dSCOPE: a software to detect sequences critical for liquid-liquid phase separation

Deciphering the role of liquid-liquid phase separation in sarcoma: Implications for pathogenesis and treatment

Liquid-liquid phase separation (LLPS) is a significant reversible and dynamic process in organisms. Cells form droplets that are distinct from membrane-bound cell organelles by phase separation to keep biochemical processes in order. Nevertheless, the pathological state of LLPS contributes to the progression of a variety of tumor-related pathogenic issues. Sarcoma is one kind of highly malignant tumor characterized by aggressive metastatic potential and resistance to conventional therapeutic agents. Despite the significant clinical relevance, research on phase separation in sarcomas currently faces several major challenges. These include the limited availability of sarcoma samples, insufficient attention from the research community, and the complex genetic heterogeneity of sarcomas. Recently, emerging evidence have elaborated the specific effects and pathways of phase separation on different sarcoma subtypes, including the effect of sarcoma fusion proteins and other physicochemical factors on phase separation. This review aims to summarize the multiple roles of phase separation in sarcoma and novel molecular inhibitors that target phase separation. These insights will broaden the understanding of the mechanisms concerning sarcoma and offer new perspectives for future therapeutic strategies.

Protocol for in vitro observation of HDAC4 condensation during induced cardiac reprogramming

Here, we present a protocol to monitor and stimulate cardiac reprogramming via the disruption of Hdac4 condensates mediated by PC14-3-3 activation. We describe steps for inducing cardiac reprogramming in vitro, validating condensate formation ability of Hdac4, and observing Hdac4 condensates during reprogramming. We then detail procedures for disrupting these condensates using PC14-3-3 activation strategies. For complete details on the use and execution of this protocol, please refer to Liu et al.1.

Peptide-mediated liquid-liquid phase separation and biomolecular condensates

Liquid-liquid phase separation (LLPS) is a cornerstone of cellular organization, driving the formation of biomolecular condensates that regulate diverse biological processes and inspire innovative applications. This review explores the molecular mechanisms underlying peptide-mediated LLPS, emphasizing the roles of intermolecular interactions such as hydrophobic effects, electrostatic interactions, and π-π stacking in phase separation. The influence of environmental factors, such as pH, temperature, ionic strength, and molecular crowding on the stability and dynamics of peptide coacervates is examined, highlighting their tunable properties. Additionally, the unique physicochemical properties of peptide coacervates, including their viscoelastic behavior, interfacial dynamics, and stimuli-responsiveness, are discussed in the context of their biological relevance and engineering potential. Peptide coacervates are emerging as versatile platforms in biotechnology and medicine, particularly in drug delivery, tissue engineering, and synthetic biology. By integrating fundamental insights with practical applications, this review underscores the potential of peptide-mediated LLPS as a transformative tool for advancing science and healthcare.

The MTR4/hnRNPK complex surveils aberrant polyadenylated RNAs with multiple exons

RNA surveillance systems degrade aberrant RNAs that result from defective transcriptional termination, splicing, and polyadenylation. Defective RNAs in the nucleus are recognized by RNA-binding proteins and MTR4, and are degraded by the RNA exosome complex. Here, we detect aberrant RNAs in MTR4-depleted cells using long-read direct RNA sequencing and 3' sequencing. MTR4 destabilizes intronic polyadenylated transcripts generated by transcriptional read-through over one or more exons, termed 3' eXtended Transcripts (3XTs). MTR4 also associates with hnRNPK, which recognizes 3XTs with multiple exons. Moreover, the aberrant protein translated from KCTD13 3XT is a target of the hnRNPK-MTR4-RNA exosome pathway and forms aberrant condensates, which we name KCTD13 3eXtended Transcript-derived protein (KeXT) bodies. Our results suggest that RNA surveillance in human cells inhibits the formation of condensates of a defective polyadenylated transcript-derived protein.

MX2 forms nucleoporin-comprising cytoplasmic biomolecular condensates that lure viral capsids

Human myxovirus resistance 2 (MX2) can restrict HIV-1 and herpesviruses at a post-entry step through a process requiring an interaction between MX2 and the viral capsids. The involvement of other host cell factors, however, remains poorly understood. Here, we mapped the proximity interactome of MX2, revealing strong enrichment of phenylalanine-glycine (FG)-rich proteins related to the nuclear pore complex as well as proteins that are part of cytoplasmic ribonucleoprotein granules. MX2 interacted with these proteins to form multiprotein cytoplasmic biomolecular condensates that were essential for its anti-HIV-1 and anti-herpes simplex virus 1 (HSV-1) activity. MX2 condensate formation required the disordered N-terminal region and MX2 dimerization. Incoming HIV-1 and HSV-1 capsids associated with MX2 at these dynamic cytoplasmic biomolecular condensates, preventing nuclear entry of their viral genomes. Thus, MX2 forms cytoplasmic condensates that likely act as nuclear pore decoys, trapping capsids and inducing premature viral genome release to interfere with nuclear targeting of HIV-1 and HSV-1.

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.

AlphaFold2-based prediction of the co-condensation propensity of proteins

The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.

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.

Tannic acid as a biphasic modulator of tau protein liquid-liquid phase separation

Tannic acid (TA) is a natural polyphenol that shows great potential in the field of biomedicine due to its anti-inflammatory, anti-oxidant, anti-bacterial, anti-tumor, anti-virus, and neuroprotective activities. Recent studies have revealed that liquid-liquid phase separation (LLPS) is closely associated with protein aggregation. Therefore, modulating LLPS offers new insights into the treatment of neurodegenerative diseases. In this study, we investigated the influence of TA on the LLPS of the Alzheimer's-related protein tau and the underlying mechanism. Our findings indicate that TA affects the LLPS of tau in a biphasic manner, with initial promotion and subsequent suppression as the TA to tau molar ratio increases. TA modulates tau phase separation through a combination of hydrophobic interactions and hydrogen bonds. The balance between TA-tau and tau-tau interactions is found to be relevant to the material properties of TA-induced tau condensates. We further illustrate that the modulatory activity of TA in phase separation is highly dependent on the target proteins. These findings enhance our understanding of the forces driving tau LLPS under different conditions, and may facilitate the identification and optimization of compounds that can rationally modulate protein phase transition in the future.

Aromatic and arginine content drives multiphasic condensation of protein-RNA mixtures

Multiphasic architectures are found ubiquitously in biomolecular condensates and are thought to have important implications for the organization of multiple chemical reactions within the same compartment. Many of these multiphasic condensates contain RNA in addition to proteins. Here, we investigate the importance of different interactions in multiphasic condensates comprising two different proteins and RNA using computer simulations with a residue-resolution coarse-grained model of proteins and RNA. We find that in multilayered condensates containing RNA in both phases, protein-RNA interactions dominate, with aromatic residues and arginine forming the key stabilizing interactions. The total aromatic and arginine content of the two proteins must be appreciably different for distinct phases to form, and we show that this difference increases as the system is driven toward greater multiphasicity. Using the trends observed in the different interaction energies of this system, we demonstrate that we can also construct multilayered condensates with RNA preferentially concentrated in one phase. The "rules" identified can thus enable the design of synthetic multiphasic condensates to facilitate further study of their organization and function.

Sinorhizobium meliloti BR-bodies promote fitness during host colonization

Biomolecular condensates, such as the nucleoli or P-bodies, are non-membrane-bound assemblies of proteins and nucleic acids that facilitate specific cellular processes. Like eukaryotic P-bodies, the recently discovered bacterial ribonucleoprotein bodies (BR-bodies) organize the mRNA decay machinery, yet the similarities in molecular and cellular functions across species have been poorly explored. Here, we examine the functions of BR-bodies in the nitrogen-fixing endosymbiont Sinorhizobium meliloti, which colonizes the roots of compatible legume plants. Assembly of BR-bodies into visible foci in S. meliloti cells requires the C-terminal intrinsically disordered region (IDR) of RNase E, and foci fusion is readily observed in vivo, suggesting they are liquid-like condensates that form via mRNA sequestration. Using Rif-seq to measure mRNA lifetimes, we found a global slowdown in mRNA decay in a mutant deficient in BR-bodies, indicating that compartmentalization of the degradation machinery promotes efficient mRNA turnover. While BR-bodies are constitutively present during exponential growth, the abundance of BR-bodies increases upon cell stress, whereby they promote stress resistance. Finally, using Medicago truncatula as host, we show that BR-bodies enhance competitiveness during colonization and appear to be required for effective symbiosis, as mutants without BR-bodies failed to stimulate plant growth. These results suggest that BR-bodies provide a fitness advantage for bacteria during infection, perhaps by enabling better resistance against the host immune response.

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.

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.