Biology subjects

Emenecker, R. J.

Publications and source records attributed to Emenecker, R. J..

13 recordsLinked to original sources

bioRxiv2026

C9ORF72-derived polyGR polypeptides disrupt passive nucleocytoplasmic transport by tuning protein affinity for the nuclear pore barrier

Nucleocytoplasmic partitioning is an essential determinant of eukaryotic cellular function, governed by the nuclear pore complex, a molecular portal filled by a disordered phenylalanine-glycine (FG)-rich phase that governs selective entry and exit. Disruption of nucleocytoplasmic partitioning is seen across many different diseases, including viral infection, cancer, and neurodegeneration. However, what determines whether a given protein mislocalises when nucleocytoplasmic transport is disrupted remains unknown. This question is central to amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD), where cytosolic mislocalisation of the nuclear RNA-binding protein TDP-43 is a defining pathological feature. The most common genetic cause of these diseases is a G4C2 repeat expansion in the gene C9ORF72, which produces aberrant neurotoxic polypeptides that induce nucleocytoplasmic transport defects. Here, we show how the highly toxic poly(glycine-arginine/GR) polypeptide engages the nuclear pore FG-rich selectivity barrier and retunes passive nucleocytoplasmic transport according to client surface chemistry. Using coarse-grained simulations, in vitro FG-phase reconstitution and human cell lines and neurons, we find that polyGR produces a non-linear, biphasic modulation of nuclear pore passage. Proteins with low affinity for the FG phase are unaffected, whereas proteins with higher affinity due to solvent-exposed hydrophobic residues exhibit enhanced transport up to a critical threshold, beyond which highly hydrophobic proteins experience transport suppression, cytoplasmic accumulation and aggregation. Together, these findings establish how disease-associated polypeptides retune the physicochemical rules governing passive nuclear pore transport, leading to biphasic outcomes determined by protein surface chemistry that alter protein compartmentalisation and aggregation. This provides a biophysical mechanism by which polyGR drives selective protein vulnerability to nuclear pore dysfunction in C9ORF72-associated ALS/FTD.

cell biology↗

bioRxiv2025

Accurate predictions of conformational ensembles of disordered proteins with STARLING

Intrinsically disordered proteins and regions (collectively IDRs) are found across all kingdoms of life and play critical roles in virtually every eukaryotic cellular process. In contrast to folded proteins, IDRs lack a stable 3D structure and are instead described in terms of a conformational ensemble, a collection of energetically accessible interconverting structures. This unique structural plasticity facilitates diverse molecular recognition and function; thus, a convenient way to view IDRs is through their ensembles. Here, we combine advances in physics-based force fields for IDPs with the power of modern multi-scale generative modeling to develop STARLING, an approach for the rapid and accurate prediction of IDR ensembles directly from sequence. STARLING enables ensembles of hundreds of conformers to be generated in seconds and works on GPUs and CPUs. This, in turn, dramatically lowers the barrier to the computational interrogation of IDR function through the lens of emergent biophysical properties complementing bioinformatic protein sequence analysis. We evaluate STARLINGs accuracy against extant experimental data and offer a series of vignettes illustrating how STARLING can enable rapid hypothesis generation for IDR function and aid the interpretation of experimental data.

biophysics↗

bioRxiv2024

Metapredict enables accurate disorder prediction across the Tree of Life

Intrinsically disordered proteins and protein regions (collectively IDRs) are critical in numerous cellular processes. To understand how IDRs facilitate function, we need tools to accurately and rapidly identify them from sequence. While many methods for disorder prediction exist, we are currently limited by throughput and accuracy for evolutionary scale analyses. To bridge this gap, we developed metapredict V3, an updated version of our disorder predictor that enables evolutionary-scale disorder prediction. Metapredict V3 enables proteome-scale prediction with state-of-the-art accuracy in seconds and was developed with a focus on usability. It is distributed as a web server, Python software package, command-line interface, and Google Colab notebook. Here, we leverage the accuracy and throughput of metapredict V3 to predict disorder for over 20,000 proteomes to evaluate the prevalence of disorder across the kingdoms of life.

bioinformatics↗

bioRxiv2024

Protein surface chemistry encodes an adaptive resistance to desiccation

Cellular desiccation - the loss of nearly all water from the cell - is a recurring stress in an increasing number of ecosystems that can drive protein unfolding and aggregation. For cells to survive, at least some of the proteome must resume function upon rehydration. Which proteins tolerate desiccation, and the molecular determinants that underlie this tolerance, are largely unknown. Here, we apply quantitative and structural proteomic mass spectrometry to show that certain proteins possess an innate capacity to tolerate rehydration following extreme water loss. Structural analysis points to protein surface chemistry as a key determinant for desiccation tolerance, which we test by showing that rational surface mutants can convert a desiccation sensitive protein into a tolerant one. Desiccation tolerance also has strong overlap with cellular function, with highly tolerant proteins responsible for production of small molecule building blocks, and intolerant proteins involved in energy-consuming processes such as ribosome biogenesis. As a result, the rehydrated proteome is preferentially enriched with metabolite and small molecule producers and depleted of some of the cells heaviest consumers. We propose this functional bias enables cells to kickstart their metabolism and promote cell survival following desiccation and rehydration. TeaserProteins can resist extreme dryness by tuning the amino acids on their surfaces.

biophysics↗

bioRxiv2024

Direct prediction of intermolecular interactions driven by disordered regions

Intrinsically disordered regions (IDRs) are critical for a wide variety of cellular functions, many of which involve interactions with partner proteins. Molecular recognition is typically considered through the lens of sequence-specific binding events. However, a growing body of work has shown that IDRs often interact with partners in a manner that does not depend on the precise order of the amino acid order, instead driven by complementary chemical interactions leading to disordered bound-state complexes. Despite this emerging paradigm, we lack tools to describe, quantify, predict, and interpret these types of structurally heterogeneous interactions from the underlying amino acid sequences. Here, we repurpose the chemical physics developed originally for molecular simulations to develop an approach for predicting intermolecular interactions between IDRs and partner proteins. Our approach enables the direct prediction of phase diagrams, the identification of chemically-specific interaction hotspots on IDRs, and a route to develop and test mechanistic hypotheses regarding IDR function in the context of molecular recognition. We use our approach to examine a range of systems and questions to highlight its versatility and applicability.

biophysics↗

bioRxiv2024

Structural dynamics of the intrinsically disordered linker region of cardiac troponin T

The cardiac troponin complex, composed of troponins I, T, and C, plays a central role in regulating the calcium-dependent interactions between myosin and the thin filament. Mutations in troponin can cause cardiomyopathies; however, it is still a major challenge to connect how changes in sequence affect troponins function. Recent high-resolution structures of the thin filament revealed critical insights into the structure-function relationship of troponin, but there remain large, unresolved segments of troponin, including the troponin-T linker region that is a hotspot for cardiomyopathy mutations. This linker region is predicted to be intrinsically disordered, with behaviors that are not well described by traditional structural approaches; however, this proposal has not been experimentally verified. Here, we used a combination of single-molecule Forster resonance energy transfer (FRET), molecular dynamics simulations, and functional reconstitution assays to investigate the troponin-T linker region. We show that in the context of both isolated troponin and the fully regulated troponin complex, the linker behaves as a dynamic, intrinsically disordered region. This region undergoes polyampholyte expansion in the presence of high salt and distinct conformational changes during the assembly of the troponin complex. We also examine the {Delta}E160 hypertrophic cardiomyopathy mutation in the linker and demonstrate that it does not affect the conformational dynamics of the linker, rather it allosterically affects interactions with other troponin complex subunits, leading to increased molecular contractility. Taken together, our data clearly demonstrate the importance of disorder within the troponin-T linker and provide new insights into the molecular mechanisms driving the pathogenesis of cardiomyopathies. SIGNIFICANCE STATEMENTTroponin plays a central role in regulating heart contraction, and mutations in troponin can cause human cardiomyopathies. There are several functionally-significant regions of troponin that have not been structurally resolved, including the troponin T linker region that contains multiple cardiomyopathy mutations. In these unresolved regions, it is not possible to understand how changes in sequence affect function. We used computational and experimental techniques to demonstrate that this linker is dynamic and intrinsically disordered both in isolation and in the fully regulated thin filament. Moreover, we show how a cardiomyopathy mutation in this region affects function via allosteric disruption of intermolecular interactions. Our results highlight the need to consider how key mutations affect troponin disorder rather than the structure-function relationship.

biophysics↗

bioRxiv2023

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

Despite lacking a stable three-dimensional structure, intrinsically disordered protein regions (IDRs) are ubiquitous across all kingdoms of life and play essential cellular roles. While rational design of folded proteins has seen substantial recent progress, our ability to design IDRs remains limited. Here, we present GOOSE, a comprehensive computational framework for the rational design of IDRs. GOOSEs versatility and throughput enable us to test thousands of IDR sequences, revealing distinct sequence-ensemble-function relationships. By using GOOSE to rapidly explore these relationships, we design IDRs that can alter their ensembles in response to cell volume changes, create scaffolds for the self-assembly of specific clients, and protect cells from desiccation. Taken together, this work highlights how sequence-function relationships are encoded in IDRs and provides a powerful tool for further exploration.

biophysics↗

bioRxiv2023

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 single 3D structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means IDRs are largely absent from the PDB, 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 IDR ensemble dimensions from sequence. ALBATROSS enables the instantaneous prediction of ensemble average properties at 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 in the cloud. 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 emergent biophysical behavior of IDRs within and between proteomes. Update from previous versionO_LIThis preprint reports an updated version of the ALBATROSS network weights trained on simulations of over 42,000 sequences. C_LIO_LIIn addition, we provide new colab notebooks that enable proteome-wide IDR prediction and annotation in minutes. C_LIO_LIAll conclusions and observations made in versions 1 and 2 of this manuscript remain true and robust. C_LI

biophysics↗

bioRxiv2022

Metapredict V2: An update to metapredict, a fast, accurate, and easy-to-use predictor of consensus disorder and structure

Intrinsically disordered proteins and protein regions make up 20-40% of most eukaryotic proteomes and play essential roles in a wide gamut of cellular processes, from intracellular trafficking to epigenetic silencing. Given their importance, the ability to robustly, quickly, and easily identify IDRs within large proteins is critical. Here we present metapredict V2, an update to our deep-learning-based disorder predictor metapredict. Metapredict V2 has substantially improved accuracy, more features, and a more user-friendly interface via our web server (https://metapredict.net/), Python package, and command-line tool. To illustrate V2s improved performance we undertake a systematic analysis of human transcription factors, as well as illustrate that metapredict V2 works well for synthetic or non-natural proteins. KEY POINTSO_LIMetapredict is a fast and easy-to-use disorder predictor released in 2021. C_LIO_LIMetapredict V2 was released in March 2022 and includes improved accuracy and new features. C_LIO_LIMetapredict V2 is now the default metapredict implementation, although the original implementation is available as legacy metapredict. C_LIO_LIThis manuscript provides a summary of how we improved the accuracy of metapredict and compares the original version (legacy) to our improved version (V2) C_LIO_LIThis manuscript will not be submitted to a journal; if you use metapredict V2 please cite the original paper and make reference to the fact that V2 is being used. C_LI

bioinformatics↗

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↗

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

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↗