Biology subjects

Holehouse, A. S.

Publications and source records attributed to Holehouse, A. S..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Sequence- and chemical specificity define the functional landscape of intrinsically disordered regions

Intrinsically disordered protein regions (IDRs) pervasively engage in essential molecular functions, yet they are often poorly conserved as assessed by sequence alignment. To understand the seeming paradox of how sequence variability is compatible with persistent function, we examined the functional determinants for a poorly conserved but essential IDR. We show that IDR function depends on two distinct but related properties: sequence- and chemical specificity. While sequence-specificity works via linear binding motifs, chemical specificity reflects the sequence-encoded chemistry of multivalent interactions through amino acids across an IDR. Unexpectedly, a binding motif that is essential in the wild-type IDR can be removed if compensatory changes to the sequence chemistry are made, highlighting the orthogonality and interoperability of both properties and providing a much deeper sequence space compatible with function. Our results provide a general framework to understand the functional constraints on IDR sequence evolution.

biophysics↗

bioRxiv2022

The formation of a fuzzy complex in the negative arm regulates the robustness of the circadian clock.

The circadian clock times cellular processes to the day/night cycle via a Transcription-Translation negative Feedback Loop (TTFL). However, a mechanistic understanding of the negative arm in both the timing of the TTFL and its control of output is lacking. We posited that the formation of negative-arm protein complexes was fundamental to clock regulation stemming from the negative arm. Using a modified peptide microarray approach termed Linear motif discovery using rational design (LOCATE), we characterized the interaction of the disordered negative-arm clock protein FREQUENCY to its partner protein FREQUENCY-Interacting RNA helicase. LOCATE identified a specific Short Linear Motif (SLiM) and interaction "hotspot" as well as positively charged "islands" that mediate electrostatic interactions, suggesting a model where negative arm proteins form a "fuzzy" complex essential for clock timing and robustness. Further analysis revealed that the positively charged islands were an evolutionarily conserved feature in higher eukaryotes and contributed to proper clock function.

molecular biology↗

bioRxiv2021

Hidden structure in disordered proteins is adaptive to intracellular changes

Intrinsically disordered proteins and protein regions (IDPs) are essential to cellular function in all proteomes. Unlike folded proteins, IDPs exist in an ensemble of rapidly interchanging conformations. IDP sequences encode interactions that create structural biases within the ensemble. Such structural biases determine the three-dimensional shape of IDP ensembles and can affect their activity. However, the plasticity and sensitivity of IDP ensembles means structural biases, often measured in vitro, may differ in the dynamic and heterogeneous intracellular environment. Here we reveal that structural biases found in vitro in well-studied IDPs persist inside human-derived cells. We further show that a subset of IDPs are able to sense changes in cellular physical-chemical composition and modulate their ensemble in response. We propose that IDP ensembles can evolve to sense and respond to intracellular physicochemical changes, or to resist them. This property can be leveraged for biological function, be the underlying cause of IDP-driven pathology, or be leveraged for the design of disorder-based biosensors and actuators.

biophysics↗

bioRxiv2021

Molecular Swiss Army Knives: Tardigrade CAHS Proteins Mediate Desiccation Tolerance Through Multiple Mechanisms

Tardigrades, also known as water bears, make up a phylum of small but extremely hardy animals, renowned for their ability to survive extreme stresses, including desiccation. How tardigrades survive desiccation is one of the enduring mysteries of animal physiology. Here we show that CAHS D, an intrinsically disordered protein belonging to a unique family of proteins possessed only by tardigrades, undergoes a liquid-to-gel phase transition in a concentration dependent manner. Unlike other gelling proteins, such as gelatin, our data support a mechanism in which gel formation of CAHS D is driven by intermolecular {beta}-{beta} interactions. We find that gel formation corresponds with strong coordination of water and slowing of water diffusion. The degree of water coordination correlates with the ability of CAHS D to protect lactate dehydrogenase from unfolding when dried. This implies that the mechanism for unfolding protection can be attributed to a combination of hydration and slowed molecular motion. Conversely, rapid diffusion leading to efficient molecular shielding appears to be the predominant mechanism preventing protein aggregation. Our study demonstrates that distinct mechanisms are required for holistic protection during desiccation, and that protectants, such as CAHS D, can act as molecular Swiss Army Knives capable of providing protection through several different mechanisms simultaneously.

biophysics↗

bioRxiv2021

metapredict: a fast, accurate, and easy-to-use cross-platform predictor of consensus disorder

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes where they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike. Statement of SignificanceIntrinsically disordered regions are found across all kingdoms of life where they play a variety of essential roles. Being able to accurately and quickly identify disordered regions in proteins using just the amino acid sequence is critical for the appropriate design and interpretation of experiments. Despite this, performing large-scale disorder prediction on thousands of sequences is challenging using extant disorder predictors due to various difficulties including general installation and computational requirements. We have developed an accurate, high-performance and easy-to-use predictor of protein disorder and structure. Our predictor, metapredict, was designed for both proteome-scale analysis and individual sequence predictions alike. Metapredict is implemented as a collection of local tools and an online web server, and is appropriate for both seasoned computational biologists and novices alike.

bioinformatics↗

bioRxiv2021

PARROT: a flexible recurrent neural network framework for analysis of large protein datasets

The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex non-linear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.

bioinformatics↗

bioRxiv2021

Conformational buffering underlies functional selection in intrinsically disordered protein regions

Many disordered proteins conserve essential functions in the face of extensive sequence variation. This makes it challenging to identify the forces responsible for functional selection. Viruses are robust model systems to investigate functional selection and they take advantage of protein disorder to acquire novel traits. Here, we combine structural and computational biophysics with evolutionary analysis to determine the molecular basis for functional selection in the intrinsically disordered adenovirus early gene 1A (E1A) protein. E1A competes with host factors to bind the retinoblastoma (Rb) protein, triggering early S-phase entry and disrupting normal cellular proliferation. We show that the ability to outcompete host factors depends on the picomolar binding affinity of E1A for Rb, which is driven by two binding motifs tethered by a hypervariable disordered linker. Binding affinity is determined by the spatial dimensions of the linker, which constrain the relative position of the two binding motifs. Despite substantial sequence variation across evolution, the linker dimensions are finely optimized through compensatory changes in amino acid sequence and sequence length, leading to conserved linker dimensions and maximal affinity. We refer to the mechanism that conserves spatial dimensions despite large-scale variations in sequence as conformational buffering. Conformational buffering explains how variable disordered proteins encode functions and could be a general mechanism for functional selection within disordered protein regions.

biophysics↗

bioRxiv2021

Shelterin components modulate nucleic acids condensation and phase-separation

Telomeres are nucleoprotein complexes that protect the ends of chromosomes and are essential for chromosome stability in Eukaryotes. In cells, individual telomeres form distinct globules of finite size that appear to be smaller than expected for bare DNA. Moreover, upon changes in their protein composition, telomeres can cluster to form telomere-induced-foci (TIFs) or co-localize with promyelocytic leukemia (PML) nuclear bodies. The physical basis for collapse of individual telomeres and coalescence of multiple ones remains unclear, as does the relationship between these two phenomena. By combining single-molecule measurements, optical microscopy, turbidity assays, and simulations, we show that the telomere scaffolding protein TRF2 can condense individual DNA chains and drives coalescence of multiple DNA molecules, leading to phase separation and the formation of liquid-like droplets. Addition of the TRF2 binding protein hRap1 modulates phase boundaries and tunes the specificity of solution demixing while simultaneously altering the degree of DNA compaction. Our results suggest that the condensation of single telomeres and formation of biomolecular condensates containing multiple telomeres are two different outcomes driven by the same set of molecular interactions. Moreover, binding partners, such as other telomere components, can alter those interactions to promote single-chain DNA compaction over multiple-chain phase separation.

biophysics↗

bioRxiv2021

Sequence determinants of in cell condensate assembly morphology, dynamics, and oligomerization as measured by number and brightness analysis

BackgroundBiomolecular 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. MethodsIn 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), which is a transcription factor integral to the signaling pathway for the plant hormone auxin. ARF19 contains a large central glutamine-rich IDR and a C-terminal Phox Bem1 (PB1) oligomerization domain and forms cytoplasmic condensates. ResultsOur 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. ConclusionsIDR 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.

biochemistry↗

bioRxiv2021

The arrested state of processing bodies supports mRNA regulation in early development

Biomolecular condensates that form via liquid-liquid phase separation can exhibit diverse physical states. Despite considerable progress, the relevance of condensate physical states for in vivo biological function remains limited. Here, we investigated the physical properties of in vivo processing bodies (P bodies) and their impact on mRNA storage in mature Drosophila oocytes. We show that the conserved DEAD-box RNA helicase Me31B forms P body condensates which adopt a less dynamic, arrested physical state. We demonstrate that structurally distinct proteins and hydrophobic and electrostatic interactions, together with RNA and intrinsically disordered regions, regulate the physical properties of P bodies. Finally, using live imaging, we show that the arrested state of P bodies is required to prevent the premature release of bicoid (bcd) mRNA, a body axis determinant, and that P body dissolution leads to bcd release. Together, this work establishes a role for arrested states of biomolecular condensates in regulating cellular function in a developing organism.

cell biology↗

bioRxiv2021

SWI/SNF senses carbon starvation with a pH-sensitive low complexity sequence

It is increasingly appreciated that intracellular pH changes are important biological signals. This motivates the elucidation of molecular mechanisms of pH-sensing. We determined that a nucleocytoplasmic pH oscillation was required for the transcriptional response to carbon starvation in S. cerevisiae. The SWI/SNF chromatin remodeling complex is a key mediator of this transcriptional response. We found that a glutamine-rich low complexity sequence (QLC) in the SNF5 subunit of this complex, and histidines within this sequence, were required for efficient transcriptional reprogramming during carbon starvation. Furthermore, the SNF5 QLC mediated pH-dependent recruitment of SWI/SNF to a model promoter in vitro. Simulations showed that protonation of histidines within the SNF5 QLC lead to conformational expansion, providing a potential biophysical mechanism for regulation of these interactions. Together, our results indicate that that pH changes are a second messenger for transcriptional reprogramming during carbon starvation, and that the SNF5 QLC acts as a pH-sensor.

molecular biology↗

bioRxiv2021

A modular platform for engineering function ofnatural and synthetic biomolecular condensates

Phase separation is emerging as a universal principle for how cells use dynamic subcompartmentalization to organize biochemical reactions in time and space1,2. Yet, whether the emergent physical properties of these biomolecular condensates are important for their biological function remains unclear. The intrinsically disordered protein PopZ forms membraneless condensates at the poles of the bacterium Caulobacter crescentus and selectively sequesters kinase-signaling cascades to regulate asymmetric cell division3-5. By dissecting the molecular grammar underlying PopZ phase separation, we find that unlike many eukaryotic examples, where unstructured regions drive condensation6,7, a structured domain of PopZ drives condensation, while conserved repulsive features of the disordered region modulate material properties. By generating rationally designed PopZ mutants, we find that the exact material properties of PopZ condensates directly determine cellular fitness, providing direct evidence for the physiological importance of the emergent properties of biomolecular condensates. Our work codifies a clear set of design principles illuminating how sequence variation in a disordered domain alters the function of a widely conserved bacterial condensate. We used these insights to repurpose PopZ as a modular platform for generating synthetic condensates of tunable function in human cells.

cell biology↗

bioRxiv2020

SARS-CoV-2 Requires Cholesterol for Viral Entry and Pathological Syncytia Formation

Many enveloped viruses induce multinucleated cells (syncytia), reflective of membrane fusion events caused by the same machinery that underlies viral entry. These syncytia are thought to facilitate replication and evasion of the host immune response. Here, we report that co-culture of human cells expressing the receptor ACE2 with cells expressing SARS-CoV-2 spike, results in synapse-like intercellular contacts that initiate cell-cell fusion, producing syncytia resembling those we identify in lungs of COVID-19 patients. To assess the mechanism of spike/ACE2-driven membrane fusion, we developed a microscopy-based, cell-cell fusion assay to screen [~]6000 drugs and >30 spike variants. Together with cell biological and biophysical approaches, the screen reveals an essential role for membrane cholesterol in spike-mediated fusion, which extends to replication-competent SARS-CoV-2 isolates. Our findings provide a molecular basis for positive outcomes reported in COVID-19 patients taking statins, and suggest new strategies for therapeutics targeting the membrane of SARS-CoV-2 and other fusogenic viruses. HighlightsO_LICell-cell fusion at ACE2-spike clusters cause pathological syncytia in COVID-19 C_LIO_LIDrug screen reveals critical role for membrane lipid composition in fusion C_LIO_LISpikes unusual membrane-proximal cysteines and aromatics are essential for fusion C_LIO_LICholesterol tunes relative infectivity of SARS-CoV-2 viral particles C_LI

cell biology↗

bioRxiv2020

Human transcriptional activation domains require hydrophobic and acidic residues

Acidic activation domains are intrinsically disordered regions of transcription factors that bind coactivators. The intrinsic disorder and low conservation of activation domains have made it difficult to identify general rules governing their function. To address this problem, we designed thousands of variants in seven acidic activation domains and measured their activities with a new high-throughput assay in human cell culture. From these data we identify three design principles of acidic activation domains: hydrophobic motifs must be balanced by acidic residues; acidic residues make large contributions to activity when they are adjacent to motifs; and motif composition reflects the structural constraints of coactivator interactions. These design principles motivated a simple predictor of activation domains in the human proteome: scanning for clusters of aromatic and leucine residues embedded in regions of high acidity identifies known activation domains and accurately predicts new ones. Our results support a flexible model in which acidic residues solubilize hydrophobic motifs so that they can interact with coactivators.

systems biology↗

bioRxiv2020

Probing the Hidden Sensitivity of Intrinsically Disordered Proteins to their Chemical Environment

Intrinsically disordered proteins and protein-regions (IDRs) make up roughly 30% of the human proteome and play vital roles in a wide variety of biological processes. Given a lack of persistent tertiary structure, all of the residues in an IDR are, to some extent, solvent exposed. This extensive surface area, coupled with the absence of strong intramolecular contacts, makes IDRs inherently sensitive to their chemical environment. Despite this sensitivity, our understanding of how IDR structural ensembles are influenced by changes in their chemical environment is limited. This is particularly relevant given a growing body of evidence showing that IDR function is linked to the underlying structural ensemble. We develop and use a combined experimental, computational, and analytical framework for high-throughput characterization of IDR sensitivity we call solution space scanning. Our framework reveals that IDRs show sequence-dependent sensitivity to solution chemistry, with complex behavior that can be interpreted through relatively simple polymer models. Our results imply that solution-responsive IDRs are ubiquitous and can provide an additional layer of biological regulation.

biophysics↗

bioRxiv2020

Hydration-dependent phase separation of a prion-like protein regulates seed germination during water stress.

Many organisms evolved strategies to survive and thrive under extreme desiccation. Plant seeds protect dehydrated embryos from a variety of stressors and can even lay dormant for millennia. While hydration is the key trigger that reactivates metabolism and kick-starts germination, the exact mechanism by which the embryo senses water remains unresolved. We identified an uncharacterized Arabidopsis thaliana prion-like protein we named FLOE1, which phase separates upon hydration and allows the embryo to sense water stress. We demonstrate that the biophysical states of FLOE1 condensates modulate its biological function in vivo in suppressing seed germination under unfavorable environments. We also find intragenic, intraspecific, and interspecific natural variations in phase separation propensity of FLOE1 homologs. These findings demonstrate a physiological role of phase separation in a multicellular organism and have direct implications for plant ecology and agriculture, especially the design of drought resistant crops, in the face of climate change.

cell biology↗

bioRxiv2020

The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA

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.

biophysics↗

bioRxiv2020

Connecting coil-to-globule transitions to full phase diagrams for intrinsically disordered proteins

Phase separation is thought to underlie spatial and temporal organization that is required for controlling biochemical reactions in cells. Multivalence of interaction motifs also known as stickers is a defining feature of proteins that drive phase separation. Intrinsically disordered proteins with stickers uniformly distributed along the linear sequence can serve as scaffold molecules that drive phase separation. The sequence-intrinsic contributions of disordered proteins to phase separation can be discerned by computing or measuring sequence-specific phase diagrams. These help to delineate the combinations of protein concentration and a suitable control parameter such as temperature that support phase separation. Here, we present an approach that combines detailed simulations with a numerical adaptation of an analytical Gaussian cluster theory to enable the calculation of sequence-specific phase diagrams. Our approach leverages the known equivalence between the driving forces for single chain collapse in dilute solutions and the driving forces for phase separation in concentrated solutions. We demonstrate the application of the theory-aided computations through calculation of phase diagrams for a set of archetypal intrinsically disordered low complexity domains. STATEMENT OF SIGNIFICANCEIntrinsically disordered proteins that have the requisite valence of adhesive linear motifs can drive phase separation and give rise to membraneless biomolecular condensates. Knowledge of how phase diagrams vary with amino acid sequence and changes to solution conditions is essential for understanding how proteins contribute to condensate assembly and dissolution. In this work, we introduce a new two-pronged computational approach to predict sequence-specific phase diagrams. This approach starts by extracting key parameters from simulations of single-chain coil-to-globule transitions. We use these parameters in our numerical implementation of the Gaussian cluster theory (GCT) for polymer solutions to construct sequences-specific phase diagrams. The method is efficient and demonstrably accurate and should pave the way for high-throughput assessments of phase behavior.

biophysics↗