Computational Screening of Phase-separating Proteins

Advances in the understanding of androgen receptor structure and function and in the development of next-generation AR-targeted therapeutics

Androgen receptor (AR) and its ligand androgens are important for development and physiology of various tissues. AR and its ligands also play critical role in the development of various diseases, making it a valuable therapeutic target. AR ligands, both agonists and antagonists, are being widely used to treat pathological conditions, including prostate cancer and hypogonadism. Despite AR being studied widely over the last five decades, the last decade has seen striking advances in the knowledge on AR and discoveries that have the potential to translate to the clinic. This review provides an overview of the advances in AR biology, AR molecular mechanisms of action, and next generation molecules that are currently in development. Several of the areas described in the review are just unraveling and the next decade will bring more clarity on these developments that will put AR at the forefront of both basic biology and drug development.

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.

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.

The role of phase separation in RNA modification: both cause and effect

Phase separation is a critical mechanism for partitioning cellular functions by specific aggregation of biological macromolecules. Recent studies have found that phase separation is widely contributed in various biological functions, particularly in RNA related processes. Over 170 different post-transcriptional modifications occur in RNA, which is considered to be one of the most important physiological and pathogenic epigenetic mechanisms. Here, we discuss the role of phase separation in regulating RNA modification processing to ensure orderly RNA metabolism and function. Enzymes responsible for RNA modification undergo compartmentalization, enabling them to traffic client RNAs and amplify modifying efficacy. Meanwhile, altered RNA affects the formation, dissolution, and biophysical properties of phase separation conversely. These findings deeper our understanding of the interplay between phase separation and RNAs that governs a wide range of cellular processes. Finally, we concluded pathological roles of phase separation in RNA modification towards clinical applications and outlined perspectives to research RNA modification through the lens of phase separation.

Long way up: rethink diseases in light of phase separation and phase transition

Biomolecular condensation, driven by multivalency, serves as a fundamental mechanism within cells, facilitating the formation of distinct compartments, including membraneless organelles that play essential roles in various cellular processes. Perturbations in the delicate equilibrium of condensation, whether resulting in gain or loss of phase separation, have robustly been associated with cellular dysfunction and physiological disorders. As ongoing research endeavors wholeheartedly embrace this newly acknowledged principle, a transformative shift is occurring in our comprehension of disease. Consequently, significant strides have been made in unraveling the profound relevance and potential causal connections between abnormal phase separation and various diseases. This comprehensive review presents compelling recent evidence that highlight the intricate associations between aberrant phase separation and neurodegenerative diseases, cancers, and infectious diseases. Additionally, we provide a succinct summary of current efforts and propose innovative solutions for the development of potential therapeutics to combat the pathological consequences attributed to aberrant phase separation.

Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions

The burgeoning comprehension of protein phase separation (PS) has ushered in a wealth of bioinformatics tools for the prediction of phase-separating proteins (PSPs). These tools often skew towards PSPs with a high content of intrinsically disordered regions (IDRs), thus frequently undervaluing potential PSPs without IDRs. Nonetheless, PS is not only steered by IDRs but also by the structured modular domains and interactions that aren't necessarily reflected in amino acid sequences. In this work, we introduce PSPire, a machine learning predictor that incorporates both residue-level and structure-level features for the precise prediction of PSPs. Compared to current PSP predictors, PSPire shows a notable improvement in identifying PSPs without IDRs, which underscores the crucial role of non-IDR, structure-based characteristics in multivalent interactions throughout the PS process. Additionally, our biological validation experiments substantiate the predictive capacity of PSPire, with 9 out of 11 chosen candidate PSPs confirmed to form condensates within cells.

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.

Phase separation-mediated biomolecular condensates and their relationship to tumor

Phase separation is a cellular phenomenon where macromolecules aggregate or segregate, giving rise to biomolecular condensates resembling "droplets" and forming distinct, membrane-free compartments. This process is pervasive in biological cells, contributing to various essential cellular functions. However, when phase separation goes awry, leading to abnormal molecular aggregation, it can become a driving factor in the development of diseases, including tumor. Recent investigations have unveiled the intricate connection between dysregulated phase separation and tumor pathogenesis, highlighting its potential as a novel therapeutic target. This article provides an overview of recent phase separation research, with a particular emphasis on its role in tumor, its therapeutic implications, and outlines avenues for further exploration in this intriguing field.

Derailed protein turnover in the aging mammalian brain

Efficient protein turnover is essential for cellular homeostasis and organ function. Loss of proteostasis is a hallmark of aging culminating in severe dysfunction of protein turnover. To investigate protein turnover dynamics as a function of age, we performed continuous in vivo metabolic stable isotope labeling in mice along the aging continuum. First, we discovered that the brain proteome uniquely undergoes dynamic turnover fluctuations during aging compared to heart and liver tissue. Second, trends in protein turnover in the brain proteome during aging showed sex-specific differences that were tightly tied to cellular compartments. Next, parallel analyses of the insoluble proteome revealed that several cellular compartments experience hampered turnover, in part due to misfolding. Finally, we found that age-associated fluctuations in proteasome activity were associated with the turnover of core proteolytic subunits, which was recapitulated by pharmacological suppression of proteasome activity. Taken together, our study provides a proteome-wide atlas of protein turnover across the aging continuum and reveals a link between the turnover of individual proteasome subunits and the age-associated decline in proteasome activity.

Phase separation as a possible mechanism for dosage sensitivity

BACKGROUND

Deletion of haploinsufficient genes or duplication of triplosensitive ones results in phenotypic effects in a concentration-dependent manner, and the mechanisms underlying these dosage-sensitive effects remain elusive. Phase separation drives functional compartmentalization of biomolecules in a concentration-dependent manner as well, which suggests a potential link between these two processes, and warrants further systematic investigation.

RESULTS

Here we provide bioinformatic and experimental evidence to show a close link between phase separation and dosage sensitivity. We first demonstrate that haploinsufficient or triplosensitive gene products exhibit a higher tendency to undergo phase separation. Assessing the well-established dosage-sensitive genes HNRNPK, PAX6, and PQBP1 with experiments, we show that these proteins undergo phase separation. Critically, pathogenic variations in dosage-sensitive genes disturb the phase separation process either through reduced protein levels, or loss of phase-separation-prone regions. Analysis of multi-omics data further demonstrates that loss-of-function genetic perturbations on phase-separating genes cause similar dysfunction phenotypes as dosage-sensitive gene perturbations. In addition, dosage-sensitive scores derived from population genetics data predict phase-separating proteins with much better performance than available sequence-based predictors, further illustrating close ties between these two parameters.

CONCLUSIONS

Together, our study shows that phase separation is functionally linked to dosage sensitivity and provides novel insights for phase-separating protein prediction from the perspective of population genetics data.

The juxtamembrane linker of synaptotagmin 1 regulates Ca2+ binding via liquid-liquid phase separation

Synaptotagmin (syt) 1, a Ca2+ sensor for synaptic vesicle exocytosis, functions in vivo as a multimer. Syt1 senses Ca2+ via tandem C2-domains that are connected to a single transmembrane domain via a juxtamembrane linker. Here, we show that this linker segment harbors a lysine-rich, intrinsically disordered region that is necessary and sufficient to mediate liquid-liquid phase separation (LLPS). Interestingly, condensate formation negatively regulates the Ca2+-sensitivity of syt1. Moreover, Ca2+ and anionic phospholipids facilitate the observed phase separation, and increases in [Ca2+]i promote the fusion of syt1 droplets in living cells. Together, these observations suggest a condensate-mediated feedback loop that serves to fine-tune the ability of syt1 to trigger release, via alterations in Ca2+ binding activity and potentially through the impact of LLPS on membrane curvature during fusion reactions. In summary, the juxtamembrane linker of syt1 emerges as a regulator of syt1 function by driving self-association via LLPS.

The juxtamembrane linker of synaptotagmin 1 regulates Ca2+ binding via liquid-liquid phase separation

Synaptotagmin (syt) 1, a Ca2+ sensor for synaptic vesicle exocytosis, functions in vivo as a multimer. Syt1 senses Ca2+ via tandem C2-domains that are connected to a single transmembrane domain via a juxtamembrane linker. Here, we show that this linker segment harbors a lysine-rich, intrinsically disordered region that is necessary and sufficient to mediate liquid-liquid phase separation (LLPS). Interestingly, condensate formation negatively regulates the Ca2+-sensitivity of syt1. Moreover, Ca2+ and anionic phospholipids facilitate the observed phase separation, and increases in [Ca2+]i promote the fusion of syt1 droplets in living cells. Together, these observations suggest a condensate-mediated feedback loop that serves to fine-tune the ability of syt1 to trigger release, via alterations in Ca2+ binding activity and potentially through the impact of LLPS on membrane curvature during fusion reactions. In summary, the juxtamembrane linker of syt1 emerges as a regulator of syt1 function by driving self-association via 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.

ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level

We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase-separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain-level organization and compute a sequence-based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visiting https://stevewhitten.github.io/Parse_v2_FASTA to quickly identify phase-separating proteins within large sequence sets, or by visiting https://stevewhitten.github.io/Parse_v2_web to evaluate individual protein sequences.

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.

De novo amyloid peptides with subtle sequence variations differ in their self-assembly and nanomechanical properties

Proteinaceous amyloids are well known for their widespread pathological roles but lately have emerged also as key components in several biological functions. The remarkable ability of amyloid fibers to form tightly packed conformations in a cross β-sheet arrangement manifests in their robust enzymatic and structural stabilities. These characteristics of amyloids make them attractive for designing proteinaceous biomaterials for various biomedical and pharmaceutical applications. In order to design customizable and tunable amyloid nanomaterials, it is imperative to understand the sensitivity of the peptide sequence for subtle changes based on amino acid position and chemistry. Here we report our results from four rationally-designed amyloidogenic decapeptides that subtly differ in hydrophobicity and polarity at positions 5 and 6. We show that making the two positions hydrophobic renders the peptide with enhanced aggregation and material properties while introducing polar residues in position 5 dramatically changes the structure and nanomechanical properties of the fibrils formed. A charged residue at position 6, however, abrogates amyloid formation. In sum, we show that subtle changes in the sequence do not make the peptide innocuous but rather sensitive to aggregation, reflected in the biophysical and nanomechanical properties of the fibrils. We conclude that tolerance of peptide amyloid for changes in the sequence, however small they may be, should not be neglected for the effective design of customizable amyloid nanomaterials.

Intrinsically disordered regions that drive phase separation form a robustly distinct protein class

Protein phase separation is thought to be a primary driving force for the formation of membrane-less organelles, which control a wide range of biological functions from stress response to ribosome biogenesis. Among phase-separating (PS) proteins, many have intrinsically disordered regions (IDRs) that are needed for phase separation to occur. Accurate identification of IDRs that drive phase separation is important for testing the underlying mechanisms of phase separation, identifying biological processes that rely on phase separation, and designing sequences that modulate phase separation. To identify IDRs that drive phase separation, we first curated datasets of folded, ID, and PS ID sequences. We then used these sequence sets to examine how broadly existing amino acid property scales can be used to distinguish between the three classes of protein regions. We found that there are robust property differences between the classes and, consequently, that numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. This result indicates that multiple, redundant mechanisms contribute to the formation of phase-separated droplets from IDRs. The top-performing scales were used to further optimize our previously developed predictor of PS IDRs, ParSe. We then modified ParSe to account for interactions between amino acids and obtained reasonable predictive power for mutations that have been designed to test the role of amino acid interactions in driving protein phase separation. Collectively, our findings provide further insight into the classification of IDRs and the elements involved in protein phase separation.

Drosophila insulator proteins exhibit in vivo liquid-liquid phase separation properties

Drosophila insulator proteins and the cohesin subunit Rad21 coalesce in vivo to form liquid-droplet condensates, suggesting that liquid–liquid phase separation mediates their function in 3D genome organization.

Mounting evidence implicates liquid–liquid phase separation (LLPS), the condensation of biomolecules into liquid-like droplets in the formation and dissolution of membraneless intracellular organelles (MLOs). Cells use MLOs or condensates for various biological processes, including emergency signaling and spatiotemporal control over steady-state biochemical reactions and heterochromatin formation. Insulator proteins are architectural elements involved in establishing independent domains of transcriptional activity within eukaryotic genomes. In Drosophila, insulator proteins form nuclear foci known as insulator bodies in response to osmotic stress. However, the mechanism through which insulator proteins assemble into bodies is yet to be investigated. Here, we identify signatures of LLPS by insulator bodies, including high disorder tendency in insulator proteins, scaffold–client–dependent assembly, extensive fusion behavior, sphericity, and sensitivity to 1,6-hexanediol. We also show that the cohesin subunit Rad21 is a component of insulator bodies, adding to the known insulator protein constituents and γH2Av. Our data suggest a concerted role of cohesin and insulator proteins in insulator body formation and under physiological conditions. We propose a mechanism whereby these architectural proteins modulate 3D genome organization through LLPS.

Thermostable Proteins from HaCaT Keratinocytes Identify a Wide Breadth of Intrinsically Disordered Proteins and Candidates for Liquid-Liquid Phase Separation

Intrinsically disordered proteins (IDPs) move through an ensemble of conformations which allows multitudinous roles within a cell. Keratinocytes, the predominant cell type in mammalian epidermis, have had only a few individual proteins assessed for intrinsic disorder and its possible contribution to liquid-liquid phase separation (LLPS), especially in regard to what functions or structures these proteins provide. We took a holistic approach to keratinocyte IDPs starting with enrichment via the isolation of thermostable proteins. The keratinocyte protein involucrin, known for its resistance to heat denaturation, served as a marker. It and other thermostable proteins were identified by liquid chromatography tandem mass spectrometry and subjected to extensive bioinformatic analysis covering gene ontology, intrinsic disorder, and potential for LLPS. Numerous proteins unique to keratinocytes and other proteins with shared expression in multiple cell types were identified to have IDP traits (e.g., compositional bias, nucleic acid binding, and repeat motifs). Among keratinocyte-specific proteins, many that co-assemble with involucrin into the cell-specific structure known as the cornified envelope scored highly for intrinsic disorder and potential for LLPS. This suggests intrinsic disorder and LLPS are previously unrecognized traits for assembly of the cornified envelope, echoing the contribution of intrinsic disorder and LLPS to more widely encountered features such as stress granules and PML bodies.

Modulating biomolecular condensates: a novel approach to drug discovery

In the past decade, membraneless assemblies known as biomolecular condensates have been reported to play key roles in many cellular functions by compartmentalizing specific proteins and nucleic acids in subcellular environments with distinct properties. Furthermore, growing evidence supports the view that biomolecular condensates often form by phase separation, in which a single-phase system demixes into a two-phase system consisting of a condensed phase and a dilute phase of particular biomolecules. Emerging understanding of condensate function in normal and aberrant cellular states, and of the mechanisms of condensate formation, is providing new insights into human disease and revealing novel therapeutic opportunities. In this Perspective, we propose that such insights could enable a previously unexplored drug discovery approach based on identifying condensate-modifying therapeutics (c-mods), and we discuss the strategies, techniques and challenges involved.

Screening membraneless organelle participants with machine-learning models that integrate multimodal features

Protein self-assembly is one of the formation mechanisms of biomolecular condensates. However, most phase-separating systems (PS) demand multiple partners in biological conditions. In this study, we divided PS proteins into two groups according to the mechanism by which they undergo PS: PS-Self proteins can self-assemble spontaneously to form droplets, while PS-Part proteins interact with partners to undergo PS. Analysis of the amino acid composition revealed differences in the sequence pattern between the two protein groups. Existing PS predictors, when evaluated on two test protein sets, preferentially predicted self-assembling proteins. Thus, a comprehensive predictor is required. Herein, we propose that properties other than sequence composition can provide crucial information in screening PS proteins. By incorporating phosphorylation frequencies and immunofluorescence image-based droplet-forming propensity with other PS-related features, we built two independent machine-learning models to separately predict the two protein categories. Results of independent testing suggested the superiority of integrating multimodal features. We performed experimental verification on the top-scored proteins DHX9, K_i-67, and NIFK. Their PS behavior in vitro revealed the effectiveness of our models in PS prediction. Further validation on the proteome of membraneless organelles confirmed the ability of our models to identify PS-Part proteins. We implemented a web server named PhaSePred (http://predict.phasep.pro/) that incorporates our two models together with representative PS predictors. PhaSePred displays proteome-level quantiles of different features, thus profiling PS propensity and providing crucial information for identification of candidate proteins.

Phase-Separated Subcellular Compartmentation and Related Human Diseases

In live cells, proteins and nucleic acids can associate together through multivalent interactions, and form relatively isolated phases that undertake designated biological functions and activities. In the past decade, liquid–liquid phase separation (LLPS) has gradually been recognized as a general mechanism for the intracellular organization of biomolecules. LLPS regulates the assembly and composition of dozens of membraneless organelles and condensates in cells. Due to the altered physiological conditions or genetic mutations, phase-separated condensates may undergo aberrant formation, maturation or gelation that contributes to the onset and progression of various diseases, including neurodegenerative disorders and cancers. In this review, we summarize the properties of different membraneless organelles and condensates, and discuss multiple phase separation-regulated biological processes. Based on the dysregulation and mutations of several key regulatory proteins and signaling pathways, we also exemplify how aberrantly regulated LLPS may contribute to human diseases.

The Regulatory Roles of Intrinsically Disordered Linker in VRN1-DNA Phase Separation

Biomacromolecules often form condensates to function in cells. VRN1 is a transcriptional repressor that plays a key role in plant vernalization. Containing two DNA-binding domains connected by an intrinsically disordered linker (IDL), VRN1 was shown to undergo liquid-like phase separation with DNA, and the length and charge pattern of IDL play major regulatory roles. However, the underlying mechanism remains elusive. Using a polymer chain model and lattice-based Monte-Carlo simulations, we comprehensively investigated how the IDL regulates VRN1 and DNA phase separation. Using a worm-like chain model, we showed that the IDL controls the binding affinity of VRN1 to DNA, by modulating the effective local concentration of the VRN1 DNA-binding domains. The predicted binding affinities, under different IDL lengths, were in good agreement with previously reported experimental results. Our simulation of the phase diagrams of the VRN1 variants with neutral IDLs and DNA revealed that the ability of phase separation first increased and then decreased, along with the increase in the linker length. The strongest phase separation ability was achieved when the linker length was between 40 and 80 residues long. Adding charged patches to the IDL resulted in robust phase separation that changed little with IDL length variations. Our study provides mechanism insights on how IDL regulates VRN1 and DNA phase separation, and why naturally occurring VRN1-like proteins evolve to contain the charge segregated IDL sequences, which may also shed light on the molecular mechanisms of other IDL-regulated phase separation processes in living cells.

Protein phase separation hotspots at the presynapse

Fundamental discoveries have shaped our molecular understanding of presynaptic processes, such as neurotransmitter release, active zone organization and mechanisms of synaptic vesicle (SV) recycling. However, certain regulatory steps still remain incompletely understood. Protein liquid-liquid phase separation (LLPS) and its role in SV clustering and active zone regulation now introduce a new perception of how the presynapse and its different compartments are organized. This article highlights the newly emerging concept of LLPS at the synapse, providing a systematic overview on LLPS tendencies of over 500 presynaptic proteins, spotlighting individual proteins and discussing recent progress in the field. Newly discovered LLPS systems like ELKS/liprin-alpha and Eps15/FCho are put into context, and further LLPS candidate proteins, including epsin1, dynamin, synaptojanin, complexin and rabphilin-3A, are highlighted.

Probing Liquid-Liquid Phase Separation of RNA-Binding Proteins In Vitro and In Vivo

Biomolecular condensates and the concept of liquid-liquid phase separation (LLPS) have transformed cell biology in recent years. Condensates organize cellular content and compartmentalize biochemical reactions, in particular many processes involving RNA. This protocol is aimed at readers new to the LLPS field who want to test their protein or cellular structure of interest. We describe the basic principles of liquid-liquid phase separation, and outline initial approaches-both in vitro and in yeast cells-for the characterization of a candidate cellular condensate. First, we focus on strategies to purify phase-separating proteins and to reconstitute condensates from recombinant proteins in vitro for observation by light microscopy. Second, we describe in vivo experiments (including fluorescence recovery after photobleaching (FRAP) microscopy and 1,6-Hexanediol treatment) to test whether a subcellular structure displays liquid-like behavior in cells.

Distinctive Network Topology of Phase-Separated Proteins in Human Interactome

Liquid-liquid phase separation (LLPS) is an important mechanism that mediates the formation of biomolecular condensates. Despite the immense interest in LLPS, phase-separated proteins verified by experiments are still limited, and identification of phase-separated proteins at proteome-scale is a challenging task. Multivalent interaction among macromolecules is the driving force of LLPS, which suggests that phase-separated proteins may harbor distinct biological characteristics in protein-protein interactions (PPIs). In this study, we constructed an integrated human PPI network (HPIN) and mapped phase-separated proteins into it. Analysis of the network parameters revealed differences of network topology between phase-separated proteins and others. The results further suggested the efficiency when applying topological similarities in distinguishing components of MLOs. Furthermore, we found that affinity purification mass spectrometry (AP-MS) detects PPIs more effectively than yeast-two hybrid system (Y2H) in phase separation-driven condensates. Our work provides the first global view of the distinct network topology of phase-separated proteins in human interactome, suggesting incorporation of PPI network for LLPS prediction in further studies.

Quantifying the phase separation property of chromatin-associated proteins under physiological conditions using an anti-1,6-hexanediol index

BACKGROUND

Liquid-liquid phase separation (LLPS) is an important organizing principle for biomolecular condensation and chromosome compartmentalization. However, while many proteins have been reported to undergo LLPS, quantitative and global analysis of chromatin LLPS property remains absent.

RESULTS

Here, by combining chromatin-associated protein pull-down, quantitative proteomics and 1,6-hexanediol (1,6-HD) treatment, we develop Hi-MS and define an anti-1,6-HD index of chromatin-associated proteins (AICAP) to quantify 1,6-HD sensitivity of chromatin-associated proteins under physiological conditions. Compared with known physicochemical properties involved in phase separation, we find that proteins with lower AICAP are associated with higher content of disordered regions, higher hydrophobic residue preference, higher mobility and higher predicted LLPS potential. We also construct BL-Hi-C libraries following 1,6-HD treatment to study the sensitivity of chromatin conformation to 1,6-HD treatment. We find that the active chromatin and high-order structures, as well as the proteins enriched in corresponding regions, are more sensitive to 1,6-HD treatment.

CONCLUSIONS

Our work provides a global quantitative measurement of LLPS properties of chromatin-associated proteins and higher-order chromatin structure. Hi-MS and AICAP data provide an experimental tool and quantitative resources valuable for future studies of biomolecular condensates.

Sequence Determinants of the Aggregation of Proteins Within Condensates Generated by Liquid-liquid Phase Separation

The transition between the native and amyloid states of proteins can proceed via a deposition pathway via oligomeric intermediates or via a condensation pathway involving liquid droplet intermediates generated through liquid-liquid phase separation. While several computational methods are available to perform sequence-based predictions of the propensity of proteins to aggregate via the deposition pathway, much less is known about the physico-chemical principles that underlie aggregation within condensates. Here we investigate the sequence determinants of aggregation via the condensation pathway, and identify three relevant features: droplet-promoting propensity, aggregation-promoting propensity and multimodal interactions quantified by the binding mode entropy. By using this approach, we show that it is possible to predict aggregation-promoting mutations in droplet-forming proteins associated with amyotrophic lateral sclerosis (ALS). This analysis provides insights into the amino acid code for the conversion of proteins between liquid-like and solid-like condensates.