LLPSDB v2.0: an updated database of proteins undergoing liquid-liquid phase separation in vitro

Compartmentation of multiple metabolic enzymes and their preparation in vitro and in cellulo

Compartmentalization of multiple enzymes in cellulo and in vitro is a means of controlling the cascade reaction of metabolic enzymes. The compartmentation of enzymes through liquid-liquid phase separation may facilitate the reversible control of biocatalytic cascade reactions, thereby reducing the transcriptional and translational burden. This has attracted attention as a potential application in bioproduction. Recent research has demonstrated the existence and regulatory mechanisms of various enzyme compartments within cells. Mounting evidence suggests that enzyme compartmentation allows in vitro and in vivo regulation of cellular metabolism. However, the comprehensive regulatory mechanisms of enzyme condensates in cells and ideal organization of cellular systems remain unknown. This review provides an overview of the recent progress in multiple enzyme compartmentation in cells and summarizes strategies to reconstruct multiple enzyme assemblies in vitro and in cellulo. By examining parallel examples, we have evaluated the consensus and future perspectives of enzyme condensation.

PSTP: accurate residue-level phase separation prediction using protein conformational and language model embeddings

Phase separation (PS) is essential in cellular processes and disease mechanisms, highlighting the need for predictive algorithms to analyze uncharacterized sequences and accelerate experimental validation. Current high-accuracy methods often rely on extensive annotations or handcrafted features, limiting their generalizability to sequences lacking such annotations and making it difficult to identify key protein regions involved in PS. We introduce Phase Separation's Transfer-learning Prediction (PSTP), which combines conformational embeddings with large language model embeddings, enabling state-of-the-art PS predictions from protein sequences alone. PSTP performs well across various prediction scenarios and shows potential for predicting novel-designed artificial proteins. Additionally, PSTP provides residue-level predictions that are highly correlated with experimentally validated PS regions. By analyzing 160 000+ variants, PSTP characterizes the strong link between the incidence of pathogenic variants and residue-level PS propensities in unconserved intrinsically disordered regions, offering insights into underexplored mutation effects. PSTP's sliding-window optimization reduces its memory usage to a few hundred megabytes, facilitating rapid execution on typical CPUs and GPUs. Offered via both a web server and an installable Python package, PSTP provides a versatile tool for decoding protein PS behavior and supporting disease-focused research.

In silico nanoscope to study the interplay of genome organization and transcription regulation

In eukaryotic genomes, regulated access and communication between cis-regulatory elements (CREs) are necessary for enhancer-mediated transcription of genes. The molecular framework of the chromatin organization underlying such communication remains poorly understood. To better understand it, we develop a multiscale modeling pipeline to build near-atomistic models of the 200 kb Nanog gene locus in mouse embryonic stem cells comprising nucleosomes, transcription factors, co-activators, and RNA polymerase II-mediator complexes. By integrating diverse experimental data, including protein localization, genomic interaction frequencies, cryo-electron microscopy, and single-molecule fluorescence studies, our model offers novel insights into chromatin organization and its role in enhancer-promoter communication. The models equilibrated by high-performance molecular dynamics simulations span a scale of ∼350 nm, revealing an experimentally consistent local and global organization of chromatin and transcriptional machinery. Our models elucidate that the sequence-regulated chromatin accessibility facilitates the recruitment of transcription regulatory proteins exclusively at CREs, guided by the contrasting nucleosome organization compared to other regions. By constructing an experimentally consistent near-atomic model of chromatin in the cellular environment, our approach provides a robust framework for future studies on nuclear compartmentalization, chromatin organization, and transcription regulation.

Disordered Regions of Condensate-promoting Proteins Have Distinct Molecular Signatures Associated with Cellular Function

Disordered regions of proteins play crucial roles in cellular functions through diverse mechanisms. Some disordered regions function by promoting the formation of biomolecular condensates through dynamic multivalent interactions. While many have assumed that interactions among these condensate-promoting disordered regions are non-specific, recent studies have shown that distinct sequence compositions and patterning lead to specific condensate compositions associated with cellular function. Despite in-depth characterization of several key examples, the full chemical diversity of condensate-promoting disordered regions has not been surveyed. Here, we define a list of disordered regions of condensate-promoting proteins to survey the relationship between sequence and function. We find that these disordered regions show amino acid biases associated with different cellular functions. These amino acid biases are evolutionarily conserved in the absence of positional sequence conservation. Overall, our analysis highlights the relationship between sequence features and function for condensate-promoting disordered regions. This analysis suggests that molecular signatures encoded within disordered regions could impart functional specificity.

catGRANULE 2.0: accurate predictions of liquid-liquid phase separating proteins at single amino acid resolution

Liquid-liquid phase separation (LLPS) enables the formation of membraneless organelles, essential for cellular organization and implicated in diseases. We introduce catGRANULE 2.0 ROBOT, an algorithm integrating physicochemical properties and AlphaFold-derived structural features to predict LLPS at single-amino-acid resolution. The method achieves high performance and reliably evaluates mutation effects on LLPS propensity, providing detailed predictions of how specific mutations enhance or inhibit phase separation. Supported by experimental validations, including microscopy data, it predicts LLPS across diverse organisms and cellular compartments, offering valuable insights into LLPS mechanisms and mutational impacts. The tool is freely available at https://tools.tartaglialab.com/catgranule2 and https://doi.org/10.5281/zenodo.14205831 .

Liquid-liquid phase separation of tau and α-synuclein: A new pathway of overlapping neuropathologies

Liquid-liquid phase separation (LLPS) is a critical phenomenon that leads to the formation of liquid-like membrane-less organelles within cells. Advances in our understanding of condensates reveal their significant roles in biology and highlight how their dysregulation may contribute to disease. Recent evidence indicates that the high protein concentration in coacervates may lead to abnormal protein aggregation associated with several neurodegenerative diseases. The presence of condensates containing multiple amyloidogenic proteins may play a role in the co-deposition and comorbidity seen in neurodegeneration. This review first provides a brief overview of the physicochemical bases and molecular determinants of LLPS. It then summarizes our understanding of Tau and α-synuclein (AS) phase separation, key proteins in Alzheimer's and Parkinson's diseases. By integrating recent findings on complex Tau and AS coacervation, this article offers a fresh perspective on how LLPS may contribute to the pathological overlap in neurodegenerative disorders and provide a novel therapeutic target to mitigate or prevent such conditions.

Long-range transcription factor binding sites clustered regions may mediate transcriptional regulation through phase-separation interactions in early human embryo

In mammals, during the post-fertilization pre-implantation phase, the expression of cell type-specific genes is crucial for normal embryonic development, which is regulated by cis-regulatory elements (CREs). TFs control gene expression by interacting with CREs. Research shows that transcription factor binding sites (TFBSs) reflect the general characteristics of the regulatory genome. Here, we identified TFBSs from chromatin accessibility data in five stages of early human embryonic development, and quantified transcription factor binding sites-clustered regions (TFCRs) and their complexity (TC). Assigning TC values to TFCRs has made it possible to assess the functionality of these regulatory elements in a more quantitative way. Our findings reveal a robust correlation between TFCR complexity and gene expression starting from the 8Cell stage, which is when the zygotic genome is activated in humans. Furthermore, we have defined long-range TFCRs (LR-TFCRs) and conjecture that LR-TFCRs may regulate gene expression through phase-separation mechanisms during the early stages of human embryonic development.

Heterogeneous Slowdown of Dynamics in the Condensate of an Intrinsically Disordered Protein

The high concentration of proteins and other biological macromolecules inside biomolecular condensates leads to dense and confined environments, which can affect the dynamic ensembles and the time scales of the conformational transitions. Here, we use atomistic molecular dynamics (MD) simulations of the intrinsically disordered low complexity domain (LCD) of the human fused in sarcoma (FUS) RNA-binding protein to study how self-crowding inside a condensate affects the dynamic motions of the protein. We found a heterogeneous retardation of the protein dynamics in the condensate with respect to the dilute phase, with large-amplitude motions being strongly slowed by up to 2 orders of magnitude, whereas small-scale motions, such as local backbone fluctuations and side-chain rotations, are less affected. The results support the notion of a liquid-like character of the condensates and show that different protein motions respond differently to the environment.

Identification of RNA‐dependent liquid‐liquid phase separation proteins using an artificial intelligence strategy

RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA-dependent and 606 non-RNA-dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K-spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA-dependent LLPS proteins. To enhance the accessibility of the model, a user-centric web server has been established and can be accessed via the link: http://rpp.lin-group.cn.

A protein pre-trained model-based approach for the identification of the liquid-liquid phase separation (LLPS) proteins

Liquid-liquid phase separation (LLPS) regulates many biological processes including RNA metabolism, chromatin rearrangement, and signal transduction. Aberrant LLPS potentially leads to serious diseases. Therefore, the identification of the LLPS proteins is crucial. Traditionally, biochemistry-based methods for identifying LLPS proteins are costly, time-consuming, and laborious. In contrast, artificial intelligence-based approaches are fast and cost-effective and can be a better alternative to biochemistry-based methods. Previous research methods employed word2vec in conjunction with machine learning or deep learning algorithms. Although word2vec captures word semantics and relationships, it might not be effective in capturing features relevant to protein classification, like physicochemical properties, evolutionary relationships, or structural features. Additionally, other studies often focused on a limited set of features for model training, including planar π contact frequency, pi-pi, and β-pairing propensities. To overcome such shortcomings, this study first constructed a reliable dataset containing 1206 protein sequences, including 603 LLPS and 603 non-LLPS protein sequences. Then a computational model was proposed to efficiently identify the LLPS proteins by perceiving semantic information of protein sequences directly; using an ESM2-36 pre-trained model based on transformer architecture in conjunction with a convolutional neural network. The model could achieve an accuracy of 85.68% and 89.67%, respectively on training data and test data, surpassing the accuracy of previous studies. The performance demonstrates the potential of our computational methods as efficient alternatives for identifying LLPS proteins.

Decoding Missense Variants by Incorporating Phase Separation via Machine Learning

Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To address this issue, we introduce phase separation, which is tightly linked to IDRs, into the investigation of missense variants. Phase separation is vital for multiple physiological processes. By leveraging missense variants that alter phase separation propensity, we develop a machine learning approach named PSMutPred to predict the impact of missense mutations on phase separation. PSMutPred demonstrates robust performance in predicting missense variants that affect natural phase separation. In vitro experiments further underscore its validity. By applying PSMutPred on over 522,000 ClinVar missense variants, it significantly contributes to decoding the pathogenesis of disease variants, especially those in IDRs. Our work provides insights into the understanding of a vast number of VUSs in IDRs, expediting clinical interpretation and diagnosis.

Liquid-liquid phase separation: a new perspective on respiratory diseases

Liquid-liquid phase separation (LLPS) is integral to various biological processes, facilitating signal transduction by creating a condensed, membrane-less environment that plays crucial roles in diverse physiological and pathological processes. Recent evidence has underscored the significance of LLPS in human health and disease. However, its implications in respiratory diseases remain poorly understood. This review explores current insights into the mechanisms and biological roles of LLPS, focusing particularly on its relevance to respiratory diseases, aiming to deepen our understanding and propose a new paradigm for studying phase separation in this context.

A two-task predictor for discovering phase separation proteins and their undergoing mechanism

Liquid-liquid phase separation (LLPS) is one of the mechanisms mediating the compartmentalization of macromolecules (proteins and nucleic acids) in cells, forming biomolecular condensates or membraneless organelles. Consequently, the systematic identification of potential LLPS proteins is crucial for understanding the phase separation process and its biological mechanisms. A two-task predictor, Opt_PredLLPS, was developed to discover potential phase separation proteins and further evaluate their mechanism. The first task model of Opt_PredLLPS combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) through a fully connected layer, where the CNN utilizes evolutionary information features as input, and BiLSTM utilizes multimodal features as input. If a protein is predicted to be an LLPS protein, it is input into the second task model to predict whether this protein needs to interact with its partners to undergo LLPS. The second task model employs the XGBoost classification algorithm and 37 physicochemical properties following a three-step feature selection. The effectiveness of the model was validated on multiple benchmark datasets, and in silico saturation mutagenesis was used to identify regions that play a key role in phase separation. These findings may assist future research on the LLPS mechanism and the discovery of potential phase separation proteins.

VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation

The diagnostic odysseys for rare disease patients are getting shorter as next-generation sequencing becomes more widespread. However, the complex genetic diversity and factors influencing expressivity continue to challenge accurate diagnosis, leaving more than 50% of genetic variants categorized as variants of uncertain significance.Genomic expression intricately hinges on localized interactions among its products. Conventional variant prioritization, biased towards known disease genes and the structure-function paradigm, overlooks the potential impact of variants shaping the composition, location, size, and properties of biomolecular condensates, genuine membraneless organelles swiftly sensing and responding to environmental changes, and modulating expressivity.To address this complexity, we propose to focus on the nexus of genetic variants within biomolecular condensates determinants. Scrutinizing variant effects in these membraneless organelles could refine prioritization, enhance diagnostics, and unveil the molecular underpinnings of rare diseases. Integrating comprehensive genome sequencing, transcriptomics, and computational models can unravel variant pathogenicity and disease mechanisms, enabling precision medicine. This paper presents the rationale driving our proposal and describes a protocol to implement this approach. By fusing state-of-the-art knowledge and methodologies into the clinical practice, we aim to redefine rare diseases diagnosis, leveraging the power of scientific advancement for more informed medical decisions.

MolPhase, an advanced prediction algorithm for protein phase separation

We introduce MolPhase, an advanced algorithm for predicting protein phase separation (PS) behavior that improves accuracy and reliability by utilizing diverse physicochemical features and extensive experimental datasets. MolPhase applies a user-friendly interface to compare distinct biophysical features side-by-side along protein sequences. By additional comparison with structural predictions, MolPhase enables efficient predictions of new phase-separating proteins and guides hypothesis generation and experimental design. Key contributing factors underlying MolPhase include electrostatic pi-interactions, disorder, and prion-like domains. As an example, MolPhase finds that phytobacterial type III effectors (T3Es) are highly prone to homotypic PS, which was experimentally validated in vitro biochemically and in vivo in plants, mimicking their injection and accumulation in the host during microbial infection. The physicochemical characteristics of T3Es dictate their patterns of association for multivalent interactions, influencing the material properties of phase-separating droplets based on the surrounding microenvironment in vivo or in vitro. Robust integration of MolPhase's effective prediction and experimental validation exhibit the potential to evaluate and explore how biomolecule PS functions in biological systems.

Technologies for studying phase-separated biomolecular condensates

Biomolecular condensates, also referred to as membrane-less organelles, function as fundamental organizational units within cells. These structures primarily form through liquid-liquid phase separation, a process in which proteins and nucleic acids segregate from the surrounding milieu to assemble into micron-scale structures. By concentrating functionally related proteins and nucleic acids, these biomolecular condensates regulate a myriad of essential cellular processes. To study these significant and intricate organelles, a range of technologies have been either adapted or developed. In this review, we provide an overview of the most utilized technologies in this rapidly evolving field. These include methods used to identify new condensates, explore their components, investigate their properties and spatiotemporal regulation, and understand the organizational principles governing these condensates. We also discuss potential challenges and review current advancements in applying the principles of biomolecular condensates to the development of new technologies, such as those in synthetic biology.

A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning

Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (FIDR ) is identified as the most crucial feature. While, in the further three-class LLPS-Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83. This work indicates using sequence features and a machine learning model, proteins undergoing LLPS or forming amyloid fibrils can be identified.

Liquid-liquid phase separation in Alzheimer's disease

The pathological aggregation and misfolding of tau and amyloid-β play a key role in Alzheimer's disease (AD). However, the underlying pathological mechanisms remain unclear. Emerging evidences indicate that liquid-liquid phase separation (LLPS) has great impacts on regulating human health and diseases, especially neurodegenerative diseases. A series of studies have revealed the significance of LLPS in AD. In this review, we summarize the latest progress of LLPS in AD, focusing on the impact of metal ions, small-molecule inhibitors, and proteinaceous partners on tau LLPS and aggregation, as well as toxic oligomerization, the role of LLPS on amyloid-β (Aβ) aggregation, and the cross-interactions between amyloidogenic proteins in AD. Eventually, the fundamental methods and techniques used in LLPS study are introduced. We expect to present readers a deeper understanding of the relationship between LLPS and AD.

Thermodynamic forces from protein and water govern condensate formation of an intrinsically disordered protein domain

Liquid-liquid phase separation (LLPS) can drive a multitude of cellular processes by compartmentalizing biological cells via the formation of dense liquid biomolecular condensates, which can function as membraneless organelles. Despite its importance, the molecular-level understanding of the underlying thermodynamics of this process remains incomplete. In this study, we use atomistic molecular dynamics simulations of the low complexity domain (LCD) of human fused in sarcoma (FUS) protein to investigate the contributions of water and protein molecules to the free energy changes that govern LLPS. Both protein and water components are found to have comparably sizeable thermodynamic contributions to the formation of FUS condensates. Moreover, we quantify the counteracting effects of water molecules that are released into the bulk upon condensate formation and the waters retained within the protein droplets. Among the various factors considered, solvation entropy and protein interaction enthalpy are identified as the most important contributions, while solvation enthalpy and protein entropy changes are smaller. These results provide detailed molecular insights on the intricate thermodynamic interplay between protein- and solvation-related forces underlying the formation of biomolecular condensates.

PredLLPS_PSSM: a novel predictor for liquid-liquid protein separation identification based on evolutionary information and a deep neural network

The formation of biomolecular condensates by liquid-liquid phase separation (LLPS) has become a universal mechanism for spatiotemporal coordination of biological activities in cells and has been widely observed to directly regulate the key cellular processes involved in cancer cell pathology. However, the complexity of protein sequences and the diversity of conformations are inherently disordered, which poses great challenges for LLPS protein calculations and experimental research. Herein, we proposed a novel predictor named PredLLPS_PSSM for LLPS protein identification based only on sequence evolution information. Because finding real and reliable samples is the cornerstone of building predictors, we collected anew and collated the LLPS proteins from the latest versions of three databases. By comparing the performance of the position-specific score matrix (PSSM) and word embedding, PredLLPS_PSSM combined PSSM-based information and two deep learning frameworks. Independent tests using three existing independent test datasets and two newly constructed independent test datasets demonstrated the superiority of PredLLPS_PSSM compared with state-of-the-art methods. Furthermore, we tested PredLLPS_PSSM on nine experimentally identified LLPS proteins from three insects that were not included in any of the databases. In addition, the powerful Shapley Additive exPlanation algorithm and heatmap were applied to find the most critical amino acids relevant to LLPS.

Ensembles of synthetic polymers mimic biological fluids

Recently a report by Ruan et al. in Nature described how relatively simple random heteropolymers can replicate the properties of biological fluids. These polymers capture the segmental-level interactions between proteins and could enhance folding of membrane proteins, improve stability, and enable DNA sequestration in a chemistry specific manner.

Theoretical and Data-Driven Approaches for Biomolecular Condensates

Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.

Condensation of the β-cell secretory granule luminal cargoes pro/insulin and ICA512 RESP18 homology domain

ICA512/PTPRN is a receptor tyrosine-like phosphatase implicated in the biogenesis and turnover of the insulin secretory granules (SGs) in pancreatic islet beta cells. Previously we found biophysical evidence that its luminal RESP18 homology domain (RESP18HD) forms a biomolecular condensate and interacts with insulin in vitro at close-to-neutral pH, that is, in conditions resembling those present in the early secretory pathway. Here we provide further evidence for the relevance of these findings by showing that at pH 6.8 RESP18HD interacts also with proinsulin-the physiological insulin precursor found in the early secretory pathway and the major luminal cargo of β-cell nascent SGs. Our light scattering analyses indicate that RESP18HD and proinsulin, but also insulin, populate nanocondensates ranging in size from 15 to 300 nm and 10e2 to 10e6 molecules. Co-condensation of RESP18HD with proinsulin/insulin transforms the initial nanocondensates into microcondensates (size >1 μm). The intrinsic tendency of proinsulin to self-condensate implies that, in the ER, a chaperoning mechanism must arrest its spontaneous intermolecular condensation to allow for proper intramolecular folding. These data further suggest that proinsulin is an early driver of insulin SG biogenesis, in a process in which its co-condensation with RESP18HD participates in their phase separation from other secretory proteins in transit through the same compartments but destined to other routes. Through the cytosolic tail of ICA512, proinsulin co-condensation with RESP18HD may further orchestrate the recruitment of cytosolic factors involved in membrane budding and fission of transport vesicles and nascent SGs.

Population-based heteropolymer design to mimic protein mixtures

Biological fluids, the most complex blends, have compositions that constantly vary and cannot be molecularly defined1. Despite these uncertainties, proteins fluctuate, fold, function and evolve as programmed2-4. We propose that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters5,6; synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. Here, we extracted the chemical characteristics and sequential arrangement along a protein chain at the segmental level from natural protein libraries and used the information to design heteropolymer ensembles as mixtures of disordered, partially folded and folded proteins. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration, enhancing the thermal stability of proteins and behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials and, ultimately, realize matter-to-life transformations.

A New Phase of Networking: The Molecular Composition and Regulatory Dynamics of Mammalian Stress Granules

Stress granules (SGs) are cytosolic biomolecular condensates that form in response to cellular stress. Weak, multivalent interactions between their protein and RNA constituents drive their rapid, dynamic assembly through phase separation coupled to percolation. Though a consensus model of SG function has yet to be determined, their perceived implication in cytoprotective processes (e.g., antiviral responses and inhibition of apoptosis) and possible role in the pathogenesis of various neurodegenerative diseases (e.g., amyotrophic lateral sclerosis and frontotemporal dementia) have drawn great interest. Consequently, new studies using numerous cell biological, genetic, and proteomic methods have been performed to unravel the mechanisms underlying SG formation, organization, and function and, with them, a more clearly defined SG proteome. Here, we provide a consensus SG proteome through literature curation and an update of the user-friendly database RNAgranuleDB to version 2.0 (http://rnagranuledb.lunenfeld.ca/). With this updated SG proteome, we use next-generation phase separation prediction tools to assess the predisposition of SG proteins for phase separation and aggregation. Next, we analyze the primary sequence features of intrinsically disordered regions (IDRs) within SG-resident proteins. Finally, we review the protein- and RNA-level determinants, including post-translational modifications (PTMs), that regulate SG composition and assembly/disassembly dynamics.

Development and Validation of a Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Low-Grade Gliomas

Aim

To explore whether the liquid-liquid phase separation- (LLPS-) related genes were potential prognostic markers that could contribute to the further classification of low-grade gliomas (LGGs).

Methods

The LLPS-related genes were subjected to functional enrichment analysis. The univariable, least absolute shrinkage and selection operator, and multivariable stepwise Cox regression analyses were performed to develop an LLPS-related gene signature (GS) in the discovery data set. The biological characteristics of the high-risk LGG were explored using gene set enrichment analysis. Two independent external data sets were used to validate the LLPS-related GS.

Results

LLPS-related genes are involved in multiple important cancer-related biological processes and pathways in LGG. Nine LLPS-related genes were identified to construct the LLPS-related GS, which was significantly associated with the prognosis of LGG patients. The LLPS-related GS could successfully divide patients with LGG into high- and low-risk groups, and the high-risk group showed a poorer prognosis than the low-risk group. Furthermore, the LLPS-related GS was independent of IDH and 1p19q status. Several cancer-related pathways may be more active in high-risk LGGs, such as IL6 JAK STAT3 signaling pathway. The LLPS-related GS was successfully validated with two independent external data sets.

Conclusion

We developed and validated a novel LLPS-related GS for risk stratification of LGG. Our findings may provide more precise management for LGGs and a useful reference for LLPS mechanism to link LGG studies.

PhaSepDB in 2022: annotating phase separation-related proteins with droplet states, co-phase separation partners and other experimental information

Phase separation (PS) proteins form droplets to regulate myriad membraneless organelles (MLOs) and cellular pathways such as transcription, signaling transduction and protein degeneration. PS droplets are usually liquid-like and can convert to hydrogel/solid-like under certain conditions. The PS behavior of proteins is regulated by co-PS partners and mutations, modifications, oligomerizations, repeat regions and alternative splicing of the proteins. With growing interest in PS condensates and associated proteins, we established PhaSepDB 1.0, which provided experimentally verified PS proteins and MLO-related proteins. The past few years witnessed a surge in PS-related research works; thus, we kept updating PhaSepDB. The current PhaSepDB contains 1419 PS entries, 770 low-throughput MLO-related entries and 7303 high-throughput MLO-related entries. We provided more detailed annotations of PS proteins, including PS verification experiments, regions used in experiments, phase diagrams of different experimental conditions, droplet states, co-PS partners and PS regulatory information. We believe that researchers can go further in studying PS proteins with the updated PhaSepDB (http://db.phasep.pro/).

Protein conformation and biomolecular condensates

Protein conformation and cell compartmentalization are fundamental concepts and subjects of vast scientific endeavors. In the last two decades, we have witnessed exciting advances that unveiled the conjunction of these concepts. An avalanche of studies highlighted the central role of biomolecular condensates in membraneless subcellular compartmentalization that permits the spatiotemporal organization and regulation of myriads of simultaneous biochemical reactions and macromolecular interactions. These studies have also shown that biomolecular condensation, driven by multivalent intermolecular interactions, is mediated by order-disorder transitions of protein conformation and by protein domain architecture. Conceptually, protein condensation is a distinct level in protein conformational landscape in which collective folding of large collections of molecules takes place. Biomolecular condensates arise by the physical process of phase separation and comprise a variety of bodies ranging from membraneless organelles to liquid condensates to solid-like conglomerates, spanning lengths from mesoscopic clusters (nanometers) to micrometer-sized objects. In this review, we summarize and discuss recent work on the assembly, composition, conformation, material properties, thermodynamics, regulation, and functions of these bodies. We also review the conceptual framework for future studies on the conformational dynamics of condensed proteins in the regulation of cellular processes.

BIAPSS: A Comprehensive Physicochemical Analyzer of Proteins Undergoing Liquid-Liquid Phase Separation

The liquid-liquid phase separation (LLPS) of biomolecules is a phenomenon which is nowadays recognized as the driving force for the biogenesis of numerous functional membraneless organelles and cellular bodies. The interplay between the protein primary sequence and phase separation remains poorly understood, despite intensive research. To uncover the sequence-encoded signals of protein capable of undergoing LLPS, we developed a novel web platform named BIAPSS (Bioinformatics Analysis of LLPS Sequences). This web server provides on-the-fly analysis, visualization, and interpretation of the physicochemical and structural features for the superset of curated LLPS proteins.