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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. Video Abstract

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

Regulation of AUXIN RESPONSE FACTOR condensation and nucleo-cytoplasmic partitioning

Auxin critically regulates plant growth and development. Auxin-driven transcriptional responses are mediated through the AUXIN RESPONSE FACTOR (ARF) family of transcription factors. ARF protein condensation attenuates ARF activity, resulting in dramatic shifts in the auxin transcriptional landscape. Here, we perform a forward genetics screen for ARF hypercondensation, identifying an F-box protein, which we named AUXIN RESPONSE FACTOR F-BOX1 (AFF1). Functional characterization of SC

Adaptable P body physical states differentially regulate bicoid mRNA storage during early Drosophila development.

Ribonucleoprotein condensates can exhibit diverse physical states in vitro and in vivo. Despite considerable progress, the relevance of condensate physical states for in vivo biological function remains limited. Here, we investigated the physical properties of 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 viscous P body condensates, which adopt an arrested physical state. We demonstrate that structurally distinct proteins and protein-protein interactions, together with RNA, regulate the physical properties of P bodies. Using live imaging and in situ hybridization, we show that the arrested state and integrity of P bodies support the storage of bicoid (bcd) mRNA and that egg activation modulates P body properties, leading to the release of bcd for translation in the early embryo. Together, this work provides an example of how physical states of condensates regulate cellular function in development

Auxin-Abscisic Acid Interactions in Plant Growth and Development

Plant hormones regulate many aspects of plant growth, development, and response to biotic and abiotic stress. Much research has gone into our understanding of individual plant hormones, focusing primarily on their mechanisms of action and the processes that they regulate. However, recent research has begun to focus on a more complex problem; how various plant hormones work together to regulate growth and developmental processes. In this review, we focus on two phytohormones, abscisic acid (ABA) and auxin. We begin with brief overviews of the hormones individually, followed by in depth analyses of interactions between auxin and ABA, focusing on interactions in individual tissues and how these interactions are occurring where possible. Finally, we end with a brief discussion and future prospects for the field

CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins

International audienceIntrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring d ynamic ID beha vior e xperimentall y on a large scale remains difficult, scores of published ID predictor s ha ve tried to fill this gap. Unfortunatel y, their heterogeneity makes it difficult to compare perf ormance, conf ounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a priv ate dashboar d. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins. The server is available at the URL: https://caid.idpcentral.org