern to minimize occupancy of the cytosol. The underlying molecular mechanism and the influence on cellular physiology remain unknown. Using genetic, biochemical and imaging approaches, here we identify GvpU from Priestia megaterium as a protein that regulates GV clustering in vitro and upon expression in Escherichia coli. GvpU binds to the C-terminal tail of the core GV shell protein and undergoes a phase transition to form clusters in subsaturated solution. These properties of GvpU tune GV clustering and directly modulate bacterial fitness. GV variants can be designed with controllable sensitivity to GvpU-mediated clustering, enabling design of genetically tunable biosensors. Our findings elucidate the molecular mechanisms and functional roles of GV clustering, enabling its programmability for biomedical applications.

Disordered proteins interact with the chemical environment to tune their protective function during drying.

The conformational ensemble and function of intrinsically disordered proteins (IDPs) are sensitive to their solution environment. The inherent malleability of disordered proteins combined with the exposure of their residues accounts for this sensitivity. One context in which IDPs play important roles that is concomitant with massive changes to the intracellular environment is during desiccation (extreme drying). The ability of organisms to survive desiccation has long been linked to the accumulation of high levels of cosolutes such as trehalose or sucrose as well as the enrichment of IDPs, such as late embryogenesis abundant (LEA) proteins or cytoplasmic abundant heat soluble (CAHS) proteins. Despite knowing that IDPs play important roles and are co-enriched alongside endogenous, species-specific cosolutes during desiccation, little is known mechanistically about how IDP-cosolute interactions influence desiccation tolerance. Here, we test the notion that the protective function of desiccation-related IDPs is enhanced through conformational changes induced by endogenous cosolutes. We find that desiccation-related IDPs derived from four different organisms spanning two LEA protein families and the CAHS protein family, synergize best with endogenous cosolutes during drying to promote desiccation protection. Yet the structural parameters of protective IDPs do not correlate with synergy for either CAHS or LEA proteins. We further demonstrate that for CAHS, but not LEA proteins, synergy is related to self-assembly and the formation of a gel. Our results demonstrate that functional synergy between IDPs and endogenous cosolutes is a convergent desiccation protection strategy seen among different IDP families and organisms, yet, the mechanisms underlying this synergy differ between IDP families.

Direct prediction of intrinsically disordered protein conformational properties from sequence.

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.

Structural biases in disordered proteins are prevalent in the cell.

Intrinsically disordered proteins and protein regions (IDPs) are prevalent in all proteomes and are essential to cellular function. Unlike folded proteins, IDPs exist in an ensemble of dissimilar conformations. Despite this structural plasticity, intramolecular interactions create sequence-specific structural biases that determine an IDP ensemble's three-dimensional shape. Such structural biases can be key to IDP function and are often measured in vitro, but whether those biases are preserved inside the cell is unclear. Here we show that structural biases in IDP ensembles found in vitro are recapitulated inside human-derived cells. We further reveal that structural biases can change in a sequence-dependent manner due to changes in the intracellular milieu, subcellular localization, and intramolecular interactions with tethered well-folded domains. We propose that the structural sensitivity of IDP ensembles can be leveraged for biological function, can be the underlying cause of IDP-driven pathology or can be used to design disorder-based biosensors and actuators.

The molecular basis for cellular function of intrinsically disordered protein regions.

Intrinsically disordered protein regions exist in a collection of dynamic interconverting conformations that lack a stable 3D structure. These regions are structurally heterogeneous, ubiquitous and found across all kingdoms of life. Despite the absence of a defined 3D structure, disordered regions are essential for cellular processes ranging from transcriptional control and cell signalling to subcellular organization. Through their conformational malleability and adaptability, disordered regions extend the repertoire of macromolecular interactions and are readily tunable by their structural and chemical context, making them ideal responders to regulatory cues. Recent work has led to major advances in understanding the link between protein sequence and conformational behaviour in disordered regions, yet the link between sequence and molecular function is less well defined. Here we consider the biochemical and biophysical foundations that underlie how and why disordered regions can engage in productive cellular functions, provide examples of emerging concepts and discuss how protein disorder contributes to intracellular information processing and regulation of cellular function.

Dissecting the biophysics and biology of intrinsically disordered proteins.

Intrinsically disordered regions (IDRs) 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 physiology and disease processes, with a focus on cancer.

The disordered N-terminal tail of SARS-CoV-2 Nucleocapsid protein forms a dynamic complex with RNA.

The SARS-CoV-2 Nucleocapsid (N) protein is responsible for condensation of the viral genome. Characterizing the mechanisms controlling nucleic acid binding is a key step in understanding how condensation is realized. Here, we focus on the role of the RNA binding domain (RBD) and its flanking disordered N-terminal domain (NTD) tail, using single-molecule Förster Resonance Energy Transfer and coarse-grained simulations. We quantified contact site size and binding affinity for nucleic acids and concomitant conformational changes occurring in the disordered region. We found that the disordered NTD increases the affinity of the RBD for RNA by about 50-fold. Binding of both nonspecific and specific RNA results in a modulation of the tail configurations, which respond in an RNA length-dependent manner. Not only does the disordered NTD increase affinity for RNA, but mutations that occur in the Omicron variant modulate the interactions, indicating a functional role of the disordered tail. Finally, we found that the NTD-RBD preferentially interacts with single-stranded RNA and that the resulting protein:RNA complexes are flexible and dynamic. We speculate that this mechanism of interaction enables the Nucleocapsid protein to search the viral genome for and bind to high-affinity motifs.

Sequence-ensemble-function relationships for disordered proteins in live cells.

Intrinsically disordered protein regions (IDRs) are ubiquitous across all kingdoms of life and play a variety of essential cellular roles. IDRs exist in a collection of structurally distinct conformers known as an ensemble. An IDR's amino acid sequence determines its ensemble, which in turn can play an important role in dictating molecular function. Yet a clear link connecting IDR sequence, its ensemble properties, and its molecular function in living cells has not been directly established. Here, we set out to test this sequence-ensemble-function paradigm using a novel computational method (GOOSE) that enables the rational design of libraries of IDRs by systematically varying specific sequence properties. Using ensemble FRET, we measured the ensemble dimensions of a library of rationally designed IDRs in human-derived cell lines, revealing how IDR sequence influences ensemble dimensions in situ. Furthermore, we show that the interplay between sequence and ensemble can tune an IDR's ability to sense changes in cell volume - a de novo molecular function for these synthetic sequences. Our results establish biophysical rules for intracellular sequence-ensemble relationships, enable a new route for understanding how IDR sequences map to function in live cells, and set the ground for the design of synthetic IDRs with de novo function.

Intrinsically disordered regions are poised to act as sensors of cellular chemistry.

Intrinsically disordered proteins and protein regions (IDRs) are abundant in eukaryotic proteomes and play a wide variety of essential roles. Instead of folding into a stable structure, IDRs exist in an ensemble of interconverting conformations whose structure is biased by sequence-dependent interactions. The absence of a stable 3D structure, combined with high solvent accessibility, means that IDR conformational biases are inherently sensitive to changes in their environment. Here, we argue that IDRs are ideally poised to act as sensors and actuators of cellular physicochemistry. We review the physical principles that underlie IDR sensitivity, the molecular mechanisms that translate this sensitivity to function, and recent studies where environmental sensing by IDRs may play a key role in their downstream function.

Conserved molecular recognition by an intrinsically disordered region in the absence of sequence conservation.

Intrinsically disordered regions (IDRs) are critical for cellular function, yet often appear to lack sequence conservation when assessed by multiple sequence alignments. This raises the question of if and how function can be encoded and preserved in these regions despite massive sequence variation. To address this question, we have applied coarse-grained molecular dynamics simulations to investigate non-specific RNA binding of coronavirus nucleocapsid proteins. Coronavirus nucleocapsid proteins consist of multiple interspersed disordered and folded domains that bind RNA. We focussed here on the first two domains of coronavirus nucleocapsid proteins, the disordered N-terminal domain (NTD) followed by the folded RNA binding domain (RBD). While the NTD is highly variable across evolution, the RBD is structurally conserved. This combination makes the NTD-RBD a convenient model system to explore the interplay between an IDR adjacent to a folded domain, and how changes in IDR sequence can influence molecular recognition of a partner. Our results reveal a surprising degree of sequence-specificity encoded by both the composition and the precise order of the amino acids in the NTD. The presence of an NTD can - depending on the sequence - either suppress or enhance RNA binding. Despite this sensitivity, large-scale variation in NTD sequences is possible while certain sequence features are retained. Consequently, a conformationally-conserved fuzzy RNA:protein complex is found across nucleocapsid protein orthologs, despite large-scale changes in both NTD sequence and RBD surface chemistry. Taken together, these insights shed light on the ability of disordered regions to preserve functional characteristics despite their sequence variability.

SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets.

MOTIVATION: The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, sanity checking, integrating, and analyzing complex sequence annotations remains logistically challenging and introduces a major barrier to entry for even superficial integrative bioinformatics. RESULTS: To address this technical burden, we have developed SHEPHARD, a Python framework that trivializes large-scale integrative protein bioinformatics. SHEPHARD combines an object-oriented hierarchical data structure with database-like features, enabling programmatic annotation, integration, and analysis of complex datatypes. Importantly SHEPHARD is easy to use and enables a Pythonic interrogation of largescale protein datasets with millions of unique annotations. We use SHEPHARD to examine three orthogonal proteome-wide questions relating protein sequence to molecular function, illustrating its ability to uncover novel biology. AVAILABILITY AND IMPLEMENTATION: We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab).

SOURSOP: A Python Package for the Analysis of Simulations of Intrinsically Disordered Proteins.

Conformational heterogeneity is a defining hallmark of intrinsically disordered proteins and protein regions (IDRs). The functions of IDRs and the emergent cellular phenotypes they control are associated with sequence-specific conformational ensembles. Simulations of conformational ensembles that are based on atomistic and coarse-grained models are routinely used to uncover the sequence-specific interactions that may contribute to IDR functions. These simulations are performed either independently or in conjunction with data from experiments. Functionally relevant features of IDRs can span a range of length scales. Extracting these features requires analysis routines that quantify a range of properties. Here, we describe a new analysis suite simulation analysis of unfolded regions of proteins (SOURSOP), an object-oriented and open-source toolkit designed for the analysis of simulated conformational ensembles of IDRs. SOURSOP implements several analysis routines motivated by principles in polymer physics, offering a unique collection of simple-to-use functions to characterize IDR ensembles. As an extendable framework, SOURSOP supports the development and implementation of new analysis routines that can be easily packaged and shared.

Poly(A)-binding protein is an ataxin-2 chaperone that regulates biomolecular condensates.

Biomolecular condensation underlies the biogenesis of an expanding array of membraneless assemblies, including stress granules (SGs), which form under a variety of cellular stresses. Advances have been made in understanding the molecular grammar of a few scaffold proteins that make up these phases, but how the partitioning of hundreds of SG proteins is regulated remains largely unresolved. While investigating the rules that govern the condensation of ataxin-2, an SG protein implicated in neurodegenerative disease, we unexpectedly identified a short 14 aa sequence that acts as a condensation switch and is conserved across the eukaryote lineage. We identify poly(A)-binding proteins as unconventional RNA-dependent chaperones that control this regulatory switch. Our results uncover a hierarchy of cis and trans interactions that fine-tune ataxin-2 condensation and reveal an unexpected molecular function for ancient poly(A)-binding proteins as regulators of biomolecular condensate proteins. These findings may inspire approaches to therapeutically target aberrant phases in disease.

A functional link between lariat debranching enzyme and the intron-binding complex is defective in non-photosensitive trichothiodystrophy.

The pre-mRNA life cycle requires intron processing; yet, how intron-processing defects influence splicing and gene expression is unclear. Here, we find that TTDN1/MPLKIP, which is encoded by a gene implicated in non-photosensitive trichothiodystrophy (NP-TTD), functionally links intron lariat processing to spliceosomal function. The conserved TTDN1 C-terminal region directly binds lariat debranching enzyme DBR1, whereas its N-terminal intrinsically disordered region (IDR) binds the intron-binding complex (IBC). TTDN1 loss, or a mutated IDR, causes significant intron lariat accumulation, as well as splicing and gene expression defects, mirroring phenotypes observed in NP-TTD patient cells. A Ttdn1-deficient mouse model recapitulates intron-processing defects and certain neurodevelopmental phenotypes seen in NP-TTD. Fusing DBR1 to the TTDN1 IDR is sufficient to recruit DBR1 to the IBC and circumvents the functional requirement for TTDN1. Collectively, our findings link RNA lariat processing with splicing outcomes by revealing the molecular function of TTDN1.

FIREBALL: A tool to fit protein phase diagrams based on mean-field theories for polymer solutions.

Biomolecular condensates form via phase transitions of condensate-specific biomacromolecules. Intrinsically disordered regions featuring the appropriate sequence grammars can contribute via homotypic and heterotypic interactions to the driving forces for phase separation of multivalent proteins. Experiments and computations have matured to the point where the concentrations of coexisting dense and dilute phases can be measured or computed for individual intrinsically disordered regions in complex milieus. For a macromolecule such as a disordered protein in a solvent, the locus of points that connects concentrations of the two coexisting phases defines a phase boundary, or binodal. Often, only a few points along the binodal are accessible via measurements. In such cases, and for quantitative and comparative analysis of parameters that describe the driving forces for phase separation, it is useful to fit measured or computed binodals to mean-field free energies for polymer solutions. The nonlinearity of the underlying free energy functions makes it challenging to put mean-field theories into practice. Here, we present FIREBALL, a suite of computational tools designed to enable efficient construction, analysis, and fitting to experimental or computed data of binodals. We show that depending on the theory being used, one can also extract information regarding coil-to-globule transitions of individual macromolecules.