ESpritz: accurate and fast prediction of protein disorder

SGF29 nuclear condensates reinforce cellular aging

Phase separation, a biophysical segregation of subcellular milieus referred as condensates, is known to regulate transcription, but its impacts on physiological processes are less clear. Here, we demonstrate the formation of liquid-like nuclear condensates by SGF29, a component of the SAGA transcriptional coactivator complex, during cellular senescence in human mesenchymal progenitor cells (hMPCs) and fibroblasts. The Arg 207 within the intrinsically disordered region is identified as the key amino acid residue for SGF29 to form phase separation. Through epigenomic and transcriptomic analysis, our data indicated that both condensate formation and H3K4me3 binding of SGF29 are essential for establishing its precise chromatin location, recruiting transcriptional factors and co-activators to target specific genomic loci, and initiating the expression of genes associated with senescence, such as CDKN1A. The formation of SGF29 condensates alone, however, may not be sufficient to drive H3K4me3 binding or achieve transactivation functions. Our study establishes a link between phase separation and aging regulation, highlighting nuclear condensates as a functional unit that facilitate shaping transcriptional landscapes in aging.

CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions

Molecular recognition features (MoRFs) are a commonly occurring type of intrinsically disordered regions (IDRs) that undergo disorder-to-order transition upon binding to partner molecules. We focus on recently characterized and functionally important membrane-binding MoRFs (MemMoRFs). Motivated by the lack of computational tools that predict MemMoRFs, we use a dataset of experimentally annotated MemMoRFs to conceptualize, design, evaluate and release an accurate sequence-based predictor. We rely on state-of-the-art tools that predict residues that possess key characteristics of MemMoRFs, such as intrinsic disorder, disorder-to-order transition and lipid-binding. We identify and combine results from three tools that include flDPnn for the disorder prediction, DisoLipPred for the prediction of disordered lipid-binding regions, and MoRFCHiBiLight for the prediction of disorder-to-order transitioning protein binding regions. Our empirical analysis demonstrates that combining results produced by these three methods generates accurate predictions of MemMoRFs. We also show that use of a smoothing operator produces predictions that closely mimic the number and sizes of the native MemMoRF regions. The resulting CoMemMoRFPred method is available as an easy-to-use webserver at http://biomine.cs.vcu.edu/servers/CoMemMoRFPred. This tool will aid future studies of MemMoRFs in the context of exploring their abundance, cellular functions, and roles in pathologic phenomena.

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.

The preferential transport of NO3- by full-length Guillardia theta anion channelrhodopsin 1 is enhanced by its extended cytoplasmic domain

Previous research of anion channelrhodopsins (ACRs) has been performed using cytoplasmic domain (CPD)-deleted constructs and therefore have overlooked the native functions of full-length ACRs and the potential functional role(s) of the CPD. In this study, we used the recombinant expression of full-length Guillardia theta ACR1 (GtACR1_full) for pH measurements in Pichia pastoris cell suspensions as an indirect method to assess its anion transport activity and for absorption spectroscopy and flash photolysis characterization of the purified protein. The results show that the CPD, which was predicted to be intrinsically disordered and possibly phosphorylated, enhanced NO3- transport compared to Cl- transport, which resulted in the preferential transport of NO3-. This correlated with the extended lifetime and large accumulation of the photocycle intermediate that is involved in the gate-open state. Considering that the depletion of a nitrogen source enhances the expression of GtACR1 in native algal cells, we suggest that NO3- transport could be the natural function of GtACR1_full in algal cells.

Intrinsic disorder may drive the interaction of PROS1 and MERTK in uveal melanoma

BACKGROUND

Class 2 uveal melanomas are associated with the inactivation of the BRCA1 ((breast cancer type 1 susceptibility protein)-associated protein 1 (BAP1)) gene. Inactivation of BAP1 promotes the upregulation of vitamin K-dependent protein S (PROS1), which interacts with the tyrosine-protein kinase Mer (MERTK) receptor on M2 macrophages to induce an immunosuppressive environment.

METHODS

We simulated the interaction of PROS1 with MERTK with ColabFold. We evaluated PROS1 and MERTK for the presence of intrinsically disordered protein regions (IDPRs) and disorder-to-order (DOT) regions to understand their protein-protein interaction (PPI). We first evaluated the structure of each protein with AlphaFold. We then analyzed specific sequence-based features of the PROS1 and MERTK with a suite of bioinformatics tools.

RESULTS

With high-resolution, moderate confidence, we successfully modeled the interaction between PROS1 and MERTK (predicted local distance difference test score (pDLLT) = 70.68). Our structural analysis qualitatively demonstrated IDPRs (i.e., spaghetti-like entities) in PROS1 and MERK. PROS1 was 23.37 % disordered, and MERTK was 23.09 % disordered, classifying them as moderately disordered and flexible proteins. PROS1 was significantly enriched in cysteine, the most order-promoting residue (p-value <0.05). Our IUPred analysis demonstrated that there are two disorder-to-order transition (DOT) regions in PROS1. MERTK was significantly enriched in proline, the most disorder-promoting residue (p-value <0.05), but did not contain DOT regions. Our STRING analysis demonstrated that the PPI network between PROS1 and MERTK is more complex than their assumed one-to-one binding (p-value <2.0 × 10-6).

CONCLUSION

Our findings present a novel prediction for the interaction between PROS1 and MERTK. Our findings show that PROS1 and MERTK contain elements of intrinsic disorder. PROS1 has two DOT regions that are attractive immunotherapy targets. We recommend that IDPRs and DOT regions found in PROS1 and MERTK should be considered when developing immunotherapies targeting this PPI.

Phase-separated ribosome-nascent chain complexes in genotoxic stress response

Assemblysomes are EDTA- and RNase-resistant ribonucleoprotein (RNP) complexes of paused ribosomes with protruding nascent polypeptide chains. They have been described in yeast and human cells for the proteasome subunit Rpt1, and the disordered amino-terminal part of the nascent chain was found to be indispensable for the accumulation of the Rpt1-RNP into assemblysomes. Motivated by this, to find other assemblysome-associated RNPs we used bioinformatics to rank subunits of Saccharomyces cerevisiae protein complexes according to their amino-terminal disorder propensity. The results revealed that gene products involved in DNA repair are enriched among the top candidates. The Sgs1 DNA helicase was chosen for experimental validation. We found that indeed nascent chains of Sgs1 form EDTA-resistant RNP condensates, assemblysomes by definition. Moreover, upon exposure to UV, SGS1 mRNA shifted from assemblysomes to polysomes, suggesting that external stimuli are regulators of assemblysome dynamics. We extended our studies to human cell lines. The BLM helicase, ortholog of yeast Sgs1, was identified upon sequencing assemblysome-associated RNAs from the MCF7 human breast cancer cell line, and mRNAs encoding DNA repair proteins were overall enriched. Using the radiation-resistant A549 cell line, we observed by transmission electron microscopy that 1,6-hexanediol, an agent known to disrupt phase-separated condensates, depletes ring ribosome structures compatible with assemblysomes from the cytoplasm of cells and makes the cells more sensitive to X-ray treatment. Taken together, these findings suggest that assemblysomes may be a component of the DNA damage response from yeast to human.

Dissolution of oncofusion transcription factor condensates for cancer therapy

Cancer-associated chromosomal rearrangements can result in the expression of numerous pathogenic fusion proteins. The mechanisms by which fusion proteins contribute to oncogenesis are largely unknown, and effective therapies for fusion-associated cancers are lacking. Here we comprehensively scrutinized fusion proteins found in various cancers. We found that many fusion proteins are composed of phase separation-prone domains (PSs) and DNA-binding domains (DBDs), and these fusions have strong correlations with aberrant gene expression patterns. Furthermore, we established a high-throughput screening method, named DropScan, to screen drugs capable of modulating aberrant condensates. One of the drugs identified via DropScan, LY2835219, effectively dissolved condensates in reporter cell lines expressing Ewing sarcoma fusions and partially rescued the abnormal expression of target genes. Our results indicate that aberrant phase separation is likely a common mechanism for these PS-DBD fusion-related cancers and suggest that modulating aberrant phase separation is a potential route to treat these diseases.

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.

Protein intrinsically disordered region prediction by combining neural architecture search and multi-objective genetic algorithm

BACKGROUND

Intrinsically disordered regions (IDRs) are widely distributed in proteins and related to many important biological functions. Accurately identifying IDRs is of great significance for protein structure and function analysis. Because the long disordered regions (LDRs) and short disordered regions (SDRs) share different characteristics, the existing predictors fail to achieve better and more stable performance on datasets with different ratios between LDRs and SDRs. There are two main reasons. First, the existing predictors construct network structures based on their own experiences such as convolutional neural network (CNN) which is used to extract the feature of neighboring residues in protein, and long short-term memory (LSTM) is used to extract the long-distance dependencies feature of protein residues. But these networks cannot capture the hidden feature associated with the length-dependent between residues. Second, many algorithms based on deep learning have been proposed but the complementarity of the existing predictors is not fully explored and used.

RESULTS

In this study, the neural architecture search (NAS) algorithm was employed to automatically construct the network structures so as to capture the hidden features in protein sequences. In order to stably predict both the LDRs and SDRs, the model constructed by NAS was combined with length-dependent models for capturing the unique features of SDRs or LDRs and general models for capturing the common features between LDRs and SDRs. A new predictor called IDP-Fusion was proposed.

CONCLUSIONS

Experimental results showed that IDP-Fusion can achieve more stable performance than the other existing predictors on independent test sets with different ratios between SDRs and LDRs.

Canonical structural-binding modes in the calmodulin-target protein complexes

Intracellular calcium sensor protein calmodulin (CaM) belongs to the large EF-hand protein superfamily. CaM shows a unique and not fully understood ability to bind to multiple targets, allows them to participate in a variety of regulatory processes. The protein has two approximately symmetrical globular domains (the N- and C-lobes). Analysis of the CaM-binding sites of target proteins showed that they have two hydrophobic 'anchor' amino acids separated by 10 to 17 residues. Consequently, several CaM-binding motifs: {1-10}, {1-11}, {1-13}, {1-14}, {1-16}, {1-17}, differing by the distance between the two anchor residues along the amino acid sequence, have been identified. Despite extensive structural information on the role of target-protein amino acid residues in the formation of complexes with CaM, much less is known about the role of amino acids from CaM contributing to these interactions. In this work, a quantitative analysis of the contact surfaces of CaM and target proteins has been carried out for 35 representative three-dimensional structures. It has been shown that, in addition to the two hydrophobic terminal residues of the target fragment, the interaction also involves residues that are 4 residues earlier in the sequence (binding mode {1-5}). It has also been found that the N- and C-lobes of CaM bind the {1-5} motif located at the ends of the target in a structurally identical manner. Methionine residues at positions 51 (corresponding to 124 in the C-lobe), 71 (144), and 72 (145) of the CaM amino acid sequence are key hydrophobic residues for this interaction. They are located at the N- and C-boundaries of the even EF-hand motifs. The hydrophobic core of CaM ('Ф-quatrefoil') consists of 10 amino acids in the N-lobe (and in the C-lobe): Phe16 (Phe89), Phe19 (Phe92), Ile27 (Ile100), Thr29 (Ala102), Leu32 (Leu105), Ile52 (Ile125), Val55 (Ala128), Ile63 (Val136), Phe65 (Tyr138), and Phe68 (Phe141) and do not intersect with the target-binding methionine residues. CaM belongs to the 'dynamic' group of EF-hand proteins, in which calcium and protein ligand binding causes only global conformational changes but does not alter the conservative 'black' and 'grey' clusters described in our earlier works (PLoS One. 2014; 9(10):e109287). The membership of CaM in the 'dynamic' group is determined by the triggering and protective methionine layer: Met51 (Met124), Met71 (Met144) and Met72 (Met145). HIGHLIGHTSInterchain interactions in the unique 35 CaM complex structures were analyzed.Methionine amino acids of the N- and C-lobes of CaM form triggering and protective layers.Interactions of the target terminal residues with these methionine layers are structurally identical.CaM belonging to the 'dynamic' group is determined by the triggering and protective methionine layer.Communicated by Ramaswamy H. Sarma.

Surface frustration re-patterning underlies the structural landscape and evolvability of fungal orphan candidate effectors

Pathogens secrete effector proteins to subvert host physiology and cause disease. Effectors are engaged in a molecular arms race with the host resulting in conflicting evolutionary constraints to manipulate host cells without triggering immune responses. The molecular mechanisms allowing effectors to be at the same time robust and evolvable remain largely enigmatic. Here, we show that 62 conserved structure-related families encompass the majority of fungal orphan effector candidates in the Pezizomycotina subphylum. These effectors diversified through changes in patterns of thermodynamic frustration at surface residues. The underlying mutations tended to increase the robustness of the overall effector protein structure while switching potential binding interfaces. This mechanism could explain how conserved effector families maintained biological activity over long evolutionary timespans in different host environments and provides a model for the emergence of sequence-unrelated effector families with conserved structures.

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.

Intrinsic disorder in PRAME and its role in uveal melanoma

INTRODUCTION

The PReferentially expressed Antigen in MElanoma (PRAME) protein has been shown to be an independent biomarker for increased risk of metastasis in Class 1 uveal melanomas (UM). Intrinsically disordered proteins and regions of proteins (IDPs/IDPRs) are proteins that do not have a well-defined three-dimensional structure and have been linked to neoplastic development. Our study aimed to evaluate the presence of intrinsic disorder in PRAME and the role these structureless regions have in PRAME( +) Class 1 UM.

METHODS

A bioinformatics study to characterize PRAME's propensity for the intrinsic disorder. We first used the AlphaFold tool to qualitatively assess the protein structure of PRAME. Then we used the Compositional Profiler and a set of per-residue intrinsic disorder predictors to quantify the intrinsic disorder. The Database of Disordered Protein Prediction (D2P2) platform, IUPred, FuzDrop, fIDPnn, AUCpred, SPOT-Disorder2, and metapredict V2 allowed us to evaluate the potential functional disorder of PRAME. Additionally, we used the Search Tool for the Retrieval of Interacting Genes (STRING) to analyze PRAME's potential interactions with other proteins.

RESULTS

Our structural analysis showed that PRAME contains intrinsically disordered protein regions (IDPRs), which are structureless and flexible. We found that PRAME is significantly enriched with serine (p-value < 0.05), a disorder-promoting amino acid. PRAME was found to have an average disorder score of 16.49% (i.e., moderately disordered) across six per-residue intrinsic disorder predictors. Our IUPred analysis revealed the presence of disorder-to-order transition (DOT) regions in PRAME near the C-terminus of the protein (residues 475-509). The D2P2 platform predicted a region from approximately 140 and 175 to be highly concentrated with post-translational modifications (PTMs). FuzDrop predicted the PTM hot spot of PRAME to be a droplet-promoting region and an aggregation hotspot. Finally, our analysis using the STRING tool revealed that PRAME has significantly more interactions with other proteins than expected for randomly selected proteins of the same size, with the ability to interact with 84 different partners (STRING analysis result: p-value < 1.0 × 10-16; model confidence: 0.400).

CONCLUSION

Our study revealed that PRAME has IDPRs that are possibly linked to its functionality in the context of Class 1 UM. The regions of functionality (i.e., DOT regions, PTM sites, droplet-promoting regions, and aggregation hotspots) are localized to regions of high levels of disorder. PRAME has a complex protein-protein interaction (PPI) network that may be secondary to the structureless features of the polypeptide. Our findings contribute to our understanding of UM and suggest that IDPRs and DOT regions in PRAME may be targeted in developing new therapies for this aggressive cancer. Video Abstract.

Nucleolar- and Nuclear-Stress-Induced Membrane-Less Organelles: A Proteome Analysis through the Prism of Liquid-Liquid Phase Separation

Radical changes in the idea of the organization of intracellular space that occurred in the early 2010s made it possible to consider the formation and functioning of so-called membrane-less organelles (MLOs) based on a single physical principle: the liquid-liquid phase separation (LLPS) of biopolymers. Weak non-specific inter- and intramolecular interactions of disordered polymers, primarily intrinsically disordered proteins, and RNA, play a central role in the initiation and regulation of these processes. On the other hand, in some cases, the "maturation" of MLOs can be accompanied by a "liquid-gel" phase transition, where other types of interactions can play a significant role in the reorganization of their structure. In this work, we conducted a bioinformatics analysis of the propensity of the proteomes of two membrane-less organelles, formed in response to stress in the same compartment, for spontaneous phase separation and examined their intrinsic disorder predispositions. These MLOs, amyloid bodies (A-bodies) formed in the response to acidosis and heat shock and nuclear stress bodies (nSBs), are characterized by a partially overlapping composition, but show different functional activities and morphologies. We show that the proteomes of these biocondensates are differently enriched in proteins, and many have high potential for spontaneous LLPS that correlates with the different morphology and function of these organelles. The results of these analyses allowed us to evaluate the role of weak interactions in the formation and functioning of these important organelles.

High subtelomeric GC content in the genome of a zoonotic Cryptosporidium species

Cryptosporidium canis is a zoonotic species causing cryptosporidiosis in humans in addition to its natural hosts dogs and other fur animals. To understand the genetic basis for host adaptation, we sequenced the genomes of C. canis from dogs, minks, and foxes and conducted a comparative genomics analysis. While the genomes of C. canis have similar gene contents and organisations, they (~41.0 %) and C. felis (39.6 %) have GC content much higher than other Cryptosporidium spp. (24.3-32.9 %) sequenced to date. The high GC content is mostly restricted to subtelomeric regions of the eight chromosomes. Most of these GC-balanced genes encode Cryptosporidium-specific proteins that have intrinsically disordered regions and are involved in host-parasite interactions. Natural selection appears to play a more important role in the evolution of codon usage in GC-balanced C. canis, and most of the GC-balanced genes have undergone positive selection. While the identity in whole genome sequences between the mink- and dog-derived isolates is 99.9 % (9365 SNVs), it is only 96.0 % (362 894 SNVs) between them and the fox-derived isolate. In agreement with this, the fox-derived isolate possesses more subtelomeric genes encoding invasion-related protein families. Therefore, the change in subtelomeric GC content appears to be responsible for the more GC-balanced C. canis genomes, and the fox-derived isolate could represent a new Cryptosporidium species.

Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins

We expand studies of AlphaFold2 (AF2) in the context of intrinsic disorder prediction by comparing it against a broad selection of 20 accurate, popular and recently released disorder predictors. We use 25% larger benchmark dataset with 646 proteins and cover protein-level predictions of disorder content and fully disordered proteins. AF2-based disorder predictions secure a relatively high Area Under receiver operating characteristic Curve (AUC) of 0.77 and are statistically outperformed by several modern disorder predictors that secure AUCs around 0.8 with median runtime of about 20 s compared to 1200 s for AF2. Moreover, AF2 provides modestly accurate predictions of fully disordered proteins (F1 = 0.59 vs. 0.91 for the best disorder predictor) and disorder content (mean absolute error of 0.21 vs. 0.15). AF2 also generates statistically more accurate disorder predictions for about 20% of proteins that have relatively short sequences and a few disordered regions that tend to be located at the sequence termini, and which are absent of disordered protein-binding regions. Interestingly, AF2 and the most accurate disorder predictors rely on deep neural networks, suggesting that these models are useful for protein structure and disorder predictions.

ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers

Intrinsically disordered proteins (IDPs) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT (Attention DisOrder PredicTor), a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue-level representations from Facebook's Evolutionary Scale Modeling library. The latter uses a database of nuclear magnetic resonance chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and a test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features that are relevant for the prediction performance and show that good performance can already be gained with <100 features. ADOPT is available as a stand-alone package at https://github.com/PeptoneLtd/ADOPT and as a web server at https://adopt.peptone.io/.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

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

Adaptive protein evolution through length variation of short tandem repeats in Arabidopsis

Intrinsically disordered protein regions are of high importance for biotic and abiotic stress responses in plants. Tracts of identical amino acids accumulate in these regions and can vary in length over generations because of expansions and retractions of short tandem repeats at the genomic level. However, little attention has been paid to what extent length variation is shaped by natural selection. By environmental association analysis on 2514 length variable tracts in 770 whole-genome sequenced Arabidopsis thaliana, we show that length variation in glutamine and asparagine amino acid homopolymers, as well as in interaction hotspots, correlate with local bioclimatic habitat. We determined experimentally that the promoter activity of a light-stress gene depended on polyglutamine length variants in a disordered transcription factor. Our results show that length variations affect protein function and are likely adaptive. Length variants modulating protein function at a global genomic scale has implications for understanding protein evolution and eco-evolutionary biology.

A hybrid positive unlabeled learning framework for uncovering scaffolds across human proteome by measuring the propensity to drive phase separation

Scaffold proteins drive liquid-liquid phase separation (LLPS) to form biomolecular condensates and organize various biochemical reactions in cells. Dysregulation of scaffolds can lead to aberrant condensate assembly and various complex diseases. However, bioinformatics predictors dedicated to scaffolds are still lacking and their development suffers from an extreme imbalance between limited experimentally identified scaffolds and unlabeled candidates. Here, using the joint distribution of hybrid multimodal features, we implemented a positive unlabeled (PU) learning-based framework named PULPS that combined ProbTagging and penalty logistic regression (PLR) to profile the propensity of scaffolds. PULPS achieved the best AUC of 0.8353 and showed an area under the lift curve (AUL) of 0.8339 as an estimation of true performance. Upon reviewing recent experimentally verified scaffolds, we performed a partial recovery with 2.85% increase in AUL from 0.8339 to 0.8577. In comparison, PULPS showed a 45.7% improvement in AUL compared with PLR, whereas 8.2% superiority over other existing tools. Our study first proved that PU learning is more suitable for scaffold prediction and demonstrated the widespread existence of phase separation states. This profile also uncovered potential scaffolds that co-drive LLPS in the human proteome and generated candidates for further experiments. PULPS is free for academic research at http://pulps.zbiolab.cn.

Alternatively spliced exon regulates context-dependent MEF2D higher-order assembly during myogenesis

During muscle cell differentiation, the alternatively spliced, acidic β-domain potentiates transcription of Myocyte-specific Enhancer Factor 2 (Mef2D). Sequence analysis by the FuzDrop method indicates that the β-domain can serve as an interaction element for Mef2D higher-order assembly. In accord, we observed Mef2D mobile nuclear condensates in C2C12 cells, similar to those formed through liquid-liquid phase separation. In addition, we found Mef2D solid-like aggregates in the cytosol, the presence of which correlated with higher transcriptional activity. In parallel, we observed a progress in the early phase of myotube development, and higher MyoD and desmin expression. In accord with our predictions, the formation of aggregates was promoted by rigid β-domain variants, as well as by a disordered β-domain variant, capable of switching between liquid-like and solid-like higher-order states. Along these lines, NMR and molecular dynamics simulations corroborated that the β-domain can sample both ordered and disordered interactions leading to compact and extended conformations. These results suggest that β-domain fine-tunes Mef2D higher-order assembly to the cellular context, which provides a platform for myogenic regulatory factors and the transcriptional apparatus during the developmental process.

Structural intrinsic disorder in a functionalized potyviral coat protein as a main viability determinant of its assembled nanoparticles

The viability of viral-derived nanoparticles (virions and VLPs) aimed to nanobiotechnological functionalizations of the coat protein (CP) of turnip mosaic virus has been studied by means of advanced computational methodologies that include molecular dynamics. The study has allowed to model the structure of the complete CP and its functionalization with three different peptides and obtain essential structural features such as order/disorder, interactions, and electrostatic potentials of their constituent domains. The results provide for the first time a dynamic view of a complete potyvirus CP, since experimental available structures so far obtained lack N- and C-terminal segments. The relevance of disorder in the most distal N-terminal subdomain, and the interaction of the less distal N-terminal subdomain with the highly ordered CP core, stand out as crucial characteristic for a viable CP. Preserving them proved of outmost importance to obtain viable potyviral CPs presenting peptides at their N-terminus.

Computational prediction of disordered binding regions

One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.

Orthoparamyxovirinae C Proteins Have a Common Origin and a Common Structural Organization

The protein C is a small viral protein encoded in an overlapping frame of the P gene in the subfamily Orthoparamyxovirinae. This protein, expressed by alternative translation initiation, is a virulence factor that regulates viral transcription, replication, and production of defective interfering RNA, interferes with the host-cell innate immunity systems and supports the assembly of viral particles and budding. We expressed and purified full-length and an N-terminally truncated C protein from Tupaia paramyxovirus (TupV) C protein (genus Narmovirus). We solved the crystal structure of the C-terminal part of TupV C protein at a resolution of 2.4 Å and found that it is structurally similar to Sendai virus C protein, suggesting that despite undetectable sequence conservation, these proteins are homologous. We characterized both truncated and full-length proteins by SEC-MALLS and SEC-SAXS and described their solution structures by ensemble models. We established a mini-replicon assay for the related Nipah virus (NiV) and showed that TupV C inhibited the expression of NiV minigenome in a concentration-dependent manner as efficiently as the NiV C protein. A previous study found that the Orthoparamyxovirinae C proteins form two clusters without detectable sequence similarity, raising the question of whether they were homologous or instead had originated independently. Since TupV C and SeV C are representatives of these two clusters, our discovery that they have a similar structure indicates that all Orthoparamyxovirine C proteins are homologous. Our results also imply that, strikingly, a STAT1-binding site is encoded by exactly the same RNA region of the P/C gene across Paramyxovirinae, but in different reading frames (P or C), depending on which cluster they belong to.

Disease association and comparative genomics of compositional bias in human proteins

Background: The evolutionary rate of disordered proteins varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of intrinsically disordered regions (IDRs) across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards low complexity regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, low complexity proteins across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of low complexity, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Disease association and comparative genomics of compositional bias in human proteins

Background: The evolutionary rate of disordered protein regions varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of compositional bias, indicative of disorder, across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards biased regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, proteins with compositional bias across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of compositional bias, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

Liaisons dangereuses: Intrinsic Disorder in Cellular Proteins Recruited to Viral Infection-Related Biocondensates

Liquid-liquid phase separation (LLPS) is responsible for the formation of so-called membrane-less organelles (MLOs) that are essential for the spatio-temporal organization of the cell. Intrinsically disordered proteins (IDPs) or regions (IDRs), either alone or in conjunction with nucleic acids, are involved in the formation of these intracellular condensates. Notably, viruses exploit LLPS at their own benefit to form viral replication compartments. Beyond giving rise to biomolecular condensates, viral proteins are also known to partition into cellular MLOs, thus raising the question as to whether these cellular phase-separating proteins are drivers of LLPS or behave as clients/regulators. Here, we focus on a set of eukaryotic proteins that are either sequestered in viral factories or colocalize with viral proteins within cellular MLOs, with the primary goal of gathering organized, predicted, and experimental information on these proteins, which constitute promising targets for innovative antiviral strategies. Using various computational approaches, we thoroughly investigated their disorder content and inherent propensity to undergo LLPS, along with their biological functions and interactivity networks. Results show that these proteins are on average, though to varying degrees, enriched in disorder, with their propensity for phase separation being correlated, as expected, with their disorder content. A trend, which awaits further validation, tends to emerge whereby the most disordered proteins serve as drivers, while more ordered cellular proteins tend instead to be clients of viral factories. In light of their high disorder content and their annotated LLPS behavior, most proteins in our data set are drivers or co-drivers of molecular condensation, foreshadowing a key role of these cellular proteins in the scaffolding of viral infection-related MLOs.

Evolution of the orthopoxvirus core genome

Orthopoxviruses comprise several relevant pathogens, including the causative agent of smallpox and monkeypox virus. Analysis of orthopoxvirus genome evolution mainly focused on gene gains/losses. We instead analyzed core genes, which are conserved in all orthopoxviruses. We show that, despite their strong constraint, some genes involved in viral morphogenesis and transcription/replication were targets of pervasive positive selection, which was relatively uncommon in immunomodulatory genes. However at least three of the positively selected genes, E3L, A24R, and H3L, might have evolved in response to immune selection. Episodic positive selection was particularly common on the internal branches of the orthopox phylogeny and on the monkeypox virus lineage. The latter showed evidence of episodic positive selection at the D14L gene, which encodes a modulator of complement activation (MOPICE). Notably, two genes (B1R and A33R) targeted by episodic selection on more than one branch are involved in forms of intra-genomic conflict. Finally, we found that, in orthopoxvirus proteomes, intrinsically disordered regions (IDRs) tend to be less constrained and are common targets of positive selection. Extension of our analysis to all poxviruses showed no evidence that the IDR fraction differs with host range. Conversely, we found a strong effect of base composition, which was however not sufficient to explain IDR fraction. We thus suggest that, in poxviruses, the IDR fraction is maintained by modulating GC content to accommodate disorder-promoting codons. Overall, our data provide novel insight in orthopoxvirus evolution and provide a list of genes and sites that are expected to modulate viral phenotypes.

Identification of Phase Separating Proteins With Distributed Reduced Alphabet Representations of Sequences

Phase separation of proteins play key roles in cellular physiology including bacterial division, tumorigenesis etc. Consequently, understanding the molecular forces that drive phase separation has gained considerable attention and several factors including hydrophobicity, protein dynamics, etc., have been implicated in phase separation. Data-driven identification of new phase separating proteins can enable in-depth understanding of cellular physiology and may pave way towards developing novel methods of tackling disease progression. In this work, we exploit the existing wealth of data on phase separating proteins to develop sequence-based machine learning method for prediction of phase separating proteins. We use reduced alphabet schemes based on hydrophobicity and conformational similarity along with distributed representation of protein sequences and biochemical properties as input features to Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. We used both curated and balanced dataset for building the models. RF trained on balanced dataset with hydropathy, conformational similarity embeddings and biochemical properties achieved accuracy of 97%. Our work highlights the use of conformational similarity, a feature that reflects amino acid flexibility, and hydrophobicity for predicting phase separating proteins. Use of such "interpretable" features obtained from the ever-growing knowledgebase of phase separation is likely to improve prediction performances further.

Unveiling the Metal-Dependent Aggregation Properties of the C-terminal Region of Amyloidogenic Intrinsically Disordered Protein Isoforms DPF3b and DPF3a

Double-PHD fingers 3 (DPF3) is a BAF-associated human epigenetic regulator, which is increasingly recognised as a major contributor to various pathological contexts, such as cardiac defects, cancer, and neurodegenerative diseases. Recently, we unveiled that its two isoforms (DPF3b and DPF3a) are amyloidogenic intrinsically disordered proteins. DPF3 isoforms differ from their C-terminal region (C-TERb and C-TERa), containing zinc fingers and disordered domains. Herein, we investigated the disorder aggregation properties of C-TER isoforms. In agreement with the predictions, spectroscopy highlighted a lack of a highly ordered structure, especially for C-TERa. Over a few days, both C-TERs were shown to spontaneously assemble into similar antiparallel and parallel β-sheet-rich fibrils. Altered metal homeostasis being a neurodegeneration hallmark, we also assessed the influence of divalent metal cations, namely Cu²⁺, Mg²⁺, Ni²⁺, and Zn²⁺, on the C-TER aggregation pathway. Circular dichroism revealed that metal binding does not impair the formation of β-sheets, though metal-specific tertiary structure modifications were observed. Through intrinsic and extrinsic fluorescence, we found that metal cations differently affect C-TERb and C-TERa. Cu²⁺ and Ni²⁺ have a strong inhibitory effect on the aggregation of both isoforms, whereas Mg²⁺ impedes C-TERb fibrillation and, on the contrary, enhances that of C-TERa. Upon Zn²⁺ binding, C-TERb aggregation is also hindered, and the amyloid autofluorescence of C-TERa is remarkably red-shifted. Using electron microscopy, we confirmed that the metal-induced spectral changes are related to the morphological diversity of the aggregates. While metal-treated C-TERb formed breakable and fragmented filaments, C-TERa fibrils retained their flexibility and packing properties in the presence of Mg²⁺ and Zn²⁺ cations.

Bassoon contributes to tau-seed propagation and neurotoxicity

Tau aggregation is a defining histopathological feature of Alzheimer's disease and other tauopathies. However, the cellular mechanisms involved in tau propagation remain unclear. Here, we performed an unbiased quantitative proteomic study to identify proteins that specifically interact with this tau seed. We identified Bassoon (BSN), a presynaptic scaffolding protein, as an interactor of the tau seed isolated from a mouse model of tauopathy, and from Alzheimer's disease and progressive supranuclear palsy postmortem samples. We show that BSN exacerbates tau seeding and toxicity in both mouse and Drosophila models for tauopathy, and that BSN downregulation decreases tau spreading and overall disease pathology, rescuing synaptic and behavioral impairments and reducing brain atrophy. Our findings improve the understanding of how tau seeds can be stabilized by interactors such as BSN. Inhibiting tau-seed interactions is a potential new therapeutic approach for neurodegenerative tauopathies.

Sequence-Based Prediction of Protein Phase Separation: The Role of Beta-Pairing Propensity

The formation of droplets of bio-molecular condensates through liquid-liquid phase separation (LLPS) of their component proteins is a key factor in the maintenance of cellular homeostasis. Different protein properties were shown to be important in LLPS onset, making it possible to develop predictors, which try to discriminate a positive set of proteins involved in LLPS against a negative set of proteins not involved in LLPS. On the other hand, the redundancy and multivalency of the interactions driving LLPS led to the suggestion that the large conformational entropy associated with non specific side-chain interactions is also a key factor in LLPS. In this work we build a LLPS predictor which combines the ability to form pi-pi interactions, with an unrelated feature, the propensity to stabilize the β-pairing interaction mode. The cross-β structure is formed in the amyloid aggregates, which are involved in degenerative diseases and may be the final thermodynamically stable state of protein condensates. Our results show that the combination of pi-pi and β-pairing propensity yields an improved performance. They also suggest that protein sequences are more likely to be involved in phase separation if the main chain conformational entropy of the β-pairing maintained droplet state is increased. This would stabilize the droplet state against the more ordered amyloid state. Interestingly, the entropic stabilization of the droplet state appears to proceed according to different mechanisms, depending on the fraction of "droplet-driving" proteins present in the positive set.

MobiDB: 10 years of intrinsically disordered proteins

The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes.

DeepCLD: An Efficient Sequence-Based Predictor of Intrinsically Disordered Proteins

Intrinsic disorder is common in proteins, plays important roles in protein functionality, and is commonly associated with various human diseases. To have an accurate tool for the annotation of intrinsic disorder in proteins, this paper proposes a novel algorithm, DeepCLD, for sequence-based prediction of intrinsically disordered proteins. This algorithm uses amino acid position specific scoring matrix (PSSM) to capture the intrinsic variability characteristic of sequence patterns, ResNet to preserve feature space structure, and bidirectional CudnnLSTM as recurrent layer to further improve the efficiency. Futhermore, DeepCLD also utilized the attention mechanism to solve the problem of gradient disappearing in deep network. Comparative analyses show that DeepCLD has faster training speed and higher prediction accuracy than comparable methods.

SETH predicts nuances of residue disorder from protein embeddings

Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of AlphaFold2 results for UniProt. However, many proteins have so-called intrinsically disordered regions (IDRs) that do not adopt unique structures in isolation. These IDRs are associated with several diseases, including Alzheimer's Disease. We showed that three recent disorder measures of AlphaFold2 predictions (pLDDT, "experimentally resolved" prediction and "relative solvent accessibility") correlated to some extent with IDRs. However, expert methods predict IDRs more reliably by combining complex machine learning models with expert-crafted input features and evolutionary information from multiple sequence alignments (MSAs). MSAs are not always available, especially for IDRs, and are computationally expensive to generate, limiting the scalability of the associated tools. Here, we present the novel method SETH that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input. Thereby, our method, relying on a relatively shallow convolutional neural network, outperformed much more complex solutions while being much faster, allowing to create predictions for the human proteome in about 1 hour on a consumer-grade PC with one NVIDIA GeForce RTX 3060. Trained on a continuous disorder scale (CheZOD scores), our method captured subtle variations in disorder, thereby providing important information beyond the binary classification of most methods. High performance paired with speed revealed that SETH's nuanced disorder predictions for entire proteomes capture aspects of the evolution of organisms. Additionally, SETH could also be used to filter out regions or proteins with probable low-quality AlphaFold2 3D structures to prioritize running the compute-intensive predictions for large data sets. SETH is freely publicly available at: https://github.com/Rostlab/SETH.

Looking at the Pathogenesis of the Rabies Lyssavirus Strain Pasteur Vaccins through a Prism of the Disorder-Based Bioinformatics

Rabies is a neurological disease that causes between 40,000 and 70,000 deaths every year. Once a rabies patient has become symptomatic, there is no effective treatment for the illness, and in unvaccinated individuals, the case-fatality rate of rabies is close to 100%. French scientists Louis Pasteur and Émile Roux developed the first vaccine for rabies in 1885. If administered before the virus reaches the brain, the modern rabies vaccine imparts long-lasting immunity to the virus and saves more than 250,000 people every year. However, the rabies virus can suppress the host's immune response once it has entered the cells of the brain, making death likely. This study aimed to make use of disorder-based proteomics and bioinformatics to determine the potential impact that intrinsically disordered protein regions (IDPRs) in the proteome of the rabies virus might have on the infectivity and lethality of the disease. This study used the proteome of the Rabies lyssavirus (RABV) strain Pasteur Vaccins (PV), one of the best-understood strains due to its use in the first rabies vaccine, as a model. The data reported in this study are in line with the hypothesis that high levels of intrinsic disorder in the phosphoprotein (P-protein) and nucleoprotein (N-protein) allow them to participate in the creation of Negri bodies and might help this virus to suppress the antiviral immune response in the host cells. Additionally, the study suggests that there could be a link between disorder in the matrix (M) protein and the modulation of viral transcription. The disordered regions in the M-protein might have a possible role in initiating viral budding within the cell. Furthermore, we checked the prevalence of functional disorder in a set of 37 host proteins directly involved in the interaction with the RABV proteins. The hope is that these new insights will aid in the development of treatments for rabies that are effective after infection.

Prediction of protein-protein interaction sites in intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles.

Intrinsic Disorder in BAP1 and Its Association with Uveal Melanoma

Background: Specific subvariants of uveal melanoma (UM) are associated with increased rates of metastasis compared to other subvariants. BRCA1 (BReast CAncer gene 1)-associated protein-1 (BAP1) is encoded by a gene that has been linked to aggressive behavior in UM. Methods: We evaluated BAP1 for the presence of intrinsically disordered protein regions (IDPRs) and its protein−protein interactions (PPI). We evaluated specific sequence-based features of the BAP1 protein using a set of bioinformatic databases, predictors, and algorithms. Results: We show that BAP1’s structure contains extensive IDPRs as it is highly enriched in proline residues (the most disordered amino acid; p-value < 0.05), the average percent of predicted disordered residues (PPDR) was 57.34%, and contains 9 disorder-based binding sites (ie. molecular recognition features (MoRFs)). BAP1’s intrinsic disorder allows it to engage in a complex PPI network with at least 49 partners (p-value < 1.0 × 10−16). Conclusion: These findings show that BAP1 contains IDPRs and an intricate PPI network. Mutations in UM that are associated with the BAP1 gene may alter the function of the IDPRs embedded into its structure. These findings develop the understanding of UM and may provide a target for potential novel therapies to treat this aggressive neoplasm.

Intrinsically disordered proteins in viral pathogenesis and infections

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

Aberrant liquid-liquid phase separation and amyloid aggregation of proteins related to neurodegenerative diseases

Recent evidence has shown that the processes of liquid-liquid phase separation (LLPS) or liquid-liquid phase transitions (LLPTs) are a crucial and prevalent phenomenon that underlies the biogenesis of numerous membrane-less organelles (MLOs) and biomolecular condensates within the cells. Findings show that processes associated with LLPS play an essential role in physiology and disease. In this review, we discuss the physical and biomolecular factors that contribute to the development of LLPS, the associated functions, as well as their consequences for cell physiology and neurological disorders. Additionally, the finding of mis-regulated proteins, which have long been linked to aggregates in neuropathology, are also known to induce LLPS/LLPTs, prompting a lot of interest in understanding the connection between aberrant phase separation and disorder conditions. Moreover, the methods used in recent and ongoing studies in this field are also explored, as is the possibility that these findings will encourage new lines of inquiry into the molecular causes of neurodegenerative diseases.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

Machine-learning analysis of intrinsically disordered proteins identifies key factors that contribute to neurodegeneration-related aggregation

Protein structure is determined by the amino acid sequence and a variety of post-translational modifications, and provides the basis for physiological properties. Not all proteins in the proteome attain a stable conformation; roughly one third of human proteins are unstructured or contain intrinsically disordered regions exceeding 40% of their length. Proteins comprising or containing extensive unstructured regions are termed intrinsically disordered proteins (IDPs). IDPs are known to be overrepresented in protein aggregates of diverse neurodegenerative diseases. We evaluated the importance of disordered proteins in the nematode Caenorhabditis elegans, by RNAi-mediated knockdown of IDPs in disease-model strains that mimic aggregation associated with neurodegenerative pathologies. Not all disordered proteins are sequestered into aggregates, and most of the tested aggregate-protein IDPs contribute to important physiological functions such as stress resistance or reproduction. Despite decades of research, we still do not understand what properties of a disordered protein determine its entry into aggregates. We have employed machine-learning models to identify factors that predict whether a disordered protein is found in sarkosyl-insoluble aggregates isolated from neurodegenerative-disease brains (both AD and PD). Machine-learning predictions, coupled with principal component analysis (PCA), enabled us to identify the physiochemical properties that determine whether a disordered protein will be enriched in neuropathic aggregates.

Intrinsically disordered BMP4 morphogen and the beak of the finch: Co-option of an ancient axial patterning system

Darwin's finches, with the primary diversity in the shape and size of their beaks, represent an excellent model system to study speciation and adaptive evolution. It is generally held that evolution depends on the natural selection of heritable phenotypic variations originating from the genetic mutations. However, it is now increasingly evident that epigenetic transgenerational inheritance of phenotypic variation can also guide evolutionary change. Several studies have shown that the bone morphogenetic protein BMP4 is a major driver of beak morphology. A recent study explored variability of the morphological, genetic, and epigenetic differences in the adjacent "urban" and "rural" populations of two species of ground Darwin's finches on the Galápagos Islands and revealed significant changes in methylation patterns in several genes including those involved in the BMP/TGFß pathway in the sperm DNA compared to erythrocyte DNA. These observations indicated that epigenetic changes caused by environmental fluctuations can be passed on to the offspring. Nonetheless, the mechanism by which dysregulated expression of BMP4 impacts beak morphology remains poorly understood. Here, we show that BMP4 is an intrinsically disordered protein and present a causal a link between epigenetic changes, BMP4 dysregulation and the evolution of the beak of the finch by natural selection.

Hepatitis C Virus Infection and Intrinsic Disorder in the Signaling Pathways Induced by Toll-Like Receptors

In this study, we examined the interplay between protein intrinsic disorder, hepatitis C virus (HCV) infection, and signaling pathways induced by Toll-like receptors (TLRs). To this end, 10 HCV proteins, 10 human TLRs, and 41 proteins from the TLR-induced downstream pathways were considered from the prevalence of intrinsic disorder. Mapping of the intrinsic disorder to the HCV-TLR interactome and to the TLR-based pathways of human innate immune response to the HCV infection demonstrates that substantial levels of intrinsic disorder are characteristic for proteins involved in the regulation and execution of these innate immunity pathways and in HCV-TLR interaction. Disordered regions, being commonly enriched in sites of various posttranslational modifications, may play important functional roles by promoting protein-protein interactions and support the binding of the analyzed proteins to other partners such as nucleic acids. It seems that this system represents an important illustration of the role of intrinsic disorder in virus-host warfare.

Structural characterisation of amyloidogenic intrinsically disordered zinc finger protein isoforms DPF3b and DPF3a

Double PHD fingers 3 (DPF3) is a zinc finger protein, found in the BAF chromatin remodelling complex, and is involved in the regulation of gene expression. Two DPF3 isoforms have been identified, respectively named DPF3b and DPF3a. Very limited structural information is available for these isoforms, and their specific functionality still remains poorly studied. In a previous work, we have demonstrated the first evidence of DPF3a being a disordered protein sensitive to amyloid fibrillation. Intrinsically disordered proteins (IDPs) lack a defined tertiary structure, existing as a dynamic conformational ensemble, allowing them to act as hubs in protein-protein interaction networks. In the present study, we have more thoroughly characterised DPF3a in vitro behaviour, as well as unravelled and compared the structural properties of the DPF3b isoform, using an array of predictors and biophysical techniques. Predictions, spectroscopy, and dynamic light scattering have revealed a high content in disorder: prevalence of random coil, aromatic residues partially to fully exposed to the solvent, and large hydrodynamic diameters. DPF3a appears to be more disordered than DPF3b, and exhibits more expanded conformations. Furthermore, we have shown that they both time-dependently aggregate into amyloid fibrils, as revealed by typical circular dichroism, deep-blue autofluorescence, and amyloid-dye binding assay fingerprints. Although spectroscopic and microscopic analyses have unveiled that they share a similar aggregation pathway, DPF3a fibrillates at a faster rate, likely through reordering of its C-terminal domain.

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning

BACKGROUND

Degrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality disrupt control of protein abundance due to mistargeting of proteins destined for degradation and often result in pathologies. Targeting degrons by small molecules also emerges as an exciting drug design strategy to upregulate the expression of specific proteins. Despite their essential function and disease targetability, reliable identification of degrons remains a conundrum. Here, we developed a deep learning-based model named Degpred that predicts general degrons directly from protein sequences.

RESULTS

We showed that the BERT-based model performed well in predicting degrons singly from protein sequences. Then, we used the deep learning model Degpred to predict degrons proteome-widely. Degpred successfully captured typical degron-related sequence properties and predicted degrons beyond those from motif-based methods which use a handful of E3 motifs to match possible degrons. Furthermore, we calculated E3 motifs using predicted degrons on the substrates in our collected E3-substrate interaction dataset and constructed a regulatory network of protein degradation by assigning predicted degrons to specific E3s with calculated motifs. Critically, we experimentally verified that a predicted SPOP binding degron on CBX6 prompts CBX6 degradation and mediates the interaction with SPOP. We also showed that the protein degradation regulatory system is important in tumorigenesis by surveying degron-related mutations in TCGA.

CONCLUSIONS

Degpred provides an efficient tool to proteome-wide prediction of degrons and binding E3s singly from protein sequences. Degpred successfully captures typical degron-related sequence properties and predicts degrons beyond those from previously used motif-based methods, thus greatly expanding the degron landscape, which should advance the understanding of protein degradation, and allow exploration of uncharacterized alterations of proteins in diseases. To make it easier for readers to access collected and predicted datasets, we integrated these data into the website http://degron.phasep.pro/ .

Are casein micelles extracellular condensates formed by liquid-liquid phase separation?

Casein micelles are extracellular polydisperse assemblies of unstructured casein proteins. Caseins are the major component of milk. Within casein micelles, casein molecules are stabilised by binding to calcium phosphate nanoclusters and, by acting as molecular chaperones, through multivalent interactions. In light of such interactions, we discuss whether casein micelles can be considered as extracellular condensates formed by liquid-liquid phase separation. We analyse the sequence, structure and interactions of caseins in comparison to proteins forming intracellular condensates. Furthermore, we review the similarities between caseins and small heat-shock proteins whose chaperone activity is linked to phase separation of proteins. By bringing these observations together, we describe a regulatory mechanism for protein condensates, as exemplified by casein micelles.