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

Ageing drop by drop: Disturbance of the membrane-less organelle biogenesis as an aging hallmark

Despite extensive research, the features associated with the aging phenotype are not all-inclusive and need to be updated on a regular basis to incorporate new findings. We propose to include the dysfunction of membrane-less organelle (MLO) as a new aging hallmark. Special scaffold proteins with a high degree of intrinsic disorder drive the formation of MLOs via the liquid-liquid phase separation (LLPS) process. Aberrant behavior of MLOs was shown to be associated with the pathogenesis of many neurodegenerative diseases. In this work, we challenge the aging through bidirectional bioinformatics analysis of human proteins found in Granulome consisting of 7264 protein and Ageome containing 1624 aging-related proteins. The analysis indicates the interconnectivity of MLOs and aging. Approximately 67 % of the Ageome are presented in Granulome thereby constituting the Intersectome that include 1084 proteins showing an enrichment significantly higher than for the random datasets of the same size. Furthermore, for proteins in 10 representative MLOs, we analyzed in detail molecular functions, association with the already known aging hallmarks, and the roles in MLO formation (scaffold, client, or regulator). Cumulatively, our results strengthen the hypothesis that the dysfunction of MLOs can serve as a potent new aging hallmark.

LCRAnnotationsDB: a database of low complexity regions functional and structural annotations

Low Complexity Regions (LCRs) are segments of proteins with a low diversity of amino acid composition. These regions play important roles in proteins. However, annotations describing these functions are dispersed across databases and scientific literature. LCRAnnotationsDB aims to consolidate knowledge about LCRs and store relevant annotations in a single place. To unify redundant annotations, we assigned them categories based on similarity in function, protein structure, and biological process. Categories are organized hierarchically by linking them to Gene Ontology terms. The LCRAnnotationsDB database can be accessed at https://lcrannotdb.lcr-lab.org/ .

The RNA-dependent association of phosphatidylinositol 4,5-bisphosphate with intrinsically disordered proteins contribute to nuclear compartmentalization

The RNA content is crucial for the formation of nuclear compartments, such as nuclear speckles and nucleoli. Phosphatidylinositol 4,5-bisphosphate (PIP2) is found in nuclear speckles, nucleoli, and nuclear lipid islets and is involved in RNA polymerase I/II transcription. Intriguingly, the nuclear localization of PIP2 was also shown to be RNA-dependent. We therefore investigated whether PIP2 and RNA cooperate in the establishment of nuclear architecture. In this study, we unveiled the RNA-dependent PIP2-associated (RDPA) nuclear proteome in human cells by mass spectrometry. We found that intrinsically disordered regions (IDRs) with polybasic PIP2-binding K/R motifs are prevalent features of RDPA proteins. Moreover, these IDRs of RDPA proteins exhibit enrichment for phosphorylation, acetylation, and ubiquitination sites. Our results show for the first time that the RDPA protein Bromodomain-containing protein 4 (BRD4) associates with PIP2 in the RNA-dependent manner via electrostatic interactions, and that altered PIP2 levels affect the number of nuclear foci of BRD4 protein. Thus, we propose that PIP2 spatiotemporally orchestrates nuclear processes through association with RNA and RDPA proteins and affects their ability to form foci presumably via phase separation. This suggests the pivotal role of PIP2 in the establishment of a functional nuclear architecture competent for gene expression.

SeqDance: A Protein Language Model for Representing Protein Dynamic Properties

Proteins perform their functions by folding amino acid sequences into dynamic structural ensembles. Despite the important role of protein dynamics, their complexity and the absence of efficient representation methods have limited their integration into studies on protein function and mutation fitness, especially in deep learning applications. To address this, we present SeqDance, a protein language model designed to learn representation of protein dynamic properties directly from sequence alone. SeqDance is pre-trained on dynamic biophysical properties derived from over 30,400 molecular dynamics trajectories and 28,600 normal mode analyses. Our results show that SeqDance effectively captures local dynamic interactions, co-movement patterns, and global conformational features, even for proteins lacking homologs in the pre-training set. Additionally, we showed that SeqDance enhances the prediction of protein fitness landscapes, disorder-to-order transition binding regions, and phase-separating proteins. By learning dynamic properties from sequence, SeqDance complements conventional evolution- and static structure-based methods, offering new insights into protein behavior and function.

Role of liquid-liquid phase separation in cancer: Mechanisms and therapeutic implications

Liquid-liquid phase separation (LLPS) has emerged as a pivotal biological phenomenon involved in various cellular processes, including the formation of membrane-less organelles and the regulation of biomolecular condensates through precise spatiotemporal coordination of signaling pathways in cells. Dysregulation of LLPSs results in aberrant biomolecular condensates, which are widely implicated in tumorigenesis and cancer progression. Here, we comprehensively summarize the multifaceted roles of LLPS in tumor biology from the perspective of cancer hallmarks, including genomic stability, metabolic reprogramming progression, ferroptosis, and metastasis, to unveil the intricate mechanisms by which LLPS occurs in tumorigenesis. We discuss current discoveries related to therapeutic involvement and potential clinical applications of LLPS in cancer treatment, highlighting the potential of targeting LLPS-driven processes as novel therapeutic strategies. Additionally, we discuss the challenges associated with new approaches for cancer treatment based on LLPS. This in-depth discussion of the impact of LLPS on fundamental aspects of tumor biology provides new insights into overcoming cancer.

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.

Emerging roles of liquid-liquid phase separation in liver innate immunity

Biomolecular condensates formed by liquid-liquid phase separation (LLPS) have become an extensive mechanism of macromolecular metabolism and biochemical reactions in cells. Large molecules like proteins and nucleic acids will spontaneously aggregate and assemble into droplet-like structures driven by LLPS when the physical and chemical properties of cells are altered. LLPS provides a mature molecular platform for innate immune response, which tightly regulates key signaling in liver immune response spatially and physically, including DNA and RNA sensing pathways, inflammasome activation, and autophagy. Take this, LLPS plays a promoting or protecting role in a range of liver diseases, such as viral hepatitis, non-alcoholic fatty liver disease, liver fibrosis, hepatic ischemia-reperfusion injury, autoimmune liver disease, and liver cancer. This review systematically describes the whole landscape of LLPS in liver innate immunity. It will help us to guide a better-personalized approach to LLPS-targeted immunotherapy for liver diseases.

Exploring the anticancer mechanism of cardiac glycosides using proteome integral solubility alteration approach

BACKGROUND AND AIMS

Cardiac glycosides (CGs), traditionally used for heart failure, have shown potential as anti-cancer agents. This study aims to explore their multifaceted mechanisms in cancer cell biology using proteome integral solubility alteration (PISA), focusing on the interaction with key proteins implicated in cellular metabolism and mitochondrial function.

METHODS

We conducted lysate-based and intact-cell PISA assays on cancer cells treated with CGs (Digoxin, Digitoxin, Ouabain) to analyze protein solubility changes. This was followed by mass spectrometric analysis and bioinformatics to identify differentially soluble proteins (DSPs). Molecular docking simulations were performed to predict protein-CG interactions. Public data including gene expression changes upon CG treatment were re-analyzed for validation.

RESULTS

The PISA assays revealed CGs' broad-spectrum interactions, particularly affecting proteins like PKM2, ANXA2, SLC16A1, GOT2 and GLUD1. Molecular docking confirmed stable interactions between CGs and these DSPs. Re-analysis of public data supported the impact of CGs on cancer metabolism and cell signaling pathways.

CONCLUSION

Our findings suggest that CGs could be repurposed for cancer therapy by modulating cellular processes. The PISA data provide insights into the polypharmacological effects of CGs, warranting further exploration of their mechanisms and clinical potential.

Biomolecular condensates and disease pathogenesis

Biomolecular condensates or membraneless organelles (MLOs) formed by liquid-liquid phase separation (LLPS) divide intracellular spaces into discrete compartments for specific functions. Dysregulation of LLPS or aberrant phase transition that disturbs the formation or material states of MLOs is closely correlated with neurodegeneration, tumorigenesis, and many other pathological processes. Herein, we summarize the recent progress in development of methods to monitor phase separation and we discuss the biogenesis and function of MLOs formed through phase separation. We then present emerging proof-of-concept examples regarding the disruption of phase separation homeostasis in a diverse array of clinical conditions including neurodegenerative disorders, hearing loss, cancers, and immunological diseases. Finally, we describe the emerging discovery of chemical modulators of phase separation.

Bioinformatic approaches of liquid-liquid phase separation in human disease

ABSTRACT

Biomolecular aggregation within cellular environments via liquid-liquid phase separation (LLPS) spontaneously forms droplet-like structures, which play pivotal roles in diverse biological processes. These structures are closely associated with a range of diseases, including neurodegenerative disorders, cancer and infectious diseases, highlighting the significance of understanding LLPS mechanisms for elucidating disease pathogenesis, and exploring potential therapeutic interventions. In this review, we delineate recent advancements in LLPS research, emphasizing its pathological relevance, therapeutic considerations, and the pivotal role of bioinformatic tools and databases in facilitating LLPS investigations. Additionally, we undertook a comprehensive analysis of bioinformatic resources dedicated to LLPS research in order to elucidate their functionality and applicability. By providing comprehensive insights into current LLPS-related bioinformatics resources, this review highlights its implications for human health and disease.

Hsp90α forms condensate engaging client proteins with RG motif repeats

Hsp90α, a pivotal canonical chaperone, is renowned for its broad interaction with numerous protein clients to maintain protein homeostasis, chromatin remodeling, and cell growth. Recent studies indicate its role in modifying various components of membraneless organelles (MLOs) such as stress granules and processing bodies, suggesting its participation in the regulation of protein condensates. In this study, we found that Hsp90α possesses an inherent ability to form dynamic condensates in vitro. Utilizing LC-MS/MS, we further pinpointed proteins in cell lysates that preferentially integrate into Hsp90α condensates. Significantly, we observed a prevalence of RG motif repeats in client proteins of Hsp90α condensates, many of which are linked to various MLOs. Moreover, each of the three domains of Hsp90α was found to undergo phase separation, with numerous solvent-exposed negatively charged residues on these domains being crucial for driving Hsp90α condensation through multivalent weak electrostatic interactions. Additionally, various clients like TDP-43 and hnRNPA1, along with poly-GR and PR dipeptide repeats, exhibit varied impacts on the dynamic behavior of Hsp90α condensates. Our study spotlights various client proteins associated with Hsp90α condensates, illustrating its intricate adaptive nature in interacting with diverse clients and its functional adaptability across multiple MLOs.

High-throughput and proteome-wide discovery of endogenous biomolecular condensates

Phase separation inside mammalian cells regulates the formation of the biomolecular condensates that are related to gene expression, signalling, development and disease. However, a large population of endogenous condensates and their candidate phase-separating proteins have yet to be discovered in a quantitative and high-throughput manner. Here we demonstrate that endogenously expressed biomolecular condensates can be identified across a cell's proteome by sorting proteins across varying oligomeric states. We employ volumetric compression to modulate the concentrations of intracellular proteins and the degree of crowdedness, which are physical regulators of cellular biomolecular condensates. The changes in degree of the partition of proteins into condensates or phase separation led to varying oligomeric states of the proteins, which can be detected by coupling density gradient ultracentrifugation and quantitative mass spectrometry. In total, we identified 1,518 endogenous condensate proteins, of which 538 have not been reported before. Furthermore, we demonstrate that our strategy can identify condensate proteins that respond to specific biological processes.

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.

Cyclophilin A supports translation of intrinsically disordered proteins and affects haematopoietic stem cell ageing

Loss of protein function is a driving force of ageing. We have identified peptidyl-prolyl isomerase A (PPIA or cyclophilin A) as a dominant chaperone in haematopoietic stem and progenitor cells. Depletion of PPIA accelerates stem cell ageing. We found that proteins with intrinsically disordered regions (IDRs) are frequent PPIA substrates. IDRs facilitate interactions with other proteins or nucleic acids and can trigger liquid-liquid phase separation. Over 20% of PPIA substrates are involved in the formation of supramolecular membrane-less organelles. PPIA affects regulators of stress granules (PABPC1), P-bodies (DDX6) and nucleoli (NPM1) to promote phase separation and increase cellular stress resistance. Haematopoietic stem cell ageing is associated with a post-transcriptional decrease in PPIA expression and reduced translation of IDR-rich proteins. Here we link the chaperone PPIA to the synthesis of intrinsically disordered proteins, which indicates that impaired protein interaction networks and macromolecular condensation may be potential determinants of haematopoietic stem cell ageing.

Revolutionizing viral disease treatment: Phase separation and lysosome/exosome targeting as new areas and new paradigms for antiviral drug research

With the advancement of globalization, our world is becoming increasingly interconnected. However, this interconnection means that once an infectious disease emerges, it can rapidly spread worldwide. Specifically, viral diseases pose a growing threat to human health. The COVID-19 pandemic has underscored the pressing need for expedited drug development to combat emerging viral diseases. Traditional drug discovery methods primarily rely on random screening and structure-based optimization, and new approaches are required to address more complex scenarios in drug discovery. Emerging antiviral strategies include phase separation and lysosome/exosome targeting. The widespread implementation of these innovative drug design strategies will contribute towards tackling existing viral infections and future outbreaks.

Liquid-liquid phase separation in plants: Advances and perspectives from model species to crops

Membraneless biomolecular condensates play important roles in both normal biological activities and responses to environmental stimuli in living organisms. Liquid‒liquid phase separation (LLPS) is an organizational mechanism that has emerged in recent years to explain the formation of biomolecular condensates. In the past decade, advances in LLPS research have contributed to breakthroughs in disease fields. By contrast, although LLPS research in plants has progressed over the past 5 years, it has been concentrated on the model plant Arabidopsis, which has limited relevance to agricultural production. In this review, we provide an overview of recently reported advances in LLPS in plants, with a particular focus on photomorphogenesis, flowering, and abiotic and biotic stress responses. We propose that many potential LLPS proteins also exist in crops and may affect crop growth, development, and stress resistance. This possibility presents a great challenge as well as an opportunity for rigorous scientific research on the biological functions and applications of LLPS in crops.

Single-cell sequencing and transcriptome analyses in the construction of a liquid-liquid phase separation-associated gene model for rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is a disabling autoimmune disease that affects multiple joints. Accumulating evidence suggests that imbalances in liquid-liquid phase separation (LLPS) can lead to altered spatiotemporal coordination of biomolecular condensates, which play important roles in carcinogenesis and inflammatory diseases. However, the role of LLPS in the development and progression of RA remains unclear. Methods: We screened RA and normal samples from GSE12021, GSE55235, and GSE55457 transcriptome datasets and GSE129087 and GSE109449 single-cell sequencing datasets from Gene Expression Omnibus database to investigate the pathogenesis of LLPS-related hub genes at the transcriptome and single cell sequencing levels. Machine learning algorithms and weighted gene co-expression network analysis were applied to screen hub genes, and hub genes were validated using correlation studies. Results: Differential analysis showed that 36 LLPS-related genes were significantly differentially expressed in RA, further random forest and support vector machine identified four and six LLPS-related genes, respectively, and weighted gene co-expression network analysis identified 396 modular genes. Hybridization of the three sets revealed two hub genes, MYC and MAP1LC3B, with AUCs of 0.907 and 0.911, respectively. Further ROC analysis of the hub genes in the GSE55457 dataset showed that the AUCs of MYC and MAP1LC3B were 0.815 and 0.785, respectively. qRT-PCR showed that the expression of MYC and MAP1LC3B in RA synovial tissues was significantly lower than that in the normal control synovial tissues. Correlation analysis between hub genes and the immune microenvironment and single-cell sequencing analysis revealed that both MYC and MAP1LC3B were significantly correlated with the degree of infiltration of various innate and acquired immune cells. Conclusion: Our study reveals a possible mechanism for LLPS in RA pathogenesis and suggests that MYC and MAP1LC3B may be potential novel molecular markers for RA with immunological significance.

Liquid-Liquid Phase Separation Sheds New Light upon Cardiovascular Diseases

Liquid-liquid phase separation (LLPS) is a biophysical process that mediates the precise and complex spatiotemporal coordination of cellular processes. Proteins and nucleic acids are compartmentalized into micron-scale membrane-less droplets via LLPS. These droplets, termed biomolecular condensates, are highly dynamic, have concentrated components, and perform specific functions. Biomolecular condensates have been observed to organize diverse key biological processes, including gene transcription, signal transduction, DNA damage repair, chromatin organization, and autophagy. The dysregulation of these biological activities owing to aberrant LLPS is important in cardiovascular diseases. This review provides a detailed overview of the regulation and functions of biomolecular condensates, provides a comprehensive depiction of LLPS in several common cardiovascular diseases, and discusses the revolutionary therapeutic perspective of modulating LLPS in cardiovascular diseases and new treatment strategies relevant to LLPS.

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.

Investigating phase separation properties of chromatin-associated proteins using gradient elution of 1,6-hexanediol

BACKGROUND

Chromatin-associated phase separation proteins establish various biomolecular condensates via liquid-liquid phase separation (LLPS), which regulates vital biological processes spatially and temporally. However, the widely used methods to characterize phase separation proteins are still based on low-throughput experiments, which consume time and could not be used to explore protein LLPS properties in bulk.

RESULTS

By combining gradient 1,6-hexanediol (1,6-HD) elution and quantitative proteomics, we developed chromatin enriching hexanediol separation coupled with liquid chromatography-mass spectrometry (CHS-MS) to explore the LLPS properties of different chromatin-associated proteins (CAPs). First, we found that CAPs were enriched more effectively in the 1,6-HD treatment group than in the isotonic solution treatment group. Further analysis showed that the 1,6-HD treatment group could effectively enrich CAPs prone to LLPS. Finally, we compared the representative proteins eluted by different gradients of 1,6-HD and found that the representative proteins of the 2% 1,6-HD treatment group had the highest percentage of IDRs and LCDs, whereas the 10% 1,6-HD treatment group had the opposite trend.

CONCLUSION

This study provides a convenient high-throughput experimental method called CHS-MS. This method can efficiently enrich proteins prone to LLPS and can be extended to explore LLPS properties of CAPs in different biological systems.

Sequence variations of phase-separating proteins and resources for studying biomolecular condensates

Phase separation (PS) is an important mechanism underlying the formation of biomolecular condensates. Physiological condensates are associated with numerous biological processes, such as transcription, immunity, signaling, and synaptic transmission. Changes in particular amino acids or segments can disturb the protein's phase behavior and interactions with other biomolecules in condensates. It is thus presumed that variations in the phase-separating-prone domains can significantly impact the properties and functions of condensates. The dysfunction of condensates contributes to a number of pathological processes. Pharmacological perturbation of these condensates is proposed as a promising way to restore physiological states. In this review, we characterize the variations observed in PS proteins that lead to aberrant biomolecular compartmentalization. We also showcase recent advancements in bioinformatics of membraneless organelles (MLOs), focusing on available databases useful for screening PS proteins and describing endogenous condensates, guiding researchers to seek the underlying pathogenic mechanisms of biomolecular condensates.

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.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

Study liquid-liquid phase separation with optical microscopy: A methodology review

Intracellular liquid-liquid phase separation (LLPS) is a critical process involving the dynamic association of biomolecules and the formation of non-membrane compartments, playing a vital role in regulating biomolecular interactions and organelle functions. A comprehensive understanding of cellular LLPS mechanisms at the molecular level is crucial, as many diseases are linked to LLPS, and insights gained can inform drug/gene delivery processes and aid in the diagnosis and treatment of associated diseases. Over the past few decades, numerous techniques have been employed to investigate the LLPS process. In this review, we concentrate on optical imaging methods applied to LLPS studies. We begin by introducing LLPS and its molecular mechanism, followed by a review of the optical imaging methods and fluorescent probes employed in LLPS research. Furthermore, we discuss potential future imaging tools applicable to the LLPS studies. This review aims to provide a reference for selecting appropriate optical imaging methods for LLPS investigations.

Towards sequence-based principles for protein phase separation predictions

The phenomenon of protein phase separation, which underlies the formation of biomolecular condensates, has been associated with numerous cellular functions. Recent studies indicate that the amino acid sequences of most proteins may harbour not only the code for folding into the native state but also for condensing into the liquid-like droplet state and the solid-like amyloid state. Here we review the current understanding of the principles for sequence-based methods for predicting the propensity of proteins for phase separation. A guiding concept is that entropic contributions are generally more important to stabilise the droplet state than they are for the native and amyloid states. Although estimating these entropic contributions has proven difficult, we describe some progress that has been recently made in this direction. To conclude, we discuss the challenges ahead to extend sequence-based prediction methods of protein phase separation to include quantitative in vivo characterisations of this process.

Comparison of Biomolecular Condensate Localization and Protein Phase Separation Predictors

Research in the field of biochemistry and cellular biology has entered a new phase due to the discovery of phase separation driving the formation of biomolecular condensates, or membraneless organelles, in cells. The implications of this novel principle of cellular organization are vast and can be applied at multiple scales, spawning exciting research questions in numerous directions. Of fundamental importance are the molecular mechanisms that underly biomolecular condensate formation within cells and whether insights gained into these mechanisms provide a gateway for accurate predictions of protein phase behavior. Within the last six years, a significant number of predictors for protein phase separation and condensate localization have emerged. Herein, we compare a collection of state-of-the-art predictors on different tasks related to protein phase behavior. We show that the tested methods achieve high AUCs in the identification of biomolecular condensate drivers and scaffolds, as well as in the identification of proteins able to phase separate in vitro. However, our benchmark tests reveal that their performance is poorer when used to predict protein segments that are involved in phase separation or to classify amino acid substitutions as phase-separation-promoting or -inhibiting mutations. Our results suggest that the phenomenological approach used by most predictors is insufficient to fully grasp the complexity of the phenomenon within biological contexts and make reliable predictions related to protein phase behavior at the residue level.