ESpritz: accurate and fast prediction of protein disorder

Proteome-wide identification and comprehensive profiling of intrinsic disorder in Fasciola gigantica

Despite the wealth of proteome sequences from multicellular parasitic helminths, studies on intrinsically disordered proteins (IDPs) in these organisms remain limited, particularly compared to viruses, bacteria, and unicellular parasites. We provide a comprehensive analysis of intrinsic disorder within the proteome of Fasciola gigantica, a parasitic liver fluke, using multiple predictive tools. Out of 12,537 proteins analyzed, a significant portion exhibited a distinct amino acid composition, characterized by an enrichment of polar and charged residues and a relative depletion of hydrophobic and aromatic residues, which are hallmarks of IDPs. These compositional features likely confer structural flexibility and functional adaptability, facilitating the survival of the parasite in diverse and hostile environments within its host. The presence of IDPs was further supported by compositional profiling of experimentally validated proteins in the DisProt database. Approximately 34.15 % of the F. gigantica proteome comprises highly disordered proteins, while 59.27 % is moderately disordered, as calculated from six well-established predictors integrated under the RIDAO platform. The consistent findings across various predictors, including PONDR® and IUPred, underscore the reliability of these results. Additionally, a detailed analysis of the distribution of charged residues in the proteome was performed. The high prevalence of IDPs in F. gigantica suggests their critical role in host-pathogen interactions, potentially providing functional advantages such as binding promiscuity and adaptability, which are essential for the survival of the parasite within the host. This study highlights the importance of IDPs in the biology of F. gigantica and provides insights into their potential roles in the parasite's pathogenesis and interactions with the host immune system.

Altered protein homeostasis in cardiovascular diseases contributes to Alzheimer's-like neuropathology

Cardiovascular diseases (CVDs) are the leading cause of death worldwide. CVD is known to increase the risk of subsequent neurodegeneration but the mechanism(s) and proteins involved have yet to be elucidated. We previously showed that myocardial infarction (MI), induced in mice and compared to sham-MI mice, leads to increases in protein aggregation, endoplasmic reticulum (ER) stress in both heart and brain, and changes in proteostatic pathways. In this study, we further investigate the molecular mechanisms altered by induced MI in mice, which were also implicated by proteomics of postmortem human hippocampal aggregates from Alzheimer's disease (AD) and cardiovascular disease (CVD) patients, vs. age-matched controls (AMC). We utilized intra-aggregate crosslinking to identify protein-protein contacts or proximities, and thus to reconstruct aggregate "contactomes" (nonfunctional interactomes). We used leave-one-out analysis (LOOA) to determine the contribution of each protein to overall aggregate cohesion, and gene ontology meta-analyses of constituent proteins to define critical organelles, processes, and pathways that distinguish AD and/or CVD from AMC aggregates. We identified influential proteins in both AD and CVD aggregates, many of which are associated with pathways or processes previously implicated in neurodegeneration such as mitochondrial, oxidative, and endoplasmic-reticulum stress; protein aggregation and proteostasis; the ubiquitin proteasome system and autophagy; axonal transport; and synapses.

MobiDB-lite 4.0: faster prediction of intrinsic protein disorder and structural compactness

MOTIVATION

In recent years, many disorder predictors have been developed to identify intrinsically disordered regions (IDRs) in proteins, achieving high accuracy. However, it may be difficult to interpret differences in predictions across methods. Consensus methods offer a simple solution, highlighting reliable predictions while filtering out uncertain positions. Here, we present a new version of MobiDB-lite, a consensus method designed to predict long IDRs and classify them based on compositional biases and conformational properties.

RESULTS

MobiDB-lite 4.0 pipeline was optimized to be ten times faster than the previous version. It now provides compactness annotations based on predicted apparent scaling exponent. The newly added features and disorder subclassifications allow the users to get a comprehensive insight into the protein's function and characteristics. MobiDB-lite 4.0 is integrated into the MobiDB and DisProt databases. A version without the compactness predictor is integrated into InterProScan, propagating MobiDB-lite annotations to UniProtKB.

AVAILABILITY AND IMPLEMENTATION

The MobiDB-lite 4.0 source code and a Docker container are available from the GitHub repository: https://github.com/BioComputingUP/MobiDB-lite.

Cell wall-resident proteins with internal repeats (PIRs) show an inverted architecture in Neurospora crassa, but maintain their role as wall stabilizers

Proteins with internal repeats (PIRs) are the second most abundant class of fungal cell wall resident proteins. In yeasts, PIRs preserve the stability of the cell wall under stressful conditions. They are characterized by conserved N-terminal amino acid sequences repeated in tandem (PIR motifs), and a cysteine (Cys)-rich C-terminal domain. PIRs have been identified in several filamentous fungi genomes; however, they have not been studied beyond yeasts. In this work, the diversity, evolution, and biological role of PIRs, with a particular focus on a new PIRs class, was addressed. Bioinformatic inference of PIRs in fungi indicated they were an innovation in Ascomycota. Predicted PIRs clustered in two main groups: classical yeasts PIRs (N-terminal PIR motifs; C-terminal Cys-rich domain), and PIRs from filamentous fungi with an inverted architecture (N-terminal Cys-rich domain; C-terminal PIR motifs), which could harbor additional glycosylphosphatidylinositol (GPI) addition-signals. As representatives of the second group, Neurospora crassa (Nc) PIR-1 (NCU04033) and PIR-2 (NCU07569) were studied. Confocal microscopy of eGFP-labeled Nc PIR-1 and Nc PIR-2 revealed they accumulate in apical plugs; additionally, PIR-1 requires the Kex2 processing site for correct maturation and harbors a predicted GPI modification signal. Moreover, Nc Δpir-1 and Δpir-2 single mutants showed a growth rate similar to that of Nc wild-type (WT), but the double mutant Nc Δpir-1/Δpir-2 grew significantly slower. Similarly, Nc Δpir-1 and Nc Δpir-2 were mildly sensitive to calcofluor white, although Nc Δpir-1/Δpir-2 double mutant was severely impaired. Despite the inverted architecture of Nc PIR-1 and Nc PIR-2, they maintain a role as cell wall stabilizers like classical yeast PIRs.

Intrinsic Disorder and Phase Separation Coordinate Exocytosis, Motility, and Chromatin Remodeling in the Human Acrosomal Proteome

Intrinsic disorder refers to protein regions that lack a fixed three-dimensional structure under physiological conditions, enabling conformational plasticity. This flexibility allows for diverse functions, including transient interactions, signaling, and phase separation via disorder-to-order transitions upon binding. Our study focused on investigating the role of intrinsic disorder and liquid-liquid phase separation (LLPS) in the human acrosome, a sperm-specific organelle essential for fertilization. Using computational prediction models, network analysis, Structural Classification of Proteins (SCOP) functional assessments, and Gene Ontology, we analyzed 250 proteins within the acrosomal proteome. Our bioinformatic analysis yielded 97 proteins with high levels (>30%) of structural disorder. Further analysis of functional enrichment identified associations between disordered regions overlapping with SCOP domains and critical acrosomal processes, including vesicle trafficking, membrane fusion, and enzymatic activation. Examples of disordered SCOP domains include the PLC-like phosphodiesterase domain, the t-SNARE domain, and the P-domain of calnexin/calreticulin. Protein-protein interaction networks revealed acrosomal proteins as hubs in tightly interconnected systems, emphasizing their functional importance. LLPS propensity modeling determined that over 30% of these proteins are high-probability LLPS drivers (>60%), underscoring their role in dynamic compartmentalization. Proteins such as myristoylated alanine-rich C-kinase substrate and nuclear transition protein 2 exhibited both high LLPS propensities and high levels of structural disorder. A significant relationship (p < 0.0001, R² = 0.649) was observed between the level of intrinsic disorder and LLPS propensity, showing the role of disorder in facilitating phase separation. Overall, these findings provide insights into how intrinsic disorder and LLPS contribute to the structural adaptability and functional precision required for fertilization, with implications for understanding disorders associated with the human acrosome reaction.

Intrinsic disorder in protein interaction networks linking cancer with metabolic diseases

Complex diseases are usually driven by numerous proteins that operate as intricate network systems. Deciphering of their signals is required for more advanced level understanding of the cellular processes driven by protein interactions. Therefore, placing diseases into a framework, where they can be viewed together, represents an important and fruitful approach. The goal of this study was to assess the intrinsic disorder present in the proteins forming PPI networks linking cancer with different human diseases. To this end, we used a set of bioinformatics tools to explore intrinsic disorder and liquid-liquid phase separation predispositions of 340 proteins reported earlier by Hirsch et al. (Cancer Cell (2010) 17(4), 348-361; doi: 10.1016/j.ccr.2010.01.022) as differently expressed in common chronic diseases, such as cancer, obesity, diabetes, and cardiovascular diseases. We further examined selected proteins from this set for their interactability and intrinsic disorder-based functionality. Our analyses demonstrated that intrinsically disordered proteins and proteins with intrinsically disordered regions may act as active network promoters and operate as highly connected hubs, which further enables them to play key roles in the disease pathways. The study also indicated that differently expressed proteins involved in disease progression could be characterized by diverse degrees of intrinsic disorder and LLPS propensity. We hope that our findings in combination with the outputs of the proteomic and functional genomic analyses contain essential clues that can be used in further medical research leading to the design of better therapies.

Advancements in one-dimensional protein structure prediction using machine learning and deep learning

The accurate prediction of protein structures remains a cornerstone challenge in structural bioinformatics, essential for understanding the intricate relationship between protein sequence, structure, and function. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have revolutionized this field, offering innovative approaches to tackle one- dimensional (1D) protein structure annotations, including secondary structure, solvent accessibility, and intrinsic disorder. This review highlights the evolution of predictive methodologies, from early machine learning models to sophisticated deep learning frameworks that integrate sequence embeddings and pretrained language models. Key advancements, such as AlphaFold's transformative impact on structure prediction and the rise of protein language models (PLMs), have enabled unprecedented accuracy in capturing sequence-structure relationships. Furthermore, we explore the role of specialized datasets, benchmarking competitions, and multimodal integration in shaping state-of-the-art prediction models. By addressing challenges in data quality, scalability, interpretability, and task-specific optimization, this review underscores the transformative impact of ML, DL, and PLMs on 1D protein prediction while providing insights into emerging trends and future directions in this rapidly evolving field.

Prediction of interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins from dihedral angles

Intrinsically disordered proteins (IDPs) are proteins that do not form uniquely defined three-dimensional (3-D) structures. Experimental research on IDPs is difficult since they go against the traditional protein structure-function paradigm. Although there are several predictors of disorder based on amino acid sequences, but very limited based on the 3-D structures of proteins. Dihedral angles have a significant role in predicting protein structure because they establish a protein's backbone, which, coupled with its side chain, establishes its overall shape. Here, we have carried out atomistic Molecular Dynamics (MD) simulations on four different proteins: one ordered protein (Monellin), two partially disordered proteins (p53-TAD and Amyloid beta (Aβ1-42) peptide), and one completely disordered protein (Histatin 5). The MD simulation trajectories for the corresponding four proteins were used to conduct dihedral angle (ϕ and ѱ) analysis. Then, the average dihedral angles for each of the residues were calculated and plotted against the residue index. We noticed steep rises or falls in the average ϕ value at certain locations in the plot. These sudden shifts in the average ϕ value reflect the interface between regions of varying degrees of order or disorderness in intrinsically disordered proteins. Using this method, the probable conformer of a protein with a higher degree of disorder can be found among the ensembles of structures sampled during the MD simulations. The results of our study offer new understandings on precisely identifying regions of various degrees of disorder in intrinsically disordered proteins.

1000 fold Ultra-Photosensitized Fluorescent Protein Mimics Toward Photocatalytic Proximity Labeling and Proteomic Profiling Functions

Photosensitizing fluorescent proteins (FP) (e.g. KillerRed) have been shown not capable of photo-catalytic protein proximity labeling for downstream proteomic profiling applications. To acquire such a function, FP chromophores are engineered in a 12 × 12 combinatorial matrix of synthetic analoges, achieving up to 1000 fold enhancement of reactive oxygen species (ROS) production compared to the natural FPs. FP chromophores are shown with larger dipole moments exhibit higher ROS yield toward protein labeling. By conjugating the ultra-photosensitized FP chromophore to HaloTag (namely upsFP tag), its photo-catalytic protein proximity labeling function is demonstrated using nucleophilic amino substrates. Through photochemical characterizations, theoretical calculation, and tandem mass spectrometry, a radical-mediated labeling mechanism is revealed with expanded reactivity toward diverse protein residues via a type I photosensitization pathway. Finally, a proteomic profiling application is showcased using the upsFP tag to resolve the dynamic interactome variations upon TAR DNA-binding protein 43 (TDP43) phase separation and suborganellar translocation. Together, this work demonstrates three orders of magnitude ultra-photosensitization of fluorescent protein chromophore enables photocatalytic protein proximity labeling and profiling functions that are impractical for natural fluorescent proteins.

PUNCH2: Explore the strategy for intrinsically disordered protein predictor

Intrinsically disordered proteins (IDPs) and their intrinsically disordered regions (IDRs) lack stable three-dimensional structures, posing significant challenges for computational prediction. This study introduces PUNCH2 and PUNCH2-light, advanced predictors designed to address these challenges through curated datasets, innovative feature extraction, and optimized neural architectures. By integrating experimental datasets from PDB (PDB_missing) and fully disordered sequences from DisProt (DisProt_FD), we enhanced model performance and robustness. Three embedding strategies-One-Hot, MSA-based, and PLM-based embeddings-were evaluated, with ProtTrans emerging as the most effective single embedding and combined embeddings achieving the best results. The predictors employ a 12-layer convolutional network (CNN_L12_narrow), offering a balance between accuracy and computational efficiency. PUNCH2 combines One-Hot, ProtTrans, and MSA-Transformer embeddings, while PUNCH2-light provides a faster alternative excluding MSA-based embeddings. PUNCH2 and its streamlined variant, PUNCH2-light, are competitive with other predictors on the CAID2 benchmark and rank as the top two predictors in the CAID3 competition. These tools provide efficient, accurate solutions to advance IDP research and understanding.

Uncovering the structural stability of Magnaporthe oryzae effectors: a secretome-wide in silico analysis

Rice blast, caused by the ascomycete fungus Magnaporthe oryzae, is a deadly disease and a major threat to global food security. The pathogen secretes small proteinaceous effectors, virulence factors, inside the host to manipulate and perturb the host immune system, allowing the pathogen to colonize and establish a successful infection. While the molecular functions of several effectors are characterized, very little is known about the structural stability of these effectors. We analyzed a total of 554 small secretory proteins (SSPs) from the M. oryzae secretome to decipher key features of intrinsic disorder (ID) and the structural dynamics of the selected putative effectors through thorough and systematic in silico studies. Our results suggest that out of the total SSPs, 66% were predicted as effector proteins, released either into the apoplast or cytoplasm of the host cell. Of these, 68% were found to be intrinsically disordered effector proteins (IDEPs). Among the six distinct classes of disordered effectors, we observed peculiar relationships between the localization of several effectors in the apoplast or cytoplasm and the degree of disorder. We determined the degree of structural disorder and its impact on protein foldability across all the putative small secretory effector proteins from the blast pathogen, further validated by molecular dynamics simulation studies. This study provides definite clues toward unraveling the mystery behind the importance of structural distortions in effectors and their impact on plant-pathogen interactions. The study of these dynamical segments may help identify new effectors as well.Communicated by Ramaswamy H. Sarma.

DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction

The prediction of intrinsic disorder regions has significant implications for understanding protein functions and dynamics. It can help to discover novel protein-protein interactions essential for designing new drugs and enzymes. Recently, a new generation of predictors based on protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accuracy without calculating time-consuming multiple sequence alignments (MSAs). This article introduces the new DisorderUnetLM disorder predictor, which builds upon the idea of ProteinUnet. It uses the Attention U-Net convolutional network and incorporates features from the ProtTrans pLM. DisorderUnetLM achieves top results in the direct comparison with recent predictors exploiting MSAs and pLMs. Moreover, among 43 predictors on the latest CAID-2 benchmark, it ranks 1st for the NOX subset in terms of the ROC-AUC metric (0.844) and 2nd for the AP metric (0.596). For the CAID-2 PDB subset, it ranks in the top 10 (ROC-AUC of 0.924 and AP of 0.862). The code and model are publicly available and fully reproducible at doi.org/10.24433/CO.7350682.v1.

A Practical Guide to Computational Tools for Engineering Biocatalytic Properties

The growing demand for efficient, selective, and stable enzymes has fueled advancements in computational enzyme engineering, a field that complements experimental methods to accelerate enzyme discovery. With a plethora of software and tools available, researchers from different disciplines often face challenges in selecting the most suitable method that meets their requirements and available starting data. This review categorizes the computational tools available for enzyme engineering based on their capacity to enhance the following specific biocatalytic properties of biotechnological interest: (i) protein-ligand affinity/selectivity, (ii) catalytic efficiency, (iii) thermostability, and (iv) solubility for recombinant enzyme production. By aligning tools with their respective scoring functions, we aim to guide researchers, particularly those new to computational methods, in selecting the appropriate software for the design of protein engineering campaigns. De novo enzyme design, involving the creation of novel proteins, is beyond this review's scope. Instead, we focus on practical strategies for fine-tuning enzymatic performance within an established reference framework of natural proteins.

The Balbiani body is formed by microtubule-controlled molecular condensation of Buc in early oogenesis

Vertebrate oocyte polarity has been observed for two centuries and is essential for embryonic axis formation and germline specification, yet its underlying mechanisms remain unknown. In oocyte polarization, critical RNA-protein (RNP) granules delivered to the oocyte's vegetal pole are stored by the Balbiani body (Bb), a membraneless organelle conserved across species from insects to humans. However, the mechanisms of Bb formation are still unclear. Here, we elucidate mechanisms of Bb formation in zebrafish through developmental biomolecular condensation. Using super-resolution microscopy, live imaging, biochemical, and genetic analyses in vivo, we demonstrate that Bb formation is driven by molecular condensation through phase separation of the essential intrinsically disordered protein Bucky ball (Buc). Live imaging, molecular analyses, and fluorescence recovery after photobleaching (FRAP) experiments in vivo reveal Buc-dependent changes in the Bb condensate's dynamics and apparent material properties, transitioning from liquid-like condensates to a solid-like stable compartment. Furthermore, we identify a multistep regulation by microtubules that controls Bb condensation: first through dynein-mediated trafficking of early condensing Buc granules, then by scaffolding condensed granules, likely through molecular crowding, and finally by caging the mature Bb to prevent overgrowth and maintain shape. These regulatory steps ensure the formation of a single intact Bb, which is considered essential for oocyte polarization and embryonic development. Our work offers insight into the long-standing question of the origins of embryonic polarity in non-mammalian vertebrates, supports a paradigm of cellular control over molecular condensation by microtubules, and highlights biomolecular condensation as a key process in female reproduction.

MOBIDB in 2025: integrating ensemble properties and function annotations for intrinsically disordered proteins

The MobiDB database (URL: https://mobidb.org/) aims to provide structural and functional information about intrinsic protein disorder, aggregating annotations from the literature, experimental data, and predictions for all known protein sequences. Here, we describe the improvements made to our resource to capture more information, simplify access to the aggregated data, and increase documentation of all MobiDB features. Compared to the previous release, all underlying pipeline modules were updated. The prediction module is ten times faster and can detect if a predicted disordered region is structurally extended or compact. The PDB component is now able to process large cryo-EM structures extending the number of processed entries. The entry page has been restyled to highlight functional aspects of disorder and all graphical modules have been completely reimplemented for better flexibility and faster rendering. The server has been improved to optimise bulk downloads. Annotation provenance has been standardised by adopting ECO terms. Finally, we propagated disorder function (IDPO and GO terms) from the DisProt database exploiting sequence similarity and protein embeddings. These improvements, along with the addition of comprehensive training material, offer a more intuitive interface and novel functional knowledge about intrinsic disorder.

InterPro: the protein sequence classification resource in 2025

InterPro (https://www.ebi.ac.uk/interpro) is a freely accessible resource for the classification of protein sequences into families. It integrates predictive models, known as signatures, from multiple member databases to classify sequences into families and predict the presence of domains and significant sites. The InterPro database provides annotations for over 200 million sequences, ensuring extensive coverage of UniProtKB, the standard repository of protein sequences, and includes mappings to several other major resources, such as Gene Ontology (GO), Protein Data Bank in Europe (PDBe) and the AlphaFold Protein Structure Database. In this publication, we report on the status of InterPro (version 101.0), detailing new developments in the database, associated web interface and software. Notable updates include the increased integration of structures predicted by AlphaFold and the enhanced description of protein families using artificial intelligence. Over the past two years, more than 5000 new InterPro entries have been created. The InterPro website now offers access to 85 000 protein families and domains from its member databases and serves as a long-term archive for retired databases. InterPro data, software and tools are freely available.

Techniques for Bioinformatic Applications in Protein Dynamics

A method is described by which bioinformatic concepts and tools can be applied to the study of protein dynamic properties. Sequences are transformed into numerical strings by representing each amino acid by a residue specific average value of the crystallographic alpha carbon B factor. These dynamic sequences are then Fourier transformed. The Fourier coefficients, each of which contains information about the entire sequence, viewed on a specific length scale, can then be used to study a wide variety of dynamic characteristics in a manner which is completely inaccessible using conventional tools.

Two decades of advances in sequence-based prediction of MoRFs, disorder-to-order transitioning binding regions

INTRODUCTION

Molecular recognition features (MoRFs) are regions in protein sequences that undergo induced folding upon binding partner molecules. MoRFs are common in nature and can be predicted from sequences based on their distinctive sequence signatures.

AREAS COVERED

We overview 20 years of progress in the sequence-based prediction of MoRFs which resulted in the development of 25 predictors of MoRFs that interact with proteins, peptides, and lipids. These methods range from simple discriminant analysis to sophisticated deep transformer networks that use protein language models. They generate relatively accurate predictions as evidenced by the results of a recently published community-driven assessment.

EXPERT OPINION

MoRFs prediction is a mature field of research that is poised to continue at a steady pace in the foreseeable future. We anticipate further expansion of the scope of MoRF predictions to additional partner molecules, such as nucleic acids, and continued use of recent machine learning advances. Other future efforts should concentrate on improving availability of MoRF predictions by releasing, maintaining, and popularizing web servers and by depositing MoRF predictions to large databases of protein structure and function predictions. Furthermore, accurate MoRF predictions should be coupled with the equally accurate prediction and modeling of the resulting structures of complexes.

Targeting Protein Disorder for the Remediation of Antimicrobial Resistance

The remediation of antimicrobial resistance (AMR) is a fundamental challenge for global healthcare. Intrinsically disordered proteins (IDPs) are recognized drug targets for neurodegeneration and cancer but have not been considered to date for AMR. Here, a novel link between structural disorder and AMR is identified by mapping predicted disorder profiles onto existing transcriptomic data for resistant and susceptible E. coli isolates. The AMR-relevant IDPs fall into two distinct classes, those involved in the bacterial stress response and those differentially expressed between resistant and susceptible strains following antibiotic exposure. A residue-wise conservation analysis of relevant bacterial IDPs identified mutations within intrinsically disordered regions that correlate with pronounced changes in antimicrobial susceptibility, providing valuable insight into the functional importance of bacterial intrinsic disorder in the ESKAPEE pathogens. The identification of susceptibility-inducing IDPs in E. coli highlights the potential of disorder-based antimicrobial drug discovery for the remediation of drug-resistant bacterial infections.

Functional specialization of the subdomains of a bactofilin driving stalk morphogenesis in Asticcacaulis biprosthecum

Bactofilins are a recently discovered class of cytoskeletal protein, widely implicated in subcellular organization and morphogenesis in bacteria and archaea. Several lines of evidence suggest that bactofilins polymerize into filaments using a central β-helical core domain, flanked by variable N- and C-terminal domains that may be important for scaffolding and other functions. However, a systematic exploration of the characteristics of these domains has yet to be performed. In Asticcacaulis biprosthecum, the bactofilin BacA serves as a topological organizer of stalk synthesis, localizing to the stalk base and coordinating the synthesis of these long, thin extensions of the cell envelope. The easily distinguishable phenotypes of wild-type A. biprosthecum stalks and ΔbacA "pseudostalks" make this an ideal system for investigating how mutations in BacA affect its functions in morphogenesis. Here, we redefine the core domain of A. biprosthecum BacA using various bioinformatics and biochemical approaches to precisely delimit the N- and C- terminal domains. We then show that loss of these terminal domains leads to cells with severe morphological abnormalities, typically presenting a pseudostalk phenotype. BacA mutants lacking the N- and C- terminal domains also exhibit localization defects, implying that the terminal domains of BacA may be involved in its subcellular positioning, whether through membrane interactions through the N-terminal domain or through interactions with the stalk-specific morphological regulator SpmX through the C-terminal domain. We further show that point mutations that render BacA defective for polymerization lead to stalk synthesis defects. Overall, our study suggests that BacA's polymerization, membrane association, and interactions with other morphological factors all play a crucial role in the protein's function as a morphogenic regulator. The specialization and modularity of the terminal domains may underlie the remarkable functional versatility of the bactofilins in different species.

Survey of the Aβ-peptide structural diversity: molecular dynamics approaches

The review deals with the application of Molecular Dynamics (MD) to the structure modeling of beta-amyloids (Aβ), currently classified as intrinsically disordered proteins (IDPs). In this review, we strive to relate the main advances in this area but specifically focus on the approaches and methodology. All relevant papers on the Aβ modeling are cited in the Tables in Supplementary Data, including a concise description of the applied approaches, sorted according to the types of the studied systems: modeling of the monomeric Aβ and Aβ aggregates. Similar sections focused according to the type of modeled object are present in the review. In the final part of the review, novel methods of general IDP modeling not confined to Aβ are described.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-024-01253-y.

Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses

Intrinsic disorder predictors were evaluated in several studies including the two large CAID experiments. However, these studies are biased towards eukaryotic proteins and focus primarily on the residue-level predictions. We provide first-of-its-kind assessment that comprehensively covers the taxonomy and evaluates predictions at the residue and disordered region levels. We curate a benchmark dataset that uniformly covers eukaryotic, archaeal, bacterial, and viral proteins. We find that predictive performance differs substantially across taxonomy, where viruses are predicted most accurately, followed by protists and higher eukaryotes, while bacterial and archaeal proteins suffer lower levels of accuracy. These trends are consistent across predictors. We also find that current tools, except for flDPnn, struggle with reproducing native distributions of the numbers and sizes of the disordered regions. Moreover, analysis of two variants of disorder predictions derived from the AlphaFold2 predicted structures reveals that they produce accurate residue-level propensities for archaea, bacteria and protists. However, they underperform for higher eukaryotes and generally struggle to accurately identify disordered regions. Our results motivate development of new predictors that target bacteria and archaea and which produce accurate results at both residue and region levels. We also stress the need to include the region-level assessments in future assessments.

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.

The potent PHL4 transcription factor effector domain contains significant disorder

The phosphate-starvation response transcription-factor protein family is essential to plant response to low-levels of phosphate. Proteins in this transcription factor (TF) family act by altering various gene expression levels, such as increasing levels of the acid phosphatase proteins which catalyze the conversion of inorganic phosphates to bio-available compounds. There are few structural characterizations of proteins in this TF family, none of which address the potent TF activation domains. The phosphate-starvation response-like protein-4 (PHL4) protein from this family has garnered interest due to the unusually high TF activation activity of the N-terminal domain. Here, we demonstrate using solution nuclear magnetic resonance (NMR) measurements that the PHL4 N-terminal activating TF effector domain is mainly an intrinsically disordered domain of over 200 residues, and that the C-terminal region of PHL4 is also disordered. Additionally, we present evidence from size-exclusion chromatography, diffusion NMR measurements, and a cross-linking assay suggesting full-length PHL4 forms a trimeric or tetrameric assembly. Together, the data indicate the N- and C-terminal disordered domains in PHL4 flank a central folded region that likely forms the ordered oligomer of PHL4. This work provides a foundation for future studies detailing how the conformations and molecular motions of PHL4 change as it acts as a potent activator of gene expression in phosphate metabolism. Such a detailed mechanistic understanding of TF function will benefit genetic engineering efforts that take advantage of this activity to boost transcriptional activation of genes across different organisms.

Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational ensembles

Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions, challenging traditional structure-based prediction methods. This review explores how modern deep learning approaches, which have revolutionized structure prediction for globular proteins, have impacted protein disorder predictions. We highlight the role of community-driven efforts in curating data and assessing state-of-the-art, which have been crucial in advancing the field. We also review state-of-the-art methods utilizing deep learning techniques, highlighting innovative approaches. We also address advancements in characterizing protein conformational ensembles directly from sequence data using novel machine learning methods.

Intrinsic structural disorder on proteins is involved in the interactome evolution

New mathematical tools help understand cell functions, adaptability, and evolvability to discover hidden variables to predict phenotypes that could be tested in the future in wet labs. Different models have been successfully used to discover the properties of the protein-protein interaction networks or interactomes. I found that in the hyperbolic Popularity-Similarity model, cellular proteins with the highest contents of structural intrinsic disorder cluster together in many different eukaryotic interactomes and this is not the case for the prokaryotic E. coli, where proteins with high degree of intrinsic disorder are scarce. I also found that the normalized theta variable from the Popularity-Similarity model for orthologues proteins correlate to the complexity of the organisms in analysis.

Chaperone BiP controls ER stress sensor Ire1 through interactions with its oligomers

The complex multistep activation cascade of Ire1 involves changes in the Ire1 conformation and oligomeric state. Ire1 activation enhances ER folding capacity, in part by overexpressing the ER Hsp70 molecular chaperone BiP; in turn, BiP provides tight negative control of Ire1 activation. This study demonstrates that BiP regulates Ire1 activation through a direct interaction with Ire1 oligomers. Particularly, we demonstrated that the binding of Ire1 luminal domain (LD) to unfolded protein substrates not only trigger conformational changes in Ire1-LD that favour the formation of Ire1-LD oligomers but also exposes BiP binding motifs, enabling the molecular chaperone BiP to directly bind to Ire1-LD in an ATP-dependent manner. These transient interactions between BiP and two short motifs in the disordered region of Ire1-LD are reminiscent of interactions between clathrin and another Hsp70, cytoplasmic Hsc70. BiP binding to substrate-bound Ire1-LD oligomers enables unfolded protein substrates and BiP to synergistically and dynamically control Ire1-LD oligomerisation, helping to return Ire1 to its deactivated state when an ER stress response is no longer required.

Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences

We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed previously, which encodes the intrinsic dynamic properties of the naturally occurring amino acids. We Fourier analyze the resulting sequences. It is demonstrated that classification models built using several different supervised learning methods are able to successfully distinguish folded from intrinsically disordered proteins from sequence alone. It is further shown that the most important sequence property for this discrimination is the sequence mobility, which is the sequence averaged value of the residue-specific average alpha carbon B factor. This is in agreement with previous work, in which we have demonstrated the central role played by the sequence mobility in protein dynamic bioinformatics and biophysics. This finding opens a path to the application of dynamic bioinformatics, in combination with machine learning algorithms, to a range of significant biomedical problems.

Intrinsic disorder and salt-dependent conformational changes of the N-terminal region of TFIP11 splicing factor

Tuftelin Interacting Protein 11 (TFIP11) was identified as a critical human spliceosome assembly regulator, interacting with multiple proteins and localising in membrane-less organelles. However, a lack of structural information on TFIP11 limits the rationalisation of its biological role. TFIP11 is predicted as an intrinsically disordered protein (IDP), and more specifically concerning its N-terminal (N-TER) region. IDPs lack a defined tertiary structure, existing as a dynamic conformational ensemble, favouring protein-protein and protein-RNA interactions. IDPs are involved in liquid-liquid phase separation (LLPS), driving the formation of subnuclear compartments. Combining disorder prediction, molecular dynamics, and spectroscopy methods, this contribution shows the first evidence TFIP11 N-TER is a polyampholytic IDP, exhibiting a structural duality with the coexistence of ordered and disordered assemblies, depending on the ionic strength. Increasing the salt concentration enhances the protein conformational flexibility, presenting a more globule-like shape, and a fuzzier unstructured arrangement that could favour LLPS and protein-RNA interaction. The most charged and hydrophilic regions are the most impacted, including the G-Patch domain essential to TFIP11 function. This study gives a better understanding of the salt-dependent conformational behaviour of the N-TER TFIP11, supporting the hypothesis of the formation of different types of protein assembly, in line with its multiple biological roles.

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.

flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins

Prediction of the intrinsic disorder in protein sequences is an active research area, with well over 100 predictors that were released to date. These efforts are motivated by the functional importance and high levels of abundance of intrinsic disorder, combined with relatively low amounts of experimental annotations. The disorder predictors are periodically evaluated by independent assessors in the Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiments. The recently completed CAID2 experiment assessed close to 40 state-of-the-art methods demonstrating that some of them produce accurate results. In particular, flDPnn2 method, which is the successor of flDPnn that performed well in the CAID1 experiment, secured the overall most accurate results on the Disorder-NOX dataset in CAID2. flDPnn2 implements a number of improvements when compared to its predecessor including changes to the inputs, increased size of the deep network model that we retrained on a larger training set, and addition of an alignment module. Using results from CAID2, we show that flDPnn2 produces accurate predictions very quickly, modestly improving over the accuracy of flDPnn and reducing the runtime by half, to about 27 s per protein. flDPnn2 is freely available as a convenient web server at http://biomine.cs.vcu.edu/servers/flDPnn2/.

Data-driven probabilistic definition of the low energy conformational states of protein residues

Protein dynamics and related conformational changes are essential for their function but difficult to characterise and interpret. Amino acids in a protein behave according to their local energy landscape, which is determined by their local structural context and environmental conditions. The lowest energy state for a given residue can correspond to sharply defined conformations, e.g. in a stable helix, or can cover a wide range of conformations, e.g. in intrinsically disordered regions. A good definition of such low energy states is therefore important to describe the behaviour of a residue and how it changes with its environment. We propose a data-driven probabilistic definition of six low energy conformational states typically accessible for amino acid residues in proteins. This definition is based on solution NMR information of 1322 proteins through a combined analysis of structure ensembles with interpreted chemical shifts. We further introduce a conformational state variability parameter that captures, based on an ensemble of protein structures from molecular dynamics or other methods, how often a residue moves between these conformational states. The approach enables a different perspective on the local conformational behaviour of proteins that is complementary to their static interpretation from single structure models.

Methodological approaches to studying phase separation and HIV-1 replication: Current and future perspectives

This article reviews tried-and-tested methodologies that have been employed in the first studies on phase separating properties of structural, RNA-binding and catalytic proteins of HIV-1. These are described here to stimulate interest for any who may want to initiate similar studies on virus-mediated liquid-liquid phase separation. Such studies serve to better understand the life cycle and pathogenesis of viruses and open the door to new therapeutics.

Revealing cis- and trans-regulatory elements underlying nuclear distribution and function of the Arabidopsis histone H2B.8 variant

The H2B.8 variant has been diverged from other variants by its extended N-terminal region that possesses a conserved domain. We generated transgenic Arabidopsis plants expressing H2B.9 (class I), H2B.5 (class II) and H2B.8 (class III) fused to GFP under the 35 S promoter and studied their nuclear distribution and function. H2B.8-GFP showed peculiar nuclear localization at chromocenters in all cell types examined, while H2B.5-GFP and H2B.9-GFP displayed various patterns often dependent on cell types. H2B variants faithfully assembled onto nucleosomes showing no effect on nuclear organization; H2B.8-GFP appeared as three distinct isoforms in which one isoform appeared to be SUMOylated. Interestingly, transient expression in protoplasts revealed H2B.8 nuclear localization distinct from transgenic plants as it was restricted to the nuclear periphery generating a distinctive ring-like appearance accompanied by nuclear size reduction. This unique appearance was abolished by deletion of the N-terminal conserved domain or when H2B.8-GFP is transiently expressed in ddm1 protoplasts. GFP-TRAP-coupled proteome analysis uncovered H2B.8-partner proteins including H2A.W.12, which characterizes heterochromatin. Thus, our data highlight H2B.8 as a unique variant evolved in angiosperms to control chromatin compaction/aggregation and uncover cis- and trans-regulatory elements underlying its nuclear distribution and function.

DR-BERT: A protein language model to annotate disordered regions

Despite their lack of a rigid structure, intrinsically disordered regions (IDRs) in proteins play important roles in cellular functions, including mediating protein-protein interactions. Therefore, it is important to computationally annotate IDRs with high accuracy. In this study, we present Disordered Region prediction using Bidirectional Encoder Representations from Transformers (DR-BERT), a compact protein language model. Unlike most popular tools, DR-BERT is pretrained on unannotated proteins and trained to predict IDRs without relying on explicit evolutionary or biophysical data. Despite this, DR-BERT demonstrates significant improvement over existing methods on the Critical Assessment of protein Intrinsic Disorder (CAID) evaluation dataset and outperforms competitors on two out of four test cases in the CAID 2 dataset, while maintaining competitiveness in the others. This performance is due to the information learned during pretraining and DR-BERT's ability to use contextual information.

Nuclear aggregates of NONO/SFPQ and A-to-I-edited RNA in Parkinson's disease and dementia with Lewy bodies

Neurodegenerative diseases are commonly classified as proteinopathies that are defined by the aggregation of a specific protein. Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are classified as synucleinopathies since α-synuclein (α-syn)-containing inclusions histopathologically define these diseases. Unbiased biochemical analysis of PD and DLB patient material unexpectedly revealed novel pathological inclusions in the nucleus comprising adenosine-to-inosine (A-to-I)-edited mRNAs and NONO and SFPQ proteins. These inclusions showed no colocalization with Lewy bodies and accumulated at levels comparable to α-syn. NONO and SFPQ aggregates reduced the expression of the editing inhibitor ADAR3, increasing A-to-I editing mainly within human-specific, Alu-repeat regions of axon, synaptic, and mitochondrial transcripts. Inosine-containing transcripts aberrantly accumulated in the nucleus, bound tighter to recombinant purified SFPQ in vitro, and potentiated SFPQ aggregation in human dopamine neurons, resulting in a self-propagating pathological state. Our data offer new insight into the inclusion composition and pathophysiology of PD and DLB.

Specificity profiling of deubiquitylases against endogenously generated ubiquitin-protein conjugates

Deubiquitylating enzymes (DUBs) remove ubiquitin from proteins thereby regulating their stability or activity. Our understanding of DUB-substrate specificity is limited because DUBs are typically not compared to each other against many physiological substrates. By broadly inhibiting DUBs in Xenopus egg extract, we generated hundreds of ubiquitylated proteins and compared the ability of 30 DUBs to deubiquitylate them using quantitative proteomics. We identified five high-impact DUBs (USP7, USP9X, USP36, USP15, and USP24) that each reduced ubiquitylation of over 10% of the isolated proteins. Candidate substrates of high-impact DUBs showed substantial overlap and were enriched for disordered regions, suggesting this feature may promote substrate recognition. Other DUBs showed lower impact and non-overlapping specificity, targeting distinct non-disordered proteins including complexes such as the ribosome or the proteasome. Altogether our study identifies candidate DUB substrates and defines patterns of functional redundancy and specificity, revealing substrate characteristics that may influence DUB-substrate recognition.

Global protein turnover quantification in Escherichia coli reveals cytoplasmic recycling under nitrogen limitation

Protein turnover is critical for proteostasis, but turnover quantification is challenging, and even in well-studied E. coli, proteome-wide measurements remain scarce. Here, we quantify the turnover rates of ~3200 E. coli proteins under 13 conditions by combining heavy isotope labeling with complement reporter ion quantification and find that cytoplasmic proteins are recycled when nitrogen is limited. We use knockout experiments to assign substrates to the known cytoplasmic ATP-dependent proteases. Surprisingly, none of these proteases are responsible for the observed cytoplasmic protein degradation in nitrogen limitation, suggesting that a major proteolysis pathway in E. coli remains to be discovered. Lastly, we show that protein degradation rates are generally independent of cell division rates. Thus, we present broadly applicable technology for protein turnover measurements and provide a rich resource for protein half-lives and protease substrates in E. coli, complementary to genomics data, that will allow researchers to study the control of proteostasis.

The Potent PHL4 Transcription Factor Effector Domain Contains Significant Disorder

The phosphate-starvation response transcription-factor protein family is essential to plant response to low-levels of phosphate. Proteins in this transcription factor (TF) family act by altering various gene expression levels, such as increasing levels of the acid phosphatase proteins which catalyze the conversion of inorganic phosphates to bio-available compounds. There are few structural characterizations of proteins in this TF family, none of which address the potent TF activation domains. The phosphate-starvation response-like protein-4 (PHL4) protein from this family has garnered interest due to the unusually high TF activation activity of the N-terminal domain. Here, we demonstrate using solution nuclear magnetic resonance (NMR) measurements that the PHL4 N-terminal activating TF effector domain is mainly an intrinsically disordered domain of over 200 residues, and that the C-terminal region of PHL4 is also disordered. Additionally, we present evidence from size-exclusion chromatography, diffusion NMR measurements, and a cross-linking assay suggesting full-length PHL4 forms a tetrameric assembly. Together, the data indicate the N- and C-terminal disordered domains in PHL4 flank a central folded region that likely forms the ordered oligomer of PHL4. This work provides a foundation for future studies detailing how the conformations and molecular motions of PHL4 change as it acts as a potent activator of gene expression in phosphate metabolism. Such a detailed mechanistic understanding of TF function will benefit genetic engineering efforts that take advantage of this activity to boost transcriptional activation of genes across different organisms.

The structural landscape and diversity of Pyricularia oryzae MAX effectors revisited

Magnaporthe AVRs and ToxB-like (MAX) effectors constitute a family of secreted virulence proteins in the fungus Pyricularia oryzae (syn. Magnaporthe oryzae), which causes blast disease on numerous cereals and grasses. In spite of high sequence divergence, MAX effectors share a common fold characterized by a ß-sandwich core stabilized by a conserved disulfide bond. In this study, we investigated the structural landscape and diversity within the MAX effector repertoire of P. oryzae. Combining experimental protein structure determination and in silico structure modeling we validated the presence of the conserved MAX effector core domain in 77 out of 94 groups of orthologs (OG) identified in a previous population genomic study. Four novel MAX effector structures determined by NMR were in remarkably good agreement with AlphaFold2 (AF2) predictions. Based on the comparison of the AF2-generated 3D models we propose a classification of the MAX effectors superfamily in 20 structural groups that vary in the canonical MAX fold, disulfide bond patterns, and additional secondary structures in N- and C-terminal extensions. About one-third of the MAX family members remain singletons, without strong structural relationship to other MAX effectors. Analysis of the surface properties of the AF2 MAX models also highlights the high variability within the MAX family at the structural level, potentially reflecting the wide diversity of their virulence functions and host targets.

C19ORF84 connects piRNA and DNA methylation machineries to defend the mammalian germ line

In the male mouse germ line, PIWI-interacting RNAs (piRNAs), bound by the PIWI protein MIWI2 (PIWIL4), guide DNA methylation of young active transposons through SPOCD1. However, the underlying mechanisms of SPOCD1-mediated piRNA-directed transposon methylation and whether this pathway functions to protect the human germ line remain unknown. We identified loss-of-function variants in human SPOCD1 that cause defective transposon silencing and male infertility. Through the analysis of these pathogenic alleles, we discovered that the uncharacterized protein C19ORF84 interacts with SPOCD1. DNMT3C, the DNA methyltransferase responsible for transposon methylation, associates with SPOCD1 and C19ORF84 in fetal gonocytes. Furthermore, C19ORF84 is essential for piRNA-directed DNA methylation and male mouse fertility. Finally, C19ORF84 mediates the in vivo association of SPOCD1 with the de novo methylation machinery. In summary, we have discovered a conserved role for the human piRNA pathway in transposon silencing and C19ORF84, an uncharacterized protein essential for orchestrating piRNA-directed DNA methylation.

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.

Technologies for studying phase-separated biomolecular condensates

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

Protein ensemble modeling and analysis with MMMx

Proteins, especially of eukaryotes, often have disordered domains and may contain multiple folded domains whose relative spatial arrangement is distributed. The MMMx ensemble modeling and analysis toolbox (https://github.com/gjeschke/MMMx) can support the design of experiments to characterize the distributed structure of such proteins, starting from AlphaFold2 predictions or folded domain structures. Weak order can be analyzed with reference to a random coil model or to peptide chains that match the residue-specific Ramachandran angle distribution of the loop regions and are otherwise unrestrained. The deviation of the mean square end-to-end distance of chain sections from their average over sections of the same sequence length reveals localized compaction or expansion of the chain. The shape sampled by disordered chains is visualized by superposition in the principal axes frame of their inertia tensor. Ensembles of different sizes and with weighted conformers can be compared based on a similarity parameter that abstracts from the ensemble width.

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.

Evidence for widespread cytoplasmic structuring into mesoscale condensates

Compartmentalization is an essential feature of eukaryotic life and is achieved both via membrane-bound organelles, such as mitochondria, and membrane-less biomolecular condensates, such as the nucleolus. Known biomolecular condensates typically exhibit liquid-like properties and are visualized by microscopy on the scale of ~1 µm (refs. 1,2). They have been studied mostly by microscopy, examining select individual proteins. So far, several dozen biomolecular condensates are known, serving a multitude of functions, for example, in the regulation of transcription3, RNA processing4 or signalling5,6, and their malfunction can cause diseases7,8. However, it remains unclear to what extent biomolecular condensates are utilized in cellular organization and at what length scale they typically form. Here we examine native cytoplasm from Xenopus egg extract on a global scale with quantitative proteomics, filtration, size exclusion and dilution experiments. These assays reveal that at least 18% of the proteome is organized into mesoscale biomolecular condensates at the scale of ~100 nm and appear to be stabilized by RNA or gelation. We confirmed mesoscale sizes via imaging below the diffraction limit by investigating protein permeation into porous substrates with defined pore sizes. Our results show that eukaryotic cytoplasm organizes extensively via biomolecular condensates, but at surprisingly short length scales.

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.

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.

Substrate identification and specificity profiling of deubiquitylases against endogenously-generated ubiquitin-protein conjugates

Deubiquitylating enzymes (DUBs) remove ubiquitin from proteins thereby regulating their stability or activity. Our understanding of DUB-substrate specificity is limited because DUBs are typically not compared to each other against many physiological substrates. By broadly inhibiting DUBs in Xenopus egg extract, we generated hundreds of ubiquitylated proteins and compared the ability of 30 DUBs to deubiquitylate them using quantitative proteomics. We identified five high impact DUBs (USP7, USP9X, USP36, USP15 and USP24) that each reduced ubiquitylation of over ten percent of the isolated proteins. Candidate substrates of high impact DUBs showed substantial overlap and were enriched for disordered regions, suggesting this feature may promote substrate recognition. Other DUBs showed lower impact and non-overlapping specificity, targeting distinct non-disordered proteins including complexes such as the ribosome or the proteasome. Altogether our study identifies candidate DUB substrates and defines patterns of functional redundancy and specificity, revealing substrate characteristics that may influence DUB-substrate recognition.

Critical assessment of protein intrinsic disorder prediction (CAID) - Results of round 2

Protein intrinsic disorder (ID) is a complex and context-dependent phenomenon that covers a continuum between fully disordered states and folded states with long dynamic regions. The lack of a ground truth that fits all ID flavors and the potential for order-to-disorder transitions depending on specific conditions makes ID prediction challenging. The CAID2 challenge aimed to evaluate the performance of different prediction methods across different benchmarks, leveraging the annotation provided by the DisProt database, which stores the coordinates of ID regions when there is experimental evidence in the literature. The CAID2 challenge demonstrated varying performance of different prediction methods across different benchmarks, highlighting the need for continued development of more versatile and efficient prediction software. Depending on the application, researchers may need to balance performance with execution time when selecting a predictor. Methods based on AlphaFold2 seem to be good ID predictors but they are better at detecting absence of order rather than ID regions as defined in DisProt. The CAID2 predictors can be freely used through the CAID Prediction Portal, and CAID has been integrated into OpenEBench, which will become the official platform for running future CAID challenges.